Evaluating External Contamination of Polybrominated Diphenyl Ethers in Human Hair
Bibliographic record
Abstract
BACKGROUND: Human hair is a well-validated matrix for detecting a variety of xenobiotics, including drugs of abuse (cocaine, tetrahydrocannabinol, and morphine) and fatty acid ethyl ethers. Recent studies have shown that hair can also be useful in determining an individual's exposure to polybrominated diphenyl ethers (PBDEs), flame retardants that contaminate the dust in our daily environment. Hair processing before assay varies with each analyte; in particular, the wash protocol must be optimized to remove external contaminants while not affecting levels of the chemical of interest. The aim of this study was to determine whether hair needs to be washed before analysis for PBDEs, and if so, which protocol is most effective to ensure that the level of PBDEs is neither overestimated nor underestimated. METHOD: Individual hair samples from 10 adults (5 men and 5 women) were subjected to 4 different wash protocols: (1) no wash, (2) water, (3) 10% sodium dodecyl sulfate (SDS), and (4) hexane. Both the washes and hair were analyzed for 8 PBDEs by gas chromatography/mass spectrometry. RESULTS: The sum of PBDEs (ΣPBDEs) in the washes was (1) no wash: 0 pg/mg, (2) water: 0.39 ± 0.19 (mean ± SEM), (3) 10% SDS: 1.34 ± 0.68, and (4) hexane: 1.92 ± 0.87. The ΣPBDEs in the hair were: (1) no wash: 20.32 ± 3.05, (2) water: 20.30 ± 2.41, (3) 10% SDS: 19.27 ± 1.87, and (4) hexane: 16.91 ± 2.89. Washing with water, 10% SDS, and hexane decreased the PBDE levels by 1.9%, 7%, and 11.4%, respectively (P < 0.05). CONCLUSIONS: Thus, of the washes evaluated, water is the wash that had the least effect on total PBDE concentrations, providing the best evaluation of an individual's exposure to PBDEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".