揭示复杂网络在冠状病毒增殖研究中的潜力

IF 0.7 Q4 ENGINEERING, BIOMEDICAL International Journal of Biomedical Engineering and Technology Pub Date : 2023-01-01 DOI:10.1504/ijbet.2023.133719

S. Sankararaman

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引用次数: 0

摘要

开发新的方法来了解病毒复制是COVID-19大流行时期的需要。本研究提出了一种新的基于替代图的方法来理解SARS-CoV-2的复制。从病毒与正常细胞相互作用的视频中构建时程模式(THP)矩阵，确定惯性矩(IM)和复杂网络特征。IM的变化和图形特征与SARS-CoV-2的增殖相关。因此，这项工作提出了复杂网络和IM分析来了解病毒感染动力学的可能性。

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Unveiling the potential of complex network in coronavirus proliferation study

The development of novel methods for understanding virus replication is the need of the time of the COVID-19 pandemic. The present work proposes a novel surrogate graph-based method for understanding SARS-CoV-2 replication. Constructing a time history pattern (THP) matrix from the video of the virus interaction with normal cells, the inertia moment (IM) and complex network features are determined. The variation of IM and the graph features are correlated with the proliferation of SARS-CoV-2. Thus, the work suggests the possibility of complex network and IM analyses to understand the kinetics of the virus infection.

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来源期刊

International Journal of Biomedical Engineering and Technology ENGINEERING, BIOMEDICAL-

CiteScore

1.60

自引率

0.00%

发文量

期刊介绍： IJBET addresses cutting-edge research in the multi-disciplinary area of biomedical engineering and technology. Medical science incorporates scientific/technological advances combining to produce more accurate diagnoses, effective treatments with fewer side effects, and improved ability to prevent disease and provide superior-quality healthcare. A key field here is biomedical engineering/technology, offering a synthesis of physical, chemical, mathematical and computational sciences combined with engineering principles to enhance R&D in biology, medicine, behaviour, and health. Topics covered include Artificial organs Automated patient monitoring Advanced therapeutic and surgical devices Application of expert systems and AI to clinical decision making Biomaterials design Biomechanics of injury and wound healing Blood chemistry sensors Computer modelling of physiologic systems Design of optimal clinical laboratories Medical imaging systems Sports medicine.