Two‐dimensional high performance liquid chromatography purification of underivatized urinary testosterone and metabolites for compound‐specific stable carbon isotope analysis
Bibliographic record
Abstract
Abstract Testosterone doping in sports is detected through the measurement of the carbon isotopic signature (δ 13 C) of testosterone and its metabolites in urine. A critical step in achieving accurate and precise δ 13 C values during compound‐specific stable carbon isotope analysis (CSIA) is the removal of interfering matrix components. To this end, the World Anti‐Doping Agency (WADA) recommends the use of high‐performance liquid chromatography (HPLC) as a method of sample pretreatment. We provide a description of an automated two‐dimensional HPLC (2D‐HPLC) purification method for urine extracts that has made possible the CSIA of underivatized steroids, requiring only 36 min per sample. Eight urinary steroids including testosterone (T) and dehydroepiandrosterone (DHEA) and four of their metabolites as well as two endogenous reference compounds were collected during HPLC purification. Comparative GC chromatograms are used to contrast the efficiency of two‐dimensional (2D) purification to a previously established 1D‐HPLC method. The 2D purification leads to improved sample purity while simultaneously decreasing the analysis time, allowing for unprecedented sample throughput. Precision of δ 13 C for all analyzed compounds in negative and positive controls was 0.5‰ or better, which is comparable with the precision of pure reference materials at similar intensities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".