Detection and characterization of betamethasone metabolites in human urine by LC‐MS/MS
Bibliographic record
Abstract
Glucocorticosteroids are prohibited in sports when administered by systemic routes and allowed using other administrations for therapeutic reasons. Therefore, markers to distinguish between routes of administration through the analysis of urine samples are needed in anti-doping control. As a first step to achieve that goal, the metabolism of betamethasone (BET) was investigated in the present work. Urine samples obtained after BET intramuscular injection were hydrolyzed with β-glucuronidase and subjected to liquid-liquid extraction with ethyl acetate in alkaline conditions. The extracts were analyzed by liquid chromatography coupled to tandem mass spectrometry. Common open screening methods for fluorine containing corticosteroids (precursor ion scan method of m/z 121, 147, 171, and neutral loss (NL) scan methods of 20 and 38 Da in positive ionization, and 46 and 76 Da in negative ionization) were applied to detect BET metabolites. Moreover, an NL method was applied to detect A-ring reduced metabolites of BET, which are ionized as [M+NH4 ](+) (NL of 55, 73, and 91 Da, corresponding to the consecutive losses of NH3 , HF and one, two and three water molecules, respectively). BET and 24 metabolites were detected. Six metabolites were identified by comparison with standards, and for ten, feasible structures were proposed based on mass spectrometric data. Eleven of the characterized metabolites had not been previously reported. Metabolites resulting from 11-oxidation, 6-hydroxylation, C20 or 4-ene-3-one reduction and combination of some of them were detected. Moreover one metabolite resulting from cleavage of the side chain with subsequent oxidation of carbon at C17 was also detected.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".