101
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Joshi G, Borah P, Thakur S, Sharma P, Mayank, Poduri R. Exploring the COVID-19 vaccine candidates against SARS-CoV-2 and its variants: where do we stand and where do we go? Hum Vaccin Immunother 2021; 17:4714-4740. [PMID: 34856868 PMCID: PMC8726002 DOI: 10.1080/21645515.2021.1995283] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/20/2021] [Accepted: 10/15/2021] [Indexed: 12/23/2022] Open
Abstract
As of September 2021, 117 COVID-19 vaccines are in clinical development, and 194 are in preclinical development as per the World Health Organization (WHO) published draft landscape. Among the 117 vaccines undergoing clinical trials, the major platforms include protein subunit; RNA; inactivated virus; viral vector, among others. So far, USFDA recognized to approve the Pfizer-BioNTech (Comirnaty) COVID-19 vaccine for its full use in individuals of 16 years of age and older. Though the approved vaccines are being manufactured at a tremendous pace, the wealthiest countries have about 28% of total vaccines despite possessing only 10.8% of the total world population, suggesting an inequity of vaccine distribution. The review comprehensively summarizes the history of vaccines, mainly focusing on vaccines for SARS-CoV-2. The review also connects relevant topics, including measurement of vaccines efficacy against SARS-CoV-2 and its variants, associated challenges, and limitations, as hurdles in global vaccination are also kept forth.
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Affiliation(s)
- Gaurav Joshi
- School of Pharmacy, Graphic Era Hill University, Dehradun, India
- Department of Pharmaceutical Sciences and Natural Products, School of Pharmaceutical Sciences, Central University of Punjab, Bathinda, India
| | - Pobitra Borah
- School of Pharmacy, Graphic Era Hill University, Dehradun, India
| | - Shweta Thakur
- Laboratory of Genomic Instability and Diseases, Department of Infectious Disease Biology, Institute of Life Sciences, Bhubaneshwar, India
| | - Praveen Sharma
- Department of Pharmaceutical Sciences and Natural Products, School of Pharmaceutical Sciences, Central University of Punjab, Bathinda, India
| | - Mayank
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, India
| | - Ramarao Poduri
- Department of Pharmaceutical Sciences and Natural Products, School of Pharmaceutical Sciences, Central University of Punjab, Bathinda, India
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102
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Ma L, Li H, Lan J, Hao X, Liu H, Wang X, Huang Y. Comprehensive analyses of bioinformatics applications in the fight against COVID-19 pandemic. Comput Biol Chem 2021; 95:107599. [PMID: 34773807 PMCID: PMC8560182 DOI: 10.1016/j.compbiolchem.2021.107599] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/24/2021] [Accepted: 10/29/2021] [Indexed: 02/07/2023]
Abstract
Novel coronavirus disease 2019 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which can be transmitted from person to person. As of September 21, 2021, over 228 million cases were diagnosed as COVID-19 infection in more than 200 countries and regions worldwide. The death toll is more than 4.69 million and the mortality rate has reached about 2.05% as it has gradually become a global plague, and the numbers are growing. Therefore, it is important to gain a deeper understanding of the genome and protein characteristics, clinical diagnostics, pathogenic mechanisms, and the development of antiviral drugs and vaccines against the novel coronavirus to deal with the COVID-19 pandemic. The traditional biology technologies are limited for COVID-19-related studies to understand the pandemic happening. Bioinformatics is the application of computational methods and analytical tools in the field of biological research which has obvious advantages in predicting the structure, product, function, and evolution of unknown genes and proteins, and in screening drugs and vaccines from a large amount of sequence information. Here, we comprehensively summarized several of the most important methods and applications relating to COVID-19 based on currently available reports of bioinformatics technologies, focusing on future research for overcoming the virus pandemic. Based on the next-generation sequencing (NGS) and third-generation sequencing (TGS) technology, not only virus can be detected, but also high quality SARS-CoV-2 genome could be obtained quickly. The emergence of data of genome sequences, variants, haplotypes of SARS-CoV-2 help us to understand genome and protein structure, variant calling, mutation, and other biological characteristics. After sequencing alignment and phylogenetic analysis, the bat may be the natural host of the novel coronavirus. Single-cell RNA sequencing provide abundant resource for discovering the mechanism of immune response induced by COVID-19. As an entry receptor, angiotensin-converting enzyme 2 (ACE2) can be used as a potential drug target to treat COVID-19. Molecular dynamics simulation, molecular docking and artificial intelligence (AI) technology of bioinformatics methods based on drug databases for SARS-CoV-2 can accelerate the development of drugs. Meanwhile, computational approaches are helpful to identify suitable vaccines to prevent COVID-19 infection through reverse vaccinology, Immunoinformatics and structural vaccinology.
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Affiliation(s)
- Lifei Ma
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China,College of Lab Medicine, Hebei North University, Zhangjiakou, Hebei 075000, China,Corresponding author at: State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China
| | - Huiyang Li
- Tianjin Key Laboratory of Biomaterial Research, Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin 300192, China
| | - Jinping Lan
- College of Lab Medicine, Hebei North University, Zhangjiakou, Hebei 075000, China
| | - Xiuqing Hao
- The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei 075000, China
| | - Huiying Liu
- The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei 075000, China
| | - Xiaoman Wang
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China,Corresponding authors
| | - Yong Huang
- College of Lab Medicine, Hebei North University, Zhangjiakou, Hebei 075000, China,Corresponding authors
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103
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Machado AS, Castelo PM, Capela E Silva F, Lamy E. Covid-19: Signs and symptoms related to the feeding behavior. Physiol Behav 2021; 242:113605. [PMID: 34600920 PMCID: PMC8482655 DOI: 10.1016/j.physbeh.2021.113605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 08/03/2021] [Accepted: 09/23/2021] [Indexed: 12/15/2022]
Abstract
COVID-19 reached pandemic level in March 2020 and the number of confirmed cases continued to increase worldwide. The clinical course of the disease has not yet been fully characterized, and some specific symptoms related to smell, taste, and feeding behavior require further examination. The present study aimed to assess the presence of symptoms related to the feeding behavior occurred during and/or after COVID-19 in adults residing in Portugal and to link them to disease severity using a multivariate approach. Data were collected from May to September 2020, through a questionnaire answered online containing questions about general and specific symptoms before, during and after COVID-19. 362 participants were included: 201 were symptomatic, being 15 hospitalized and 186 non-hospitalized. Cluster analysis grouped the symptomatic non-hospitalized participants as mild and severe cases. For these patients, the most frequent symptoms related to the feeding process were smell disorders in 40% and 62%, taste disorders in 37% and 60%, and dry mouth, in 23% and 48% of the mild and severe cases, respectively. Dry mouth was significantly associated with difficulty to swallow, pain during swallow, choking when eating or drinking, and preference for mushy/pasty foods (p < 0.01; Chi-squared test). Among the severe cases, the incidence of coughing during the meal (31%), difficulty (19%) and pain during swallow (17%), preference for mushy/pasty foods (10%) and choking when eating or drinking (6%) were clinically relevant and may indicate the presence of swallowing disorders. This group also showed a higher frequency of general symptoms, such as fever, headache, abdominal pain, tiredness, diarrhea, nausea, and shortness of breath (p < 0.05; Chi-squared test). Smell disorders, taste disorders and dry mouth were the most frequent symptoms related to the feeding behavior for both mild and severe cases. Dry mouth was significantly associated with swallowing difficulties and future research should investigate it as a frequent symptom and as a predictive of the presence of eating and swallowing disorders in COVID-19 cases.
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Affiliation(s)
| | - Paula Midori Castelo
- Department of Pharmaceutical Sciences, Institute of Environmental, Chemical and Pharmaceutical Sciences, University Federal de São Paulo (UNIFESP), Brazil
| | - Fernando Capela E Silva
- MED - Mediterranean Institute for Agriculture, Environment and Development, IIFA - Instituto de Investigação e Formação Avançada, University of Évora, Portugal; Department of Medical and Health Sciences, School of Health and Human Development, University of Évora, Évora, Portugal
| | - Elsa Lamy
- MED - Mediterranean Institute for Agriculture, Environment and Development, IIFA - Instituto de Investigação e Formação Avançada, University of Évora, Portugal.
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104
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Antonarelli G, Corti C, Tarantino P, Ascione L, Cortes J, Romero P, Mittendorf E, Disis M, Curigliano G. Therapeutic cancer vaccines revamping: technology advancements and pitfalls. Ann Oncol 2021; 32:1537-1551. [PMID: 34500046 PMCID: PMC8420263 DOI: 10.1016/j.annonc.2021.08.2153] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/21/2021] [Accepted: 08/29/2021] [Indexed: 12/12/2022] Open
Abstract
Cancer vaccines (CVs) represent a long-sought therapeutic and prophylactic immunotherapy strategy to obtain antigen (Ag)-specific T-cell responses and potentially achieve long-term clinical benefit. However, historically, most CV clinical trials have resulted in disappointing outcomes, despite promising signs of immunogenicity across most formulations. In the past decade, technological advances regarding vaccine delivery platforms, tools for immunogenomic profiling, and Ag/epitope selection have occurred. Consequently, the ability of CVs to induce tumor-specific and, in some cases, remarkable clinical responses have been observed in early-phase clinical trials. It is notable that the record-breaking speed of vaccine development in response to the coronavirus disease-2019 pandemic mainly relied on manufacturing infrastructures and technological platforms already developed for CVs. In turn, research, clinical data, and infrastructures put in place for the severe acute respiratory syndrome coronavirus 2 pandemic can further speed CV development processes. This review outlines the main technological advancements as well as major issues to tackle in the development of CVs. Possible applications for unmet clinical needs will be described, putting into perspective the future of cancer vaccinology.
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Affiliation(s)
- G. Antonarelli
- Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Milan, Italy,Department of Oncology and Haematology (DIPO), University of Milan, Milan, Italy
| | - C. Corti
- Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Milan, Italy,Department of Oncology and Haematology (DIPO), University of Milan, Milan, Italy
| | - P. Tarantino
- Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Milan, Italy,Department of Oncology and Haematology (DIPO), University of Milan, Milan, Italy
| | - L. Ascione
- Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Milan, Italy,Department of Oncology and Haematology (DIPO), University of Milan, Milan, Italy
| | - J. Cortes
- International Breast Cancer Center (IBCC), Quironsalud Group, Barcelona, Spain,Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - P. Romero
- Department of Fundamental Oncology, University of Lausanne, Lausanne, Switzerland
| | - E.A. Mittendorf
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, USA,Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, USA
| | - M.L. Disis
- UW Medicine Cancer Vaccine Institute, University of Washington, Seattle, USA
| | - G. Curigliano
- Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Milan, Italy,Department of Oncology and Haematology (DIPO), University of Milan, Milan, Italy,Correspondence to: Prof. Giuseppe Curigliano, Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141 Milan, Italy. Tel: +39-0257489599
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105
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Application of Reverse Vaccinology and Immunoinformatic Strategies for the Identification of Vaccine Candidates Against Shigella flexneri. Methods Mol Biol 2021. [PMID: 34784029 DOI: 10.1007/978-1-0716-1900-1_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Reverse vaccinology (RV) was first introduced by Rappuoli for the development of an effective vaccine against serogroup B Neisseria meningitidis (MenB). With the advances in next generation sequencing technologies, the amount of genomic data has risen exponentially. Since then, the RV approach has widely been used to discover potential vaccine protein targets by screening whole genome sequences of pathogens using a combination of sophisticated computational algorithms and bioinformatic tools. In contrast to conventional vaccine development strategies, RV offers a novel method to facilitate rapid vaccine design and reduces reliance on the traditional, relatively tedious, and labor-intensive approach based on Pasteur"s principles of isolating, inactivating, and injecting the causative agent of an infectious disease. Advances in biocomputational techniques have remarkably increased the significance for the rapid identification of the proteins that are secreted or expressed on the surface of pathogens. Immunogenic proteins which are able to induce the immune response in the hosts can be predicted based on the immune epitopes present within the protein sequence. To date, RV has successfully been applied to develop vaccines against a variety of infectious pathogens. In this chapter, we apply a pipeline of bioinformatic programs for identification of Shigella flexneri potential vaccine candidates as an illustration immunoinformatic tools available for RV.
