Your browser doesn't support javascript.
loading
Emerging Electrochemical Biosensing Trends for Rapid Diagnosis of COVID-19 Biomarkers as Point-of-Care Platforms: A Critical Review.
Madhurantakam, Sasya; Muthukumar, Sriram; Prasad, Shalini.
Afiliação
  • Madhurantakam S; Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas 75080, United States.
  • Muthukumar S; EnLiSense LLC, Allen, Texas 75013, United States.
  • Prasad S; Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas 75080, United States.
ACS Omega ; 7(15): 12467-12473, 2022 Apr 19.
Article em En | MEDLINE | ID: mdl-35474766
ABSTRACT
Rapid diagnosis is a critical aspect associated with controlling the spread of COVID-19. Electrochemical sensor platforms are ideally suited for rapid and highly sensitive detection of biomolecules. This review focuses on state-of-the-art of COVID-19 biomarker detection by utilizing electrochemical biosensing platforms. Point-of-care (POC) sensing is one of the most promising and emerging fields in detecting and quantifying health biomarkers. Electrochemical biosensors play a major role in the development of point-of-care devices because of their high sensitivity, specificity, and ability for rapid analysis. Integration of electrochemistry with point-of-care technologies in the context of COVID-19 diagnosis and screening has facilitated in convenient operation, miniaturization, and portability. Identification of potential biomarkers in disease diagnosis is crucial for patient monitoring concerning severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this review, we will discuss the choice of biomarkers in addition to the various types of electrochemical sensors that have been developed to meet the needs of rapid detection and disease severity analysis.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article