Evidence map›Paper›PMID 35393791›Full record

ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022

Virus Detection: From State-of-the-Art Laboratories to Smartphone-Based Point-of-Care Testing.

Meng Xiao, Feng Tian, Xin Liu, Qiaoqiao Zhou, Jiangfei Pan, Zhaofan Luo, Mo Yang, Changqing Yi

Abstract readReview
In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed
–field-weighted citation impact
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

58 citing papers in PubMed.

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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

8 authors.

Meng XiaoGuangdong Provincial Key Laboratory of Sensing Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518107, P. R. China.
Feng TianDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hunghom, Hong Kong, 999077, P. R. China.
Xin LiuGuangdong Provincial Key Laboratory of Sensing Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518107, P. R. China.
Qiaoqiao ZhouGuangdong Provincial Key Laboratory of Sensing Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518107, P. R. China.
Jiangfei PanGuangdong Provincial Key Laboratory of Sensing Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518107, P. R. China.
Zhaofan LuoDepartment of Clinical Laboratory, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, P. R. China.
Mo YangDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hunghom, Hong Kong, 999077, P. R. China.
Changqing YiGuangdong Provincial Key Laboratory of Sensing Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518107, P. R. China.ORCID 0000-0001-5477-902X

Funding

Guangdong Natural Science Foundation 2021A1515220020Hong Kong Research Council Collaborative Research Grant C5110-20GShenzhen Basic Research Program JCYJ20210324140004013Shenzhen-Hong Kong-Macao Science and Technology Plan Project SGDX2020110309260000
6 · The paper itself

Abstract

Infectious virus outbreaks pose a significant challenge to public healthcare systems. Early and accurate virus diagnosis is critical to prevent the spread of the virus, especially when no specific vaccine or effective medicine is available. In clinics, the most commonly used viral detection methods are molecular techniques that involve the measurement of nucleic acids or proteins biomarkers. However, most clinic-based methods require complex infrastructure and expensive equipment, which are not suitable for low-resource settings. Over the past years, smartphone-based point-of-care testing (POCT) has rapidly emerged as a potential alternative to laboratory-based clinical diagnosis. This review summarizes the latest development of virus detection. First, laboratory-based and POCT-based viral diagnostic techniques are compared, both of which rely on immunosensing and nucleic acid detection. Then, various smartphone-based POCT diagnostic techniques, including optical biosensors, electrochemical biosensors, and other types of biosensors are discussed. Moreover, this review covers the development of smartphone-based POCT diagnostics for various viruses including COVID-19, Ebola, influenza, Zika, HIV, et al. Finally, the prospects and challenges of smartphone-based POCT diagnostics are discussed. It is believed that this review will aid researchers better understand the current challenges and prospects for achieving the ultimate goal of containing disease-causing viruses worldwide.

Indexed as

COVID-19Zika VirusZika Virus InfectionClinical Laboratory TechniquesHumansLaboratoriesPoint-of-Care TestingSmartphonebiosensorslaboratory-based diagnosticspoint-of-care testingsmartphonesvirus

Identifiers

PMID35393791
PMCPMC9110880

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.