Effect of oral fluid in urine samples on the analysis of selected erythropoietin receptor agonists and detection of saliva-specific markers for doping control purposes
Bibliographic record
Abstract
Due to their performance-enhancing effect, erythropoiesis-stimulating agents (ESAs) are banned at all times by the World Anti-Doping Agency (WADA) in competitive sports. Doping control analyses for such compounds are routinely performed using gel electrophoretic and immunoblotting techniques, and degradation of the analytes can severely impair detection, results evaluation and interpretation. As oral fluid (OF) contains significant amounts of proteases, the question of whether its addition to a doping control urine sample may impede anti-doping analysis needs to be addressed. Intentional tampering attempts are likewise prohibited by WADA and require a detection strategy. It was observed that the addition of OF can indeed lead to impairments of ESA analyses, though the fraction of unidentifiable ESA signals varies depending on several factors, such as the individual composition of the OF, the sex of the OF donor, the time of sampling, the OF volume and the incubation conditions. Overall, 20 % of all generally valid analyses were classified as unidentifiable, 12 % as impaired, and 69 % as identifiable, highlighting the relevance for strategies that allow for the identification of OF in urine. While human salivary α-amylase was found insufficiently reliable as a marker, peptides of salivary proline rich proteins (saPRP) were shown to be both specific for OF and traceable with adequate sensitivity using a newly developed LC-HRMS/MS method. The approach was comprehensively characterized shown to be fit-for-purpose for routine doping controls where tampering attempts with OF are suspected. • First systematic investigation of effects of oral fluid on urinary proteins. • Salivary α-amylase can contribute to detecting oral fluid contamination in urine. • Salivary proline-rich peptides are superior target analytes for identifying oral fluid in human urine. • A new test method contributing to future routine doping controls concerning potential sample manipulation was developed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
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".