Detection of sgRNA via SHERLOCK as Potential CRISPR Related Gene Doping Control Strategy
Bibliographic record
Abstract
Apprehensions about gene doping have grown consistently due to advancements in gene engineering techniques, particularly with the emergence of clustered regularly interspaced short palindromic repeats/CRISPR-associated (CRISPR/Cas)-based tools. These tools not only provide unprecedented possibilities for illicit performance enhancement by athletes but also offer new avenues for the detection of gene doping through biosensing of nucleic acids. Hence, pursuing on a previous study, an analytical method based on reverse transcriptase-recombinase polymerase amplification (RT-RPA) and subsequent qualitative nucleic acid detection by means of Specific High Sensitive Enzymatic Reporter UnLOCKing (SHERLOCK) was optimized for the direct detection of sgRNA associated with Streptococcus pyogenes in serum. Detection device, assay parameters, and sample handling were adjusted, to overcome previously determined assay limitations. The conducted method characterization confirmed the methods’ specificity and increased detection sensitivity from 100 pM to 1 fM sgRNA in 100 μL of serum. Furthermore, reanalysis of in vivo mouse administration samples collected in a previous proof-of-concept study was conducted with successful identification of sgRNA in all anticipated postadministration samples within the 24-h collection period. Those findings support the applicability of the refined analytical procedure for the detection of illegal doping attempts via ribonucleoprotein-based CRISPR/Cas application through sgRNA identification, offering a new potential doping control strategy for CRISPR related gene doping.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".