Autonomous Exponential Amplification via Rolling Circle–DNAzyme Feedback Programming
Bibliographic record
Abstract
Rolling circle amplification (RCA) is a powerful isothermal strategy for nucleic acid detection, but its linear kinetics and dependence on externally supplied primers limit its sensitivity and programmability. Here, we report an exponential RCA (E-RCA) platform that integrates primer regeneration and signal amplification into a single DNA-encoded system. The design uses a circular DNA template encoding the I-R3 self-cleaving DNAzyme sequence; during RCA, tandem I-R3 units are generated within the DNA amplicons, which then catalyze site-specific cleavage to release new primers. This self-sustained (RCA ↔ DNAzyme) amplification circuit enables robust exponential signal growth using only a circular DNA probe and a DNA polymerase without requiring external primers or protein enzymes. We elucidate the mechanism through biochemical experiments and kinetic modeling and validate the system in multiplexed intracellular microRNA imaging and quantitative dual-marker profiling of clinical breast cancer tissues. The E-RCA strategy achieved high diagnostic accuracy (AUC = 0.914; specificity = 100%; sensitivity = 81.3%), demonstrating its potential for sensitive, programmable, and autonomous molecular analysis in both biological and clinical contexts.
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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 | 0.001 | 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".