DNA-Templated Click Ligation Chain Reaction Catalyzed by Heterogeneous Cu<sub>2</sub>O for Enzyme-Free Amplification and Ultrasensitive Detection of Nucleic Acids
Bibliographic record
Abstract
Nucleic acids play a pivotal role in the diagnosis of diseases. However, rapid, cost-efficient, and ultrasensitive identification of nucleic acid targets still represents a significant challenge. Herein, we describe an enzyme-free DNA amplification method capable of achieving accurate and ultrasensitive nucleic acid detection via D NA- t emplated c lick l igation c hain r eaction (DT-CLCR) catalyzed by a h eterogeneous n anocatalyst made of Cu 2 O (hnCu 2 O). This hnCu 2 O–DT-CLCR method is built on two cross-amplifying hnCu 2 O-catalyzed DNA-templated azide–alkyne cycloaddition-driven DNA ligation reactions that boast a fast reaction rate and a high DNA ligation yield in minutes, enabling rapid exponential amplification of specific DNA targets. This newly developed hnCu 2 O–DT-CLCR-enabled DNA amplification strategy is further integrated with two signal reporting mechanisms to achieve low-cost and easy-to-use biosensors: an electrochemical sensor through the conjugation of a methylene blue redox reporter to a DNA probe used in hnCu 2 O–DT-CLCR and a colorimetric sensor through the incorporation of the split-to-intact G-quadruplex DNAzyme encoded into hnCu 2 O–DT-CLCR. Both sensors are able to achieve specific detection of the intended DNA target with a limit of detection at aM ranges, even when challenged in complex biological matrices. The combined hnCu 2 O–DT-CLCR and sensing strategies offer attractive universal platforms for enzyme-free and yet efficient detection of specific nucleic acid 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 | 0.000 | 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".