A novel method to detect mutation in DNA by utilizing exponential amplification reaction triggered by the CRISPR-Cas9 system
Bibliographic record
Abstract
We, herein, describe a novel method to detect mutation in DNA by utilizing exponential amplification reaction (EXPAR) triggered by clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9, called CRISPR-EXPAR. The CRISPR system consisting of two Cas9/sgRNA complexes was designed to cut out a specific mutation region within the target DNA, which would consequently promote EXPAR by continuously repeated extension and nicking reactions. As a consequence, a large number of final EXPAR products, which can be monitored through duplex-specific fluorescent staining, are produced. Based on this design principle, we successfully identified a model target mutation within the human epidermal growth factor receptor 2 (HER2) gene down to 437 aM with excellent specificity. The practical capability of this method was verified by reliably identifying the target mutation directly from the genomic DNA (gDNA) extracted from the lung cancer cell line, NCI-H1781 (H1781), and its universal applicability was further confirmed by identifying another EFGF L858R mutation. This technique could serve as a new isothermal platform to identify various mutations by rationally redesigning single guide RNA (sgRNA) according to the target mutation site.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".