One-pot DTECT enables rapid and efficient capture of genetic signatures for precision genome editing and clinical diagnostics
Bibliographic record
Abstract
The detection of genomic sequences and their alterations is crucial for basic research and clinical diagnostics. However, current methodologies are costly and time-consuming and require outsourcing sample preparation, processing, and analysis to genomic companies. Here, we establish One-pot DTECT, a platform that expedites the detection of genetic signatures, only requiring a short incubation of a PCR product in an optimized one-pot mixture. One-pot DTECT enables qualitative, quantitative, and visual detection of biologically relevant variants, such as cancer mutations, and nucleotide changes introduced by prime editing and base editing into cancer cells and human primary T cells. Notably, One-pot DTECT achieves quantification accuracy for targeted genetic signatures comparable with Sanger and next-generation sequencing. Furthermore, its effectiveness as a diagnostic platform is demonstrated by successfully detecting sickle cell variants in blood and saliva samples. Altogether, One-pot DTECT offers an efficient, versatile, adaptable, and cost-effective alternative to traditional methods for detecting genomic signatures.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".