Electrophoretic detection of black market myostatin propeptide
Bibliographic record
Abstract
Myostatin propeptide is prohibited according to chapter S4 of the "WADA 2022 List of Prohibited Substances and Methods." So far, no approved myostatin-propeptide pharmaceuticals are available. Nevertheless, myostatin-propeptides can be bought on the black market for "research purposes." A study on black market myostatin propeptide products is presented as well as electrophoretic detection methods for serum and urine. Out of the 12 tested products, only nine actually contained the protein. Separation by SDS-PAGE revealed that the nine products were relatively impure and that the main compound had a much higher mass (approximately 54-55 kDa) than expected (approximately 33 kDa). Further analyses by mass spectrometry showed that the elevated molecular mass was due to the presence of a full length GST-tag on the propeptide. The developed detection method for serum is based on immunoprecipitation (IP) followed by SDS-PAGE and Western blotting. In total, three anti-myostatin propeptide antibodies were tested. All of them were well suited for either IP or immunoblotting. The final protocol applies a biotinylated polyclonal antibody, streptavidin-coated magnetic beads, and a monoclonal detection antibody. For a sample volume of 500 μL serum, the detection limit of the method is approximately 2.5 ng/mL. The urine method applies a commercial ELISA for IP and performs with a limit of detection (LOD) of approximately 0.4 ng/mL. Furthermore, practically all currently available myostatin propeptide standards were also investigated. Due to the significant molecular mass difference of the black market products, an unambiguous differentiation from endogenous myostatin propeptide is possible.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".