ArticleNanoscale2020
Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness.
Article in Nanoscale, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Salt-Mediated Electro-Deformation of AAV Capsids Revealed by Nanopipette-Based Single-Particle Analysis.Analytical chemistry · 2026Article
- Numerical characterization of electrochemical transport in three-dimensional rhombic zero-depth pores.Scientific reports · 2026Article
- Ring-Electrode AC Plasmonic Nanopore Sensing for DNA Load Characterization of Single Adeno-Associated Viruses.Sensors (Basel, Switzerland) · 2026Article
- Single-Particle Analysis of Cargo-Dependent Deformation in Lipid Nanoparticles Using Resistive Pulse Sensing: Implications for Formulation Optimization and Quality Control of mRNA Nanomedicine.ACS applied nano materials · 2026Article
- The evolution of nanopore measurements: from biological out-of-plane pores to plastic in-plane pores.Lab on a chip · 2026Review
- Mechanisms of Adeno-Associated Virus Serotype 9 Vector Characterization and Quality Control through Solid-State Nanopores.ACS nano · 2026Article
- Mislabel Identification Using Transfer Learning-Based Ensemble Method.IEEE access : practical innovations, open solutions · 2026Article
- Metrology of Nanoscale Objects Using Nanopores.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Plasmonic Nanopore Sensing to Probe the DNA Loading Status of Adeno-Associated Viruses.Chemosensors (Basel, Switzerland) · 2025Article
- Enhanced Discriminability of Viral Vectors in Viscous Nanopores.Small methods · 2025Article
- A Robust Parallel Computing Data Extraction Framework for Nanopore Experiments.Small methods · 2024Article
- A Novel Method for Separating Full and Empty Adeno-Associated Viral Capsids Using Ultrafiltration.Membranes · 2024Article
- Machine-learning-guided Directed Evolution for AAV Capsid Engineering.Current pharmaceutical design · 2024Review
- High Sensitivity Extended Nano-Coulter Counter for Detection of Viral Particles and Extracellular Vesicles.Analytical chemistry · 2023Article
- Fine-tuning-based Transfer Learning for Characterization of Adeno-Associated Virus.Journal of signal processing systems · 2022Article
- PCR-Based Analytical Methods for Quantification and Quality Control of Recombinant Adeno-Associated Viral Vector Preparations.Pharmaceuticals (Basel, Switzerland) · 2021Review
- Nanopore sensors for viral particle quantification: current progress and future prospects.Bioengineered · 2021Review
- A Guide to Signal Processing Algorithms for Nanopore Sensors.ACS sensors · 2021Article
- Modulation of electrophoresis, electroosmosis and diffusion for electrical transport of proteins through a solid-state nanopore.RSC advances · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Solid-state nanopore (SSN)-based analytical methods have found abundant use in genomics and proteomics with fledgling contributions to virology - a clinically critical field with emphasis on both infectious and designer-drug carriers. Here we demonstrate the ability of SSN to successfully discriminate adeno-associated viruses (AAVs) based on their genetic cargo [double-stranded DNA (AAVdsDNA), single-stranded DNA (AAVssDNA) or none (AAVempty)], devoid of digestion steps, through nanopore-induced electro-deformation (characterized by relative current change; ΔI/I0). The deformation order was found to be AAVempty > AAVssDNA > AAVdsDNA. A deep learning algorithm was developed by integrating support vector machine with an existing neural network, which successfully classified AAVs from SSN resistive-pulses (characteristic of genetic cargo) with >95% accuracy - a potential tool for clinical and biomedical applications. Subsequently, the presence of AAVempty in spiked AAVdsDNA was flagged using the ΔI/I0 distribution characteristics of the two types for mixtures composed of ∼75 : 25% and ∼40 : 60% (in concentration) AAVempty : AAVdsDNA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.