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106
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Lv H, Shi L, Berkenpas JW, Dao FY, Zulfiqar H, Ding H, Zhang Y, Yang L, Cao R. Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design. Brief Bioinform 2021; 22:bbab320. [PMID: 34410360 PMCID: PMC8511807 DOI: 10.1093/bib/bbab320] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/15/2021] [Accepted: 07/22/2021] [Indexed: 12/13/2022] Open
Abstract
The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.
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Affiliation(s)
- Hao Lv
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Lei Shi
- Department of Spine Surgery, Changzheng Hospital, Naval Medical University, Shanghai 200433, China
| | | | - Fu-Ying Dao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hasan Zulfiqar
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Liming Yang
- Department of Pathophysiology, Harbin Medical University-Daqing, Daqing, 163319, China
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
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107
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Tayara H, Abdelbaky I, To Chong K. Recent omics-based computational methods for COVID-19 drug discovery and repurposing. Brief Bioinform 2021; 22:6355836. [PMID: 34423353 DOI: 10.1093/bib/bbab339] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 07/09/2021] [Indexed: 12/22/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.
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Affiliation(s)
- Hilal Tayara
- School of international Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Ibrahim Abdelbaky
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Jeollabukdo 54896, Republic of Korea.,Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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108
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He S, Leanse LG, Feng Y. Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases. Adv Drug Deliv Rev 2021; 178:113922. [PMID: 34461198 DOI: 10.1016/j.addr.2021.113922] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 07/14/2021] [Accepted: 08/09/2021] [Indexed: 12/23/2022]
Abstract
In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.
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Affiliation(s)
- Sheng He
- Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA.
| | - Leon G Leanse
- Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Yanfang Feng
- Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA.
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109
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Qian K, Schmitt M, Zheng H, Koike T, Han J, Liu J, Ji W, Duan J, Song M, Yang Z, Ren Z, Liu S, Zhang Z, Yamamoto Y, Schuller BW. Computer Audition for Fighting the SARS-CoV-2 Corona Crisis-Introducing the Multitask Speech Corpus for COVID-19. IEEE INTERNET OF THINGS JOURNAL 2021; 8:16035-16046. [PMID: 35782182 PMCID: PMC8768988 DOI: 10.1109/jiot.2021.3067605] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 02/24/2021] [Accepted: 03/17/2021] [Indexed: 05/29/2023]
Abstract
Computer audition (CA) has experienced a fast development in the past decades by leveraging advanced signal processing and machine learning techniques. In particular, for its noninvasive and ubiquitous character by nature, CA-based applications in healthcare have increasingly attracted attention in recent years. During the tough time of the global crisis caused by the coronavirus disease 2019 (COVID-19), scientists and engineers in data science have collaborated to think of novel ways in prevention, diagnosis, treatment, tracking, and management of this global pandemic. On the one hand, we have witnessed the power of 5G, Internet of Things, big data, computer vision, and artificial intelligence in applications of epidemiology modeling, drug and/or vaccine finding and designing, fast CT screening, and quarantine management. On the other hand, relevant studies in exploring the capacity of CA are extremely lacking and underestimated. To this end, we propose a novel multitask speech corpus for COVID-19 research usage. We collected 51 confirmed COVID-19 patients' in-the-wild speech data in Wuhan city, China. We define three main tasks in this corpus, i.e., three-category classification tasks for evaluating the physical and/or mental status of patients, i.e., sleep quality, fatigue, and anxiety. The benchmarks are given by using both classic machine learning methods and state-of-the-art deep learning techniques. We believe this study and corpus cannot only facilitate the ongoing research on using data science to fight against COVID-19, but also the monitoring of contagious diseases for general purpose.
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Affiliation(s)
- Kun Qian
- Educational Physiology Laboratory, Graduate School of EducationThe University of TokyoTokyo113-0033Japan
| | - Maximilian Schmitt
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Huaiyuan Zheng
- Department of Hand SurgeryWuhan Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430074China
| | - Tomoya Koike
- Educational Physiology Laboratory, Graduate School of EducationThe University of TokyoTokyo113-0033Japan
| | - Jing Han
- Mobile Systems GroupUniversity of CambridgeCambridgeCB2 1TNU.K.
| | - Juan Liu
- Department of Plastic SurgeryCentral Hospital of Wuhan, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430074China
| | - Wei Ji
- Department of Plastic SurgeryWuhan Third Hospital and Tongren Hospital of Wuhan UniversityWuhan430072China
| | - Junjun Duan
- Department of Plastic SurgeryCentral Hospital of Wuhan, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430074China
| | - Meishu Song
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Zijiang Yang
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Zhao Ren
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Shuo Liu
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Zixing Zhang
- GLAM—the Group on Language, Audio, and MusicImperial College LondonLondonSW7 2BUU.K.
| | - Yoshiharu Yamamoto
- Educational Physiology Laboratory, Graduate School of EducationThe University of TokyoTokyo113-0033Japan
| | - Björn W. Schuller
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
- GLAM—the Group on Language, Audio, and MusicImperial College LondonLondonSW7 2BUU.K.
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110
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Wang L, Zhang Y, Wang D, Tong X, Liu T, Zhang S, Huang J, Zhang L, Chen L, Fan H, Clarke M. Artificial Intelligence for COVID-19: A Systematic Review. Front Med (Lausanne) 2021; 8:704256. [PMID: 34660623 PMCID: PMC8514781 DOI: 10.3389/fmed.2021.704256] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 08/09/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.
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Affiliation(s)
- Lian Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Yonggang Zhang
- Department of Periodical Press and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.,Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China
| | - Dongguang Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Xiang Tong
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Tao Liu
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Shijie Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Jizhen Huang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Li Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Lingmin Chen
- Department of Anesthesiology and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University and The Research Units of West China, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hong Fan
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Mike Clarke
- Northern Ireland Methodology Hub, Queen's University Belfast, Belfast, United Kingdom
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111
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Kaur J, Kaur P. Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 29:2351-2382. [PMID: 34690493 PMCID: PMC8525064 DOI: 10.1007/s11831-021-09667-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 10/01/2021] [Indexed: 06/13/2023]
Abstract
From the month of December-19, the outbreak of Coronavirus (COVID-19) triggered several deaths and overstated every aspect of individual health. COVID-19 has been designated as a pandemic by World Health Organization. The circumstances placed serious trouble on every country worldwide, particularly with health arrangements and time-consuming responses. The increase in the positive cases of COVID-19 globally spread every day. The quantity of accessible diagnosing kits is restricted because of complications in detecting the existence of the illness. Fast and correct diagnosis of COVID-19 is a timely requirement for the prevention and controlling of the pandemic through suitable isolation and medicinal treatment. The significance of the present work is to discuss the outline of the deep learning techniques with medical imaging such as outburst prediction, virus transmitted indications, detection and treatment aspects, vaccine availability with remedy research. Abundant image resources of medical imaging as X-rays, Computed Tomography Scans, Magnetic Resonance imaging, formulate deep learning high-quality methods to fight against the pandemic COVID-19. The review presents a comprehensive idea of deep learning and its related applications in healthcare received over the past decade. At the last, some issues and confrontations to control the health crisis and outbreaks have been introduced. The progress in technology has contributed to developing individual's lives. The problems faced by the radiologists during medical imaging techniques and deep learning approaches for diagnosing the COVID-19 infections have been also discussed.
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Affiliation(s)
- Jaspreet Kaur
- Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab India
| | - Prabhpreet Kaur
- Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab India
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112
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Wang T, Chen Z, Shang Q, Ma C, Chen X, Xiao E. A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19. Diagnostics (Basel) 2021; 11:diagnostics11101924. [PMID: 34679622 PMCID: PMC8534829 DOI: 10.3390/diagnostics11101924] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/10/2021] [Accepted: 10/14/2021] [Indexed: 12/23/2022] Open
Abstract
Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.
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Affiliation(s)
- Tianming Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Quanliang Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Cong Ma
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Xiangyu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Enhua Xiao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Molecular Imaging Research Center, Central South University, Changsha 410008, China
- Correspondence:
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Cai X, Li JJ, Liu T, Brian O, Li J. Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design. Brief Funct Genomics 2021; 20:289-303. [PMID: 34089044 PMCID: PMC8194884 DOI: 10.1093/bfgp/elab027] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/05/2021] [Accepted: 03/12/2021] [Indexed: 12/15/2022] Open
Abstract
Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.
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Affiliation(s)
- Xinhui Cai
- Data Science Institute, Faculty of Engineering & IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Jiao Jiao Li
- School of Biomedical Engineering, Faculty of Engineering and IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Tao Liu
- School of Life Sciences, Faculty of Science, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Oliver Brian
- Children’s Cancer Institute Australia, University of New South Wales Sydney, Children’s Cancer Institute Australia, Randwick, Sydney, 2031, New South Wales, Australia
| | - Jinyan Li
- Data Science Institute, Faculty of Engineering & IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
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Bhutta ZA, Kanwal A, Ali M, Kulyar MFEA, Yao W, Shoaib M, Ashar A, Mahfooz A, Ijaz M, Ijaz N, Asif M, Nawaz S, Mahfooz MR, Kanwal T. Emerging nanotechnology role in the development of innovative solutions against COVID-19 pandemic. NANOTECHNOLOGY 2021; 32:482001. [PMID: 34320471 DOI: 10.1088/1361-6528/ac189e] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 07/28/2021] [Indexed: 06/13/2023]
Abstract
The COVID-19 outbreak is creating severe impressions on all facets of the global community. Despite strong measures worldwide to try and re-achieve normalcy, the ability of SARS-CoV-2 to survive sturdy ecological settings may contribute to its rapid spread. Scientists from different aspects of life are working together to develop effective treatment strategies against SARS-CoV-2. Apart from using clinical devices for patient recovery, the key focus is on developing antiviral drugs and vaccines. Given the physical size of the SARS-CoV-2 pathogen and with the vaccine delivery platform currently undergoing clinical trials, the link between nanotechnology is clear, and previous antiviral research using nanomaterials confirms this link. Nanotechnology based products can effectively suppress various pathogens, including viruses, regardless of drug resistance, biological structure, or physiology. Thus, nanotechnology is opening up new dimensions for developing new strategies for diagnosing, preventing, treating COVID-19 and other viral ailments. This article describes the application of nanotechnology against the COVID-19 virus in terms of therapeutic purposes and vaccine development through the invention of nanomaterial based substances such as sanitizers (handwashing agents and surface disinfectants), masks and gowns, amongst other personal protective equipment, diagnostic tools, and nanocarrier systems, as well as the drawbacks and challenges of nanotechnology that need to be addressed.
