A screening and determinative method for the analysis of natural and synthetic steroids, stilbenes and resorcyclic acid lactones in bovine urine
Bibliographic record
Abstract
Our laboratory has four separate methods for the analysis of trenbolone, stilbenes, resorcyclic acid lactones, and estradiol in bovine urine. The method described in this paper was in response to a client request to consolidate the methods preferably into one method. A multiresidue semi-quantitative method was developed and any suspect positive samples detected by the method were subjected to the method of standard addition to accurately quantify the concentration of the positive analyte. Samples were enzymatically hydrolyzed using β-glucuronidase after which, supported liquid extraction on HM-N cartridges was performed, followed by solvent exchange into methyl tert-butyl ether (MTBE). The samples were evaporated and reconstituted into 10% methanol in water and loaded onto a SampliQ OPT SPE. The cleaned-up extract was further cleaned up on a SampliQ NH2 cartridge. The SPE eluate was split into two for analysis by gas chromatography-mass spectrometry (GC-MS) using electron ionization (EI) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) using both positive and negative electrospray. It was found that with the exception of estradiol and trenbolone all compounds could be analyzed by both GC-MS and LC-MS/MS, providing a semi-quantitative method. It is recommended that quantification is achieved using standard addition. Of the 13 compounds successfully monitored, the limits of detection (LODs), and the limits of quantification (LOQs) obtained were within the Codex limits for the target concentrations. As far as the authors are aware, the use of supported liquid extraction has not been reported for bovine urine analysis. © 2016 Her Majesty the Queen in Right of Canada. Drug Testing and Analysis © 2016 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".