Myostatin inhibitors in sports drug testing: Detection of myostatin‐neutralizing antibodies in plasma/serum by affinity purification and Western blotting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it