UKM-Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Malaysia.
Department of Communication Engineering, University of Technology, Baghdad, 00964, Iraq.
Appl Microbiol Biotechnol. 2022 May;106(9-10):3321-3336. doi: 10.1007/s00253-022-11930-1. Epub 2022 Apr 29.
The COVID-19, MERS-CoV, and SARS-CoV are hazardous epidemics that have resulted in many deaths which caused a worldwide debate. Despite control efforts, SARS-CoV-2 continues to spread, and the fast spread of this highly infectious illness has posed a grave threat to global health. The effect of the SARS-CoV-2 mutation, on the other hand, has been characterized by worrying variations that modify viral characteristics in response to the changing resistance profile of the human population. The repeated transmission of virus mutation indicates that epidemics are likely to occur. Therefore, an early identification system of ongoing mutations of SARS-CoV-2 will provide essential insights for planning and avoiding future outbreaks. This article discussed the following highlights: First, comparing the omicron mutation with other variants; second, analysis and evaluation of the spread rate of the SARS-CoV 2 variations in the countries; third, identification of mutation areas in spike protein; and fourth, it discussed the photonics approaches enabled with artificial intelligence. Therefore, our goal is to identify the SARS-CoV 2 virus directly without the need for sample preparation or molecular amplification procedures. Furthermore, by connecting through the optical network, the COVID-19 test becomes a component of the Internet of healthcare things to improve precision, service efficiency, and flexibility and provide greater availability for the evaluation of the general population. KEY POINTS: • A proposed framework of photonics based on AI for identifying and sorting SARS-CoV 2 mutations. • Comparative scatter rates Omicron variant and other SARS-CoV 2 variations per country. • Evaluating mutation areas in spike protein and AI enabled by photonic technologies for SARS-CoV 2 virus detection.
新型冠状病毒肺炎(COVID-19)、中东呼吸综合征冠状病毒(MERS-CoV)和严重急性呼吸综合征冠状病毒(SARS-CoV)是三种危害性极大的传染病,它们导致了大量死亡,引发了全球范围内的广泛讨论。尽管采取了控制措施,但 SARS-CoV-2 仍在继续传播,这种高度传染性疾病的迅速传播对全球健康构成了严重威胁。另一方面,SARS-CoV-2 的突变效应表现为令人担忧的变异,这些变异通过改变病毒特征来应对人类群体不断变化的耐药性特征。病毒突变的反复传播表明,疫情很可能再次发生。因此,建立一种 SARS-CoV-2 持续突变的早期识别系统,将为规划和避免未来疫情爆发提供重要的见解。本文讨论了以下几个重点:首先,比较了 omicron 突变与其他变体;其次,分析和评估了各国 SARS-CoV-2 变异的传播速度;第三,鉴定了刺突蛋白中的突变区域;最后,讨论了人工智能支持的光子学方法。因此,我们的目标是无需样本制备或分子扩增程序即可直接识别 SARS-CoV-2 病毒。此外,通过连接光学网络,COVID-19 测试成为医疗物联网的一个组成部分,以提高精度、服务效率和灵活性,并为评估普通人群提供更大的可用性。关键点:• 提出了一种基于人工智能的光子学框架,用于识别和分类 SARS-CoV-2 突变。• 比较了 omicron 变体和每个国家的其他 SARS-CoV-2 变体的散射率。• 评估了刺突蛋白中的突变区域和人工智能支持的光子技术在 SARS-CoV-2 病毒检测中的应用。