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Quantitative detection of dengue serotypes using a smartphone-connected handheld lab-on-chip platform.
Moser, Nicolas; Yu, Ling-Shan; Rodriguez Manzano, Jesus; Malpartida-Cardenas, Kenny; Au, Anselm; Arkell, Paul; Cicatiello, Chiara; Moniri, Ahmad; Miglietta, Luca; Wang, Wen-Hung; Wang, Sheng Fan; Holmes, Alison; Chen, Yen-Hsu; Georgiou, Pantelis.
Afiliação
  • Moser N; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom.
  • Yu LS; Institute of Biopharmaceutical Sciences, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan.
  • Rodriguez Manzano J; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom.
  • Malpartida-Cardenas K; Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom.
  • Au A; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom.
  • Arkell P; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom.
  • Cicatiello C; Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom.
  • Moniri A; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom.
  • Miglietta L; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom.
  • Wang WH; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom.
  • Wang SF; Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom.
  • Holmes A; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan.
  • Chen YH; Center for Tropical Medicine and Infectious Disease, Kaohsiung Medical University, Kaohsiung, Taiwan.
  • Georgiou P; Department of Medical Laboratory Science and Biotechnology, Kaohsiung Medical University, Kaohsiung, Taiwan.
Front Bioeng Biotechnol ; 10: 892853, 2022.
Article em En | MEDLINE | ID: mdl-36185458
ABSTRACT
Dengue is one of the most prevalent infectious diseases in the world. Rapid, accurate and scalable diagnostics are key to patient management and epidemiological surveillance of the dengue virus (DENV), however current technologies do not match required clinical sensitivity and specificity or rely on large laboratory equipment. In this work, we report the translation of our smartphone-connected handheld Lab-on-Chip (LoC) platform for the quantitative detection of two dengue serotypes. At its core, the approach relies on the combination of Complementary Metal-Oxide-Semiconductor (CMOS) microchip technology to integrate an array of 78 × 56 potentiometric sensors, and a label-free reverse-transcriptase loop mediated isothermal amplification (RT-LAMP) assay. The platform communicates to a smartphone app which synchronises results in real time with a secure cloud server hosted by Amazon Web Services (AWS) for epidemiological surveillance. The assay on our LoC platform (RT-eLAMP) was shown to match performance on a gold-standard fluorescence-based real-time instrument (RT-qLAMP) with synthetic DENV-1 and DENV-2 RNA and extracted RNA from 9 DENV-2 clinical isolates, achieving quantitative detection in under 15 min. To validate the portability of the platform and the geo-tagging capabilities, we led our study in the laboratories at Imperial College London, UK, and Kaohsiung Medical Hospital, Taiwan. This approach carries high potential for application in low resource settings at the point of care (PoC).
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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