Nanopore sensors for viral particle quantification: current progress and future prospects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rapid, inexpensive, and laboratory-free diagnostic of viral pathogens is highly critical in controlling viral pandemics. In recent years, nanopore-based sensors have been employed to detect, identify, and classify virus particles. By tracing ionic current containing target molecules across nano-scale pores, nanopore sensors can recognize the target molecules at the single-molecule level. In the case of viruses, they enable discrimination of individual viruses and obtaining important information on the physical and chemical properties of viral particles. Despite classical benchtop virus detection methods, such as amplification techniques (e.g., PCR) or immunological assays (e.g., ELISA), that are mainly laboratory-based, expensive and time-consuming, nanopore-based sensing methods can enable low-cost and real-time point-of-care (PoC) and point-of-need (PoN) monitoring of target viruses. This review discusses the limitations of classical virus detection methods in PoN virus monitoring and then provides a comprehensive overview of nanopore sensing technology and its emerging applications in quantifying virus particles and classifying virus sub-types. Afterward, it discusses the recent progress in the field of nanopore sensing, including integrating nanopore sensors with microfabrication technology, microfluidics and artificial intelligence, which have been demonstrated to be promising in developing the next generation of low-cost and portable biosensors for the sensitive recognition of viruses and emerging pathogens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it