scholarly journals Unveiling the Potential Role of Nanozymes in Combating the COVID-19 Outbreak

Nanomaterials ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1328
Author(s):  
Jafar Ali ◽  
Saira Naveed Elahi ◽  
Asghar Ali ◽  
Hassan Waseem ◽  
Rameesha Abid ◽  
...  
Keyword(s):  
Vaccine Development ◽  
Low Cost ◽  
Disease Diagnosis ◽  
Diagnostic Tools ◽  
Effective Vaccine ◽  
Future Research ◽  
Current Crisis ◽  
Public Health Challenges ◽  
Potential Applications ◽  
Effective Role

The current coronavirus disease 2019 (COVID-19) outbreak is considered as one of the biggest public health challenges and medical emergencies of the century. A global health emergency demands an urgent development of rapid diagnostic tools and advanced therapeutics for the mitigation of COVID-19. To cope with the current crisis, nanotechnology offers a number of approaches based on abundance and versatile functioning. Despite major developments in early diagnostics and control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is still a need to find effective nanomaterials with low cost, high stability and easy use. Nanozymes are nanomaterials with innate enzyme-like characteristics and exhibit great potential for various biomedical applications such as disease diagnosis and anti-viral agents. Overall the potential and contribution of nanozymes in the fight against SARS-CoV-2 infection i.e., rapid detection, inhibition of the virus at various stages, and effective vaccine development strategies, is not fully explored. This paper discusses the utility and potential of nanozymes from the perspective of COVID-19. Moreover, future research directions and potential applications of nanozymes are highlighted to overcome the challenges related to early diagnosis and therapeutics development for the SARS-CoV-2. We anticipate the current perspective will play an effective role in the existing response to the COVID-19 crisis.

scholarly journals Early survey with bibliometric analysis on machine learning approaches in controlling coronavirus

2020 ◽  
Author(s):  
Haruna Chiroma ◽  
Absalom E. Ezugwu ◽  
Fatsuma Jauro ◽  
Mohammed A. Al-Garadi ◽  
Idris N. Abdullahi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ct Scan ◽  
Bibliometric Analysis ◽  
Survey Methodology ◽  
Vaccine Development ◽  
Diagnostic Tools ◽  
Future Research ◽  
X Rays ◽  
New Perspective ◽  
Further Development

AbstractBackground and ObjectiveThe COVID-19 pandemic has caused severe mortality across the globe with the USA as the current epicenter, although the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight the COVID-19 pandemic from a different perspective. To the best of the authors’ knowledge, no comprehensive survey with bibliometric analysis has been conducted on the adoption of machine learning for fighting COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine-learning-based technologies to fight the COVID-19 pandemic from a different perspective, including an extensive systematic literature review and a bibliometric analysis.MethodsA literature survey methodology is applied to retrieve data from academic databases, and a bibliometric technique is subsequently employed to analyze the accessed records. Moreover, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis, and analysis are presented. The convolutional neural network (CNN) is found mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest computed tomography (CT) scan images. Similarly, a bibliometric analysis of machine-learning-based COVID-19-related publications in Scopus and Web of Science citation indexes is performed. Finally, a new perspective is proposed to solve the challenges identified as directions for future research. We believe that the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators.ResultsThe findings in this study reveal that machine-learning-based COVID-19 diagnostic tools received the most considerable attention from researchers. Specifically, the analyses of the results show that energy and resources are more dispensed toward COVID-19 automated diagnostic tools, while COVID-19 drugs and vaccine development remain grossly underexploited. Moreover, the machine-learning-based algorithm predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images.ConclusionsThe challenges hindering practical work on the application of machine-learning-based technologies to fight COVID-19 and a new perspective to solve the identified problems are presented in this study. We believe that the presented survey with bibliometric analysis can help researchers determine areas that need further development and identify potential collaborators at author, country, and institutional levels to advance research in the focused area of machine learning application for disease control.

scholarly journals Ethical allocation of future COVID-19 vaccines

2020 ◽  
pp. medethics-2020-106850
Author(s):  
Rohit Gupta ◽  
Stephanie R Morain
Keyword(s):  
Health Inequalities ◽  
Vaccine Development ◽  
Morbidity And Mortality ◽  
Effective Vaccine ◽  
Future Research ◽  
Instrumental Value ◽  
The Usa ◽  
Vaccination Campaigns ◽  
Scientific Questions

