Late Breaking Abstract - Assessing Inflammatory Response to Air Pollution via VOCs in Exhaled Breath
Bibliographic record
Abstract
Air pollution, particularly particulate matter less than 2.5 μg/m³ (PM2.5), is classified as a Group 1 carcinogen and contributes to lung cancer and respiratory diseases. Lung inflammation is a response to pollutants, releasing volatile organic compounds (VOCs) in breath, which could serve as non-invasive markers for assessing inflammation. This randomized double-blind crossover study examined breath VOCs over 24 hours following acute high exposure to PM2.5 in 20 non-smoking, healthy individuals. Participants underwent two separate exposures in the UBC Air Pollution Exposure Lab booth: 300 μg/m³ of PM2.5 from a diesel engine, and to filtered air, spaced a minimum of six weeks apart. Breath collected using Breath Biopsy® before exposure, immediately after, and at 0.5, 1, 3, 6, and 24 hours post-exposure. Samples were analysed by GC-MS, both untargeted and targeted analyses was conducted. iData analysis included univariate analysis and multivariate techniques such as ANOVA simultaneous component analysis (ASCA). ASCA revealed a significant effect of exposure type (300 μg/m³ PM2.5 vs. sham, p < 0.01) and a significant interaction (p < 0.05) between post-exposure time and exposure type. From targeted analysis, inflammation-related compounds significantly increased in individuals exposed to PM2.5 compared to controls. From untargeted analysis, 12 VOCs significantly increased in breath of the 300 μg/m³ PM2.5 group, which diminished in samples collected 24 hours post-exposure. A distinct breath VOC profile is detectable in individuals exposed to inflammation triggers, suggesting that breath VOCs may detect acute airway inflammation and assess respiratory diseases secondary to air pollution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".