Toxicity screening of air extracts representing different source sectors in the Greater Toronto and Hamilton areas: In vitro oxidative stress, pro-inflammatory response, and toxicogenomic analysis
Bibliographic record
Abstract
In the present study, the suitability and sensitivity of different in vitro toxicity endpoints were determined to evaluate and distinguish the specific contributions of polycyclic aromatic carbon (PAC) mixtures from various sites in Toronto (Canada), to pulmonary toxicity. Air samples were collected for two-month periods from April 2014 to March 2015 from one location, and from August 2016 to August 2017 from multiple locations reflecting different geographical areas in Toronto, and the Greater Toronto Area, with varying source emissions including background, traffic, urban, industrial and residential sites. Relative concentrations of PACs and their derivatives in these air samples were characterised. In vitro cytotoxicity, pro-inflammatory, and oxidative stress assays were employed to assess the acute pulmonary effects of urban-air-derived air pollutants. In addition, global transcriptional profiling was utilized to understand how these chemical mixtures exert their harmful effects. Lastly, the transcriptomic data and the chemical profiles for each site and season were used to relate the biological response back to individual constituents. Site-specific responses could not be derived; however, the Spring season was identified as the most responsive through benchmark concentration analysis. A combination of correlational analysis and principal component analysis revealed that nitrated and oxygenated polycyclic aromatic hydrocarbons (PAHs) drive the response at lower concentrations while specific PAHs drive the response at the highest concentration tested. Unsubstituted PAHs are the current targets for analysis as priority pollutants. The present study highlights the importance of by-products of PAH degradation in the assessment of risk. The study also demonstrates the usefulness of in vitro toxicity assays to derive meaningful data in support of risk assessment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".