Evidence map›Paper›PMID 37435542›Full record

ArticleIEEE journal of translational engineering in health and medicine2023

Automated System for Multiplexing Detection of COVID-19 and Other Respiratory Pathogens.

Parker Y L Tsang, Sunny L H Chu, Libby C W Li, Deborah M S Tai, Berry K C Cheung, Firaol Tamiru Kebede, Pete Y M Leung, Winston Wong, Teresa Chung, Cyril C Y Yip and 6 more

Open access · goldAbstract read
In one paragraph

Article in IEEE journal of translational engineering in health and medicine, 2023. 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
0.4field-weighted citation impact, top 38% 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, 5 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

16 authors at 4 institutions in 2 countries.

Parker Y L TsangEmerging Viral Diagnostics (HK) Ltd. Hong Kong China.ORCID https://orcid.org/0000-0003-2135-6359
Sunny L H ChuEmerging Viral Diagnostics (HK) Ltd. Hong Kong China.
Libby C W LiEmerging Viral Diagnostics (HK) Ltd. Hong Kong China.ORCID https://orcid.org/0000-0002-4507-5335
Deborah M S TaiEmerging Viral Diagnostics (HK) Ltd. Hong Kong China.
Berry K C CheungEmerging Viral Diagnostics (HK) Ltd. Hong Kong China.
Firaol Tamiru KebedeDepartment of Industrial and Systems EngineeringThe Hong Kong Polytechnic University Hong Kong China.
Pete Y M LeungDepartment of Industrial and Systems EngineeringThe Hong Kong Polytechnic University Hong Kong China.
Winston WongEmerging Viral Diagnostics (HK) Ltd. Hong Kong China.
Teresa ChungDepartment of Industrial and Systems EngineeringThe Hong Kong Polytechnic University Hong Kong China.ORCID https://orcid.org/0000-0001-8113-2563
Cyril C Y YipDepartment of MicrobiologyThe University of Hong Kong Hong Kong China.ORCID https://orcid.org/0000-0002-9606-8506
Rosana W S PoonDepartment of MicrobiologyThe University of Hong Kong Hong Kong China.
Jonathan H K ChenDepartment of MicrobiologyThe University of Hong Kong Hong Kong China.ORCID https://orcid.org/0000-0002-7510-1696
Kwok-Yung YuenDepartment of MicrobiologyThe University of Hong Kong Hong Kong China.
Manson FokEmerging Viral Diagnostics (HK) Ltd. Hong Kong China.
Johnson Y N LauEmerging Viral Diagnostics (HK) Ltd. Hong Kong China.
Lok-Ting LauEmerging Viral Diagnostics (HK) Ltd. Hong Kong China.ORCID https://orcid.org/0000-0001-7517-1486
Hong Kong Polytechnic University · HKHong Kong Science and Technology Parks Corporation · HKHong Kong Baptist University · HKMacau University of Science and Technology · MO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveInfectious diseases are global health challenge, impacted the communities worldwide particularly in the midst of COVID-19 pandemic. The need of rapid and accurate automated systems for detecting pathogens of concern has always been critical. Ideally, such systems shall detect a large panel of pathogens simultaneously regardless of well-equipped facilities and highly trained operators, thus realizing on-site diagnosis for frontline healthcare providers and in critical locations such as borders and airports. METHODS &

resultsAvalon Automated Multiplex System, AAMST, is developed to automate a series of biochemistry protocols to detect nucleic acid sequences from multiple pathogens in one test. Automated processes include isolation of nucleic acids from unprocessed samples, reverse transcription and two rounds of amplifications. All procedures are carried out in a microfluidic cartridge performed by a desktop analyzer. The system was validated with reference controls and showed good agreement with their laboratory counterparts. In total 63 clinical samples, 13 positives including those from COVID-19 patients and 50 negative cases were detected, consistent with clinical diagnosis using conventional laboratory methods.

conclusionsThe proposed system has demonstrated promising utility. It would benefit the screening and diagnosis of COVID-19 and other infectious diseases in a simple, rapid and accurate fashion.

Indexed as

COVID-19AirportsHealth PersonnelHumansLaboratoriesPandemicsAutomationbiochemistryclinical diagnosisCOVID-19genomicsmicrofluidics

Identifiers

PMID37435542
PMCPMC10332469
OpenAlexW4312778612

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.