Evidence map›Paper›PMID 37035101›Full record

ArticleBiomicrofluidics2023

Recent development of microfluidics-based platforms for respiratory virus detection.

Jingyu Shi, Yu Zhang, Mo Yang

Open access · hybridAbstract read
In one paragraph

Article in Biomicrofluidics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed, 11 citations in OpenAlex.

  1. Integrating metagenomics and metatranscriptomics intoThe Journal of general virology · 2026
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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

3 authors at 2 institutions in 2 countries.

Jingyu ShiDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong SAR, People's Republic of China.ORCID 0000-0001-7650-8212
Yu ZhangDepartment of Mechanical and Automotive Engineering, Royal Melbourne Institute of Technology, Melbourne, VIC 3000, Australia.ORCID 0000-0001-6751-9294
Mo YangDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong SAR, People's Republic of China.ORCID 0000-0002-3863-8187
Hong Kong Polytechnic University · HKThe Royal Melbourne Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the global outbreak of SARS-CoV-2, the inadequacies of current detection technology for respiratory viruses have been recognized. Rapid, portable, accurate, and sensitive assays are needed to expedite diagnosis and early intervention. Conventional methods for detection of respiratory viruses include cell culture-based assays, serological tests, nucleic acid detection (e.g., RT-PCR), and direct immunoassays. However, these traditional methods are often time-consuming, labor-intensive, and require laboratory facilities, which cannot meet the testing needs, especially during pandemics of respiratory diseases, such as COVID-19. Microfluidics-based techniques can overcome these demerits and provide simple, rapid, accurate, and cost-effective analysis of intact virus, viral antigen/antibody, and viral nucleic acids. This review aims to summarize the recent development of microfluidics-based techniques for detection of respiratory viruses. Recent advances in different types of microfluidic devices for respiratory virus diagnostics are highlighted, including paper-based microfluidics, continuous-flow microfluidics, and droplet-based microfluidics. Finally, the future development of microfluidic technologies for respiratory virus diagnostics is discussed.

Identifiers

PMID37035101
PMCPMC10076069
OpenAlexW4362588568

What OpenQuestion holds

Textmetadata
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.