Urine Testing for Designer Steroids by Liquid Chromatography with Androgen Bioassay Detection and Electrospray Quadrupole Time-of-Flight Mass Spectrometry Identification
Bibliographic record
Abstract
New anabolic steroids show up occasionally in sports doping and in veterinary control. The discovery of these designer steroids is facilitated by findings of illicit preparations, thus allowing bioactivity testing, structure elucidation using NMR and mass spectrometry, and final incorporation in urine testing. However, as long as these preparations remain undiscovered, new designer steroids are not screened for in routine sports doping or veterinary control urine tests since the established GC/MS and LC/MS/MS methods are set up for the monitoring of a few selected ions or MS/MS transitions of known substances only. In this study, the feasibility of androgen bioactivity testing and mass spectrometric identification is being investigated for trace analysis of designer steroids in urine. Following enzymatic deconjugation and a generic solid-phase extraction, the samples are analyzed by gradient LC with effluent splitting toward two identical 96-well fraction collectors. One well plate is used for androgen bioactivity detection using a novel robust yeast reporter gene bioassay yielding a biogram featuring a 20-s time resolution. The bioactive wells direct the identification efforts to the corresponding well numbers in the duplicate plate. These are subjected to high-resolution LC using a short column packed with 1.7-microm C18 material and coupled with electrospray quadrupole time-of-flight mass spectrometry (LC/QTOFMS) with accurate mass measurement. Element compositions are calculated and used to interrogate electronic substance databases. The feasibility of this approach for doping control is demonstrated via the screening of human urine samples spiked with the designer anabolic steroid tetrahydrogestrinone. Application of the proposed methodology, complementary to the established targeted urine screening for known anabolics, will increase the chance of finding unknown emerging designer steroids, rather then being solely dependent on findings of the illicit preparations themselves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".