ABSTRACT
Nowadays, the world confronts a highly infectious pandemic called coronavirus (COVID-19) and over 4 million people worldwide have now died from this illness. So, early detection of COVID-19 outbreak and distinguishing it from other diseases with the same physical symptoms can give enough time for treatment with true positive results and prevent coma or death. For early recognition of COVID-19, several methods for each modality are proposed. Although there are some modalities for COVID-19 detection, electrocardiography (ECG) is one of the fastest, the most accessible, the cheapest and the safest one. This paper proposed a new method for classifying COVID-19 patients from other cardiovascular disease by ECG signals. In the proposed method, Resnet50v2 which is a kind of convolutional neural network, is used for classification. In this paper because of image format of data, first data with image format are applied to the network and then for comparison, ECG images are changed to signal format and classification is done. These two strategies are used for COVID-19 classification from other cardiac abnormalities with different filter sizes and the results of strategies are compared with each other and other methods in this field. As it can be concluded from the results, signal-based data give better accuracy than image classification at best performance and it is better to change the image format to signals for classification. The second result can be found by comparing with other methods in this field, the proposed method of this paper gives better performance with high accuracy in COVID-19 classification. Copyright © 2023 National Taiwan University. National Taiwan University.
ABSTRACT
Introduction: There are a limited number of studies about COVID-19 during delivery and postpartum. Case Presentation: A 38-years-old G3p2 woman at 35 weeks and 4 days of gestation was referred with the chief complaints of dyspnea, cough, headache, and fever. Pharyngeal swab polymerase chain reaction (PCR) was negative for COVID-19;however, in chest Computed Tomography (CT) angiography, ground glass was observed in the basal lobe of the left lung. The infant was born via cesarean section with a gestational age of 36 weeks and an Apgar score of 8/9. No infant document was found about COVID-19 and other infections in several days after delivery. The patient died eight days after the onset of symptoms due to cardiovascular collapse. Discussion: we reported the first postpartum maternal death with COVID-19 and a healthy baby with no evidence of COVID-19 and a gestational age of 36 weeks. Conclusion: Physicians should be aware that pregnant women with symptoms of respiratory infection and pulse negative PCR test should be followed for infection with COVID-19. This infection may lead to maternal death.
ABSTRACT
Background and Aim: SARS-CoV-2 is the causative agent of Coronavirus 2019 or COVID-19 in the world. Novel coronavirus disease is a respiratory disease. To date, there have been challenges in the treatment for COVID-19 and emerged new variants like UK B1.1.7. Accordingly, an effective prevention regime is needed for this infection, which covers most variants. The purpose of this research was to predict the conserved epitopes of Spike and Nucleocapsid proteins from SARS-CoV-2 for the design of a novel coronavirus 2019 multi-epitope vaccine using in silico tools. Materials and Methods: Computational analysis and immunoinformatics approaches include identification of potential conserve epitopes and selection of epitopes based on allergenicity, toxicity, antigenicity, and molecular docking were used for epitope prediction and screening. In the next step, selected segments of the epitopes were attached by the suitable linkers. Finally, Maltese-bound protein (MBP) as an adjuvant was added to the novel vaccine structure. The secondary and third structures of the designed multi-epitope vaccine were predicted via immunoinformatics algorithms. Predicted structure refined and validated for attaining best stability. In the end, immunoinformatics evaluation, molecular docking, and molecular dynamics were performed to confirm vaccine efficiency. Codon optimization and in silico cloning were done to ensure the expression yield of the novel multi-epitope vaccine in the target host. Results: This study showed that our data support the suggestion that the designed vaccine could induce immune responses against SARS-CoV-2 variants. Conclusion: The structure designed had acceptable quality with software reviews. Further in vitro and in vivo experiments are needed to confirm the safety and immunogenicity of the candidate vaccine. © 2021. All Rights Reserved.
ABSTRACT
The new coronavirus disease (COVID-19) is a highly contagious and pathogenic viral infection that first appeared in the city of Wuhan, China in December 2019. The rapid spread of the virus around the world among humans is still going on. Genomic analysis has shown that the virus is 96% identical to the severe acute respiratory syndrome (SARS) coronavirus. The transmission of the virus from the origin to humans is not known yet, but its rapid transmission from human to human has been widely confirmed. The virus increases the risk of mortality in people with a history of heart disease, diabetes, kidney problems and high blood pressure. Researchers found that the mortality rate is higher in men than women and elderly male patients above 60 are also at high risk. To date, no effective and approved antiviral medicine or vaccine is available for use against COVID-19. Therefore, the World Health Organization (WHO) recommend that humans should protect themselves and prevent the spread of the virus by taking personal hygiene, keeping a distance from people infected with the virus, and even quarantine. The purpose of this article is to provide an overview of the issues and necessary points for people to identify and deal effectively with the novel coronavirus and to provide a reference for future studies on coronaviruses. © 2020, International Journal of Innovative Research and Scientific Studies. All rights reserved.