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Affiliation(s)
- Zeeshan Ahmad Bhutta
- The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, Scotland, United Kingdom
- Department of Clinical Medicine and Surgery, University of Agriculture Faisalabad, 38000, Pakistan
| | - Ayesha Kanwal
- Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, 63100, Pakistan
| | - Moazam Ali
- Department of Clinical Medicine and Surgery, University of Agriculture Faisalabad, 38000, Pakistan
| | | | - Wangyuan Yao
- College of Veterinary Medicine, Huazhong Agriculture University, Wuhan, 430070, People's Republic of China
| | - Muhammad Shoaib
- Institute of Microbiology, Faculty of Veterinary Science, University of Agriculture Faisalabad, 38000, Pakistan
| | - Ambreen Ashar
- Department of Chemistry, Government College Women University, Faisalabad, Pakistan
| | - Ashar Mahfooz
- Department of Clinical Medicine and Surgery, University of Agriculture Faisalabad, 38000, Pakistan
| | - Misbah Ijaz
- Department of Clinical Medicine and Surgery, University of Agriculture Faisalabad, 38000, Pakistan
| | - Nabeel Ijaz
- Department of Clinical Sciences, Faculty of Veterinary Sciences, Bahauddin Zakariya University, Multan, 60800, Pakistan
| | - Muhammad Asif
- Department of Surgery, University of Veterinary and Animal Sciences, Lahore, 54000, Pakistan
| | - Shah Nawaz
- Department of Pathology, University of Agriculture Faisalabad, 38000, Pakistan
| | | | - Tahreem Kanwal
- Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, 63100, Pakistan
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Rawal K, Sinha R, Abbasi BA, Chaudhary A, Nath SK, Kumari P, Preeti P, Saraf D, Singh S, Mishra K, Gupta P, Mishra A, Sharma T, Gupta S, Singh P, Sood S, Subramani P, Dubey AK, Strych U, Hotez PJ, Bottazzi ME. Identification of vaccine targets in pathogens and design of a vaccine using computational approaches. Sci Rep 2021; 11:17626. [PMID: 34475453 PMCID: PMC8413327 DOI: 10.1038/s41598-021-96863-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.
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Affiliation(s)
- Kamal Rawal
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India.
| | - Robin Sinha
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Bilal Ahmed Abbasi
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Amit Chaudhary
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Swarsat Kaushik Nath
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Priya Kumari
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - P Preeti
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Devansh Saraf
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Shachee Singh
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Kartik Mishra
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Pranjay Gupta
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Astha Mishra
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Trapti Sharma
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Srijanee Gupta
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Prashant Singh
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Shriya Sood
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Preeti Subramani
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Aman Kumar Dubey
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Ulrich Strych
- Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Peter J Hotez
- Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Biology, Baylor University, Waco, TX, USA
| | - Maria Elena Bottazzi
- Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Biology, Baylor University, Waco, TX, USA
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Bouchareb Y, Moradi Khaniabadi P, Al Kindi F, Al Dhuhli H, Shiri I, Zaidi H, Rahmim A. Artificial intelligence-driven assessment of radiological images for COVID-19. Comput Biol Med 2021; 136:104665. [PMID: 34343890 PMCID: PMC8291996 DOI: 10.1016/j.compbiomed.2021.104665] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/11/2021] [Accepted: 07/17/2021] [Indexed: 12/24/2022]
Abstract
Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Exploratory tools such as AI-based models may help explain the complex biological mechanisms and provide better understanding of the underlying pathophysiological processes. The present review focuses on AI-based COVID-19 studies as applies to chest x-ray (CXR) and computed tomography (CT) imaging modalities, and the associated challenges. Explicit radiomics, deep learning methods, and hybrid methods that combine both deep learning and explicit radiomics have the potential to enhance the ability and usefulness of radiological images to assist clinicians in the current COVID-19 pandemic. The aims of this review are: first, to outline COVID-19 AI-analysis workflows, including acquisition of data, feature selection, segmentation methods, feature extraction, and multi-variate model development and validation as appropriate for AI-based COVID-19 studies. Secondly, existing limitations of AI-based COVID-19 analyses are discussed, highlighting potential improvements that can be made. Finally, the impact of AI and radiomics methods and the associated clinical outcomes are summarized. In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated. Sample size, non-standard imaging protocols, segmentation, availability of public COVID-19 databases, combination of imaging and clinical information and full clinical validation remain major limitations and challenges. We conclude that AI-based assessment of CXR and CT images has significant potential as a viable pathway for the diagnosis, follow-up and prognosis of COVID-19.
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Affiliation(s)
- Yassine Bouchareb
- Department of Radiology and Molecular Imaging, College of Medicine and Health Science, Sultan Qaboos University, PO. Box 35, Al Khod, Muscat, 123, Oman.
| | - Pegah Moradi Khaniabadi
- Department of Radiology and Molecular Imaging, College of Medicine and Health Science, Sultan Qaboos University, PO. Box 35, Al Khod, Muscat, 123, Oman.
| | | | - Humoud Al Dhuhli
- Department of Radiology and Molecular Imaging, College of Medicine and Health Science, Sultan Qaboos University, PO. Box 35, Al Khod, Muscat, 123, Oman
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
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Sun Z, Zheng X, Ji F, Zhou M, Su X, Ren K, Li L. Mass Spectrometry Analysis of SARS-CoV-2 Nucleocapsid Protein Reveals Camouflaging Glycans and Unique Post-Translational Modifications. INFECTIOUS MICROBES & DISEASES 2021; 3:149-157. [PMID: 38630108 PMCID: PMC8454284 DOI: 10.1097/im9.0000000000000071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/18/2021] [Accepted: 06/28/2021] [Indexed: 01/08/2023]
Abstract
The devastating coronavirus disease 2019 (COVID-19) pandemic has prompted worldwide efforts to study structural biological traits of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its viral components. Compared to the Spike protein, which is the primary target for currently available vaccines or antibodies, knowledge about other virion structural components is incomplete. Using high-resolution mass spectrometry, we report a comprehensive post-translational modification (PTM) analysis of nucleocapsid phosphoprotein (NCP), the most abundant structural component of the SARS-CoV-2 virion. In addition to phosphoryl groups, we show that the SARS-CoV-2 NCP is decorated with a variety of PTMs, including N-glycans and ubiquitin. Based on newly identified PTMs, refined protein structural models of SARS-CoV-2 NCP were proposed and potential immune recognition epitopes of NCP were aligned with PTMs. These data can facilitate the design of novel vaccines or therapeutics targeting NCP, as valuable alternatives to the current vaccination and treatment paradigm that is under threat of the ever-mutating SARS-CoV-2 Spike protein.
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Affiliation(s)
- Zeyu Sun
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiaoqin Zheng
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Feiyang Ji
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Menghao Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiaoling Su
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Keyi Ren
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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118
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Aguiar VRC, Augusto DG, Castelli EC, Hollenbach JA, Meyer D, Nunes K, Petzl-Erler ML. An immunogenetic view of COVID-19. Genet Mol Biol 2021; 44:e20210036. [PMID: 34436508 PMCID: PMC8388242 DOI: 10.1590/1678-4685-gmb-2021-0036] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/12/2021] [Indexed: 02/06/2023] Open
Abstract
Meeting the challenges brought by the COVID-19 pandemic requires an interdisciplinary approach. In this context, integrating knowledge of immune function with an understanding of how genetic variation influences the nature of immunity is a key challenge. Immunogenetics can help explain the heterogeneity of susceptibility and protection to the viral infection and disease progression. Here, we review the knowledge developed so far, discussing fundamental genes for triggering the innate and adaptive immune responses associated with a viral infection, especially with the SARS-CoV-2 mechanisms. We emphasize the role of the HLA and KIR genes, discussing what has been uncovered about their role in COVID-19 and addressing methodological challenges of studying these genes. Finally, we comment on questions that arise when studying admixed populations, highlighting the case of Brazil. We argue that the interplay between immunology and an understanding of genetic associations can provide an important contribution to our knowledge of COVID-19.
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Affiliation(s)
- Vitor R. C. Aguiar
- Universidade de São Paulo, Departamento de Genética e Biologia
Evolutiva, São Paulo, SP, Brazil
| | - Danillo G. Augusto
- University of California, UCSF Weill Institute for Neurosciences,
Department of Neurology, San Francisco, CA, USA
- Universidade Federal do Paraná, Departamento de Genética, Curitiba,
PR, Brazil
| | - Erick C. Castelli
- Universidade Estadual Paulista, Faculdade de Medicina de Botucatu,
Departamento de Patologia, Botucatu, SP, Brazil
| | - Jill A. Hollenbach
- University of California, UCSF Weill Institute for Neurosciences,
Department of Neurology, San Francisco, CA, USA
| | - Diogo Meyer
- Universidade de São Paulo, Departamento de Genética e Biologia
Evolutiva, São Paulo, SP, Brazil
| | - Kelly Nunes
- Universidade de São Paulo, Departamento de Genética e Biologia
Evolutiva, São Paulo, SP, Brazil
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119
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Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021; 10:1048. [PMID: 34451513 PMCID: PMC8399076 DOI: 10.3390/pathogens10081048] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 08/11/2021] [Accepted: 08/11/2021] [Indexed: 12/15/2022] Open
Abstract
As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.
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Affiliation(s)
- Gunjan Arora
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Jayadev Joshi
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA;
| | - Rahul Shubhra Mandal
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Nitisha Shrivastava
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA;
| | - Richa Virmani
- Confo Therapeutics, Technologiepark 94, 9052 Ghent, Belgium;
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, New Delhi 110020, India;
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120
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Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021; 10:1048. [PMID: 34451513 PMCID: PMC8399076 DOI: 10.3390/pathogens10081048,] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.
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Affiliation(s)
- Gunjan Arora
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
- Correspondence: or
| | - Jayadev Joshi
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA;
| | - Rahul Shubhra Mandal
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Nitisha Shrivastava
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA;
| | - Richa Virmani
- Confo Therapeutics, Technologiepark 94, 9052 Ghent, Belgium;
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, New Delhi 110020, India;
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Ma J, Chen Y, Wu W, Chen Z. Structure and Function of N-Terminal Zinc Finger Domain of SARS-CoV-2 NSP2. Virol Sin 2021; 36:1104-1112. [PMID: 34398430 PMCID: PMC8365134 DOI: 10.1007/s12250-021-00431-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 07/15/2021] [Indexed: 12/12/2022] Open
Abstract
SARS-CoV-2 has become a global pandemic threatening human health and safety. It is urgent to find effective therapeutic agents and targets with the continuous emergence of novel mutant strains. The knowledge of the molecular basis and pathogenesis of SARS-CoV-2 in host cells requires to be understood comprehensively. The unknown structure and function of nsp2 have hindered our understanding of its role in SARS-CoV-2 infection. Here, we report the crystal structure of the N-terminal of SARS-CoV-2 nsp2 to a high resolution of 1.96 Å. This novel structure contains three zinc fingers, belonging to the C2H2, C4, and C2HC types, respectively. Structure analysis suggests that nsp2 may be involved in binding nucleic acids and regulating intracellular signaling pathways. The binding to single or double-stranded nucleic acids was mainly through the large positively charged region on the surface of nsp2, and K111, K112, K113 were key residues. Our findings lay the foundation for a better understanding of the relationship between structure and function for nsp2. It is helpful to make full use of nsp2 as further research and development of antiviral targets and drug design.
