Large Differences in Urinary Benzene Metabolite S-Phenylmercapturic Acid Quantitation: A Comparison of Five LC–MS-MS Methods
Bibliographic record
Abstract
Benzene is a known genotoxic carcinogen linked to many hematological abnormalities. S-phenylmercapturic acid (PHMA, N-acetyl-S-(phenyl)-L-cysteine, CAS# 4775-80-8) is a urinary metabolite of benzene and is used as a biomarker to assess benzene exposure. Pre-S-phenylmercapturic acid (pre-PHMA) is a PHMA precursor that dehydrates to PHMA at acidic pH. Published analytical methods that measure urinary PHMA adjust urine samples to a wide range of pH values using several types of acid, potentially leading to highly variable results depending on the concentration of pre-PHMA in a sample. Information is lacking on the variation in sample preparation among laboratories regularly measuring PHMA and the effect of those differences on PHMA quantitation in human urine samples. To investigate the differences in PHMA quantitation, we conducted an inter-laboratory comparison that included the analysis of 50 anonymous human urine samples (25 self-identified smokers and 25 self-identified non-smokers), quality control samples and commercially available reference samples in five laboratories using different analytical methods. Observed urinary PHMA concentrations were proportionally higher at lower pH, and results for anonymous urine samples varied widely among the methods. The method with the neutral preparation pH yielded results about 60% lower than the method using the most acidic conditions. Samples spiked with PHMA showed little variation, suggesting that the variability in results in human urine samples across methods is driven by the acid-mediated conversion of pre-PHMA to PHMA.
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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) | 0.001 | 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".