DNA Conformational Switches as Sensitive Electronic Sensors of Analytes
Bibliographic record
Abstract
The electrical conductivity of DNA is dependent on its conformational state. We demonstrate here that such a dependence may be harnessed for the electronic sensing of external analytes, for instance, adenosine. Such a DNA sensor incorporates an analyte "receptor", whose altered conformation in the presence of bound analyte switches the conformation, and hence, the conductive path between two DNA double-helical stems. Two distinct designs for such sensors are described here, that permit significant electrical conduction through a "detector" double-helical stem only in the presence of the bound analyte. In the first design, current flows through the analyte receptor itself, whereas in the second, current flows in a path adjacent to the receptor. The former design may be especially suitable for certain categories of analytes, including heterocycle-containing compounds such as adenosine, whereas the latter design should be generally applicable to the detection of any molecular analyte, large or small. Since analyte detection in these DNA sensors is electronic, the potential exists for their application in rapid and automated chip-based detection of small molecules as well as of proteins and other macromolecules.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".