Mechanisms of pH-Dependent Uptake of Ionizable Organic Chemicals by Fish from Oil Sands Process-Affected Water (OSPW)
Bibliographic record
Abstract
Uptake and effects of ionizable organic chemicals (IOCs) that are weak acids in aqueous solution by fish can differ as a function of pH. While the pH-dependent behavior of select IOCs is well-understood, complex mixtures of IOCs, e.g., from oil sands process-affected water (OSPW), have not yet been studied systematically. Here, we established an in vitro screening method using the rainbow trout gill cell line, RTgill-W1, to investigate pH-dependent cytotoxicity and permeation of IOCs across cultured epithelia using ultra-high-performance liquid chromatography with high-resolution mass spectrometry (UPLC-HRMS). The assay was benchmarked using model chemicals and technical mixtures, and then used to characterize fractions and reconstituted extracts of field-collected OSPW. Significant pH-dependent cytotoxicity of individual IOCs, acidic fractions, and reconstituted extracts of OSPW was observed. In vitro data were in good agreement with data from a 96 h in vivo exposure experiment with juvenile rainbow trout. Permeation of some IOCs from OSPW was mediated by active transport, as revealed by studies in which inhibitors of these active transport mechanisms were applied. We conclude that the RTgill-W1 in vitro assay is useful for the screening of pH-dependent uptake of IOCs in fish, and has applications for in vitro – in vivo extrapolation, and prioritization of chemicals in nontarget screenings.
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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".