In vitro assessment of the genotoxicity and immunotoxicity of treated and untreated municipal effluents and receiving waters in freshwater organisms
Bibliographic record
Abstract
Municipal wastewater effluent is one of the largest sources of pollution entering surface waters in the Laurentian Great Lakes. Exposure to wastewater effluent has been associated with impaired immune systems and induction of genotoxicity to aquatic animals. Due to habitat degradation and environmental pollution linked to industrial development and population growth, several regions of the Great Lakes have been designated Areas of Concern (AOCs). In this study, we assessed the effect of extracts of sewage influent, (treated) effluent and receiving surface waters from the Hamilton Harbour AOC and the Toronto and Region AOC (Ontario, Canada) on the phagocytic immune response of rainbow trout (Oncorhynchus mykiss) kidney leukocytes and the genotoxicity (DNA strand breaks) of these extracts on freshwater mussel (Eurynia dilatata) hemocytes. We identified and quantified numerous chemicals present in the various samples extracted for exposure. In freshwater mussels, extracts from Hamilton Harbour AOC induced DNA damage with the most frequency (12 out of 28 samples) regardless of sample type, reflecting past and present industrial activities. In contrast, extracts from Toronto and Region AOC induced DNA damage infrequently (2 out of 32 (summer) and 5 out of 32 (fall) samples, respectively) and from different WWTPs at different times. None of the extracts induced any significant effect on phagocytosis of rainbow trout kidney leukocytes. The present study indicates that despite overall improvements to effluent quality, treatment of influent by WWTPs may not result in a corresponding improvement of the genotoxicity of effluents. In vitro bioassays are useful and cost-effective rapid-screening tools for preliminary assessments of contamination of aquatic ecosystems.
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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) | 0.001 | 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".