Toward Streamlined Identification of Dioxin-like Compounds in Environmental Samples through Integration of Suspension Bioassay
Bibliographic record
Abstract
Effect-directed analysis (EDA) is a powerful strategy to identify biologically active compounds in environmental samples. However, in current EDA studies, fractionation and handling procedures are laborious, consist of multiple evaporation steps, and thus bear the risk of contamination and decreased recoveries of the target compounds. The low resulting throughput has been one of the major bottlenecks of EDA. Here, we propose a high-throughput EDA (HT-EDA) work-flow combining reversed phase high-performance liquid chromatography fractionation of samples into 96-well microplates, followed by toxicity assessment in the micro-EROD bioassay with the wild-type rat hepatoma H4IIE cells, and chemical analysis of bioactive fractions. The approach was evaluated using single substances, binary mixtures, and extracts of sediment samples collected at the Three Gorges Reservoir, Yangtze River, China, as well as the rivers Rhine and Elbe, Germany. Selected bioactive fractions were analyzed by highly sensitive gas chromatography-atmospheric pressure laser ionization-time-of-flight-mass spectrometry. In addition, we optimized the work-flow by seeding previously adapted suspension-cultured H4IIE cells directly into the microplate used for fractionation, which makes any transfers of fractionated samples unnecessary. The proposed HT-EDA work-flow simplifies the procedure for wider application in ecotoxicology and environmental routine programs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".