An Efficient RNA-Cleaving DNA Enzyme that Synchronizes Catalysis with Fluorescence Signaling
Bibliographic record
Abstract
DNA enzymes are single-stranded DNA molecules with catalytic capabilities that are isolated from random-sequence DNA libraries by "in vitro selection". This new class of catalytic biomolecules has the potential of being used as unique molecular tools in a variety of innovative applications. Here we describe the creation and characterization of an RNA-cleaving autocatalytic DNA, DEC22-18, that uniquely links chemical catalysis with real-time fluorescence signaling capability in the same molecule. A trans-acting DNA molecule, DET22-18, was also developed from DEC22-18 that behaves as a true enzyme with a k(cat) of approximately 7 min(-1)-a rate constant that is the second largest ever reported for a DNA enzyme. It cleaves a chimeric RNA/DNA substrate at the lone RNA linkage surrounded by a closely spaced fluorophore-quencher pair-a unique structure that permits the synchronization of the chemical cleavage with fluorescence signaling. DET22-18 has a stem-loop structure and can be conjugated with DNA aptamers to form allosteric deoxyribozyme biosensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".