Nanopore sensors for nucleic acid analysis
Bibliographic record
Abstract
In the past decade, nanometre-scale pores have been explored as the basis for technologies to analyse and sequence single nucleic acid molecules. Most approaches involve using such a pore to localize single macromolecules and interact with them to garner some information on their composition. Though nanopore sensors cannot yet claim success at deoxyribonucleic acid (DNA) sequencing, nanopore-based technologies offer one of the most promising approaches to single molecule detection and analysis. The majority of experimental work with nanopore detection of nucleic acids has involved the α-haemolysin (alpha-HL) ion channel—a heptameric protein with a ∼2 nm diameter inner pore which allows translocation of single-stranded DNA. Analysis of externally induced ion current through the pore during its interaction with DNA can provide information about the DNA molecule, including length and base composition. This review focuses on alpha-HL and its applications to single-molecule detection. Modified alpha-HL and other biological and synthetic pores for macromolecule detection are also discussed, along with a brief summary of relevant theoretical work and numerical modelling of polymer–pore interaction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".