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Clinical validation of colorimetric RT-LAMP, a fast, highly sensitive and specific COVID-19 molecular diagnostic tool that is robust to detect SARS-CoV-2 variants of concern (preprint)
medrxiv; 2021.
Preprint
Dans Anglais
| medRxiv | ID: ppzbmed-10.1101.2021.05.26.21257488
ABSTRACT
The COVID-19 pandemics unfolded due to the widespread SARS-CoV-2 transmission reinforced the urgent need for affordable molecular diagnostic alternative methods for massive testing screening. We present the clinical validation of a pH-dependent colorimetric RT-LAMP (reverse transcription loop-mediated isothermal amplification) for SARS-CoV-2 detection. The method revealed a limit of detection of 19.3 viral genomic copies/uL when using RNA extracted samples obtained from nasopharyngeal swabs collected in guanidine-containing viral transport medium. Typical RT-LAMP reactions were performed at 65 C for 30 min. When compared to RT-qPCR, up to Ct value 32, RT-LAMP presented 97% (87.4-99.4% 95% CI) sensitivity and 100% (86.2-100%) specificity for SARS-CoV-2 RNA detection targeting N gene. No cross-reactivity was detected when testing other non-SARS-CoV virus, confirming high specificity. The test is compatible with primary RNA extraction free samples. We also demonstrated that colorimetric RT-LAMP can detect SARS-CoV-2 variants of concern (VOC) and variants of interest (VOI), such as variants occurring in Brazil named P.1, P.2, B.1.1.374 and B.1.1.371. The method meets point-of-care requirements and can be deployed in the field for high-throughput COVID-19 testing campaigns, especially in countries where COVID-19 testing efforts are far from ideal to tackle the pandemics. Although RT-qPCR is considered the gold standard for SARS-CoV-2 RNA detection, it requires expensive equipments, infrastructure and highly trained personnel. In contrast, RT-LAMP emerges as an affordable, inexpensive and simple alternative for SARS-CoV-2 molecular detection that can be applied to massive COVID-19 testing campaigns and save lives.
Texte intégral:
Disponible
Collection:
Preprints
Base de données:
medRxiv
Sujet Principal:
COVID-19
langue:
Anglais
Année:
2021
Type de document:
Preprint
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