Synthesis of length-tunable DNA carriers for nanopore sensing
Bibliographic record
Abstract
Molecular carriers represent an increasingly common strategy in the field of nanopore sensing to use secondary molecules to selectively report on the presence of target analytes in solution, allowing for sensitive assays of otherwise hard-to-detect molecules such as small, weakly-charged proteins. However, existing carrier designs can often introduce drawbacks to nanopore experiments including higher levels of cost/complexity and carrier-pore interactions that lead to ambiguous signals and elevated clogging rates. In this work, we present a simple method of carrier production based on sticky-ended DNA molecules that emphasizes ease-of-synthesis and compatibility with nanopore sensing and analysis. In particular, our method incorporates the ability to flexibly control the length of the DNA carriers produced, enhancing the multiplexing potential of this carrier system through the separable nanopore signals they could generate for distinct targets. A proof-of-concept nanopore experiment is also presented, involving carriers produced by our method with multiple lengths and attached to DNA nanostructure targets, in order to validate the capabilities of the system. As the breadth of applications for nanopore sensors continues to expand, the availability of tools such as those presented here to help translate the outcomes of these applications into robust nanopore signals will be of major importance.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".