Advances in Technology to Address COVID-19
Edward Kai‐Hua Chow, Pak Kin Wong, Xianting Ding
- 发表年份
- 2020
- 引用次数
- 3
- 访问权限
- 开放获取
摘要
This year has seen an unprecedented worldwide pandemic that has been brought on by the rise of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which results in COVID-19 (coronavirus 2019) infections. COVID-19 has impacted every aspect of our lives and has required the world to rapidly mobilize to address all aspects of diagnosis and treatment of this disease. COVID-19 has brought to light the challenges of managing a completely novel infectious disease with existing diagnostics and therapeutics that were insufficient to stem the spread of COVID-19. Thus, the resources allotted toward research and development and the global cooperation of governments, scientists, and clinicians to address COVID-19 required a pace of innovation in healthcare that has never before been observed in order to address this new disease. As a result of this effort, innovations in technology to better understand, detect, and treat COVID-19 continue to be reported every day. Here at SLAS Technology, we felt it was important to highlight these advances in technology that have been made to better address all aspects of COVID-19 detection and treatment. We present here a special issue that reports how technology has been used to address COVID-19. The spread of COVID-19 across the world has shown that any hope for effective control of COVID-19 infection in the community requires the development of rapid and accurate methods for detecting COVID-19 infections. Applying existing and emerging viral detection technologies toward better COVID-19 diagnostics has resulted in incredible advances in pathogen detection innovations. Miniaturization assays that allowed for the accurate analysis and detection of SARS-CoV-2 viral nucleic acid detection or host antibody response to COVID-19 have proven to be critical.1Zhu N. Wong P.K. Advances in Viral Diagnostic Technologies for Combating COVID-19 and Future Pandemics.SLAS Technol. 2020; 25: 513-521Google Scholar, 2Tan A.S. Nerurkar S.N. Tan W.C.C. et al.The Virological, Immunological, and Imaging Approaches for COVID-19 Diagnosis and Research.SLAS Technol. 2020; 25: 522-544Google Scholar, 3Karp D.G. Cuda D. Tandel D. et al.Sensitive and Specific Detection of SARS-CoV-2 Antibodies Using a High-Throughput, Fully Automated Liquid-Handling Robotic System.SLAS Technol. 2020; 25: 545-552Google Scholar While diagnostics initially required clinical laboratory tests, these technological advances have proven critical for field testing in the community or in less well-equipped remote diagnostic testing sites. In addition to advances in detecting COVID-19 infections, leveraging technology to better understand COVID-19 disease progression and immune response is critical to developing better therapies to combat this pandemic. As a result, the molecular mechanisms of COVID-19 infection, as well as an understanding of the critical immune responses and overall biological responses to COVID-19, have been uncovered in an amazingly short amount of time. Much of this has been a result of the use of critical technologies such as single-cell analysis technologies and advances in mass cytometry.2Tan A.S. Nerurkar S.N. Tan W.C.C. et al.The Virological, Immunological, and Imaging Approaches for COVID-19 Diagnosis and Research.SLAS Technol. 2020; 25: 522-544Google Scholar The last few years have seen a paradigm shift in the development and application of artificial intelligence (AI). This has been particularly true for life sciences and biomedical applications. In order to better understand and address COVID-19, AI has played a huge role in improving detection and therapeutic drug development. Of particular importance has been the development of multiple AI-based approaches toward improving COVID-19 detection through standard chest x-ray images.4Sekeroglu B. Ozsahin I. Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks.SLAS Technol. 2020; 25: 553-565Google Scholar,5Echtioui A. Zouch W. Ghorbel M. et al.D
关键词
相关论文
The Organization of Behavior
D. O. Hebb
2005
Fractional Brownian Motions, Fractional Noises and Applications
Benoît B. Mandelbrot, John W. Van Ness
1968
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi 等 10 位作者
2021
A guide to deep learning in healthcare
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar 等 10 位作者
2018