Combined detection of inhibitors of the activin receptor signaling pathways (IASPs) by means of LC-HRMS/MS for human doping control
Bibliographic record
Abstract
Members of the transforming growth factor beta superfamily such as myostatin, activin A, and GDF-11, are dimeric cytokines signaling through activin receptors. They play important regulative roles in different biological processes as the formation of muscle and red blood cells. Therefore, inhibitors of the activin receptor signaling pathways (IASPs) are potential performance-enhancing agents in sports, which are included in sections S2 ("Peptide hormones, growth factors, related substances and mimetics") and S4 ("Hormone and metabolic modulators") of the WADA Prohibited List. Within this research project, a multiplexed detection assay for nine IASPs in doping control serum/plasma samples by means of (immuno-)affinity purification, tryptic digestion and LC-HRMS/MS was developed. The method was validated and proved to be specific and sensitive (LOD: 10-50 ng/mL). Additionally, it was modified to allow for using urine. As proof-of-concept, authentic Luspatercept serum and urine and Sotatercept serum post-administration samples were successfully analyzed. Luspatercept could be detected in both matrices up to 70 days after the initial and 7 weeks after the second dose. Sotatercept was successfully detected in a serum sample collected 43 h following injection. The presented method can be employed in doping control routine analysis as a qualitative initial testing procedure.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".