Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Digital Pathology Platform for Respiratory Tract Infection Diagnosis via Multiplex Single-Particle Detections.
ACS Sensors ( IF 8.2 ) Pub Date : 2020-09-16 , DOI: 10.1021/acssensors.0c01564 Akihide Arima 1 , Makusu Tsutsui 2 , Takeshi Yoshida 2 , Kenji Tatematsu 2 , Tomoko Yamazaki 2 , Kazumichi Yokota 3 , Shun'ichi Kuroda 2 , Takashi Washio 2 , Yoshinobu Baba 1, 4, 5 , Tomoji Kawai 2
ACS Sensors ( IF 8.2 ) Pub Date : 2020-09-16 , DOI: 10.1021/acssensors.0c01564 Akihide Arima 1 , Makusu Tsutsui 2 , Takeshi Yoshida 2 , Kenji Tatematsu 2 , Tomoko Yamazaki 2 , Kazumichi Yokota 3 , Shun'ichi Kuroda 2 , Takashi Washio 2 , Yoshinobu Baba 1, 4, 5 , Tomoji Kawai 2
Affiliation
|
The variability of bioparticles remains a key barrier to realizing the competent potential of nanoscale detection into a digital diagnosis of an 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 nanopores, and resistive-pulse waveforms were analyzed multilaterally using artificial intelligence. In the discrimination, over 99% accuracy for five 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 offer the prospect of a novel surveillance system applicable to detection of multiple viruses including new strains.
中文翻译：
通过多重单颗粒检测进行呼吸道感染诊断的数字病理平台。
生物颗粒的可变性仍然是实现纳米级检测胜任潜力转化为引起传染病的外来物的数字诊断的主要障碍。在这里，我们报告基于机器学习分类的无标签病毒识别。使用纳米孔检测单个病毒颗粒，并使用人工智能对电阻脉冲波形进行多边分析。在鉴别中，证明了五种不同病毒的准确性超过99％。通过对病毒来源的离子电流信号模式进行分类可以反映出这一进步，这些信号模式反映了它们在高维特征空间中的固有物理特性。而且，基于准确性的病毒相似性的考虑指示了识别中的贡献因素。
更新日期：2020-11-25
中文翻译：
通过多重单颗粒检测进行呼吸道感染诊断的数字病理平台。
生物颗粒的可变性仍然是实现纳米级检测胜任潜力转化为引起传染病的外来物的数字诊断的主要障碍。在这里，我们报告基于机器学习分类的无标签病毒识别。使用纳米孔检测单个病毒颗粒，并使用人工智能对电阻脉冲波形进行多边分析。在鉴别中，证明了五种不同病毒的准确性超过99％。通过对病毒来源的离子电流信号模式进行分类可以反映出这一进步，这些信号模式反映了它们在高维特征空间中的固有物理特性。而且，基于准确性的病毒相似性的考虑指示了识别中的贡献因素。
京公网安备 11010802027423号