Enantiomer Fractions of Chiral Perfluorooctanesulfonate (PFOS) in Human Sera
Bibliographic record
Abstract
Perfluorooctane sulfonate (PFOS) is the most prominent perfluoroalkyl contaminant in humans and wildlife, but there is great uncertainty in exposure pathways, particularly with respect to the importance of PFOS-precursors (PreFOS). We explored the hypothesis that nonracemic proportions of chiral PFOS in serum are qualitative and semiquantitative biomarkers of human PreFOS exposure. A new chiral HPLC-MS/MS method was developed for alpha-perfluoromethyl branched PFOS (1m-PFOS, typically 2-3% of total PFOS) and applied to enantiomer fraction (EF) analysis in biological samples. In blood and tissues of rodents exposed subchronically to electrochemical PFOS, 1m-PFOS was racemic (EF = 0.485-0.511) and no evidence for enantioselective excretion was found in this model mammal. 1m-PFOS in serum of pregnant women, from Edmonton, was significantly nonracemic, with a mean EF (±standard deviation) of 0.432 ± 0.009, similar to pooled North American serum. In a highly exposed Edmonton family (mother, father, and 5 children) living in a house where ScotchGard had been applied repeatedly to carpet and upholstery, EFs ranged from 0.35 to 0.43, significantly more nonracemic than in pregnant women. Semiquantitative estimates of % serum 1m-PFOS coming from 1m-PreFOS biotransformation in both subpopulations were in reasonable agreement with model predictions of human exposure to PFOS from PreFOS. The data were overall suggestive that the measured nonracemic EFs were influenced by the relative extent of exposure to PreFOS. The possibility of using 1m-PFOS EFs for assessing the relative contribution of 1m-PreFOS (or PreFOS in general) in biological samples requires further application before being fully validated, but could be a powerful tool for probing general sources of PFOS in environments where the importance of PreFOS is unknown.
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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".