Faculty Opinions recommendation of Circular nucleic acids: discovery, functions and applications.
Bibliographic record
Abstract
Circular nucleic acids (CNAs) are nucleic acid molecules with a closed-loop structure. This feature comes with a number of advantages including complete resistance to exonuclease degradation, much better thermodynamic stability, and the capability of being replicated by a DNA polymerase in a rolling circle manner. Circular functional nucleic acids, CNAs containing at least a ribozyme/DNAzyme or a DNA/RNA aptamer, not only inherit the advantages of CNAs but also offer some unique application opportunities, such as the design of topology-controlled or enabled molecular devices. This article will begin by summarizing the discovery, biogenesis, and applications of naturally occurring CNAs, followed by discussing the methods for constructing artificial CNAs. The exploitation of circular functional nucleic acids for applications in nanodevice engineering, biosensing, and drug delivery will be reviewed next. Finally, the efforts to couple functional nucleic acids with rolling circle amplification for ultra-sensitive biosensing and for synthesizing multivalent molecular scaffolds for unique applications in biosensing and drug delivery will be recapitulated.© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. PMID: 32176816 Funding information This work was supported by: Natural Sciences and Engineering Research Council of Canada (NSERC),
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.164 | 0.120 |
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".