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Affiliation(s)
- Jun Ma
- State Key Laboratory of Agrobiotechnology and Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Yiyun Chen
- Cherry Creek High School, 9300 East Union Avenue, Greenwood Village, 80111, USA
| | - Wei Wu
- State Key Laboratory of Agrobiotechnology and Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Zhongzhou Chen
- State Key Laboratory of Agrobiotechnology and Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
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Firouzi F, Farahani B, Daneshmand M, Grise K, Song J, Saracco R, Wang LL, Lo K, Angelov P, Soares E, Loh PS, Talebpour Z, Moradi R, Goodarzi M, Ashraf H, Talebpour M, Talebpour A, Romeo L, Das R, Heidari H, Pasquale D, Moody J, Woods C, Huang ES, Barnaghi P, Sarrafzadeh M, Li R, Beck KL, Isayev O, Sung N, Luo A. Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World. IEEE INTERNET OF THINGS JOURNAL 2021; 8:12826-12846. [PMID: 35782886 PMCID: PMC8769005 DOI: 10.1109/jiot.2021.3073904] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 03/09/2021] [Accepted: 04/02/2021] [Indexed: 05/07/2023]
Abstract
As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.
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Affiliation(s)
- Farshad Firouzi
- Electrical and Computer Engineering DepartmentDuke University Durham NC 27708 USA
| | - Bahar Farahani
- Cyberspace Research Institute, Shahid Beheshti University Tehran 1983969411 Iran
| | - Mahmoud Daneshmand
- Business Intelligence and AnalyticsStevens Institute of Technology Hoboken NJ 07030 USA
| | - Kathy Grise
- IEEE Future Directions Piscataway NJ 08854 USA
| | - Jaeseung Song
- Department of Computer and Information SecuritySejong University Seoul 15600 South Korea
| | | | - Lucy Lu Wang
- Allen Institute for Artificial Intelligence Seattle WA 98112 USA
| | - Kyle Lo
- Allen Institute for Artificial Intelligence Seattle WA 98112 USA
| | - Plamen Angelov
- School of Computing and CommunicationsLancaster University Lancashire LA1 4YW U.K
| | - Eduardo Soares
- School of Computing and CommunicationsLancaster University Lancashire LA1 4YW U.K
| | - Po-Shen Loh
- Department of Mathematical SciencesCarnegie Mellon University Pittsburgh PA 15213 USA
| | - Zeynab Talebpour
- Cyberspace Research Institute, Shahid Beheshti University Tehran 1983969411 Iran
| | - Reza Moradi
- Cyberspace Research Institute, Shahid Beheshti University Tehran 1983969411 Iran
| | - Mohsen Goodarzi
- Cyberspace Research Institute, Shahid Beheshti University Tehran 1983969411 Iran
| | | | | | - Alireza Talebpour
- Cyberspace Research Institute, Shahid Beheshti University Tehran 1983969411 Iran
| | - Luca Romeo
- Department of Information EngineeringUniversit Politecnica delle Marche 60121 Ancona Italy
| | - Rupam Das
- James Watt School of EngineeringUniversity of Glasgow Glasgow G12 8QQ U.K
| | - Hadi Heidari
- James Watt School of EngineeringUniversity of Glasgow Glasgow G12 8QQ U.K
| | - Dana Pasquale
- School of Medicine and Duke HealthDuke University Durham NC 27708 USA
| | - James Moody
- School of Medicine and Duke HealthDuke University Durham NC 27708 USA
| | - Chris Woods
- School of Medicine and Duke HealthDuke University Durham NC 27708 USA
| | - Erich S Huang
- School of Medicine and Duke HealthDuke University Durham NC 27708 USA
| | - Payam Barnaghi
- Department of Brain SciencesImperial College London London SW7 2AZ U.K
- U.K. Dementia Research Institute London U.K
| | - Majid Sarrafzadeh
- Computer Science Department & Electrical and Computer Engineering DepartmentUniversity of California at Los Angeles Los Angeles CA 90095 USA
| | - Ron Li
- Department of MedicineStanford University School of Medicine Stanford CA 94305 USA
| | | | - Olexandr Isayev
- Department of ChemistryCarnegie Mellon University Pittsburgh PA 15213 USA
| | - Nakmyoung Sung
- Korea Electronics Technology Institute Seongnam 13509 South Korea
| | - Alan Luo
- Computer Science DepartmentStanford University Stanford CA 94305 USA
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Zheng L, Perl Y, He Y, Ochs C, Geller J, Liu H, Keloth VK. Visual comprehension and orientation into the COVID-19 CIDO ontology. J Biomed Inform 2021; 120:103861. [PMID: 34224898 PMCID: PMC8252699 DOI: 10.1016/j.jbi.2021.103861] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 05/11/2021] [Accepted: 06/30/2021] [Indexed: 12/12/2022]
Abstract
The current intensive research on potential remedies and vaccinations for COVID-19 would greatly benefit from an ontology of standardized COVID terms. The Coronavirus Infectious Disease Ontology (CIDO) is the largest among several COVID ontologies, and it keeps growing, but it is still a medium sized ontology. Sophisticated CIDO users, who need more than searching for a specific concept, require orientation and comprehension of CIDO. In previous research, we designed a summarization network called "partial-area taxonomy" to support comprehension of ontologies. The partial-area taxonomy for CIDO is of smaller magnitude than CIDO, but is still too large for comprehension. We present here the "weighted aggregate taxonomy" of CIDO, designed to provide compact views at various granularities of our partial-area taxonomy (and the CIDO ontology). Such a compact view provides a "big picture" of the content of an ontology. In previous work, in the visualization patterns used for partial-area taxonomies, the nodes were arranged in levels according to the numbers of relationships of their concepts. Applying this visualization pattern to CIDO's weighted aggregate taxonomy resulted in an overly long and narrow layout that does not support orientation and comprehension since the names of nodes are barely readable. Thus, we introduce in this paper an innovative visualization of the weighted aggregate taxonomy for better orientation and comprehension of CIDO (and other ontologies). A measure for the efficiency of a layout is introduced and is used to demonstrate the advantage of the new layout over the previous one. With this new visualization, the user can "see the forest for the trees" of the ontology. Benefits of this visualization in highlighting insights into CIDO's content are provided. Generality of the new layout is demonstrated.
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Affiliation(s)
- Ling Zheng
- Computer Science and Software Engineering Department, Monmouth University, West Long Branch, NJ, USA.
| | - Yehoshua Perl
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | - James Geller
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
| | - Hao Liu
- Columbia University Irving Medical Center, New York, NY, USA
| | - Vipina K Keloth
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
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Guzmán-Torres JA, Alonso-Guzmán EM, Domínguez-Mota FJ, Tinoco-Guerrero G. Estimation of the main conditions in (SARS-CoV-2) Covid-19 patients that increase the risk of death using Machine learning, the case of Mexico. RESULTS IN PHYSICS 2021; 27:104483. [PMID: 34189026 PMCID: PMC8223079 DOI: 10.1016/j.rinp.2021.104483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 05/03/2023]
Abstract
Nowadays, society faces a catastrophic problem related to respiratory syndrome due to the coronavirus SARS-CoV-2: the Covid-19 disease. This virus has changed our coexistence rules and, in consequence, has reshaped the daily activities in modern societies. Thus, there are many efforts to understand the virus behaviour in order to reduce its negative impact, and these efforts produce an incredible amount of information and data sources every week. Data scientists, which use techniques such as Machine learning, are focusing their abilities to develop mathematical models for analysing this critical situation. This paper uses Machine Learning techniques as tools to help understand some specific new patterns in Covid patients that arise from unknown complex interactions in the transmission-dynamic models of the SARS-CoV-2 virus, and their relation with the corresponding social contact patterns which are often known or can be inferred from populations variables. One of the main motivations of this research is to find the diseases that cause an increase in the risk of death in infected people with the Covid-19 virus. Mexico is the case of study in this research. The general conditions of health that cause death are well known generally in the world. However, these conditions in each country can differ depending on different factors such as the general health status of people. The results show that the principal causes of death in Mexico are related to age, bad eating habits, chronic diseases, and contact with infected people having not proper care. Results from the analysis show a remarkable accuracy of 87%, which is considered satisfactory.
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Prasad K, Kumar V. Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2. CURRENT RESEARCH IN PHARMACOLOGY AND DRUG DISCOVERY 2021; 2:100042. [PMID: 34870150 PMCID: PMC8317454 DOI: 10.1016/j.crphar.2021.100042] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/22/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022] Open
Abstract
It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.
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Affiliation(s)
- Kartikay Prasad
- Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, UP, 201303, India
| | - Vijay Kumar
- Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, UP, 201303, India
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126
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Dong J, Wu H, Zhou D, Li K, Zhang Y, Ji H, Tong Z, Lou S, Liu Z. Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China. J Med Syst 2021; 45:84. [PMID: 34302549 PMCID: PMC8308073 DOI: 10.1007/s10916-021-01757-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 07/12/2021] [Indexed: 01/08/2023]
Abstract
COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.
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Affiliation(s)
- Jiancheng Dong
- Medical Big Data Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China.
| | - Huiqun Wu
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - Dong Zhou
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - Kaixiang Li
- Medical Big Data Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuanpeng Zhang
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
- Department of Health Technology and Informatics, The Hong Kong Polytechnical University, Hong Kong, China
| | - Hanzhen Ji
- The Third Affiliated Hospital of Nantong University, Nantong, China
| | - Zhuang Tong
- Medical Big Data Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shuai Lou
- Jiangsu Zhongkang Software Co, Ltd, Nantong, China
| | - Zhangsuo Liu
- Medical Big Data Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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Barbieri D, Giuliani E, Del Prete A, Losi A, Villani M, Barbieri A. How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18147648. [PMID: 34300099 PMCID: PMC8303245 DOI: 10.3390/ijerph18147648] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 07/07/2021] [Accepted: 07/16/2021] [Indexed: 12/19/2022]
Abstract
The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, it is time to think about the pros and cons of such technologies, including artificial intelligence—which is probably the most complex and misunderstood by non-specialists—in order to get the most out of them, and to suggest future improvements and proper adoption. The aim of this narrative review was to select the relevant papers that directly address the adoption of artificial intelligence and new technologies in the management of pandemics and communicable diseases such as SARS-CoV-2: environmental measures; acquisition and sharing of knowledge in the general population and among clinicians; development and management of drugs and vaccines; remote psychological support of patients; remote monitoring, diagnosis, and follow-up; and maximization and rationalization of human and material resources in the hospital environment.
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Affiliation(s)
- Davide Barbieri
- Department of Neuroscience and Rehabilitation, University of Ferrara, Via Savonarola 9, 44121 Ferrara, Italy;
| | - Enrico Giuliani
- Department of Biomedical, Metabolic and Neuroscience Sciences, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy;
| | - Anna Del Prete
- School of Anesthesiology and Intensive Care, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy; (A.D.P.); (A.B.)
| | - Amanda Losi
- Department of Biomedical, Metabolic and Neuroscience Sciences, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy;
- Correspondence: ; Tel.: +39-0598721234 (ext. 41125)
| | - Matteo Villani
- Department of Anesthesiology and Intensive Care, Azienda USL Piacenza, Via Antonio Anguissola 15, 29121 Piacenza, Italy;
| | - Alberto Barbieri
- School of Anesthesiology and Intensive Care, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy; (A.D.P.); (A.B.)
