Bibliographic record
Abstract
DNAzymes are catalytically active DNA molecules that are obtained via in vitro selection. RNA-cleaving DNAzymes have attracted significant attention for both therapeutic and diagnostic applications due to their excellent programmability, stability, and activity. They can be designed to cleave a specific mRNA to down-regulate gene expression. At the same time, DNAzymes can sense a broad range of analytes. By combining these two functions, theranostic DNAzymes are obtained. This review summarizes the progress of DNAzyme for theranostic applications. First, in vitro selection of DNAzymes is briefly introduced, and some representative DNAzymes related to biological applications are summarized. Then, the applications of DNAzyme for RNA cleaving are reviewed. DNAzymes have been used to cleave RNA for treating various diseases, such as viral infection, cancer, inflammation and atherosclerosis. Several formulations have entered clinical trials. Next, the use of DNAzymes for detecting metal ions, small molecules and nucleic acids related to disease diagnosis is summarized. Finally, the theranostic applications of DNAzyme are reviewed. The challenges to be addressed include poor DNAzyme activity under biological conditions, mRNA accessibility, delivery, and quantification of gene expression. Possible solutions to overcome these challenges are discussed, and future directions of the field are speculated.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".