Comprehensive plasma‐screening for known and unknown substances in doping controls
Bibliographic record
Abstract
Occasionally, doping analysis has been recognized as a competitive challenge between cheating sportsmen and the analytical capabilities of testing laboratories. Both have made immense progress during the last decades, but obviously the athletes have the questionable benefit of frequently being able to switch to new, unknown and untested compounds to enhance their performance. Thus, as analytical counteraction and for effective drug testing, a complementary approach to classical targeted methods is required in order to implement a comprehensive screening procedure for known and unknown xenobiotics. The present study provides a new analytical strategy to circumvent the targeted character of classical doping controls without losing the required sensitivity and specificity. Using 50 microL of plasma only, the method potentially identifies illicit drugs in low ng/mL concentrations. Plasma provides the biological fluid with the circulating, unmodified xenobiotics; thus the identification of unknown compounds is facilitated. After a simple protein precipitation, liquid chromatographic separation and subsequent detection by means of high resolution/high accuracy orbitrap mass spectrometry, the procedure enables the determination of numerous compounds from different classes prohibited by the World Anti-Doping Agency (WADA). A new hyphenated mass spectrometry technology was employed without precursor ion selection for higher collision energy dissociation (HCD) fragmentation experiments. Thus the mass spectra contained all the desired information to identify unknown substances retrospectively. The method was validated for 32 selected model compounds for qualitative purposes considering the parameters specificity, selectivity, limit of detection (<0.1-10 ng/mL), precision (9-28%), robustness, linearity, ion suppression and recovery (80-112%). In addition to the identification of unknown compounds, the plasma samples were simultaneously screened for known prohibited targets.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".