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128
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Babcock S, Beverley J, Cowell LG, Smith B. The Infectious Disease Ontology in the age of COVID-19. J Biomed Semantics 2021; 12:13. [PMID: 34275487 PMCID: PMC8286442 DOI: 10.1186/s13326-021-00245-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 06/21/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially for those ontologies built on the design principles of the Open Biomedical Ontologies Foundry. These principles are exemplified by the Infectious Disease Ontology (IDO), a suite of interoperable ontology modules aiming to provide coverage of all aspects of the infectious disease domain. At its center is IDO Core, a disease- and pathogen-neutral ontology covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is extended by disease and pathogen-specific ontology modules. RESULTS To assist the integration and analysis of COVID-19 data, and viral infectious disease data more generally, we have recently developed three new IDO extensions: IDO Virus (VIDO); the Coronavirus Infectious Disease Ontology (CIDO); and an extension of CIDO focusing on COVID-19 (IDO-COVID-19). Reflecting the fact that viruses lack cellular parts, we have introduced into IDO Core the term acellular structure to cover viruses and other acellular entities studied by virologists. We now distinguish between infectious agents - organisms with an infectious disposition - and infectious structures - acellular structures with an infectious disposition. This in turn has led to various updates and refinements of IDO Core's content. We believe that our work on VIDO, CIDO, and IDO-COVID-19 can serve as a model for yielding greater conformance with ontology building best practices. CONCLUSIONS IDO provides a simple recipe for building new pathogen-specific ontologies in a way that allows data about novel diseases to be easily compared, along multiple dimensions, with data represented by existing disease ontologies. The IDO strategy, moreover, supports ontology coordination, providing a powerful method of data integration and sharing that allows physicians, researchers, and public health organizations to respond rapidly and efficiently to current and future public health crises.
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Affiliation(s)
- Shane Babcock
- Department of Philosophy, Niagara University, Lewiston, NY, USA.
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA.
| | - John Beverley
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA
- Department of Philosophy, Northwestern University, Evanston, IL, USA
| | - Lindsay G Cowell
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA
- Cowell Lab, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Barry Smith
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA
- Department of Philosophy, University at Buffalo, Buffalo, NY, USA
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129
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Wang Z, He Y. Precision omics data integration and analysis with interoperable ontologies and their application for COVID-19 research. Brief Funct Genomics 2021; 20:235-248. [PMID: 34159360 PMCID: PMC8287950 DOI: 10.1093/bfgp/elab029] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 05/10/2021] [Accepted: 05/24/2021] [Indexed: 12/12/2022] Open
Abstract
Omics technologies are widely used in biomedical research. Precision medicine focuses on individual-level disease treatment and prevention. Here, we propose the usage of the term 'precision omics' to represent the combinatorial strategy that applies omics to translate large-scale molecular omics data for precision disease understanding and accurate disease diagnosis, treatment and prevention. Given the complexity of both omics and precision medicine, precision omics requires standardized representation and integration of heterogeneous data types. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, we propose a precision omics ontology hypothesis, which hypothesizes that the effectiveness of precision omics is positively correlated with the interoperability of ontologies used for data and knowledge integration. Therefore, to make effective precision omics studies, interoperable ontologies are required to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. Methods for efficient development and application of interoperable ontologies are proposed and illustrated. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying the outcomes of diseases such as COVID-19. Given extensive COVID-19 omics research, we propose the strategy of precision omics supported by interoperable ontologies, accompanied with ontology-based semantic reasoning and machine learning, leading to systematic disease mechanism understanding and rational design of precision treatment and prevention. SHORT ABSTRACT Precision medicine focuses on individual-level disease treatment and prevention. Precision omics is a new strategy that applies omics for precision medicine research, which requires standardized representation and integration of individual genetics and phenotypes, experimental conditions, and data analysis settings. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, interoperable ontologies are required in order to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying disease outcomes. The precision COVID-19 omics study is provided as the primary use case to illustrate the rationale and implementation of the precision omics strategy.
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Affiliation(s)
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, USA
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130
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Martínez-Flores D, Zepeda-Cervantes J, Cruz-Reséndiz A, Aguirre-Sampieri S, Sampieri A, Vaca L. SARS-CoV-2 Vaccines Based on the Spike Glycoprotein and Implications of New Viral Variants. Front Immunol 2021; 12:701501. [PMID: 34322129 PMCID: PMC8311925 DOI: 10.3389/fimmu.2021.701501] [Citation(s) in RCA: 118] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 06/28/2021] [Indexed: 12/12/2022] Open
Abstract
Coronavirus 19 Disease (COVID-19) originating in the province of Wuhan, China in 2019, is caused by the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), whose infection in humans causes mild or severe clinical manifestations that mainly affect the respiratory system. So far, the COVID-19 has caused more than 2 million deaths worldwide. SARS-CoV-2 contains the Spike (S) glycoprotein on its surface, which is the main target for current vaccine development because antibodies directed against this protein can neutralize the infection. Companies and academic institutions have developed vaccines based on the S glycoprotein, as well as its antigenic domains and epitopes, which have been proven effective in generating neutralizing antibodies. However, the emergence of new SARS-CoV-2 variants could affect the effectiveness of vaccines. Here, we review the different types of vaccines designed and developed against SARS-CoV-2, placing emphasis on whether they are based on the complete S glycoprotein, its antigenic domains such as the receptor-binding domain (RBD) or short epitopes within the S glycoprotein. We also review and discuss the possible effectiveness of these vaccines against emerging SARS-CoV-2 variants.
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Affiliation(s)
- Daniel Martínez-Flores
- Departamento de Biología Celular y del Desarrollo, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Jesús Zepeda-Cervantes
- Departamento de Biología Celular y del Desarrollo, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Departamento de Microbiología e Inmunología, Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Adolfo Cruz-Reséndiz
- Departamento de Biología Celular y del Desarrollo, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Sergio Aguirre-Sampieri
- Laboratorio de Fisicoquímica e Ingeniería de Proteínas, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Alicia Sampieri
- Departamento de Biología Celular y del Desarrollo, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Luis Vaca
- Departamento de Biología Celular y del Desarrollo, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
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131
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Ong E, Cooke MF, Huffman A, Xiang Z, Wong MU, Wang H, Seetharaman M, Valdez N, He Y. Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning. Nucleic Acids Res 2021; 49:W671-W678. [PMID: 34009334 PMCID: PMC8218197 DOI: 10.1093/nar/gkab279] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/29/2021] [Accepted: 04/15/2021] [Indexed: 01/12/2023] Open
Abstract
Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-based vaccine design program using reverse vaccinology with machine learning. Vaxign2 is a comprehensive web server for rational vaccine design, consisting of predictive and computational workflow components. The predictive part includes the original Vaxign filtering-based method and a new machine learning-based method, Vaxign-ML. The benchmarking results using a validation dataset showed that Vaxign-ML had superior prediction performance compared to other RV tools. Besides the prediction component, Vaxign2 implemented various post-prediction analyses to significantly enhance users' capability to refine the prediction results based on different vaccine design rationales and considerably reduce user time to analyze the Vaxign/Vaxign-ML prediction results. Users provide proteome sequences as input data, select candidates based on Vaxign outputs and Vaxign-ML scores, and perform post-prediction analysis. Vaxign2 also includes precomputed results from approximately 1 million proteins in 398 proteomes of 36 pathogens. As a demonstration, Vaxign2 was used to effectively analyse SARS-CoV-2, the coronavirus causing COVID-19. The comprehensive framework of Vaxign2 can support better and more rational vaccine design. Vaxign2 is publicly accessible at http://www.violinet.org/vaxign2.
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Affiliation(s)
- Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Michael F Cooke
- School of Information, University of Michigan, Ann Arbor, MI 48109, USA
- Undergraduate Research Opportunity Program, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Anthony Huffman
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Zuoshuang Xiang
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Mei U Wong
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Haihe Wang
- Department of Pathogenobiology, Daqing Branch of Harbin Medical University, Daqing, Helongjiang, China
| | - Meenakshi Seetharaman
- Undergraduate Research Opportunity Program, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ninotchka Valdez
- Undergraduate Research Opportunity Program, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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132
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Gupta R, Lall M. COVID-19 pandemic and artificial intelligence possibilities: A healthcare perspective. Med J Armed Forces India 2021; 77:S242-S244. [PMID: 34334887 PMCID: PMC8313062 DOI: 10.1016/j.mjafi.2021.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 06/02/2021] [Indexed: 11/30/2022] Open
Affiliation(s)
- R.M. Gupta
- MG (Med), HQ Western Command, C/o 56 APO, India
| | - Mahima Lall
- Professor, Department of Microbiology, Armed Forces Medical College, Pune, India
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133
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Nguyen DC, Ding M, Pathirana PN, Seneviratne A. Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:95730-95753. [PMID: 34812398 PMCID: PMC8545197 DOI: 10.1109/access.2021.3093633] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/27/2021] [Indexed: 05/02/2023]
Abstract
The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.
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Affiliation(s)
- Dinh C. Nguyen
- School of EngineeringDeakin UniversityWaurn PondsVIC3216Australia
| | | | | | - Aruna Seneviratne
- School of Electrical Engineering and TelecommunicationsUniversity of New South Wales (UNSW)SydneyNSW2052Australia
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134
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El-Rashidy N, Abdelrazik S, Abuhmed T, Amer E, Ali F, Hu JW, El-Sappagh S. Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic. Diagnostics (Basel) 2021; 11:1155. [PMID: 34202587 PMCID: PMC8303306 DOI: 10.3390/diagnostics11071155] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/11/2022] Open
Abstract
Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
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Affiliation(s)
- Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Samir Abdelrazik
- Information System Department, Faculty of Computer Science and Information Systems, Mansoura University, Mansoura 13518, Egypt;
| | - Tamer Abuhmed
- College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Korea
| | - Eslam Amer
- Faculty of Computer Science, Misr International University, Cairo 11828, Egypt;
| | - Farman Ali
- Department of Software, Sejong University, Seoul 05006, Korea;
| | - Jong-Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
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135
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Qaid TS, Mazaar H, Alqahtani MS, Raweh AA, Alakwaa W. Deep sequence modelling for predicting COVID-19 mRNA vaccine degradation. PeerJ Comput Sci 2021; 7:e597. [PMID: 34239977 PMCID: PMC8237341 DOI: 10.7717/peerj-cs.597] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/26/2021] [Indexed: 06/13/2023]
Abstract
The worldwide coronavirus (COVID-19) pandemic made dramatic and rapid progress in the year 2020 and requires urgent global effort to accelerate the development of a vaccine to stop the daily infections and deaths. Several types of vaccine have been designed to teach the immune system how to fight off certain kinds of pathogens. mRNA vaccines are the most important candidate vaccines because of their capacity for rapid development, high potency, safe administration and potential for low-cost manufacture. mRNA vaccine acts by training the body to recognize and response to the proteins produced by disease-causing organisms such as viruses or bacteria. This type of vaccine is the fastest candidate to treat COVID-19 but it currently facing several limitations. In particular, it is a challenge to design stable mRNA molecules because of the inefficient in vivo delivery of mRNA, its tendency for spontaneous degradation and low protein expression levels. This work designed and implemented a sequence deep model based on bidirectional GRU and LSTM models applied on the Stanford COVID-19 mRNA vaccine dataset to predict the mRNA sequences responsible for degradation by predicting five reactivity values for every position in the sequence. Four of these values determine the likelihood of degradation with/without magnesium at high pH (pH 10) and high temperature (50 degrees Celsius) and the fifth reactivity value is used to determine the likely secondary structure of the RNA sample. The model relies on two types of features, namely numerical and categorical features, where the categorical features are extracted from the mRNA sequences, structure and predicted loop. These features are represented and encoded by numbers, and then, the features are extracted using embedding layer learning. There are five numerical features depending on the likelihood for each pair of nucleotides in the RNA. The model gives promising results because it predicts the five reactivity values with a validation mean columnwise root mean square error (MCRMSE) of 0.125 using LSTM model with augmentation and the codon encoding method. Codon encoding outperforms Base encoding in MCRMSE validation error using the LSTM model meanwhile Base encoding outperforms codon encoding due to less over-fitting and the difference between the training and validation loss error is 0.008.
