Evidence map›Paper›PMID 34709987›Full record

ReviewBioengineered2021

Nanopore sensors for viral particle quantification: current progress and future prospects.

Shiva Akhtarian, Saba Miri, Ali Doostmohammadi, Satinder Kaur Brar, Pouya Rezai

Open access · goldAbstract readReview
In one paragraph

Review in Bioengineered, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.2field-weighted citation impact, top 24% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 27 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Review
  8. Article
  9. The Road Toward Nanopore Sequencing of Glycosaminoglycans.Handbook of experimental pharmacology · 2025
    Review
  10. Article
  11. Review
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Shiva AkhtarianDepartment of Mechanical Engineering, York University, Toronto, ON, Canada.
Saba MiriDepartment of Civil Engineering, York University, Toronto, ON, Canada.
Ali DoostmohammadiDepartment of Mechanical Engineering, York University, Toronto, ON, Canada.
Satinder Kaur BrarDepartment of Civil Engineering, York University, Toronto, ON, Canada.
Pouya RezaiDepartment of Mechanical Engineering, York University, Toronto, ON, Canada.
York University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Biosensing TechniquesNanoporesMachine LearningMicrofluidicsVirionartificial intelligencebiosensorsmicrofluidicsNanopore sensorresistive pulse sensingvirus classificationvirus detection

Identifiers

PMID34709987
PMCPMC8810133
OpenAlexW3209416847

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.