Ultrasensitive DNAzyme‐Based Ca<sup>2+</sup> Detection Boosted by Ethanol and a Solvent‐Compatible Scaffold for Aptazyme Design
Bibliographic record
Abstract
Abstract Functional DNA includes aptamers and DNAzymes, and metal ions are often important for achieving the chemical functions of such DNA. Biosensors based on functional DNA have mainly been tested in aqueous buffers. By introducing organic solvents with much lower dielectric constants, the interaction between metal ions and DNA can be significantly enhanced, and this might affect the performance of DNA‐based biosensors. In this work, the effect of ethanol on the activity of the EtNa DNAzyme was studied for Ca2+ detection. With 30 % ethanol, the sensor has a detection limit of 1.4 μm Ca2+, which is a 16‐fold improvement relative to that in water. This EtNa DNAzyme is unique because other tested DNAzymes are all inhibited by 50 % ethanol. Finally, by using the EtNa DNAzyme as a scaffold, the adenosine monophosphate (AMP) aptamer was inserted to construct an aptazyme, which allowed the measurement of AMP in ethanol. In summary, this study has reported the most sensitive DNA‐based sensor for Ca2+, and its sensitivity and selectivity can approach those of proteins or small‐molecule ligands. This work also provides a way to measure aptamer binding in organic solvents.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".