Gene doping: an overview and current implications for athletes
Bibliographic record
Abstract
The possibility of gene doping, defined as the transfer of nucleic acid sequences and/or the use of normal or genetically modified cells to enhance sport performance, is a real concern in sports medicine. The abuse of knowledge and techniques gained in the area of gene therapy is a form of doping, and is prohibited for competitive athletes. As yet there is no conclusive evidence that that gene doping has been practiced in sport. However, given that gene therapy techniques improve continuously, the likelihood of abuse will increase. A literature search was conducted to identify the most relevant proteins based on their current gene doping potential using articles from Pubmed, Scopus and Embase published between 2006 and 2011. The final list of selected proteins were erythropoietin, insulin-like growth factor, growth hormone, myostatin, vascular endothelial growth factor, fibroblast growth factor, endorphin and enkephalin, α actinin 3, peroxisome proliferator-activated receptor-delta (PPARδ) and cytosolic phosphoenolpyruvate carboxykinase (PEPCK-C). We discuss these proteins with respect to their potential benefits, existing gene therapy experience in humans, potential risks, and chances of detection in current and future anti-doping controls. We have identified PPARδ and PEPCK-C as having high potential for abuse. But we expect that for efficiency reasons, there will be a preference for inserting gene target combinations rather than single gene doping products. This will also further complicate detection.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".