Prioritization of 10 organic flame retardants using an avian hepatocyte toxicogenomic assay
Bibliographic record
Abstract
As the number of chemicals developed and used by industry increases, the inherent limitations of traditional toxicology approaches become an unavoidable issue. To help meet the demand for toxicity evaluation, new methods, such as high-throughput toxicity screening, are currently being developed to permit rapid determination of toxic, molecular, and/or biochemical effects of a wide range of chemicals. In the present study, we demonstrate the utility of an avian in vitro toxicogenomics screening approach to determine the cytotoxic and transcriptomic effects of 10 organic flame retardants (OFRs) currently of international priority for ecological risk evaluation to prioritize and inform future toxicological studies. Hepatocytes from 2 avian species, chicken and double-crested cormorant, were prepared and exposed for 24 h to various concentrations (0-300 μM) of the following 10 OFRs: Chemical Abstracts Service registration numbers 29761-21-5, 56803-37-3 (p-tert-butylphenyl diphenyl phosphate [BPDP]), 65652-41-7, 68937-41-7 (phenol, isopropylated, phosphate [3:1] [IPPP]), 95906-11-9, 19186-97-1, 26040-51-7, 35948-25-5, 21850-44-2, and 25713-60-4. Cell viability, the 7-ethoxyresorufin-O-deethylase assay, and transcriptomic analysis using species-specific ToxChip polymerase chain reaction arrays were performed to evaluate the in vitro effect of these OFRs. Of the 10 OFRs assessed, BPDP and IPPP elicited the strongest cytotoxic and transcriptomic responses in both chicken and double-crested cormorant hepatocytes and are therefore recommended as priority candidates for further wildlife toxicological investigations. Environ Toxicol Chem 2018;37:3134-3144. © 2018 Crown in the right of Canada. Published by Wiley Periodicals Inc. on behalf of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".