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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".