Mutagenic Azo Dyes, Rather Than Flame Retardants, Are the Predominant Brominated Compounds in House Dust
Bibliographic record
Abstract
Characterization of toxicological profiles by use of traditional targeted strategies might underestimate the risk of environmental mixtures. Unbiased identification of prioritized compounds provides a promising strategy for meeting regulatory needs. In this study, untargeted screening of brominated compounds in house dust was conducted using a data-independent precursor isolation and characteristic fragment (DIPIC-Frag) approach, which used data-independent acquisition (DIA) and a chemometric strategy to detect peaks and align precursor ions. A total of 1008 brominated compound peaks were identified in 23 house dust samples. Precursor ions and formulas were identified for 738 (73%) of the brominated compounds. A correlation matrix was used to cluster brominated compounds; three large groups were found for the 140 high-abundance brominated compounds, and only 24 (17%) of these compounds were previously known flame retardants. The predominant class of unknown brominated compounds was predicted to consist of nitrogen-containing compounds. Following further validation by authentic standards, these compounds (56%) were determined to be novel brominated azo dyes. The mutagenicity of one major component was investigated, and mutagenicity was observed at environmentally relevant concentrations. Results of this study demonstrated the existence of numerous unknown brominated compounds in house dust, with mutagenic azo dyes unexpectedly being identified as the predominant compounds.
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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 | 0.001 |
| 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".