DNAzyme Feedback Amplification: Relaying Molecular Recognition to Exponential DNA Amplification
Bibliographic record
Abstract
Technologies capable of linking DNA amplification to molecular recognition are very desirable for ultrasensitive biosensing applications. We have developed a simple but powerful isothermal DNA amplification method, termed DNAzyme feedback amplification (DFA), that is capable of relaying molecular recognition to exponential DNA amplification. The method incorporates both an RNA-cleaving DNAzyme (RCD) and rolling circle amplification (RCA) carried out by a special DNA polymerase using a circular DNA template. DFA begins with a stimulus-dependent RCA reaction, producing tandemly linked RCDs in long-chain DNA products. These RCDs cleave an RNA-containing DNA sequence to form additional primers that hybridize to the circular DNA molecule, giving rise to DNA assemblies that act as the new inputs for RCA. The RCA reaction and the cleavage event keep on feeding each other autonomously, resulting in exponential growth of repetitive DNA sequences that can be easily detected. This method can be used for the detection of both nucleic acid based targets and non-nucleic acid analytes. In this article, we discuss the conceptual framework of the feedback amplification approach, the essential features of this method as well as remaining challenges and possible solutions.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".