Towards a robust test to detect gene doping for anabolic enhancement in human athletes
Bibliographic record
Abstract
Success in gene therapy in treating human disease makes this technology attractive to enhance athletic performance, creating the need for gene doping detection. In 2021, World Anti-Doping Agency (WADA) approved the first gene doping test. Here, we describe a new method to detect doping with four additional genes, follistatin, growth hormone 1, growth hormone-releasing hormone and insulin-like growth factor 1, that may improve performance by increasing muscle size and strength. The method utilises four hydrolysis probe-based polymerase chain reaction (PCR) assays that target the transgenes based on the coding sequence of the four endogenous genes. The assays are specific, reproducible and capable to detect five copies of transgene in the presence of very similar endogenous gene in 25,000 times excess. To underpin reliable and comparable routine method performance by doping testing laboratories, a synthetic reference material for the method was designed and generated following the ISO Guide 35. The complete method was validated in blood samples using plasma as extraction matrix and QIAamp DNA blood midi DNA extraction kit. All blood samples from different donors (n = 8) simulated to be negative or positive (1500 transgene copies spiked per millilitre of blood) for the transgenes were reported correctly. The new method that targets four additional genes will extend the capabilities of laboratories involved in doping control to protect athletes' health, fairness and equality.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".