Rapid biomonitoring of perfluoroalkyl substance exposures in serum by multisegment injection‐nonaqueous capillary electrophoresis‐tandem mass spectrometry
Bibliographic record
Abstract
Abstract Perfluoroalkyl substances (PFASs) are a major contaminant class due to their ubiquitous prevalence, persistence, and putative endocrine disrupting activity that may contribute to chronic disease risk notably with exposures early in life. Herein, multisegment injection‐nonaqueous capillary electrophoresis‐tandem mass spectrometry (MSI‐NACE‐MS/MS) is introduced as a high throughput approach for PFAS screening in serum samples following a simple methyl‐tert‐butyl ether (MTBE) liquid extraction. Separation and ionization conditions were optimized to quantify low nanomolar concentration levels of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) from serum extracts when using multiple reaction monitoring under negative ion mode conditions. Multiplexed separations of PFOA and PFOS were achieved with excellent throughput (<3 min/sample), adequate concentration sensitivity (LOD ∼ 20 nM, S/N = 3) and good technical precision over three consecutive days of analysis (mean CV = 9.1%, n = 84). Accurate quantification of PFASs was demonstrated in maternal serum samples (n = 16) when using MSI‐CE‐MS/MS following pre‐column sample enrichment with median concentrations of 3.46 nM (0.7‐9.0 nM) and 3.29 nM (1.5‐6.6 nM) for PFOA and PFOS, respectively. This was lower than average PFAS exposures measured in pregnant women who had serum collected prior to 2009 likely due to subsequent phase out of their production. Overall, this method offers a convenient approach for large‐scale biomonitoring of environmental exposures to legacy PFASs and their emerging replacements that is relevant to maternal health and chronic disease risk assessment in children.
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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) | 0.001 | 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 | 0.001 | 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 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".