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Affiliation(s)
- Talal S. Qaid
- Computer Science Department, College of Computer Science, King Khalid University, Abha, Saudi Arabia
- Faculty of Computer Science, Hodeidah University, Hodeidah, Yemen
| | - Hussein Mazaar
- Computer Science Department, College of Science & Arts in Tanumah, King Khalid University, Abha, Saudi Arabia
| | - Mohammed S. Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Abeer A. Raweh
- Computer Science Department, College of Computer Science, King Khalid University, Abha, Saudi Arabia
- Faculty of Computer Science, Hodeidah University, Hodeidah, Yemen
| | - Wafaa Alakwaa
- Computer Science Department, College of Science & Arts in Tanumah, King Khalid University, Abha, Saudi Arabia
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136
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Ding W, Nayak J, Swapnarekha H, Abraham A, Naik B, Pelusi D. Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data. Neurocomputing 2021; 457:40-66. [PMID: 34149184 PMCID: PMC8206574 DOI: 10.1016/j.neucom.2021.06.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 06/02/2021] [Accepted: 06/11/2021] [Indexed: 12/11/2022]
Abstract
The unprecedented surge of a novel coronavirus in the month of December 2019, named as COVID-19 by the World Health organization has caused a serious impact on the health and socioeconomic activities of the public all over the world. Since its origin, the number of infected and deceased cases has been growing exponentially in almost all the affected countries of the world. The rapid spread of the novel coronavirus across the world results in the scarcity of medical resources and overburdened hospitals. As a result, the researchers and technocrats are continuously working across the world for the inculcation of efficient strategies which may assist the government and healthcare system in controlling and managing the spread of the COVID-19 pandemic. Therefore, this study provides an extensive review of the ongoing strategies such as diagnosis, prediction, drug and vaccine development and preventive measures used in combating the COVID-19 along with technologies used and limitations. Moreover, this review also provides a comparative analysis of the distinct type of data, emerging technologies, approaches used in diagnosis and prediction of COVID-19, statistics of contact tracing apps, vaccine production platforms used in the COVID-19 pandemic. Finally, the study highlights some challenges and pitfalls observed in the systematic review which may assist the researchers to develop more efficient strategies used in controlling and managing the spread of COVID-19.
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Affiliation(s)
- Weiping Ding
- School of Information Science and Technology, Nantong University, China
| | - Janmenjoy Nayak
- Aditya Institute of Technology and Management (AITAM), India
| | - H Swapnarekha
- Aditya Institute of Technology and Management (AITAM), India
- Veer Surendra Sai University of Technology, India
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137
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Palomares I, Martínez-Cámara E, Montes R, García-Moral P, Chiachio M, Chiachio J, Alonso S, Melero FJ, Molina D, Fernández B, Moral C, Marchena R, de Vargas JP, Herrera F. A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects. APPL INTELL 2021; 51:6497-6527. [PMID: 34764606 PMCID: PMC8192224 DOI: 10.1007/s10489-021-02264-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/04/2021] [Indexed: 12/23/2022]
Abstract
The17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity worldwide. Artificial intelligence and other digital technologies that have emerged in the last years, are being currently applied in virtually every area of society, economy and the environment. Hence, it is unsurprising that their current role in the pursuance or hampering of the SDGs has become critical. This study aims at providing a snapshot and comprehensive view of the progress made and prospects in the relationship between artificial intelligence technologies and the SDGs. A comprehensive review of existing literature has been firstly conducted, after which a series SWOT (Strengths, Weaknesses, Opportunities and Threats) analyses have been undertaken to identify the strengths, weaknesses, opportunities and threats inherent to artificial intelligence-driven technologies as facilitators or barriers to each of the SDGs. Based on the results of these analyses, a subsequent broader analysis is provided, from a position vantage, to (i) identify the efforts made in applying AI technologies in SDGs, (ii) pinpoint opportunities for further progress along the current decade, and (iii) distill ongoing challenges and target areas for important advances. The analysis is organized into six categories or perspectives of human needs: life, economic and technological development, social development, equality, resources and natural environment. Finally, a closing discussion is provided about the prospects, key guidelines and lessons learnt that should be adopted for guaranteeing a positive shift of artificial intelligence developments and applications towards fully supporting the SDGs attainment by 2030.
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Affiliation(s)
- Iván Palomares
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain.,Department of Computer Science and Information Engineering, National Cheng Kung University, 70101 Tainan, Taiwan
| | - Eugenio Martínez-Cámara
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
| | - Rosana Montes
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
| | | | - Manuel Chiachio
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
| | - Juan Chiachio
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
| | - Sergio Alonso
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
| | - Francisco J Melero
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
| | - Daniel Molina
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
| | | | - Cristina Moral
- Ferrovial S.A., C/ Principe de Vergara 135, 28002 Madrid, Spain
| | | | | | - Francisco Herrera
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain.,Royal Academy of Engineering of Spain, 28005 Madrid, Spain
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138
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Emam M, Oweda M, Antunes A, El-Hadidi M. Positive selection as a key player for SARS-CoV-2 pathogenicity: Insights into ORF1ab, S and E genes. Virus Res 2021; 302:198472. [PMID: 34118359 PMCID: PMC8190378 DOI: 10.1016/j.virusres.2021.198472] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 06/02/2021] [Accepted: 06/03/2021] [Indexed: 12/16/2022]
Abstract
The human β-coronavirus SARS-CoV-2 epidemic started in late December 2019 in Wuhan, China. It causes Covid-19 disease which has become pandemic. Each of the five-known human β-coronaviruses has four major structural proteins (E, M, N and S) and 16 non-structural proteins encoded by ORF1a and ORF1b together (ORF1ab) that are involved in virus pathogenicity and infectivity. Here, we performed detailed positive selection analyses for those six genes among the four previously known human β-coronaviruses and within 38 SARS-CoV-2 genomes to assess signatures of adaptive evolution using maximum likelihood approaches. Our results suggest that three genes (E, S and ORF1ab genes) are under strong signatures of positive selection among human β-coronavirus, influencing codons that are located in functional important protein domains. The E protein-coding gene showed signatures of positive selection in two sites, Asp 66 and Ser 68, located inside a putative transmembrane α-helical domain C-terminal part, which is preferentially composed by hydrophilic residues. Such Asp and Ser sites substitutions (hydrophilic residues) increase the stability of the transmembrane domain in SARS-CoV-2. Moreover, substitutions in the spike (S) protein S1 N-terminal domain have been found, all of them were located on the S protein surface, suggesting their importance in viral transmissibility and survival. Furthermore, evidence of strong positive selection was detected in three of the SARS-CoV-2 nonstructural proteins (NSP1, NSP3, NSP16), which are encoded by ORF1ab and play vital roles in suppressing host translation machinery, viral replication and transcription and inhibiting the host immune response. These results are insightful to assess the role of positive selection in the SARS-CoV-2 encoded proteins, which will allow to better understand the virulent pathogenicity of the virus and potentially identifying targets for drug or vaccine strategy design
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Affiliation(s)
- Mohamed Emam
- Bioinformatics group, Center for Informatics Sciences (CIS), Nile University, Giza, Egypt
| | - Mariam Oweda
- Bioinformatics group, Center for Informatics Sciences (CIS), Nile University, Giza, Egypt
| | - Agostinho Antunes
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal; Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal.
| | - Mohamed El-Hadidi
- Bioinformatics group, Center for Informatics Sciences (CIS), Nile University, Giza, Egypt
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139
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Heffron AS, McIlwain SJ, Amjadi MF, Baker DA, Khullar S, Armbrust T, Halfmann PJ, Kawaoka Y, Sethi AK, Palmenberg AC, Shelef MA, O’Connor DH, Ong IM. The landscape of antibody binding in SARS-CoV-2 infection. PLoS Biol 2021; 19:e3001265. [PMID: 34143766 PMCID: PMC8245122 DOI: 10.1371/journal.pbio.3001265] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 06/30/2021] [Accepted: 05/06/2021] [Indexed: 02/08/2023] Open
Abstract
The search for potential antibody-based diagnostics, vaccines, and therapeutics for pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has focused almost exclusively on the spike (S) and nucleocapsid (N) proteins. Coronavirus membrane (M), ORF3a, and ORF8 proteins are humoral immunogens in other coronaviruses (CoVs) but remain largely uninvestigated for SARS-CoV-2. Here, we use ultradense peptide microarray mapping to show that SARS-CoV-2 infection induces robust antibody responses to epitopes throughout the SARS-CoV-2 proteome, particularly in M, in which 1 epitope achieved excellent diagnostic accuracy. We map 79 B cell epitopes throughout the SARS-CoV-2 proteome and demonstrate that antibodies that develop in response to SARS-CoV-2 infection bind homologous peptide sequences in the 6 other known human CoVs. We also confirm reactivity against 4 of our top-ranking epitopes by enzyme-linked immunosorbent assay (ELISA). Illness severity correlated with increased reactivity to 9 SARS-CoV-2 epitopes in S, M, N, and ORF3a in our population. Our results demonstrate previously unknown, highly reactive B cell epitopes throughout the full proteome of SARS-CoV-2 and other CoV proteins.
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Affiliation(s)
- Anna S. Heffron
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Sean J. McIlwain
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- University of Wisconsin Carbone Comprehensive Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Maya F. Amjadi
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - David A. Baker
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Tammy Armbrust
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Peter J. Halfmann
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Yoshihiro Kawaoka
- Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Ajay K. Sethi
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Ann C. Palmenberg
- Department of Biochemistry, Institute for Molecular Virology, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Miriam A. Shelef
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, United States of America
| | - David H. O’Connor
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Irene M. Ong
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- University of Wisconsin Carbone Comprehensive Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Obstetrics and Gynecology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Center for Human Genomics and Precision Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
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140
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Rasheed J, Jamil A, Hameed AA, Al-Turjman F, Rasheed A. COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdiscip Sci 2021; 13:153-175. [PMID: 33886097 PMCID: PMC8060789 DOI: 10.1007/s12539-021-00431-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 04/03/2021] [Accepted: 04/09/2021] [Indexed: 12/23/2022]
Abstract
The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.
