Evidence map›Paper›PMID 34922321›Full record

ArticleEBioMedicine2022

Developing RT-LAMP assays for rapid diagnosis of SARS-CoV-2 in saliva.

Xin Huang, Gongyu Tang, Nahed Ismail, Xiaowei Wang

Open access · goldAbstract read
In one paragraph

Article in EBioMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
7.4field-weighted citation impact, top 2% 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

50 citing papers in PubMed, 108 citations in OpenAlex.

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  6. Rapid and Cost-EffectiveDiagnostics (Basel, Switzerland) · 2025
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  15. Development and field validation of a reverse transcription loop-mediated isothermal amplification assay (RT-LAMP) for the rapid detection of chikungunya virus in patient and mosquito samples.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Xin HuangDepartment of Pharmacology and Regenerative Medicine, University of Illinois Chicago Chicago, IL, United States; University of Illinois Cancer Center, Chicago, IL, United States.
Gongyu TangDepartment of Pharmacology and Regenerative Medicine, University of Illinois Chicago Chicago, IL, United States; University of Illinois Cancer Center, Chicago, IL, United States.
Nahed IsmailDepartment of Pathology, University of Illinois Chicago; Chicago, IL, United States.
Xiaowei WangDepartment of Pharmacology and Regenerative Medicine, University of Illinois Chicago Chicago, IL, United States; University of Illinois Cancer Center, Chicago, IL, United States. Electronic address: xwang317@uic.edu.
University of Illinois Urbana-Champaign · USUniversity of Illinois Chicago · US

Funding

Investigating the effectiveness of COVID-19 testing choices, community engagement, and culturally-embedded mHealth literacy delivery in a medically-underserved, community-based sampleR01ES028615 · NIEHS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI AL-HENDY, AYMAN, WALKER, CHERYL L. · 2017 to 2022
$5.0M
COMBINED COMPUTATIONAL AND EXPERIMENTAL ANALYSES OF GENE REGULATION BY MICRORNAS.R01GM089784 · NIGMS · WASHINGTON UNIVERSITY · PI WANG, XIAOWEI · 2010 to 2019
$2.8M
MICRORNA BIOMARKERS FOR OROPHARYNGEAL CANCERR01DE026471 · NIDCR · WASHINGTON UNIVERSITY · PI WANG, XIAOWEI · 2017 to 2021
$2.2M
Combined Computational and Experimental Analyses of Gene Regulation by MicroRNAsR35GM141535 · NIGMS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI WANG, XIAOWEI · 2021 to 2025
$2.1M
Regulation of blood coagulation by the ZPI/PZ anticoagulant systemR56HL149881 · NHLBI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI HUANG, XIN · 2020 to 2020
$484k
NHLBI NIH HHS R56 HL149881NIDCR NIH HHS R01 DE026471NIEHS NIH HHS R01 ES028615NIGMS NIH HHS R01 GM089784NIGMS NIH HHS R35 GM141535
6 · The paper itself

Abstract

backgroundThe coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has killed millions of people worldwide. The current crisis has created an unprecedented demand for rapid test of SARS-CoV-2 infection.

methodsReverse transcription loop-mediated isothermal amplification (RT-LAMP) is a fast and convenient method to amplify and identify the transcripts of a targeted pathogen. However, the sensitivity and specificity of RT-LAMP were generally regarded as inferior when compared with the gold standard RT-qPCR. To address this issue, we combined bioinformatic and experimental analyses to improve the assay performance for COVID-19 diagnosis.

findingsFirst, by experimental screening as well as high-throughput sequencing studies, we discovered new primer features that impacted LAMP sensitivity and specificity. These features were then used to build an improved bioinformatics algorithm to design LAMP primers targeting SARS-CoV-2. We further rigorously validated these new assays for their efficacy and specificity. We demonstrated that multiplexed RT-LAMP assay could directly detect as low as 1.5 copies/µL of SARS-CoV-2 particles in saliva, without the need of RNA isolation. We further tested this ultra-sensitive and specific RT-LAMP assay using saliva samples from COVID-19 patients. Clinical validation results indicated that the new RT-LAMP assay was comparable to standard RT-qPCR in overall assay sensitivity and specificity.

interpretationIn summary, our new LAMP primer design algorithm along with the validated assays provide a fast and reliable method for the diagnosis of COVID-19 cases.

fundingNational Institutes of Health.

Indexed as

COVID-19COVID-19 Nucleic Acid TestingMolecular Diagnostic TechniquesNucleic Acid Amplification TechniquesHumansRNA, ViralSalivaSARS-CoV-2Sensitivity and SpecificityRNA, ViralCOVID-19Diagnostic assayRT-LAMPSARS-CoV-2

Identifiers

PMID34922321
PMCPMC8674011
OpenAlexW4200086312

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.