Bibliographic record
Abstract
Abstract DNAzymes are in vitro selected DNA oligonucleotides with catalytic activities. RNA cleavage is one of the most extensively studied DNAzyme reactions. To expand the chemical functionality of DNA, various chemical modifications have been made during and after selection. In this review, we summarize examples of RNA‐cleaving DNAzymes and focus on those modifications introduced during in vitro selection. By incorporating various modified nucleotides via polymerase chain reaction (PCR) or primer extension, a few DNAzymes were obtained that can be specifically activated by metal ions such as Zn2+ and Hg2+. In addition, some modifications were introduced to mimic RNase A that can cleave RNA substrates in the absence of divalent metal ions. In addition, single modifications at the fixed regions of DNA libraries, especially at the cleavage junctions, have been tested, and examples of DNAzymes with phosphorothioate and histidine‐glycine modified tertiary amine were successfully obtained specific for Cu2+, Cd2+, Zn2+, and Ni2+. Labeling fluorophore/quencher pair right next to the cleavage junction was also used to obtain signaling DNAzymes for detecting various metal ions and cells. Furthermore, we reviewed work on the cleavage of 2′‐5′ linked RNA and L‐RNA substrates. Finally, applications of these modified DNAzymes as biosensors, RNases, and biochemical probes are briefly described with a few future research opportunities outlined at the end.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".