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Affiliation(s)
- Jawad Rasheed
- Department of Computer Engineering, Istanbul Aydin University, Istanbul, 34295, Turkey.
| | - Akhtar Jamil
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Alaa Ali Hameed
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
| | - Ahmad Rasheed
- Department of Electrical and Electronics Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
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Cuspoca AF, Díaz LL, Acosta AF, Peñaloza MK, Méndez YR, Clavijo DC, Yosa Reyes J. An Immunoinformatics Approach for SARS-CoV-2 in Latam Populations and Multi-Epitope Vaccine Candidate Directed towards the World's Population. Vaccines (Basel) 2021; 9:vaccines9060581. [PMID: 34205992 PMCID: PMC8228945 DOI: 10.3390/vaccines9060581] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 04/21/2021] [Accepted: 04/28/2021] [Indexed: 12/15/2022] Open
Abstract
The coronavirus pandemic is a major public health crisis affecting global health systems with dire socioeconomic consequences, especially in vulnerable regions such as Latin America (LATAM). There is an urgent need for a vaccine to help control contagion, reduce mortality and alleviate social costs. In this study, we propose a rational multi-epitope candidate vaccine against SARS-CoV-2. Using bioinformatics, we constructed a library of potential vaccine peptides, based on the affinity of the most common major human histocompatibility complex (HLA) I and II molecules in the LATAM population to predict immunological complexes among antigenic, non-toxic and non-allergenic peptides extracted from the conserved regions of 92 proteomes. Although HLA-C, had the greatest antigenic peptide capacity from SARS-CoV-2, HLA-B and HLA-A, could be more relevant based on COVID-19 risk of infection in LATAM countries. We also used three-dimensional structures of SARS-CoV-2 proteins to identify potential regions for antibody production. The best HLA-I and II predictions (with increased coverage in common alleles and regions evoking B lymphocyte responses) were grouped into an optimized final multi-epitope construct containing the adjuvants Beta defensin-3, TpD, and PADRE, which are recognized for invoking a safe and specific immune response. Finally, we used Molecular Dynamics to identify the multi-epitope construct which may be a stable target for TLR-4/MD-2. This would prove to be safe and provide the physicochemical requirements for conducting experimental tests around the world.
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Affiliation(s)
- Andrés Felipe Cuspoca
- Grupo de Investigación en Epidemiología Clínica de Colombia (GRECO), Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia; (A.F.C.); (L.L.D.); (A.F.A.); (M.K.P.); (Y.R.M.)
| | - Laura Lorena Díaz
- Grupo de Investigación en Epidemiología Clínica de Colombia (GRECO), Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia; (A.F.C.); (L.L.D.); (A.F.A.); (M.K.P.); (Y.R.M.)
| | - Alvaro Fernando Acosta
- Grupo de Investigación en Epidemiología Clínica de Colombia (GRECO), Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia; (A.F.C.); (L.L.D.); (A.F.A.); (M.K.P.); (Y.R.M.)
| | - Marcela Katherine Peñaloza
- Grupo de Investigación en Epidemiología Clínica de Colombia (GRECO), Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia; (A.F.C.); (L.L.D.); (A.F.A.); (M.K.P.); (Y.R.M.)
| | - Yardany Rafael Méndez
- Grupo de Investigación en Epidemiología Clínica de Colombia (GRECO), Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia; (A.F.C.); (L.L.D.); (A.F.A.); (M.K.P.); (Y.R.M.)
| | - Diana Carolina Clavijo
- Facultad de Ingeniería y Ciencias, Pontificia Universidad Javeriana Cali, Santiago de Cali 760031, Colombia;
| | - Juvenal Yosa Reyes
- Laboratorio de Simulación Molecular, Facultad de Ciencias Básicas y Biomédicas, Universidad Simón Bolívar, Barranquilla 080002, Colombia
- Correspondence:
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142
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Barkas F, Ntekouan SF, Kosmidou M, Liberopoulos E, Liontos A, Milionis H. Anakinra in hospitalized non-intubated patients with coronavirus disease 2019: a systematic review and meta-analysis. Rheumatology (Oxford) 2021; 60:5527-5537. [PMID: 33999135 PMCID: PMC8194671 DOI: 10.1093/rheumatology/keab447] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/06/2021] [Accepted: 05/10/2021] [Indexed: 02/07/2023] Open
Abstract
Background Acute respiratory distress syndrome and cytokine release syndrome are the major complications of coronavirus disease 2019 (COVID-19) associated with increased mortality risk. Objectives We performed a meta-analysis to assess the efficacy and safety of anakinra in adult hospitalized non-intubated patients with COVID-19. Search methods Relevant trials were identified by searching literature until 24 April 2021 using the following terms: anakinra, interleukin 1, coronavirus, COVID-19, SARS-CoV-2. Selection criteria Trials evaluating the effect of anakinra on the need for invasive mechanical ventilation and mortality in hospitalized non-intubated patients with COVID-19. Results Nine studies (n = 1,119) were eligible for inclusion in the present meta-analysis. Their bias risk with reference to the assessed parameters was high. In pooled analyses, anakinra reduced the need for invasive mechanical ventilation (odds ratio, OR: 0·38, 95% confidence interval, CI: 0·17–0·85, p= 0.02, I2=67%; 6 studies, n = 587) and mortality risk (OR: 0·32, 95% CI: 0·23–0·45, p< 0·00001, I2=0%; 9 studies, n = 1,119) compared with standard of care therapy. There were no differences regarding the risk of adverse events, including liver dysfunction (OR: 0·75, 95% CI: 0·48–1·16, p> 0·05, I2=28%; 5 studies, n = 591) and bacteremia (OR: 1·07, 95% CI: 0·42–2·73, p> 0·05, I2=71%; 6 studies, n = 727). Conclusions Available evidence shows that treatment with anakinra reduces both the need for invasive mechanical ventilation and mortality risk of hospitalized non-intubated patients with COVID-19 without increasing the risk of adverse events. Confirmation of efficacy and safety requires randomized placebo-controlled trials.
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Affiliation(s)
- Fotios Barkas
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Sebastian Filippas Ntekouan
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Maria Kosmidou
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Evangelos Liberopoulos
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Angelos Liontos
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Haralampos Milionis
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
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143
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Obaidullah AJ, Alanazi MM, Alsaif NA, Albassam H, Almehizia AA, Alqahtani AM, Mahmud S, Sami SA, Emran TB. Immunoinformatics-guided design of a multi-epitope vaccine based on the structural proteins of severe acute respiratory syndrome coronavirus 2. RSC Adv 2021; 11:18103-18121. [PMID: 35480208 PMCID: PMC9033181 DOI: 10.1039/d1ra02885e] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/12/2021] [Indexed: 12/14/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in a contagious respiratory tract infection that has become a global burden since the end of 2019. Notably, fewer patients infected with SARS-CoV-2 progress from acute disease onset to death compared with the progression rate associated with two other coronaviruses, SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). Several research organizations and pharmaceutical industries have attempted to develop successful vaccine candidates for the prevention of COVID-19. However, increasing evidence indicates that the SARS-CoV-2 genome undergoes frequent mutation; thus, an adequate analysis of the viral strain remains necessary to construct effective vaccines. The current study attempted to design a multi-epitope vaccine by utilizing an approach based on the SARS-CoV-2 structural proteins. We predicted the antigenic T- and B-lymphocyte responses to four structural proteins after screening all structural proteins according to specific characteristics. The predicted epitopes were combined using suitable adjuvants and linkers, and a secondary structure profile indicated that the vaccine shared similar properties with the native protein. Importantly, the molecular docking analysis and molecular dynamics simulations revealed that the constructed vaccine possessed a high affinity for toll-like receptor 4 (TLR4). In addition, multiple descriptors were obtained from the simulation trajectories, including the root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (R g), demonstrating the rigid nature and inflexibility of the vaccine and receptor molecules. In addition, codon optimization, based on Escherichia coli K12, was used to determine the GC content and the codon adaptation index (CAI) value, which further followed for the incorporation into the cloning vector pET28+(a). Collectively, these findings suggested that the constructed vaccine could be used to modulate the immune reaction against SARS-CoV-2.
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Affiliation(s)
- Ahmad J Obaidullah
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University P.O. Box 2457 Riyadh 11451 Saudi Arabia
| | - Mohammed M Alanazi
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University P.O. Box 2457 Riyadh 11451 Saudi Arabia
| | - Nawaf A Alsaif
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University P.O. Box 2457 Riyadh 11451 Saudi Arabia
| | - Hussam Albassam
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University P.O. Box 2457 Riyadh 11451 Saudi Arabia
| | - Abdulrahman A Almehizia
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University P.O. Box 2457 Riyadh 11451 Saudi Arabia
| | - Ali M Alqahtani
- Department of Pharmacology, College of Pharmacy, King Khalid University Abha 62529 Saudi Arabia
| | - Shafi Mahmud
- Microbiology Laboratory, Bioinformatics Division, Department of Genetic Engineering and Biotechnology, University of Rajshahi Rajshahi 6205 Bangladesh
| | - Saad Ahmed Sami
- Department of Pharmacy, Faculty of Biological Sciences, University of Chittagong Chittagong 4331 Bangladesh
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh Chittagong 4381 Bangladesh
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144
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Singh R, Singh PK, Kumar R, Kabir MT, Kamal MA, Rauf A, Albadrani GM, Sayed AA, Mousa SA, Abdel-Daim MM, Uddin MS. Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19. Front Pharmacol 2021; 12:652335. [PMID: 34054532 PMCID: PMC8149611 DOI: 10.3389/fphar.2021.652335] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/21/2021] [Indexed: 02/05/2023] Open
Abstract
COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has a disastrous effect on mankind due to the contagious and rapid nature of its spread. Although vaccines for SARS-CoV-2 have been successfully developed, the proven, effective, and specific therapeutic molecules are yet to be identified for the treatment. The repurposing of existing drugs and recognition of new medicines are continuously in progress. Efforts are being made to single out plant-based novel therapeutic compounds. As a result, some of these biomolecules are in their testing phase. During these efforts, the whole-genome sequencing of SARS-CoV-2 has given the direction to explore the omics systems and approaches to overcome this unprecedented health challenge globally. Genome, proteome, and metagenome sequence analyses have helped identify virus nature, thereby assisting in understanding the molecular mechanism, structural understanding, and disease propagation. The multi-omics approaches offer various tools and strategies for identifying potential therapeutic biomolecules for COVID-19 and exploring the plants producing biomolecules that can be used as biopharmaceutical products. This review explores the available multi-omics approaches and their scope to investigate the therapeutic promises of plant-based biomolecules in treating SARS-CoV-2 infection.
