Determination of <i>trans</i>,<i>trans</i>‐muconic acid in workers' urine through ultra‐performance liquid chromatography coupled to tandem mass spectrometry
Bibliographic record
Abstract
A novel method for the biological monitoring of benzene-exposed workers has been developed through ultra-performance liquid chromatography coupled to tandem mass spectrometry. The method uses trans,trans-muconic acid in urine as the benzene-exposure biomarker. The method was developed using a triple quadrupole mass spectrometer with enough sensitivity to facilitate diluting and injecting the urine samples directly, rather than performing a solid-phase extraction procedure as is common in the available protocols. Moreover, compared with a conventional high-pressure liquid chromatography system, the separation power provided by the ultra-performance liquid chromatography system allows a 10-fold reduction in run time. The method was adjusted to a dynamic range of between 198.9 and 4916.7 µg/L to cover the biological exposure index of trans,trans-muconic acid in urine. Also, the method demonstrated intra-day and inter-day precision at 98%, and accuracy within an acceptable range of 101 ± 8%. The method has been used to quantify various types of urine samples, such as workers' urine and inter-laboratory proficiency tests. Depending on the sample, the quantified levels ranged from less than the limit of quantitation to 3836.7 µg/L. No levels exceeding the calibration range were detected in the urine of workers, and the reported concentrations in urine for the proficiency tests were, as expected, based on known values. Moreover, the new method using sample dilution and faster chromatographic run was more effective, facilitating fast communication of results, as needed, to decision-makers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".