Endocrine disrupting, mutagenic, and teratogenic effects of upper Danube River sediments using effect-directed analysis
Bibliographic record
Abstract
Effect-directed analysis (EDA) can be useful in identifying and evaluating potential toxic chemicals in matrixes. Previous investigations of extracts of sediments from the upper Danube River in Germany revealed acute nonspecific and mechanism-specific toxicity as determined by several bioassays. In the present study, EDA was used to further characterize these sediments and identify groups of potentially toxic chemicals. Four extracts of sediments were subjected to a novel fractionation scheme coupled with identification of chemicals to characterize their ability to disrupt steroidogenesis or cause mutagenic and/or teratogenic effects. All four whole extracts of sediment caused significant alteration of steroidogenesis and were mutagenic as well as teratogenic. The whole extracts of sediments were separated into 18 fractions and these fractions were then subjected to the same bioassays as the whole extracts. Fractions 7 to 15 of all four extracts were consistently more potent in both the Ames fluctuation and H295R assays. Much of this toxicity could be attributed to polycyclic aromatic hydrocarbons, sterols, and in fraction 7-naphthoic acids. Because the fraction containing polychlorinated biphenyls, polychlorodibenzodioxin/furan, dichlorodiphenyltrichloroethane, and several organophosphates did not cause any observable effects on hormone production or a mutagenic response, or were not detected in any of the samples, these compounds could be eliminated as causative agents for the observed effects. These results demonstrate the value of using EDA, which uses multiple bioassays and new fractionation techniques to assess toxicity. Furthermore, to our knowledge this is the first study using the recently developed H295R assay within EDA strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".