Simultaneous Determination of 11 -Agonists in Human Urine Using High-Performance Liquid Chromatography/Tandem Mass Spectrometry with Isotope Dilution
Bibliographic record
Abstract
The misuse of β-agonists constitutes a potential risk to public health and has been forbidden in many countries. In this study, we describe a method for specific, sensitive and rapid detection of β-agonists in human urine. Urine samples were extracted with ethyl acetate, without any additional purification step, and analyzed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS-MS) with Clenbuterol-D9 and Salbuterol-D3 as internal standards. The intra- and interday precision values of the method were all <5.60% and the accuracy ranged from 94.5 to 109%. Extraction recovery for 11 β-agonists varied from 66.7 to 108%. One UPLC-MS-MS analysis could be completed within 12 min and the limits of detection for 11 β-agonists were 0.1 ng/mL in the experiment. β-Agonists in human urines from 24 volunteers were analyzed by our validated method and 1.70 ng/mL salbutamol was detected in one volunteer. The application of UPLC-MS-MS method in β-agonists detection of human urine will be helpful in veterinary control of β-agonists and for studying the effect of β-agonists on human health.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".