RT-LAMP in SARS-CoV-2 detection: point to improve primer designing and decrease molecular diagnosis pitfalls.
CT scan
Data mining
RT-LAMP
RT-qPCR
SARS-CoV-2
detection
Journal
Expert review of molecular diagnostics
ISSN: 1744-8352
Titre abrégé: Expert Rev Mol Diagn
Pays: England
ID NLM: 101120777
Informations de publication
Date de publication:
31 Oct 2022
31 Oct 2022
Historique:
pubmed:
19
10
2022
medline:
19
10
2022
entrez:
18
10
2022
Statut:
aheadofprint
Résumé
Due to the high transmission rate of SARS-CoV-2, diagnostic tests have become tools for identifying patients. The key points were the virus genomes survey to design RT-LAMP primers; comparing the sensitivity and specificity of RT-LAMP and RT-qPCR; and determining the relationship among clinical symptoms, CT scan, RT-qPCR, and RT-LAMP results. This cohort study included 444 symptomatic patients. The specificity and sensitivity of RT-LAMP were assayed. The five statistical models, simultaneously, by RapidMiner to find the best method for detecting the virus were done through the correlation between the clinical symptoms, RT-LAMP, RT-qPCR, and CT scan results. The chi-square test by SPSS 26.0 was used to calculate kappa agreement. The virus genome was detected in all the positive samples (198) by RT-qPCR and RT-LAMP. In addition, 246 samples were negative by RT-qPCR, while 88 were positive by RT-LAMP. Data mining analysis indicated that there were most associations between the RT-LAMP and CT scan data compared to RT-qPCR and CT scan data. RT-LAMP could detect SARS-CoV-2 with great simplicity, speed, and cheapness. Therefore, it is logical to screen, a large number of patients by RT-LAMP, and then RT-qPCR can be used on the limited samples.
Sections du résumé
BACKGROUND
UNASSIGNED
Due to the high transmission rate of SARS-CoV-2, diagnostic tests have become tools for identifying patients. The key points were the virus genomes survey to design RT-LAMP primers; comparing the sensitivity and specificity of RT-LAMP and RT-qPCR; and determining the relationship among clinical symptoms, CT scan, RT-qPCR, and RT-LAMP results.
METHODS
UNASSIGNED
This cohort study included 444 symptomatic patients. The specificity and sensitivity of RT-LAMP were assayed. The five statistical models, simultaneously, by RapidMiner to find the best method for detecting the virus were done through the correlation between the clinical symptoms, RT-LAMP, RT-qPCR, and CT scan results. The chi-square test by SPSS 26.0 was used to calculate kappa agreement.
RESULTS
UNASSIGNED
The virus genome was detected in all the positive samples (198) by RT-qPCR and RT-LAMP. In addition, 246 samples were negative by RT-qPCR, while 88 were positive by RT-LAMP. Data mining analysis indicated that there were most associations between the RT-LAMP and CT scan data compared to RT-qPCR and CT scan data.
CONCLUSIONS
UNASSIGNED
RT-LAMP could detect SARS-CoV-2 with great simplicity, speed, and cheapness. Therefore, it is logical to screen, a large number of patients by RT-LAMP, and then RT-qPCR can be used on the limited samples.
Identifiants
pubmed: 36254603
doi: 10.1080/14737159.2022.2136991
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM