A comprehensive UHPLC–MS/MS method for the analysis of endogenous and exogenous steroids in serum for anti‐doping purposes
Bibliographic record
Abstract
In the context of steroid analyses, the use of blood could represent a valuable complement to urine. While the blood steroid profile is currently being established to aid unveiling testosterone (T) doping, this matrix is also well suited for detection of exogenous anabolic steroids and steroid esters. In this study, a method to determine a simplified blood steroid profile in combination with the direct detection of exogenous anabolic steroids and steroid esters using just one serum aliquot was developed to obtain a comprehensive analytical workflow. Following the first chromatographic analysis of endogenous and exogenous steroids, samples were derivatised with Girard's reagent T (GT) to improve the ionisation of steroid esters and re-injected. The quantitative performance for T, androstenedione (A4) and 5α-dihydrotestosterone (DHT) was evaluated and the method was validated for qualitative analysis of exogenous analogues with estimated limits of detection (LOD) between 50 and 500 pg/ml. To demonstrate the applicability of the method, samples collected from a clinical study with an oral administration of testosterone undecanoate (TU) to 19 male volunteers were then analysed. The individual serum steroid profiles with the endogenous markers T, A4 and DHT were established as well as the concentrations of TU. TU was detected in all 19 volunteers up to 24 h, while DHT represented the most promising biomarker in endogenous steroid profile for the detection of oral TU administration. These results showed that the selected approach to combine exogenous and endogenous steroid analysis has the potential to strengthen T doping detection in the future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".