Urinary detection of conjugated and unconjugated anabolic steroids by dilute‐and‐shoot liquid chromatography‐high resolution mass spectrometry
Bibliographic record
Abstract
Anabolic androgenic steroids (AAS) are an important class of doping agents. The metabolism of these substances is generally very extensive and includes phase-I and phase-II pathways. In this work, a comprehensive detection of these metabolites is described using a 2-fold dilution of urine and subsequent analysis by liquid chromatography-high resolution mass spectrometry (LC-HRMS). The method was applied to study 32 different metabolites, excreted free or conjugated (glucuronide or sulfate), which permit the detection of misuse of at least 21 anabolic steroids. The method has been fully validated for 21 target compounds (8 glucuronide, 1 sulfate and 12 free steroids) and 18 out of 21 compounds had detection limits in the range of 1-10 ng mL(-1) in urine. For the conjugated compounds, for which no reference standards are available, metabolites were synthesized in vitro or excretion studies were investigated. The detection limits for these compounds ranged between 0.5 and 18 ng mL(-1) in urine. The simple and straightforward methodology complements the traditional methods based on hydrolysis, liquid-liquid extraction, derivatization and analysis by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".