Analysis of human serum and urine for tentative identification of potentially carcinogenic pesticide-associated N-nitroso compounds using high-resolution mass spectrometry
Bibliographic record
Abstract
Human serum and urine samples were analyzed for a suite of nitrosatable pesticides and potentially carcinogenic pesticide-associated N-nitroso (PANN) compounds. Formation of PANN compounds may occur in vivo after consumption of food or water containing trace amounts of nitrosatable pesticide residues and nitrate. Using a modified version of the Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) method, nine nitrosatable pesticides and byproducts were extracted from serum and urine from 64 individuals from two different sample populations in Atlantic Canada: (i) Prince Edward Island, a region where nitrate and trace amounts of nitrosatable pesticides have been detected in groundwater; and (ii) Halifax, Nova Scotia, a non-agricultural urban area. Samples were then analyzed using ultra-high pressure liquid chromatography (UHPLC) coupled with high-resolution accurate mass (HRAM) single-stage orbitrap mass spectrometry (MS), which allows for semi-targeted analysis and tentative identification of a virtually limitless number of exposure biomarkers. Two nitrosatable target analytes, ethylenethiourea (ETU) and 3,5,6-trichloro-2-pyridinol (TCPy) were found in serum, while atrazine (ATR) and ETU were detected in urine. Five and six PANN compounds were tentatively identified in serum and urine, respectively. The two PANN compounds that were most frequently tentatively identified in serum were N-nitroso dimethoate (N-DIM) and N-nitroso omethoate (N-OME) with detection frequencies of 78% and 95%, respectively. This is the first biomonitoring study of its kind to investigate PANN compounds in human serum and urine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".