The Application of Oligonucleotide Templated Chemical Reactions to DNA Aptamers
Bibliographic record
Abstract
The ability of an aptamer to catalyze a chemical reaction under selective conditions presents a novel avenue for the exploration of biosensors and molecular payload delivery.To date, limited research exists for pairing oligonucleotide-templated chemical reactions with the selective nature of DNA aptamers.A system can be designed wherein the conformational change in aptamer structure associated with target binding brings two previously spatially isolated reactants into proximity, thereby catalyzing their reaction through an increase in effective molarity.A rationally designed aptamer-mediated SN2 displacement of a sulfonyl-based fluorescence quencher resulted in an effective increase in fluorescence upon mixing of the aptamer with two appropriately modified complementary oligonucleotides.This increased fluorescence could be slowed by the presence of the aptamer target, permitting the development of an aptamer-based sensor for the mycotoxin ochratoxin A. Using this turn-off type sensor system, a linear dynamic range of 100 μM to 100 pM ochratoxin A could be detected with a limit of detection of 1.5 pM.Similar aptamer-based sensor systems were also developed that could take advantage of the fluorogenic copper catalyzed azide-alkyne cycloadditions between two labeled probes that could be slowed by the presence of the aptamer target.This has been demonstrated using the thrombin structure switching DNA aptamer to produce a linear response to thrombin between 100 nM and 10 pM with a limit of detection of 3.9 fM.These fluorogenic click modifiers were also incorporated directly into a structureswitching thrombin aptamer such that the G-quadruplex formed upon thrombin binding 5.3.2.3Polyacrylamide gel electrophoresis ..........
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".