Analysis of anabolic androgenic steroids as sulfate conjugates using high performance liquid chromatography coupled to tandem mass spectrometry
Bibliographic record
Abstract
Improvements in doping analysis can be effected by speeding up analysis time and extending the detection time. Therefore, direct detection of phase II conjugates of doping agents, especially anabolic androgenic steroids (AAS), is proposed. Besides direct detection of conjugates with glucuronic acid, the analysis of sulfate conjugates, which are usually not part of the routine doping control analysis, can be of high interest. Sulfate conjugates of methandienone and methyltestosterone metabolites have already been identified as long-term metabolites. This study presents the synthesis of sulfate conjugates of six commonly used AAS and their metabolites: trenbolone, nandrolone, boldenone, methenolone, mesterolone, and drostanolone. In the following these sulfate conjugates were used for development of a fast and easy analysis method based on sample preparation using solid phase extraction with a mixed-mode sorbent and detection by high performance liquid chromatography coupled to tandem mass spectrometry (HPLC-MS/MS). Validation demonstrated the suitability of the method with regard to the criteria given by the technical documents of the World Anti-Doping Agency (WADA). In addition, suitability has been proven by successful detection of the synthesized sulfate conjugates in excretion urines and routine doping control samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".