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Affiliation(s)
- Rachana Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, India
| | - Pradhyumna Kumar Singh
- Plant Molecular Biology and Biotechnology Division, Council of Scientific and Industrial Research- National Botanical Research Institute (CSIR-NBRI), Lucknow, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, India
| | | | - Mohammad Amjad Kamal
- West China School of Nursing/Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Enzymoics, Novel Global Community Educational Foundation, Hebersham, NSW, Australia
| | - Abdur Rauf
- Department of Chemistry, University of Swabi, Khyber Pakhtunkhwa, Pakistan
| | - Ghadeer M. Albadrani
- Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Amany A. Sayed
- Zoology Department, Faculty of Science, Cairo University, Giza, Egypt
| | - Shaker A. Mousa
- Pharmaceutical Research Institute, Albany College of Pharmacy and Health Sciences, Rensselaer, NY, United States
| | - Mohamed M. Abdel-Daim
- Pharmacology Department, Faculty of Veterinary Medicine, Suez Canal University, Ismailia, Egypt
| | - Md. Sahab Uddin
- Department of Pharmacy, Southeast University, Dhaka, Bangladesh
- Pharmakon Neuroscience Research Network, Dhaka, Bangladesh
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145
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Mallavarpu Ambrose J, Priya Veeraraghavan V, Kullappan M, Chellapandiyan P, Krishna Mohan S, Manivel VA. Comparison of Immunological Profiles of SARS-CoV-2 Variants in the COVID-19 Pandemic Trends: An Immunoinformatics Approach. Antibiotics (Basel) 2021; 10:535. [PMID: 34066389 PMCID: PMC8148159 DOI: 10.3390/antibiotics10050535] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/28/2021] [Accepted: 05/04/2021] [Indexed: 02/06/2023] Open
Abstract
The current dynamics of the COVID-19 pandemic have become a serious concern with the emergence of a series of mutant variants of the SARS-CoV-2 virus. Unlike the previous strain, it is reported that the descendants are associated with increased risk of transmission yet causing less impact in terms of hospital admission, the severity of illness, or mortality. Moreover, the vaccine efficacy is also not believed to vary among the population depending on the variants of the virus and ethnicity. It has been determined that the mutations recorded in the spike gene and protein of the newly evolved viruses are specificallyresponsible for this transformation in the behavior of the virus and its disease condition. Hence, this study aimed to compare the immunogenic profiles of the spike protein from the latest variants of the SARS-CoV-2 virus concerning the probability of COVID-19 severity. Genome sequences of the latest SARS-CoV-2 variants were obtained from GISAID and NCBI repositories. The translated protein sequences were run against T-cell and B-cell epitope prediction tools. Subsequently, antigenicity, immunogenicity, allergenicity, toxicity, and conservancy of the identified epitopes were ascertained using various prediction servers. Only the non-allergic and non-toxic potential epitopes were matched for population relevance by using the Human Leucocyte Antigen population registry in IEDB. Finally, the selected epitopes were validated by docking and simulation studies. The evaluated immunological parameters would concurrently reveal the severity of COVID-19, determining the infection rate of the pathogen. Our immunoinformatics approach disclosed that spike protein of the five variants was capable of forming potential T and B-cell epitopes with varying immune responses. Although the Wuhan strain showed a high number of epitope/HLA combinations, relatively less antigenicity and higher immunogenicity results in poor neutralizing capacity, which could be associated with increased disease severity. Our data demonstrate that increased viral antigenicity with moderate to high immunogenicity, and several potential epitope/HLA combinations in England strain, the USA, India, and South Africa variants, could possess a high neutralizing ability. Therefore, our findings reinforce that the newly circulating variants of SARS-CoV-2 might be associated with more infectiousness and less severe disease condition despite their greater viremia, as reported in the recent COVID-19 cases, whichconsequently determine their increased epidemiological fitness.
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Affiliation(s)
- Jenifer Mallavarpu Ambrose
- Department of Research, Panimalar Medical College Hospital & Research Institute, Varadharajapuram, Poonamallee, Chennai 600 123, Tamil Nadu, India; (J.M.A.); (M.K.)
| | - Vishnu Priya Veeraraghavan
- Department of Biochemistry, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Velappanchavadi, Chennai 600 077, Tamil Nadu, India;
| | - Malathi Kullappan
- Department of Research, Panimalar Medical College Hospital & Research Institute, Varadharajapuram, Poonamallee, Chennai 600 123, Tamil Nadu, India; (J.M.A.); (M.K.)
| | - Poongodi Chellapandiyan
- Department of Obstetrics & Gynaecological Nursing, Panimalar College of Nursing, Varadharajapuram, Poonamallee, Chennai 600 123, Tamil Nadu, India;
| | - Surapaneni Krishna Mohan
- Department of Research, Panimalar Medical College Hospital & Research Institute, Varadharajapuram, Poonamallee, Chennai 600 123, Tamil Nadu, India; (J.M.A.); (M.K.)
- Departments of Biochemistry, Molecular Virology, Clinical Skills and Simulation, Panimalar Medical College Hospital & Research Institute, Varadharajapuram, Poonamallee, Chennai 600 123, Tamil Nadu, India
| | - Vivek Anand Manivel
- Department of Research, Panimalar Medical College Hospital & Research Institute, Varadharajapuram, Poonamallee, Chennai 600 123, Tamil Nadu, India; (J.M.A.); (M.K.)
- Rudbeck Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, 75185 Uppsala, Sweden
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146
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Efficacy and Safety of COVID-19 Vaccines: A Systematic Review and Meta-Analysis of Randomized Clinical Trials. Vaccines (Basel) 2021; 9:vaccines9050467. [PMID: 34066475 PMCID: PMC8148145 DOI: 10.3390/vaccines9050467] [Citation(s) in RCA: 189] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 01/12/2023] Open
Abstract
The current study systematically reviewed, summarized and meta-analyzed the clinical features of the vaccines in clinical trials to provide a better estimate of their efficacy, side effects and immunogenicity. All relevant publications were systematically searched and collected from major databases up to 12 March 2021. A total of 25 RCTs (123 datasets), 58,889 cases that received the COVID-19 vaccine and 46,638 controls who received placebo were included in the meta-analysis. In total, mRNA-based and adenovirus-vectored COVID-19 vaccines had 94.6% (95% CI 0.936-0.954) and 80.2% (95% CI 0.56-0.93) efficacy in phase II/III RCTs, respectively. Efficacy of the adenovirus-vectored vaccine after the first (97.6%; 95% CI 0.939-0.997) and second (98.2%; 95% CI 0.980-0.984) doses was the highest against receptor-binding domain (RBD) antigen after 3 weeks of injections. The mRNA-based vaccines had the highest level of side effects reported except for diarrhea and arthralgia. Aluminum-adjuvanted vaccines had the lowest systemic and local side effects between vaccines' adjuvant or without adjuvant, except for injection site redness. The adenovirus-vectored and mRNA-based vaccines for COVID-19 showed the highest efficacy after first and second doses, respectively. The mRNA-based vaccines had higher side effects. Remarkably few experienced extreme adverse effects and all stimulated robust immune responses.
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147
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Hwang W, Lei W, Katritsis NM, MacMahon M, Chapman K, Han N. Current and prospective computational approaches and challenges for developing COVID-19 vaccines. Adv Drug Deliv Rev 2021; 172:249-274. [PMID: 33561453 PMCID: PMC7871111 DOI: 10.1016/j.addr.2021.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 12/23/2022]
Abstract
SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.
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Affiliation(s)
- Woochang Hwang
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Winnie Lei
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK
| | - Nicholas M Katritsis
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Méabh MacMahon
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Centre for Therapeutics Discovery, LifeArc, Stevenage, UK
| | - Kathryn Chapman
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Namshik Han
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
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148
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Lan L, Sun W, Xu D, Yu M, Xiao F, Hu H, Xu H, Wang X. Artificial intelligence-based approaches for COVID-19 patient management. INTELLIGENT MEDICINE 2021; 1:10-15. [PMID: 34447600 PMCID: PMC8189732 DOI: 10.1016/j.imed.2021.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/27/2021] [Accepted: 05/21/2021] [Indexed: 01/08/2023]
Abstract
During the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient management and drug/vaccine development. In this paper, we specifically review the current status of AI-based approaches for patient management. Limitations and challenges still exist, and further needs are highlighted.
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149
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Hisham Y, Ashhab Y, Hwang SH, Kim DE. Identification of Highly Conserved SARS-CoV-2 Antigenic Epitopes with Wide Coverage Using Reverse Vaccinology Approach. Viruses 2021; 13:787. [PMID: 33925069 PMCID: PMC8145845 DOI: 10.3390/v13050787] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 04/26/2021] [Accepted: 04/26/2021] [Indexed: 12/12/2022] Open
Abstract
One of the most effective strategies for eliminating new and emerging infectious diseases is effective immunization. The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) warrants the need for a maximum coverage vaccine. Moreover, mutations that arise within the virus have a significant impact on the vaccination strategy. Here, we built a comprehensive in silico workflow pipeline to identify B-cell- and T-cell-stimulating antigens of SARS-CoV-2 viral proteins. Our in silico reverse vaccinology (RV) approach consisted of two parts: (1) analysis of the selected viral proteins based on annotated cellular location, antigenicity, allele coverage, epitope density, and mutation density and (2) analysis of the various aspects of the epitopes, including antigenicity, allele coverage, IFN-γ induction, toxicity, host homology, and site mutational density. After performing a mutation analysis based on the contemporary mutational amino acid substitutions observed in the viral variants, 13 potential epitopes were selected as subunit vaccine candidates. Despite mutational amino acid substitutions, most epitope sequences were predicted to retain immunogenicity without toxicity and host homology. Our RV approach using an in silico pipeline may potentially reduce the time required for effective vaccine development and can be applicable for vaccine development for other pathogenic diseases as well.
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Affiliation(s)
- Yasmin Hisham
- Department of Bioscience and Biotechnology, Konkuk University, Seoul 05029, Korea
| | - Yaqoub Ashhab
- Palestine-Korea Biotechnology Center, Palestine Polytechnic University, Hebron 90100, Palestine
| | - Sang-Hyun Hwang
- Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Dong-Eun Kim
- Department of Bioscience and Biotechnology, Konkuk University, Seoul 05029, Korea
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150
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Xie J, Zi W, Li Z, He Y. Ontology-based Precision Vaccinology for Deep Mechanism Understanding and Precision Vaccine Development. Curr Pharm Des 2021; 27:900-910. [PMID: 33238868 DOI: 10.2174/1381612826666201125112131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 10/08/2020] [Indexed: 11/22/2022]
Abstract
Vaccination is one of the most important innovations in human history. It has also become a hot research area in a new application - the development of new vaccines against non-infectious diseases such as cancers. However, effective and safe vaccines still do not exist for many diseases, and where vaccines exist, their protective immune mechanisms are often unclear. Although licensed vaccines are generally safe, various adverse events, and sometimes severe adverse events, still exist for a small population. Precision medicine tailors medical intervention to the personal characteristics of individual patients or sub-populations of individuals with similar immunity-related characteristics. Precision vaccinology is a new strategy that applies precision medicine to the development, administration, and post-administration analysis of vaccines. Several conditions contribute to make this the right time to embark on the development of precision vaccinology. First, the increased level of research in vaccinology has generated voluminous "big data" repositories of vaccinology data. Secondly, new technologies such as multi-omics and immunoinformatics bring new methods for investigating vaccines and immunology. Finally, the advent of AI and machine learning software now makes possible the marriage of Big Data to the development of new vaccines in ways not possible before. However, something is missing in this marriage, and that is a common language that facilitates the correlation, analysis, and reporting nomenclature for the field of vaccinology. Solving this bioinformatics problem is the domain of applied biomedical ontology. Ontology in the informatics field is human- and machine-interpretable representation of entities and the relations among entities in a specific domain. The Vaccine Ontology (VO) and Ontology of Vaccine Adverse Events (OVAE) have been developed to support the standard representation of vaccines, vaccine components, vaccinations, host responses, and vaccine adverse events. Many other biomedical ontologies have also been developed and can be applied in vaccine research. Here, we review the current status of precision vaccinology and how ontological development will enhance this field, and propose an ontology-based precision vaccinology strategy to support precision vaccine research and development.
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Affiliation(s)
- Jiangan Xie
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Wenrui Zi
- Chongqing engineering research center of medical electronics and information technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Zhangyong Li
- Chongqing engineering research center of medical electronics and information technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yongqun He
- Unit of Laboratory Animal Medicine, Development of Microbiology and Immunology, Center of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, United States
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