The COVID-19 pandemic will likely recede only through development and distribution of an effective vaccine. Although there are many unknowns surrounding COVID-19 vaccine development, vaccine demand will likely outstrip early supply, making prospective planning for vaccine allocation critical for ensuring the ethical distribution of COVID-19 vaccines. Here, we propose three central goals for COVID-19 vaccination campaigns: to reduce morbidity and mortality, to minimise additional economic and societal burdens related to the pandemic and to narrow unjust health inequalities. We evaluate five prioritisation approaches, assess their likely impact on advancing the three goals of vaccine allocation and identify open scientific questions that may alter their outcomes. We argue that no single prioritisation approach will advance all three goals. Instead, we propose a multipronged approach that considers the risk of serious COVID-19 illness, instrumental value and the risk of transmission, and is guided by future research on COVID-19-specific clinical and vaccine characteristics. While we focus this assessment on the USA, our analysis can inform allocation in other contexts.

scholarly journals A Heterologous Viral Protein Scaffold for Chimeric Antigen Design: An Example PCV2 Virus Vaccine Candidate

Viruses ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 385
Author(s):  
Emilio Lamazares ◽  
Fernando Gutiérrez ◽  
Angela Hidalgo ◽  
Nicolas A. Gutiérrez ◽  
Felipe I. Espinoza ◽  
...  
Keyword(s):  
Vaccine Development ◽  
Vaccine Candidate ◽  
Low Cost ◽  
Chimeric Protein ◽  
Structural Complexity ◽  
Porcine Circovirus Type ◽  
Porcine Circovirus ◽  
Economic Losses ◽  
Tobacco Necrosis Virus ◽  
Effective Vaccine

Recombinant vaccines have low-cost manufacturing, regulatory requirements, and reduced side effects compared to attenuated or inactivated vaccines. In the porcine industry, post-weaning multisystemic disease syndrome generates economic losses, characterized by progressive weight loss and weakness in piglets, and it is caused by porcine circovirus type 2 (PCV2). We designed a chimeric antigen (Qm1) to assemble the main exposed epitopes of the Cap-PCV2 protein on the capsid protein of the tobacco necrosis virus (TNV). This design was based on the Cap-N-terminal of an isolated PCV2 virus obtained in Chile. The virus was characterized, and the sequence was clustered within the PCV2 genotype b clade. This chimeric protein was expressed as inclusion bodies in both monomeric and multimeric forms, suggesting a high-molecular-weight aggregate formation. Pigs immunized with Qm1 elicited a strong and specific antibody response, which reduced the viral loads after the PCV2 challenge. In conclusion, the implemented design allowed for the generation of an effective vaccine candidate. Our proposal could be used to express the domains or fragments of antigenic proteins, whose structural complexity does not allow for low-cost production in Escherichia coli. Hence, other antigen domains could be integrated into the TNV backbone for suitable antigenicity and immunogenicity. This work represents new biotechnological strategies, with a reduction in the costs associated with vaccine development.

scholarly journals Norovirus: Vaccine Challenges and Development

2021 ◽  
Author(s):  
Chloe Emehel ◽  
Akamu Ewunkem ◽  
Uchenna Iloghalu
Keyword(s):  
Acute Gastroenteritis ◽  
Vaccine Development ◽  
Target Group ◽  
Effective Vaccine ◽  
Future Research ◽  
Age Group ◽  
Target Groups ◽  
Virus Like Particles ◽  
Pediatric Vaccination

Noroviruses are a group of single-stranded RNA pathogens that cause acute gastroenteritis worldwide. Due to the high rates of mutation and evolution in the viruses, creating an effective vaccine to treat norovirus has been difficult. Factors such as the type of vaccine – live attenuated, subunit, or virus like particles – and target age group have limited progress in development. To better understand the best vaccine types and target groups for norovirus vaccine development, continuous research on the subjects will be encouraged. It will be shown that use of virus-like particles (VLPs) and a pediatric target group are the most effective options due to the versatility of VLPs and higher projected norovirus prevention rates for pediatric vaccination. The result of research will be useful to scientists creating norovirus vaccines and will help provide new perspectives for future research.

scholarly journals Textile-Based Mechanical Sensors: A Review

Materials ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6073
Author(s):  
Zaiwei Zhou ◽  
Nuo Chen ◽  
Hongchuan Zhong ◽  
Wanli Zhang ◽  
Yue Zhang ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Low Cost ◽  
Performance Criteria ◽  
Future Research ◽  
Human Machine Interaction ◽  
Everyday Objects ◽  
Novel Approach ◽  
Potential Applications ◽  
Mechanical Sensors ◽  
Physical And Chemical

