Digital Pathology Platform for Respiratory Tract Infection Diagnosis via Multiplex Single-Particle Detections.
ACS Sens
; 5(11): 3398-3403, 2020 11 25.
Article
en En
| MEDLINE
| ID: mdl-32933253
ABSTRACT
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.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Infecciones del Sistema Respiratorio
/
Nanoporos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
ACS Sens
Año:
2020
Tipo del documento:
Article
País de afiliación:
Japón