Hair as a Biomarker of Systemic Exposure to Polybrominated Diphenyl Ethers
Bibliographic record
Abstract
The efficacy of using hair as a biomarker for exposure to polybrominated diphenyl ether (PBDE) flame retardants was assessed in humans and an animal model. Paired human hair and serum samples were obtained from adult men and women (n = 50). In parallel, hair, serum, liver, and fat were collected from adult male Sprague-Dawley rats exposed to increasing doses of the PBDE mixture found in house dust for 70 days via the diet. All samples were analyzed by GC-MS for eight common PBDEs: BDE-28, -47, -99, -100, -153, -154, -183, and -209. Paired human hair and serum samples had five congeners (BDE-28, -47, -99, -100, and -154) with significant individual correlations (0.345-0.566). In rat samples, BDE-28 and BDE-183 were frequently below the level of detection. Significant correlations were observed for BDE-47, -99, -100, -153, -154, and -209 in rat hair, serum, liver, and fat across doses, with r values ranging from 0.803 to 0.988; weaker correlations were observed between hair and other tissues when data from the lowest dose group or for BDE-209 were analyzed. Thus, human and rat hair PBDE measurements correlate strongly with those in alternative matrices, validating the use of hair as a noninvasive biomarker of long-term PBDE exposure.
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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) | 0.000 | 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".