Enhancing Clinical Detection Accuracy of Large Structured Viral RNA via DNAzyme Cleavage and Antisense‐Assisted Rolling Circle Amplification
Bibliographic record
Abstract
Abstract Sensitive detection of viral RNA is critical for accurate diagnostic testing, particularly during outbreaks of emerging infectious diseases. Rolling circle amplification (RCA) is a powerful isothermal amplification strategy that can be directly primed by RNA, eliminating the need for reverse transcription. Previous approaches have used 10–23 DNAzymes to cleave viral RNA, generating 3′‐ends for hybridization to circular DNA templates (CDTs). However, the resulting RNA fragments often retained secondary or tertiary structures that hindered CDT binding and limited RCA efficiency. To address this challenge, we developed antisense oligonucleotide‐assisted RCA (ASO‐RCA), a general strategy that uses short upstream antisense oligonucleotides (ASOs) to remodel RNA structure and expose the CDT‐binding site. Using five DNAzyme‐CDT systems targeting distinct regions of the SARS‐CoV‐2 genome, we show that ASO inclusion improves CDT hybridization and enhances RCA output—by up to 70‐fold. This enhancement was observed using both linear and quasi‐exponential RCA formats and remained effective in 50% pooled saliva. When applied to clinical saliva samples, ASO‐assisted RCA markedly improved diagnostic performance, achieving 100% sensitivity and up to 97.5%–100% accuracy across multiple systems. These findings establish ASO‐DNAzyme‐RCA as a simple, robust, and clinically relevant platform for improving nucleic acid detection in structured RNA targets.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 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".