Methylclostebol Metabolism Discovery by Untargeted Analysis
Bibliographic record
Abstract
RATIONALE: This work introduces an alternative experimental approach by integrating high-resolution mass spectrometry (HRMS) with multivariate statistical analysis for metabolite detection and identification. The integration of these tools maximizes information extraction from data, improving accuracy and reducing the risk of false identifications. METHODS: Seven volunteers' urine samples were collected before and after oral administration of 10 mg of methylclostebol (4-chloro-17β-hydroxy-17α-methylandrost-4-en-3-one, ClMT) and assigned to three excretion time intervals. Analyses were carried out on a GC-HRMS system (Agilent 8890 GC coupled with 7250 GC/QTOF), utilizing low-energy electron ionization (< 18 eV) to preserve the native molecular skeleton, thereby simplifying mass spectrum interpretation, with acquisition in full scan mode. Raw data were then processed and subjected to multivariate analysis. RESULTS: The orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed to emphasize differences among specific sample conditions, and features that significantly contribute to classification in the OPLS-DA can be identified as important biomarkers. Samples from the three excretion intervals demonstrated clear separations, occupying distinct areas within the model's defined space. From this approach, the S-plot displayed seven features identified as biomarkers related to methylclostebol ingestion, comparing their mass spectra with an in-house library of LE-EI mass spectra. CONCLUSIONS: The application of this approach is demonstrated to enhance the identification of new markers related to the intake of prohibited substances in the anti-doping field, such as methylclostebol. Its application proved to be an alternative strategy that allows for gathering a more comprehensive range of information in the antidoping field.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".