A validated liquid chromatography/tandem mass spectrometry method for 4,4′‐methylenedianiline quantitation in human urine as a measure of 4,4′‐methylene diphenyl diisocyanate exposure
Bibliographic record
Abstract
RATIONALE: 4,4'-Methylene diphenyl diisocyanate (MDI) is a highly reactive isocyanate used in the production of polyurethanes. Workers exposed to these products may develop sensitization to the diisocyanate compounds, leading to occupational asthma. Quantifying MDI levels is necessary to ensure workplace safety. MDI is metabolized by acetylation and/or conjugation to macromolecules for excretion into urine. All metabolites can be chemically hydrolyzed to form the free diamine 4,4'-methylenedianiline (MDA) as a urinary biomarker of MDI exposure. Current methods involve long sample preparation, or have been designed using costly automation. There is therefore a need to develop a new practical method for assessing exposure to MDI. METHODS: Urine samples were acidified and heated to form MDA, followed by neutralization and liquid-liquid extraction. Extracts were separated by reversed-phase chromatography on a HSS T3 column followed by analysis on a triple quadrupole mass spectrometer in multiple reaction monitoring (MRM) mode. RESULTS: N-MDA was selected as the internal standard (IS) of choice following an investigation of internal standard stability. The hydrolysis efficiency, forming free MDA from conjugated metabolites in vivo, was evaluated using 4,4'-methylenebis(acetanilide) spiked into urine and complete hydrolysis occurred after 1 h. A dynamic range of 5 to 500 nM was achieved, and was useful for monitoring MDI exposure considering the biological guidance value (BGV) of 10 μg/L (~50 nM) proposed by the German Research Foundation (DFG). The limit of detection (LOD) and limit of quantification (LOQ) of the method were 0.8 and 2.7 nM, respectively. The intra-day and inter-day precisions were 4.33% and 4.27%, respectively. Finally, the method was tested with inter-laboratory samples from the German External Quality Assessment Scheme (G-EQUAS) program and the results submitted were all within the allowable tolerance range. CONCLUSIONS: A practical and validated method for the analysis of small- to medium-sized batches of samples has been developed for the biological monitoring of MDI exposure in human urine.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".