Innovations related to textiles-based sensors have drawn great interest due to their outstanding merits of flexibility, comfort, low cost, and wearability. Textile-based sensors are often tied to certain parts of the human body to collect mechanical, physical, and chemical stimuli to identify and record human health and exercise. Until now, much research and review work has been carried out to summarize and promote the development of textile-based sensors. As a feature, we focus on textile-based mechanical sensors (TMSs), especially on their advantages and the way they achieve performance optimizations in this review. We first adopt a novel approach to introduce different kinds of TMSs by combining sensing mechanisms, textile structure, and novel fabricating strategies for implementing TMSs and focusing on critical performance criteria such as sensitivity, response range, response time, and stability. Next, we summarize their great advantages over other flexible sensors, and their potential applications in health monitoring, motion recognition, and human-machine interaction. Finally, we present the challenges and prospects to provide meaningful guidelines and directions for future research. The TMSs play an important role in promoting the development of the emerging Internet of Things, which can make health monitoring and everyday objects connect more smartly, conveniently, and comfortably efficiently in a wearable way in the coming years.

Laser Processing by Using Fluidic Laser Beam Shaper

2013 ◽  
Author(s):  
Hong Duc Doan ◽  
Naoki Iwatani ◽  
Kazuyoshi Fushinobu
Keyword(s):  
Laser Beam ◽  
Laser Processing ◽  
Low Cost ◽  
Beam Shaping ◽  
Beam Profile ◽  
Future Research ◽  
Laser Material Processing ◽  
Laser Beam Shaping ◽  
Laser Material ◽  
Potential Applications

Laser beam shaping techniques are important to optimize a large number of laser-material processing applications and laser-material interaction studies. The authors have developed a novel fluidic laser beam shaper (FLBS) with merits such as flexiblility, versatility and low cost. This work presents a fundamentally new approach for laser beam shaping by using FLBS. A Gaussian beam profile is transformed to a flat top beam and annular beam profile in the focal plane. The shaped laser beam is used for laser drilling to investigate the influence of the laser intensity profile in laser processing. The paper concludes with suggestions for future research and potential applications for further the work.

Advances in In-silico B-cell Epitope Prediction

2019 ◽  
Vol 19 (2) ◽  
pp. 105-115 ◽  
Author(s):  
Pingping Sun ◽  
Sijia Guo ◽  
Jiahang Sun ◽  
Liming Tan ◽  
Chang Lu ◽  
...  
Keyword(s):  
B Cell ◽  
In Silico ◽  
Vaccine Development ◽  
Cell Epitope ◽  
Disease Diagnosis ◽  
Diagnostic Tools ◽  
Accurate Identification ◽  
B Cell Epitopes ◽  
B Cell Epitope ◽  
In Silico Methods

Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitopebased vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Experimental methods for B-cell epitope mapping are time consuming, costly and labor intensive; in the meantime, various in-silico methods are proposed to predict both linear and conformational B-cell epitopes. The accurate identification of B-cell epitopes presents major challenges for immunoinformaticians. In this paper, we have comprehensively reviewed in-silico methods for B-cell epitope identification. The aim of this review is to stimulate the development of better tools which could improve the identification of B-cell epitopes, and further for the development of therapeutic antibodies and diagnostic tools.

scholarly journals The History of Enterovirus A71 Outbreaks and Molecular Epidemiology in the Asia-Pacific Region

2019 ◽  
Vol 26 (1) ◽  
Author(s):  
Jiratchaya Puenpa ◽  
Nasamon Wanlapakorn ◽  
Sompong Vongpunsawad ◽  
Yong Poovorawan
Keyword(s):  
Vaccine Development ◽  
Epidemiological Data ◽  
Healthcare Services ◽  
Developed Countries ◽  
Asia Pacific ◽  
Diagnostic Tools ◽  
Future Research ◽  
Pacific Region ◽  
Asia Pacific Region ◽  
Enterovirus A71

