Myostatin inhibitors in sports drug testing: Detection of myostatin‐neutralizing antibodies in plasma/serum by affinity purification and Western blotting
Bibliographic record
Abstract
PURPOSE: Myostatin is a key regulator of skeletal muscle growth and inhibition of its signaling pathway results in an increased muscle mass and function. The aim of this study was to develop a qualitative detection assay for myostatin-neutralizing antibodies for doping control purposes by using immunological approaches. EXPERIMENTAL DESIGN: To detect different types of myostatin-neutralizing antibodies irrespective of their amino acid sequence, an immunological assay specific for antibodies directed against myostatin and having a human Fc domain was established. Affinity purification and Western blotting strategies were combined to allow extracting and identifying relevant analytes from 200 μL of plasma/serum in a non-targeted approach. The assay was characterized regarding specificity, linearity, precision, robustness, and recovery. RESULTS: The assay was found to be highly specific, robust, and linear from 0.1 to 1 μg/mL. The precision was successfully specified at three different concentrations and the recovery of the affinity purification was 58%. CONCLUSIONS AND CLINICAL RELEVANCE: Within this study, an immunological detection assay for myostatin-neutralizing antibodies present in plasma/serum specimens was developed and successfully characterized. The presented approach can easily be modified to include other therapeutic antibodies and serves as proof-of-concept for the detection of antibody-based myostatin inhibitors in doping control samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".