Elucidation of urinary metabolites of fluoxymesterone by liquid chromatography‐tandem mass spectrometry and gas chromatography‐mass spectrometry
Bibliographic record
Abstract
The suitability of liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography mass spectrometry (GC-MS) for the elucidation of fluoxymesterone metabolism has been evaluated. Electrospray ionization (ESI) and collision induced dissociation (CID) fragmentation in LC-MS/MS and electron impact spectra (EI) in GC-MS have been studied for fluoxymesterone and two commercially available metabolites. MS(n) experiments and accurate mass measurements performed by an ion-trap analyser and a QTOF instrument respectively have been used for the elucidation of the fragmentation pathway. The neutral loss scan of 20 Da (loss of HF) in LC-MS/MS has been applied for the selective detection of fluoxymesterone metabolites. In a positive fluoxymesterone doping control sample, 9 different analytes have been detected including the parent compound. Seven of these metabolites were also confirmed by GC-MS including 5 previously unreported metabolites. On the basis of the ionization, the CID fragmentation, the accurate mass of the product ions and the EI spectra of these analytes, a tentative elucidation as well as a proposal for the metabolic pathway of fluoxymesterone has been suggested. The presence of these compounds has also been confirmed by the analysis of five other positive fluoxymesterone urine 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".