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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".