Simple high-throughput analytical method using ultra-performance liquid chromatography coupled with tandem mass spectrometry to quantify total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol in urine
Bibliographic record
Abstract
BACKGROUND: Since the urinary concentration of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) is a reliable biomarker of exposure to tobacco smoke, we developed a relatively simple high-throughput chromatographic method to quantify total urinary NNAL concentrations in the general population. METHODS: The high-throughput analytical method was developed using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) to identify and quantify total urinary NNAL concentrations in 10 non-smokers and 15 otherwise healthy smokers. RESULTS: Loss of nitric oxide at m/z 30 was found to be the predominant mass transitioned, and therefore was used as the SIM transition to quantify both NNAL and NNAL-methyl-d3 in urine. The analytical method did not require sample derivatization. Standard curves for total NNAL concentrations were linear between 20 and 1500 pg/mL, with coefficients of determination >0.95. Precision and accuracy ranged from 2.2% to 8.6% (CV) and from -5.6% to 10.9% (percent error), respectively. The lowest limit of quantification was 6.7 pg/mL, and 2.0 pg/mL the lowest limit of detection (LLOD). Total urinary NNAL concentrations in non-smoker subjects were <LLOD, whereas in smokers varied between
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".