Nucleic Acid Enzyme‐Activated CRISPR‐Cas12a With Circular CRISPR RNA for Biosensing
Bibliographic record
Abstract
clustered regularly interspaced short palindromic repeats (CRISPR)-Cas systems are increasingly used in biosensor development. However, directly translating recognition events for non-nucleic acid targets by CRISPR into effective measurable signals represents an important ongoing challenge. Herein, it is hypothesized and confirmed that CRISPR RNAs (crRNAs) in a circular topology efficiently render Cas12a incapable of both site-specific double-stranded DNA cutting and nonspecific single-stranded DNA trans cleavage. Importantly, it is shown that nucleic acid enzymes (NAzymes) with RNA-cleaving activity can linearize the circular crRNAs, activating CRISPR-Cas12a functions. Using ligand-responsive ribozymes and DNAzymes as molecular recognition elements, it is demonstrated that target-triggered linearization of circular crRNAs offers great versatility for biosensing. This strategy is termed as "NAzyme-Activated CRISPR-Cas12a with Circular CRISPR RNA (NA3C)." Use of NA3C for clinical evaluation of urinary tract infections using an Escherichia coli-responsive RNA-cleaving DNAzyme to test 40 patient urine samples, providing a diagnostic sensitivity of 100% and specificity of 90%, is further demonstrated.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 | 0.001 |
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".