Bibliographic record
Abstract
Abstract DNA‐based enzymes, also known as deoxyribozymes or DNAzymes, are single‐stranded DNA molecules with catalytic activity. DNAzymes do not exist in nature but can be isolated from random‐sequence DNA pools using in vitro selection. To date, many DNAzymes that collectively catalyze a diverse range of chemical transformations have been reported. Here, examples of new DNAzymes engineered to mimic some intriguing functions of naturally occurring protein‐based enzymes are discussed. This is followed by discussions of recent examples of a particular class of DNAzymes, known as “RNA‐cleaving DNAzymes”, that have been derived specifically so that their activity is strictly dependent on a given chemical or biological stimulus. Some unique ways to employ ligand‐responsive DNAzymes for the design of bioanalytical assays and biosensors are then highlighted. Being DNA molecules, DNAzymes have proven to be entirely compatible with DNA amplification. Several approaches are then discussed, which relay the activity of an analyte‐activated DNAzyme into the production of massive amounts of DNA amplicons, via “rolling circle amplification”, in biosensing applications designed to deliver very high levels of detection sensitivity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.033 |
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".