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In ACS sensors

The variability of bio-particles remains a key barrier to realizing competent potential of nanoscale detection into digital diagnosis of extraneous object that causes an infectious disease. Here, we report label-free virus identification based on machine-learning classification. Single-virus-particles were detected using nanopore and resistive-pulse waveform were analyzed multilaterally using artificial intelligence. In the discrimination, over 99 % accuracy for 5 different virus species was demonstrated. This advance is accessed through the classification of virus-derived ionic current signal patterns reflecting their intrinsic physical properties in a high-dimensional feature space. Moreover, consideration of viral similarity based on the accuracies indicates the contributing factors in the recognitions. The present findings offers the prospect of a novel surveillance system applicable to detections of multiple viruses including new strains.

Arima Akihide, Tsutsui Makusu, Yoshida Takeshi, Tatematsu Kenji, Yamazaki Tomoko, Yokota Kazumichi, Kuroda Shun’ichi, Washio Takashi, Baba Yoshinobu, Kawai Tomoji