Evidence map›Paper›PMID 35031864›Full record

ArticleBioprocess and biosystems engineering2022

Automatic system for high-throughput and high-sensitivity diagnosis of SARS-CoV-2.

Jun Lu, Weihua Fan, Zihui Huang, Ke Fan, Jianhua Dong, Jisheng Qin, Jianzhong Luo, Zhizhong Zhang, Guodong Sun, Chaohui Duan and 3 more

Open access · bronzeAbstract read
In one paragraph

Article in Bioprocess and biosystems engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.4field-weighted citation impact, top 20% 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

4 citing papers in PubMed, 10 citations in OpenAlex.

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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

13 authors at 5 institutions in 1 country.

Jun LuBioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510320, People's Republic of China.
Weihua FanGuangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, People's Republic of China.
Zihui HuangBioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510320, People's Republic of China.
Ke FanBioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510320, People's Republic of China.
Jianhua DongBioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510320, People's Republic of China.
Jisheng QinBioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510320, People's Republic of China.
Jianzhong LuoBioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510320, People's Republic of China.
Zhizhong ZhangBioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510320, People's Republic of China.
Guodong SunBioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510320, People's Republic of China.
Chaohui DuanDepartment of Clinical Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, People's Republic of China.
Kunyi PanDepartment of Clinical Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, People's Republic of China.
Wenshen GuDepartment of Clinical Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, People's Republic of China.
Xiao ZhangBioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510320, People's Republic of China. zhang_xiao@gibh.ac.cn.
Chinese Academy of Sciences · CNGuangzhou Regenerative Medicine and Health Guangdong Laboratory · CNSun Yat-sen University · CNGuangzhou Institutes of Biomedicine and Health · CNSun Yat-sen Memorial Hospital · CN

Funding

Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) No. 198F021812Instrument Developing Project of the Chinese Academy of Sciences No. YZ201620Instrument Developing Project of the Chinese Academy of Sciences No. ZDZBGCH2018005
6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had severe consequences for health and the global economy. To control the transmission, there is an urgent demand for early diagnosis and treatment in the general population. In the present study, an automatic system for SARS-CoV-2 diagnosis is designed and built to deliver high specification, high sensitivity, and high throughput with minimal workforce involvement. The system, set up with cross-priming amplification (CPA) rather than conventional reverse transcription-polymerase chain reaction (RT-PCR), was evaluated using more than 1000 real-world samples for direct comparison. This fully automated robotic system performed SARS-CoV-2 nucleic acid-based diagnosis with 192 samples in under 180 min at 100 copies per reaction in a "specimen in data out" manner. This throughput translates to a daily screening capacity of 800-1000 in an assembly-line manner with limited workforce involvement. The sensitivity of this device could be further improved using a CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based assay, which opens the door to mixed samples, potentially include SARS-CoV-2 variants screening in extensively scaled testing for fighting COVID-19.

Indexed as

SARS-CoV-2AlgorithmsBiomedical EngineeringClustered Regularly Interspaced Short Palindromic RepeatsCOVID-19COVID-19 Nucleic Acid TestingEquipment DesignHigh-Throughput Screening AssaysHumansNucleic Acid Amplification TechniquesPandemicsRoboticsSensitivity and SpecificitySystems AnalysisHigh throughputRobotic systemSARS-CoV-2 diagnosis

Identifiers

PMID35031864
PMCPMC8760113
OpenAlexW4206293514

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.