Fast capture and multiplexed detection of short multi-arm DNA stars in solid-state nanopores
Bibliographic record
Abstract
Fast and multiplexed detection of low-abundance disease biomarkers at the point-of-need would transform medicine. Nanopores have gained attention as single-molecule counters to electrically detect a range of biological molecules in a handheld format, but challenges remain before diagnostic applications can emerge. For solid-state nanopore sensors, the specificity of the ionic current signatures and the rate of target capture required to simultaneously recognize and rapidly count a mixture of molecular targets in a complex sample are active areas of research. Herein, we study the capture and translocation characteristics of short N-arm star shaped DNA nanostructures to evaluate their potential as a family of surrogate label molecules for biomarkers of interest, designed for fast and reliable multiplexed detection based on conductance blockages. Simple hybridization of a varying number of short, easily synthesized 50 bp ssDNA strands allows the number of arms in the star shape DNA to be controlled from N = 3 to 12. By introducing more arms to the nanostructures, we show that we can controllably increase the nanopore signal-to-noise ratio for a range of pore sizes, producing conductance blockages which increase linearly with the number of arms, and we demonstrate conductance-based multiplexing through simultaneous detection of three such nanostructures. Moreover, the increased molecular signal strength facilitates detection under salt concentration asymmetries, allowing for a capture rate enhancement of two orders of magnitude without compromising the nanopore temporal and ionic signals. Together, these attributes (strong signal, multiplexing potential and increased counting rate) make the N-arm star DNA-based nanostructures promising candidates as proxy labels for the detection of multiple biomarkers of interest in future high sensitivity single-molecule solid-state nanopore-based assays.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".