Evaluation of uptake of the cytostatic methotrexate in Elliptio complanata mussels by LC–MS/MS
Bibliographic record
Abstract
Abstract Aquatic organisms are continuously exposed to emerging contaminants coming from urban effluents of wastewater treatment plants. The contamination of surface water by those effluents poses a number of environmental risks, and pharmaceuticals are part of this class of effluent contaminants. Various classes of pharmaceuticals are not treated by wastewater treatment plants and anticancer drugs are part of them. The chemotherapy drug methotrexate (MTX) is an emerging contaminant and its growing use with the increase in cancer cases worldwide raises potential risk to aquatic organisms exposed to effluent discharges. However, chemical analyses in exposed freshwater aquatic organisms for ecotoxicological studies are rarely available and no studies have been done yet to accompany ecotoxicological data of exposed filter-feeding organisms. The purpose of this study was to develop a specific and sensitive analytical LC–MS/MS method for the quantification of methotrexate uptake in mussels exposed at different concentrations of the drug. A solid/liquid extraction followed by solid phase extraction (SPE) using an MCX phase purification scheme was optimized. The optimal recovery of 65% and matrix effect of 38% allowed to achieve a limit of quantification of 0.25 ng g−1, with an accuracy of 99–106%, a precision of no more than 3% RSD, and linearity ranging from 0.25 to 25 ng g−1. This methodology was tested with mussels exposed for 96 h at different concentrations (4 to 100 µg L−1) of MTX. The data revealed tissue uptake at concentrations ranging from 0 to 2.53 ng g−1. This suggests that this drug has low uptake potential and this methodology could be used to examine tissue levels of this drug in organisms continuously exposed to urban pollution.
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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 | 0.001 | 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".