Development of a self-contained microfluidic chip and an internet-of-things-based point-of-care device for automated identification of respiratory viruses.

The COVID-19 pandemic greatly impacted the diagnostic market, leading to the development of new technologies such as point-of-care testing (POCT), multiplex testing, and digital health platforms. In this study, we present a self-contained microfluidic chip integrated with an internet-of-things (IoT)-based point-of-care (POC) device for rapid and sensitive diagnosis of respiratory viruses. Our platform enables sample-to-answer diagnostics within 70 min by automating RNA extraction, reverse transcription-loop-mediated isothermal amplification (RT-LAMP), and fluorescence detection. The microfluidic chip is designed to store all the necessary reagents for the entire diagnostic assay, including a lysis buffer, a washing buffer, an elution buffer, and a lyophilized RT-LAMP cocktail. It can perform nucleic acid extraction, aliquoting, and gene amplification in multiple reaction chambers without cross-contamination. The IoT-based POC device consists of a Raspberry Pi 4 for device control and data processing, a CMOS sensor for measuring fluorescence signals, a resistive heater panel for temperature control, and solenoid valves for controlling the movement of on-chip reagent solutions. The proposed device is portable and features a touchscreen for user control and result display. We evaluated the performance of the platform using 11 clinical respiratory virus samples, including 5 SARS-CoV-2 samples, 2 influenza A samples, and 4 influenza B samples. All tested clinical samples were accurately identified with high specificity and fidelity, demonstrating the ability to simultaneously detect multiple respiratory viruses. The combination of the integrated microfluidic chip with the POC device offers a simple, cost-effective, and scalable solution for rapid molecular diagnosis of respiratory viruses in resource-limited settings.