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Cloud-Based Software Architecture for Fully Automated Point-of-Care Molecular Diagnostic Device.
Kil, Byeong-Heon; Park, Ji-Seong; Ryu, Mun-Ho; Park, Chan-Young; Kim, Yu-Seop; Kim, Jong-Dae.
  • Kil BH; School of Software, Hallym University, Chuncheon-si 24252, Korea.
  • Park JS; Bio-IT Research Center, Hallym University, Chuncheon-si 24252, Korea.
  • Ryu MH; Biomedux Co., Ltd., Suwon-si 16226, Korea.
  • Park CY; Division of Biomedical Engineering, Jeonbuk National University, Jeonju 54896, Korea.
  • Kim YS; Research Center of Healthcare & Welfare Instrument for the Aged, Jeonbuk National University, Jeonju 54896, Korea.
  • Kim JD; School of Software, Hallym University, Chuncheon-si 24252, Korea.
Sensors (Basel) ; 21(21)2021 Oct 21.
Article in English | MEDLINE | ID: covidwho-1512556
ABSTRACT
This paper proposes a cloud-based software architecture for fully automated point-of-care molecular diagnostic devices. The target system operates a cartridge consisting of an extraction body for DNA extraction and a PCR chip for amplification and fluorescence detection. To facilitate control and monitoring via the cloud, a socket server was employed for fundamental molecular diagnostic functions such as DNA extraction, amplification, and fluorescence detection. The user interface for experimental control and monitoring was constructed with the RESTful application programming interface, allowing access from the terminal device, edge, and cloud. Furthermore, it can also be accessed through any web-based user interface on smart computing devices such as smart phones or tablets. An emulator with the proposed software architecture was fabricated to validate successful operation.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Point-of-Care Systems / Cloud Computing Type of study: Prognostic study Language: English Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Point-of-Care Systems / Cloud Computing Type of study: Prognostic study Language: English Year: 2021 Document Type: Article