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Many of the survivors of the novel coronavirus disease (COVID-19) are suffering from persistent symptoms, causing significant morbidity and decreasing their quality of life, termed "post-COVID-19 syndrome" or "long COVID". Understanding the mechanisms surrounding PCS is vital to developing the diagnosis, biomarkers, and possible treatments. Here, we describe the prevalence and manifestations of PCS, and similarities with previous SARS epidemics. Furthermore, we look at the molecular mechanisms behind the neurological features of PCS, where we highlight important neural mechanisms that may potentially be involved and pharmacologically targeted, such as glutamate reuptake in astrocytes, the role of NMDA receptors and transporters (EAAT2), ROS signaling, astrogliosis triggered by NF-κB signaling, KNDy neurons, and hypothalamic networks involving Kiss1 (a ligand for the G-protein-coupled receptor 54 (GPR54)), among others. We highlight the possible role of reactive gliosis following SARS-CoV-2 CNS injury, as well as the potential role of the hypothalamus network in PCS manifestations. [ABSTRACT FROM AUTHOR]

A pandemic caused by the novel coronavirus (COVID-19) is causing an unprecedented situation for the world's health services. Health, local communities, and government are adversely affected by the COVID-19 pandemic. Moreover, on January 21, 2020, the WHO Emergency Committee declared a global health emergency because of increasing numbers of COVID-19 case notifications from countries overseas. While the pandemic caused grave damage to socioeconomic phenomena and multiple global phenomena such as commodity prices, remittances, trade, tourism, significant job loss, and drastically lower wages, the pandemic has also negatively affected multiple global phenomena. The advent of technology has spurred significant changes in many aspects of our lives and improved the exchange of information, the presentation of data, and the management of medical resources through telemedicine. In this article, we present a mathematical data visualization approach for analyzing pandemic data behaviors, such as exponential growth and deviations using the data related to COVID-19 events. Furthermore, this article will include studies on the implications of the COVID-19 pandemic on finance sector. [ABSTRACT FROM AUTHOR]

This case highlights an atypical presentation of a patient with unknown history of mental disease who has been diagnosed with a bipolar disorder associated with severe COVID‐19 symptoms. Neuroimaging was only positive for subtle white matter changes; he was treated with antipsychotics and mood‐stabilizing agents until he reached partial remission. The authors urge clinicians to consider the impact of the COVID‐19 pandemic on patients with mental illness and the urgent need for vigilant monitoring of presenting signs and symptoms. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) is characterized as a disease caused by a novel coronavirus known as severe acute respiratory coronavirus syndrome 2 (SARS-CoV-2; formerly known as 2019-nCoV). In December 2019, COVID-19 began to appear in a few countries. By the beginning of 2020, it had spread to most countries across the world. This is when education challenges began to arise. The COVID-19 crisis led to the closure of thousands of schools and universities all over the world. Such a situation requires reliance on e-learning and robotics education for students to continue their studies to avoid the mingling between people and students. In relation to this alternative learning solution, the present study was conducted. A systematic literature review on educational robotics (ER) keywords between 2015–2020 was carried out for the purpose to review a total of 2253 articles from the selected sources; Scopus (452), Taylor&Francis (311), Science Direct (427), IEEE Xplore (221), andWeb of Science (842). This review procedure was labelled as Taxonomy 1. After filtering Taxonomy 1, it was found that 98 scientific articles formed the so-called Taxonomy II that was categorized into six categories: (i) Robotics concepts, (ii) Device, (iii) Robotic applications, (iv) Manufacturing robots, (v) Robotics analysis, and (vi) Education/taxonomy. For this study, only 35 articles in this specific field were selected, of which were then assigned into three categories: (i) Application, (ii) Platform, and (iii) Educational. The results show that the application category carries 17.4%, platform 20%, and education 22.85%. This study serves as the application platform to help students, academics, and researchers. [ABSTRACT FROM AUTHOR]

The development of mathematical modeling of infectious diseases is a key research area in various fields including ecology and epidemiology. One aim of these models is to understand the dynamics of behavior in infectious diseases. For the new strain of coronavirus (COVID-19), there is no vaccine to protect people and to prevent its spread so far. Instead, control strategies associated with health care, such as social distancing, quarantine, travel restrictions, can be adopted to control the pandemic of COVID-19. This article sheds light on the dynamical behaviors of nonlinear COVID-19 models based on two methods: the homotopy perturbation method (HPM) and the modified reduced differential transform method (MRDTM). We invoke a novel signal flow graph that is used to describe the COVID-19 model. Through our mathematical studies, it is revealed that social distancing between potentially infected individuals who are carrying the virus and healthy individuals can decrease or interrupt the spread of the virus. The numerical simulation results are in reasonable agreement with the study predictions. The free equilibrium and stability point for the COVID-19 model are investigated. Also, the existence of a uniformly stable solution is proved. [ABSTRACT FROM AUTHOR]