Making Sense of Catalysis: The Potential of DNAzymes as Biosensors
Bibliographic record
Abstract
DNA, long known as a carrier of genetic information, has recently revealed itself as a multifunctional entity. Using the powerful technique of in vitro selection, catalytic DNA molecules, known as DNAzymes or deoxyribozymes, have been isolated to catalyse numerous reactions using a range of metal-ion cofactors. Conjugation of these DNAzymes to an array of signalling platforms has led to the development of several DNAzyme-based sensor systems. By labelling DNAzymes and their nucleic acid substrates with fluorescent and quenching dyes, sensors have been designed to report the presence and concentration of specific metal ions with high sensitivity and specificity. By coupling DNAzyme activity to the aggregation state of gold nanoparticles, visual sensors have been designed that report the presence of a metal ion by a change in colour, eliminating the need for expensive detection equipment. Electrode-bound DNAzymes have been developed into electrochemical sensors offering high sensitivity and reduced background. The types of analyte that can be detected by DNAzymes have also been expanded by coupling DNAzymes to DNA aptamers that bind specific target molecules. These conjugates, called DNA aptazymes, have been developed to detect small molecules such as adenosine and adenosine triphosphate (ATP). Using an in vitro selection protocol with counter-selection steps, aptazymes that can detect molecules in complex mixtures have been isolated. This chapter will highlight innovative research that has been done to engineer DNAzyme-based sensors and discuss the prospects for using DNAzymes in future detection systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".