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Malachite Green-Based Detection of SARS-CoV-2 by One-Step Reverse Transcription Loop-Mediated Isothermal Amplification
Iranian Journal of Science ; : 2023/09/01 00:00:00.000, 2023.
Article in English | EuropePMC | ID: covidwho-2236172
ABSTRACT
The pandemic of severe acute respiratory syndrome 2 (SARS-CoV-2) revealed the necessity of diagnosis of the infected people to prevent the prevalence infection cycle. Many commercial pathogen diagnosis methods are based on the detection of genomic materials. Isothermal amplification methods such as loop-mediated-isothermal amplification (LAMP) are the method of choice in these cases. Reverse transcription steps are efficiently coupled to LAMP for the detection of pathogens with genomic RNAs such as SARS-CoV-2. Many detection systems for LAMP include fluorescent readout systems. Although such systems result in desirable limits of detection, the need for special instrumentation is the main dispute of such systems to become real point of care assays. In contrast, colorimetric detection methods would reduce costs and improve the applicability of the system. In this study one-step reverse transcription-LAMP reaction was established that enables visual detection of the SARS-CoV-2 genome. Nasopharyngeal RNA samples were first validated by reverse transcription quantitative polymerase chain reaction and then subjected to RT-LAMP. To lower the cost associated with the readout system equipment, malachite green (MG) was used. The color change of MG to blue allowed visual detection of the virus. Firstly, experiments were set up as two-step RT-LAMP reaction to identify the best primer sets. In addition, MG concentration was optimized with the significant colorimetric signal for the positive samples. Next, a one-step colorimetric method was developed for the detection of SARS-CoV-2 based on MG color shift in 2 h.
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Collection: Databases of international organizations Database: EuropePMC Type of study: Observational study / Prognostic study Language: English Journal: Iranian Journal of Science Year: 2023 Document Type: Article

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Collection: Databases of international organizations Database: EuropePMC Type of study: Observational study / Prognostic study Language: English Journal: Iranian Journal of Science Year: 2023 Document Type: Article