Gas-Phase Ambient Air Contaminants Exhibit Significant Dioxin-like and Estrogen-like Activity <i>in Vitro</i>
Bibliographic record
Abstract
Several adverse health effects, such as respiratory and cardiovascular morbidity, have been linked to exposure to particulate matter in ambient air; however, the biologic activity of gas-phase ambient organic air contaminants has not been examined as thoroughly. Using aryl hydrocarbon receptor (AHR)-based and estrogen receptor (ER)-based cell bioassay systems, we assessed the dioxin-like and estrogenic activities of gas-phase organic ambient air contaminants compared with those of particulate-phase contaminants using samples collected between seasons over 2 years from an urban and a rural location in the Greater Toronto Area, Canada. The concentration of the sum (Sigma) of polycyclic aromatic hydrocarbons, which was highest in the gas phase, was 10-100 times more abundant than that of Sigmapolychlorinated biphenyls, Sigmanitro-polycyclic aromatic hydrocarbons, and Sigmaorganochlorine pesticides, and 10(3) to 10(4) times more abundant than Sigmapolychlorinated dibenzo-p-dioxins/dibenzofurans. Gas-phase samples induced significant AHR- and ER-dependent gene expression. The activity of the gas-phase samples was greater than that of the particulate-phase samples in the estrogen assay and, in one case, in the AHR assay. We found no strong associations between either summer or winter seasons or urban or rural locations in the relative efficacy of the extracts in either the ER or AHR assay despite differences in chemical composition, concentrations, and abundance. Our results suggest that mechanistic studies of the health effects of ambient air must consider gas and particulate phases because chemicals present in both phases can affect AHR and ER signaling pathways.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".