Abstract Enterovirus A71 (EV-A71) is one of the common causative pathogens for hand foot and mouth disease (HFMD) affecting young children. HFMD outbreak can result in a substantial pediatric hospitalization and burden the healthcare services, especially in less-developed countries. Since the initial epidemic of predominantly EV-A71 in California in 1969, the high prevalence of HFMD in the Asia-pacific region and elsewhere around the world represents a significant morbidity in this age group. With the advent of rapid and accurate diagnostic tools, there has been a dramatic increase in the number of laboratory-confirmed EV-A71 infection over the past two decades. The population, cultural, and socioeconomic diversity among countries in the Asia-Pacific region all influence the transmission and morbidity associated with HFMD. This review summarizes the current state of epidemiology of EV-A71 in Asia-Pacific countries based on the most recent epidemiological data and available information on the prevalence and disease burden. This knowledge is important in guiding the prevention, control and future research on vaccine development of this highly contagious disease of significant socioeconomic implications in public health.

scholarly journals A Homogeneous Split-Luciferase Assay for Rapid and Sensitive Detection of Anti-SARS CoV-2 Antibodies

2020 ◽  
Author(s):  
Igor Stagljar ◽  
Shawn Owen ◽  
Zhong Yao ◽  
Luka Drecun ◽  
Farzaneh Aboualizadeh ◽  
...  
Keyword(s):  
Low Cost ◽  
Neutralizing Antibody ◽  
Luciferase Assay ◽  
Diagnostic Tools ◽  
Target Engagement ◽  
Igg Antibodies ◽  
The Third ◽  
Antibody Assays ◽  
Potential Applications ◽  
Proof Of Principle

Abstract To meet the urgent demand for better diagnostic tools to combat the ongoing COVID-19 pandemic, we developed a homogeneous immunoassay to detect IgG antibodies against SARS-CoV-2. This assay is based on a tri-part Nanoluciferase (tNLuc) approach, in which the spike protein of SARS-CoV-2 and protein G, fused respectively to two different tNLuc tags, are used as antibody probes. Target engagement of the probes allows reconstitution of a functional luciferase in the presence of the third tNLuc component. The assay is performed directly in liquid phase of patient sera and enables rapid, quantitative and low-cost detection. We show that tNLuc maintains a similar sensitivity to ELISA, while its readouts are highly consistent with various neutralizing antibody assays. This proof-of-principle study suggests potential applications in diagnostics and disease and vaccination management.

scholarly journals Octopi: Open configurable high-throughput imaging platform for infectious disease diagnosis in the field

2019 ◽  
Author(s):  
Hongquan Li ◽  
Hazel Soto-Montoya ◽  
Maxime Voisin ◽  
Lucas Fuentes Valenzuela ◽  
Manu Prakash
Keyword(s):  
Infectious Disease ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Low Cost ◽  
Sputum Sample ◽  
Plasmodium Falciparum Malaria ◽  
Disease Diagnosis ◽  
Large Field ◽  
Diagnostic Tools ◽  
Malaria Parasites

AbstractAccess to quantitative, robust, yet affordable diagnostic tools is necessary to reduce global infectious disease burden. Manual microscopy has served as a bedrock for diagnostics with wide adaptability, although at a cost of tedious labor and human errors. Automated robotic microscopes are poised to enable a new era of smart field microscopy but current platforms remain cost prohibitive and largely inflexible, especially for resource poor and field settings. Here we present Octopi, a low-cost ($250-$500) and reconfigurable autonomous microscopy platform capable of automated slide scanning and correlated bright-field and fluorescence imaging. Being highly modular, it also provides a framework for new disease-specific modules to be developed. We demonstrate the power of the platform by applying it to automated detection of malaria parasites in blood smears. Specifically, we discovered a spectral shift on the order of 10 nm for DAPI-stained Plasmodium falciparum malaria parasites. This shift allowed us to detect the parasites with a low magnification (equivalent to 10x) large field of view (2.56 mm2) module. Combined with automated slide scanning, real time computer vision and machine learning-based classification, Octopi is able to screen more than 1.5 million red blood cells per minute for parasitemia quantification, with estimated diagnostic sensitivity and specificity exceeding 90% at parasitemia of 50/ul and 100% for parasitemia higher than 150/l. With different modules, we further showed imaging of tissue slice and sputum sample on the platform. With roughly two orders of magnitude in cost reduction, Octopi opens up the possibility of a large robotic microscope network for improved disease diagnosis while providing an avenue for collective efforts for development of modular instruments.One sentence summaryWe developed a low-cost ($250-$500) automated imaging platform that can quantify malaria parasitemia by scanning 1.5 million red blood cells per minute.

Sign in / Sign up

Export Citation Format

Share Document