Developing an Approach for Integrating Chemical Analysis and Transcriptional Changes to Assess Contaminants in Water, Sediment, and Fish
Bibliographic record
Abstract
Abstract Pharmaceuticals in aquatic environments pose threats to aquatic organisms because of their continuous release and potential accumulation. Monitoring methods for these contaminants are inadequate, with targeted analyses falling short in assessing water quality's impact on biota. The present study advocates for integrated strategies combining suspect and targeted chemical analyses with molecular biomarker approaches to better understand the risks posed by complex chemical mixtures to nontarget organisms. The research aimed to integrate chemical analysis and transcriptome changes in fathead minnows to prioritize contaminants, assess their effects, and apply this strategy in Wascana Creek, Canada. Analysis revealed higher pharmaceutical concentrations downstream of a wastewater-treatment plant, with clozapine being the most abundant in fathead minnows, showing notable bioavailability from water and sediment sources. Considering the importance of bioaccumulation factor and biota–sediment accumulation factor in risk assessment, these coefficients were calculated based on field data collected during spring, summer, and fall seasons in 2021. Bioaccumulation was classified as very bioaccumulative with values >5000 L kg–1, suggesting the ability of pharmaceuticals to accumulate in aquatic organisms. The study highlighted the intricate relationship between nutrient availability, water quality, and key pathways affected by pharmaceuticals, personal care products, and rubber components. Prioritization of these chemicals was done through suspect analysis, supported by identifying perturbed pathways (specifically signaling and cellular processes) using transcriptomic analysis in exposed fish. This strategy not only aids in environmental risk assessment but also serves as a practical model for other watersheds, streamlining risk-assessment processes to identify environmental hazards and work toward reducing risks from contaminants of emerging concern. Environ Toxicol Chem 2024;43:2252–2273. © 2024 SETAC
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".