Extracts of Passive Samplers Deployed in Variably Contaminated Wetlands in the Athabasca Oil Sands Region Elicit Biochemical and Transcriptomic Effects in Avian Hepatocytes
Bibliographic record
Abstract
Recent contaminant monitoring in boreal wetlands situated in Alberta’s Athabasca oil sands region revealed increased concentrations of polycyclic aromatic compounds (PACs) in passive sampling devices deployed in wetlands close to bitumen surface mining operations. In this study, graded concentrations of semipermeable membrane device (SPMD) extracts, collected from 4 wetlands with variable burdens of PACs, were administered to chicken and double-crested cormorant (DCCO) embryonic hepatocytes to determine effects on 7-ethoxyresorufin- O -deethylase (EROD) activity and mRNA expression. Concentrations and composition of PACs detected in SPMDs varied among sites, and the proportion of alkyl PACs was greater than parent compounds at all sites. ΣPACs was the highest in SPMDs deployed within 10 km of mining activity (near-site wetland [5930 ng SPMD –1 ]) compared to those ∼50 km south (far-site wetland [689 ng SPMD –1 ]). Measures of EROD activity and Cyp1a4 mRNA expression allowed the ranking of wetland sites based on aryl hydrocarbon receptor-mediated end points; EROD activity and Cyp1a4 mRNA induction were the highest at the near-site wetland. ToxChip PCR arrays (one chicken and one DCCO) provided a more exhaustive transcriptomic evaluation across multiple toxicological pathways following exposure to the SPMD extracts. Study sites with the greatest PAC concentrations had the most genes altered on the chicken ToxChip (12–15/43 genes). Exposure of avian hepatocytes to SPMD extracts from variably contaminated wetlands highlighted traditional PAC-related toxicity pathways as well as other novel mechanisms of action. A novel combination of passive sampling techniques and high-throughput toxicity evaluation techniques shows promise in terms of identifying hotspots of chemical concern in the natural environment.
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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) | 0.000 | 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) | 0.000 | 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".