Enzymatic Cleavage of Nucleic Acids on Gold Nanoparticles: A Generic Platform for Facile Colorimetric Biosensors
Bibliographic record
Abstract
The enzymatic cleavage of nucleic acids (DNA or DNA with a single RNA linkage) on well-dispersed gold nanoparticles (AuNPs) is exploited in the design of facile colorimetric biosensors. The assays are performed at salt concentrations such that DNA-modified AuNPs are barely stabilized by the electrostatic and steric stabilization. Enzymatic cleavage of DNA chains on the AuNP surface destabilizes the AuNPs, resulting in a rapid aggregation driven by van der Waals attraction, and a red-to-purple color change. Two different systems are chosen, DNase I (a DNA endonuclease) and 8-17 (a Pb(2+)-depedent RNA-cleaving DNAzyme), to demonstrate the utility of our assay for the detection of metal ions and sensing enzyme activities. Compared with previous studies in which AuNP aggregates are converted into dispersed AuNPs by enzymatic cleavage of DNA crosslinkers, the present assay is technically simpler. Moreover, the accessibility of DNA to biomolecular recognition elements (e.g. enzymes) on well-dispersed AuNPs in our assay appears to be higher than that embedded inside aggregates. This biosensing system should be readily adaptable to other enzymes or substrates for detection of analytes such as small molecules, proteases and their inhibitors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".