4.6 Article

An Integrated Smartphone-Based Genetic Analyzer for Qualitative and Quantitative Pathogen Detection

期刊

ACS OMEGA
卷 5, 期 35, 页码 22208-22214

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acsomega.0c02317

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资金

  1. Engineering Research Center of Excellence Program of Korea Ministry of Science, ICT and Future Planning (MSIP)/National Research Foundation of Korea (NRF) [2014R1A5A1009799]
  2. National Research Foundation of Korea (NRF)
  3. Ministry of Science and ICT (MSIT) [2020R1A2C1003960]
  4. Korea Health Technology R&D Project/Korea Health Industry Development Institute (KHIDI), Ministry of Health and Welfare of South Korea [HI20C0644]
  5. National Research Foundation of Korea [2020R1A2C1003960] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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The use of the smartphone is an ideal platform to realize the future point-of-care (POC) diagnostic system. Herein, we propose an integrated smartphone-based genetic analyzer. It consists of a smartphone and an integrated genetic analysis unit (i-Gene), in which the power of the smartphone was utilized for heating the gene amplification reaction, and the camera function was used for imaging the colorimetric change of the reaction for quantitative and multiplex foodborne pathogens. The housing of i-Gene was fabricated by using a 3D printer, which was equipped with a macro lens, white LEDs, a disposable microfluidic chip for loop-mediated isothermal amplification (LAMP), a thin-film heater, and a power booster. The i-Gene was installed on the iPhone in alignment with a camera. The LAMP mixture for Eriochrome Black T (EBT) colorimetric detection was injected into the LAMP chip to identify Escherichia coli O157:H7, Salmonella typhimurium, and Vibrio parahaemolyticus. The proportional-integral-derivative controller-embedded film heater was powered by a 5.0 V power bank to maintain 63 degrees C for the LAMP reaction. When the LAMP proceeded, the color was changed from violet to blue, which was real-time monitored by the smartphone complementary metal oxide semiconductor camera. The images were transported to the desktop computer via Wi-Fi. The quantitative LAMP profiles were obtained by plotting the ratio of green/red intensity versus the reaction time. We could identify E. coli O157:H7 with a limit of detection of 101 copies/mu L within 60 min. Our proposed smartphone-based genetic analyzer offers a portable, simple, rapid, and cost-effective POC platform for future diagnostic markets.

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