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Highly-sensitive and rapid detection of COVID-19 in wastewater: a comprehensive review based on graphene field effect transistor biosensor
Bioscience Research ; 18:1-9, 2021.
Article in English | Web of Science | ID: covidwho-1619262
ABSTRACT
The novel Corona Virus Disease 2019 (COVID-19) has been spread from the Wuhan city of China has now affected many countries;it is still circulating worldwide. Consecutive studies of finding the RNA of this virus in sewage systems increase renewed interest about COVID-19 faucal transmission and its pathogenic issue on sanitation and wastewater systems. Municipal wastewater is typically remarked as one of the major end routes of different types of emerging contaminants such as pharmaceuticals, endocrine disruptors, antibiotics, micro plastics, pesticide and heavy metal residues associated with antimicrobial resistance. Currently all available, antibodies based and molecular base testing have some limitations for this

purpose:

whole coronavirus particles instead of pure antigen proteins need to be tested in a short time and take control of the pandemic of COVID-19. The current study helped in understanding, concept and demonstrated the potential of graphene Field Effect Transistor (FET) technology for sensitive and rapid detection of corona viruses. Therefore, extra trustworthy, quick response, economical and broadly accessible analytical devices or diagnostic approaches are crucially required. We have critically reviewed and argued the biomarkers and indicators used for COVID-19 diagnostics or SARS-CoV-2 detection. In this regard, ultrasensitive graphene FET biosensors are powerful tools in early diagnosis of COVID-19 infection via targeting virus S1 protein to assess the clinical progress and offer awareness on severity and critical trends of infection.
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Collection: Databases of international organizations Database: Web of Science Type of study: Diagnostic study / Experimental Studies Language: English Journal: Bioscience Research Year: 2021 Document Type: Article

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Collection: Databases of international organizations Database: Web of Science Type of study: Diagnostic study / Experimental Studies Language: English Journal: Bioscience Research Year: 2021 Document Type: Article