Kaushik Ajeet Kumar, Dhau Jaspreet Singh, Gohel Hardik, Mishra Yogendra Kumar, Kateb Babak, Kim Nam-Young, Goswami Dharendra Yogi
NanoBioTech Laboratory, Department of Natural Sciences, Division of Sciences, Art, & Mathematics, Florida Polytechnic University, Lakeland, Florida 33805, United States.
Molecule Inc., Tampa, Florida 33612, United States.
ACS Appl Bio Mater. 2020 Nov 16;3(11):7306-7325. doi: 10.1021/acsabm.0c01004. Epub 2020 Oct 27.
To manage the COVID-19 pandemic, development of rapid, selective, sensitive diagnostic systems for early stage β-coronavirus severe acute respiratory syndrome (SARS-CoV-2) virus protein detection is emerging as a necessary response to generate the bioinformatics needed for efficient smart diagnostics, optimization of therapy, and investigation of therapies of higher efficacy. The urgent need for such diagnostic systems is recommended by experts in order to achieve the mass and targeted SARS-CoV-2 detection required to manage the COVID-19 pandemic through the understanding of infection progression and timely therapy decisions. To achieve these tasks, there is a scope for developing smart sensors to rapidly and selectively detect SARS-CoV-2 protein at the picomolar level. COVID-19 infection, due to human-to-human transmission, demands diagnostics at the point-of-care (POC) without the need of experienced labor and sophisticated laboratories. Keeping the above-mentioned considerations, we propose to explore the compartmentalization approach by designing and developing nanoenabled miniaturized electrochemical biosensors to detect SARS-CoV-2 virus at the site of the epidemic as the best way to manage the pandemic. Such COVID-19 diagnostics approach based on a POC sensing technology can be interfaced with the Internet of things and artificial intelligence (AI) techniques (such as machine learning and deep learning for diagnostics) for investigating useful informatics via data storage, sharing, and analytics. Keeping COVID-19 management related challenges and aspects under consideration, our work in this review presents a collective approach involving electrochemical SARS-CoV-2 biosensing supported by AI to generate the bioinformatics needed for early stage COVID-19 diagnosis, correlation of viral load with pathogenesis, understanding of pandemic progression, therapy optimization, POC diagnostics, and diseases management in a personalized manner.
为应对新型冠状病毒肺炎(COVID-19)大流行,开发快速、选择性、灵敏的诊断系统以用于早期β冠状病毒严重急性呼吸综合征(SARS-CoV-2)病毒蛋白检测,正成为一种必要的应对措施,以生成高效智能诊断、优化治疗以及研究更高疗效疗法所需的生物信息学。专家建议迫切需要此类诊断系统,以便通过了解感染进展和及时做出治疗决策,实现管理COVID-19大流行所需的大规模和针对性的SARS-CoV-2检测。为完成这些任务,存在开发智能传感器以在皮摩尔水平快速、选择性地检测SARS-CoV-2蛋白的空间。由于人际传播,COVID-19感染需要在现场护理(POC)进行诊断,无需经验丰富的人员和精密实验室。考虑到上述因素,我们建议通过设计和开发纳米化小型化电化学生物传感器来探索分区方法,以在疫情现场检测SARS-CoV-2病毒,这是管理大流行的最佳方式。这种基于POC传感技术的COVID-19诊断方法可与物联网和人工智能(AI)技术(如用于诊断的机器学习和深度学习)相结合,通过数据存储、共享和分析来研究有用的信息学。考虑到与COVID-19管理相关的挑战和方面,我们在本综述中的工作提出了一种集体方法,涉及由AI支持的电化学SARS-CoV-2生物传感,以生成早期COVID-19诊断、病毒载量与发病机制的相关性、了解大流行进展、治疗优化、POC诊断以及个性化疾病管理所需的生物信息学。