Author: Taniguchi, Masateru; Minami, Shohei; Ono, Chikako; Hamajima, Rina; Morimura, Ayumi; Hamaguchi, Shigeto; Akeda, Yukihiro; Kanai, Yuta; Kobayashi, Takeshi; Kamitani, Wataru; Terada, Yutaka; Suzuki, Koichiro; Hatori, Nobuaki; Yamagishi, Yoshiaki; Washizu, Nobuei; Takei, Hiroyasu; Sakamoto, Osamu; Naono, Norihiko; Tatematsu, Kenji; Washio, Takashi; Matsuura, Yoshiharu; Tomono, Kazunori
Title: Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection Cord-id: glftfa0e Document date: 2021_6_17
ID: glftfa0e
Snippet: High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligenc
Document: High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.
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