A Selective and Sensitive Gas Chromatography-Tandem Mass Spectrometry Method for Quantitation of Synthetic Musks in Human Serum
Bibliographic record
Abstract
BACKGROUND: Synthetic musk compounds are widely used as fragrances in many consumer products; however, information on human exposure and health effects is limited. Also, analytical methods for their quantification in biological matrices are limited. OBJECTIVE: In this study, an integrated method was developed and validated for the analysis of selected synthetic musk compounds in human serum. METHOD: The method is based on liquid-liquid extraction (LLE), sample clean-up by solid-phase extraction (SPE), and separation and detection by gas chromatography coupled with tandem mass spectrometry (GC-MS/MS). RESULTS: The method demonstrated good recoveries (86-105%) and high sensitivity, with low method detection limits (MDLs) ranging from 0.04 to 0.17 µg/L. The method was applied to the analysis of 10 synthetic musk compounds in 40 serum samples collected from Canadian women aged 20-44 years (20 individual samples collected in 2014 and 20 pooled samples collected in 2006). The most commonly detected compound was Galaxolide (HHCB), with median concentrations of 0.59 µg/L in samples collected in 2006, and 0.34 µg/L for samples collected in 2014. Musk ketone (MK) was not detected in any of the samples collected in 2006, but was detected in 60% of the samples collected in 2014 with a median concentration of 0.29 µg/L. Tonalide (AHTN) was detected in only one sample above its MDL (0.12 µg/L). CONCLUSIONS: This is the first study in Canada to report levels of synthetic musks in human. The data generated from this study has been used in risk screening assessment by Environment and Climate Change Canada and Health Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".