Generic sample preparation combined with high-resolution liquid chromatography–time-of-flight mass spectrometry for unification of urine screening in doping-control laboratories
Bibliographic record
Abstract
A unification of doping-control screening procedures of prohibited small molecule substances--including stimulants, narcotics, steroids, beta2-agonists and diuretics--is highly urgent in order to free resources for new classes such as banned proteins. Conceptually this may be achieved by the use of a combination of one gas chromatography-time-of-flight mass spectrometry method and one liquid chromatography-time-of-flight mass spectrometry method. In this work a quantitative screening method using high-resolution liquid chromatography in combination with accurate-mass time-of-flight mass spectrometry was developed and validated for determination of glucocorticosteroids, beta2-agonists, thiazide diuretics, and narcotics and stimulants in urine. To enable the simultaneous isolation of all the compounds of interest and the necessary purification of the resulting extracts, a generic extraction and hydrolysis procedure was combined with a solid-phase extraction modified for these groups of compounds. All 56 compounds are determined using positive electrospray ionisation with the exception of the thiazide diuretics for which the best sensitivity was obtained by using negative electrospray ionisation. The results show that, with the exception of clenhexyl, procaterol, and reproterol, all compounds can be detected below the respective minimum required performance level and the results for linearity, repeatability, within-lab reproducibility, and accuracy show that the method can be used for quantitative screening. If qualitative screening is sufficient the instrumental analysis may be limited to positive ionisation, because all analytes including the thiazides can be detected at the respective minimum required levels in the positive mode. The results show that the application of accurate-mass time-of-flight mass spectrometry in combination with generic extraction and purification procedures is suitable for unification and expansion of the window of screening methods of doping laboratories. Moreover, the full-scan accurate-mass data sets obtained still allow retrospective examination for emerging doping agents, without re-analyzing the samples.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".