Generic sample preparation and dual polarity liquid chromatography—time‐of‐flight mass spectrometry for high‐throughput screening in doping analysis
Bibliographic record
Abstract
The requirements on initial testing in doping control are getting tighter regarding efficiency and speed while the scope of analytes is getting more diverse and, consequently, the need for high-throughput methods is apparent. In this study, a comprehensive screening method for doping agents in human urine is presented, based on solid phase extraction (SPE) and liquid chromatography-time-of-flight mass spectrometry (LCTOFMS). The method covers most of the compound groups in the list of prohibited substances by World Anti-Doping Agency (WADA). Mixed-mode SPE on two types of sorbent and the use of negative ionization mode besides the commonly used positive mode in electrospray ionization (ESI) allowed detection of acidic compounds, such as sulpho-conjugated metabolites. A run time of 8 minutes for each of the two ESI polarities was achieved. The method was validated regarding relative ionization efficiency, selectivity and signal to noise at the WADA's minimum required performance limit (MRPL) level, resulting in the acceptance of 197 compounds. A selection of 20 compounds was submitted for a more thorough validation, including extraction recovery, repeatability and linearity. Recovery and linearity (R(2)) varied mainly between 83-115% and 0.78-0.99, respectively. Median values for repeatability at the MRPL and 10 x MRPL levels were below 20%. A mean and median mass accuracy of 1.2 and 0.80 mDa, respectively, was achieved. The present method represents at the moment the widest coverage of low molecular weight prohibited substances for the screening in sports, providing an approach for further rationalisation of the analytical work-flow in the doping control laboratories.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".