New sensitive LC-MS/MS method for the simultaneous determination of 13 phenolic and carboxylic acid pesticide biomarkers in human urine, including dicamba
Bibliographic record
Abstract
The commercialization in 2016 of genetically engineered seeds tolerant to dicamba and/or 2,4-dichlorophenoxyacetic acid (2,4-D) has caused a rapid increase in the use of these herbicides. New questions about the reproductive and chronic health effects of long-term exposure to these herbicides have been raised. To assess exposure to dicamba and other pesticides of interest in the Heartland Study, a birth cohort study based in the United States, a new analytical method was needed. The present study describes the development and validation of this new solid phase extraction and liquid chromatography-tandem mass spectrometry method that detects simultaneously 13 pesticides or their metabolites in 250 μL of urine. More specifically, the method allows the analysis of dicamba, 2,4-D and 2,4,5-trichlorophenoxyacetic acid (2,4,5-T), which are herbicides, of malathion dicarboxylic acid (MDA), para-nitrophenol (PNP), 3,5,6-trichloro-2-pyridinol (TCPy), 2-diethylamino-6-methylpyrimidin-4-ol (DEAMPY) and 2-isopropyl-6-methyl-4-pyrimidinol (IMPY), which are metabolites of organophosphate insecticides, and finally of cis-3-(2,2-Dichlorovinyl)-2,2-dimethylcyclopropane carboxylic acid (cis-DCCA), trans-3-(2,2-Dichlorovinyl)-2,2-dimethylcyclopropane carboxylic acid (trans-DCCA), 3-Phenoxybenzoic acid (3-PBA), 4-Fluoro-3-phenoxybenzoic acid (4-F-3-PBA) and cis-3-(2,2-Dibromovinyl)-2,2-dimethylcyclopropane carboxylic acid (cis-DBCA), which are metabolites of synthetic pyrethroids insecticides. The method was validated under ISO/IEC 17025 guidance. The limit of detection (LOD) in urine samples was 0.10 μg/L for dicamba, while the LOD for other analytes ranged between 0.0038 μg/L and 0.091 μg/L. Accuracy was evaluated by analyzing samples from two External Quality Assessment Schemes, namely G-EQUAS and OSEQAS. Preliminary results obtained following the analysis of 91 urine samples taken from pregnant women enrolled in the Heartland Study are presented here. This method is suitable for human biomonitoring studies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".