Emerging wound-healing injectable polydeoxyribonucleotide: potential as a prohibited doping method and its simple detection via CRISPR/Cas12a system
Bibliographic record
Abstract
Polydeoxyribonucleotide (PDRN), derived from chum salmon (Oncorhynchus keta), is a mixture of hydrolyzed DNA fragments used in various clinical applications. Its therapeutic value stems from its ability to promote wound healing by upregulating growth factors like VEGF, FGF, and HIF-1. However, PDRN's regenerative properties raise concerns about its potential misuse in sports. Studies suggest it may enhance athletic performance by stimulating muscle growth, recovery, and endurance through mechanisms such as satellite cell activation, angiogenesis, and anti-inflammatory effects. These potential performance-enhancing effects could be considered gene or cell doping, prohibited by the World Anti-Doping Agency (WADA). To address this concern, we developed a sensitive and specific detection method for PDRN misuse based on the CRISPR-Cas12a system. This method targets conserved 12S and 16S rDNA sequences unique to salmonids. A direct PCR method was optimized to amplify these target sequences from human plasma and urine without prior DNA extraction. The amplified DNA was then subjected to Cas12a-mediated detection, resulting in a fluorescent signal upon successful target recognition. This method demonstrated high sensitivity, detecting as little as 0.8 pg(0.3 genome copies) of O. keta DNA in 10 μL of biological samples within 90 min, surpassing the detection limits of many current doping agents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".