Programmable DNA Nanoswitch Sensing with Solid-State Nanopores
Bibliographic record
Abstract
Sensing performance of solid-state nanopores is limited by the fast kinetics of small molecular targets. To address this challenge, we translate the presence of a small target to a large conformational change of a long polymer. In this work, we explore the performance of solid-state nanopores for sensing the conformational states of molecular nanoswitches assembled using the principles of DNA origami. These programmable single-molecule switches show great potential in molecular diagnostics and long-term information storage. We investigate the translocation properties of linear and looped nanoswitch topologies using nanopores fabricated in thin membranes, ultimately comparing the performance of our nanopore platform for detecting the presence of a DNA analogue to a sequence found in a Zika virus biomarker gene with that of conventional gel electrophoresis. We found that our system provides a high-throughput method for quantifying several target concentrations within an order of magnitude by sensing only several hundred molecules using electronics of moderate bandwidth that are conventionally used in nanopore sensing 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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".