RESUMEN
COVID-19 is an emerging pandemic. The course and management of the disease in the liver transplant setting may be difficult due to a long-standing immunosuppressive state. In Egypt, the only available option is living donor liver transplantation (LDLT). In our centre, we have transplanted 440 livers since 2008. In this study, we report a single-centre experience with COVID-19 infection in long-term liver transplant recipients. A total of 25 recipients (5.7 %) had COVID-19 infections since March 2020. Among these recipients, two developed COVID-19 infections twice, approximately three and two months apart, respectively.Copyright © 2021 The Author(s)
RESUMEN
The unprecedented global spread of the coronavirus pandemic COVID-19 has significantly promoted novel Internet-of-things (IoT)-based solutions to prevent, combat, monitor, or predict virus spread in the population. The proliferation of these technologies has fostered their utilization for different practical use-cases to offer reinforced control, discipline, and safety. This paper proposes an end-to-end smart navigation framework that uses Social IoT (SIoT) and Artificial Intelligence (AI) techniques to ensure pedestrians' navigation safety through a given geographical area. The aim is to mitigate the risks of exposure to the virus and impose social distancing practices while avoiding high-risk areas identified from the SIoT data. First, we create weighted graphs representing the social relations connecting the different IoT devices in the area of interest. Second, we regroup the devices into communities according to their SIoT relations that consider their locations and owners' friendship levels. Next, we extract CCTV recorded videos to estimate the level of social distancing practice on different roads using a computer vision model. Accordingly, the road segments are assigned weights representing their safety levels based on the extracted data. Afterward, a graph-based routing algorithm is executed to recommend the route to follow while achieving a trade-off between speed and safety. Finally, the proposed framework is generalized to enable multi-user coordinated navigation. The feasibility of the proposed approach on real-world maps and IoT datasets is corroborated in our simulation results showing an ability to balance safety and travel distance, which can be adjusted according to the user's preferences.
RESUMEN
The unprecedented worldwide spread of coronavirus disease has significantly sped up the development of technology-based solutions to prevent, combat, monitor, or predict pandemics and/or its evolution. The omnipresence of smart Internet-of-things (IoT) devices can play a predominant role in designing advanced techniques helping in minimizing the risk of contamination. In this paper, we propose a practical framework that uses the Social IoT (SIoT) concept to improve pedestrians safely navigate through a real-wold map of a smart city. The objective is to mitigate the risks of exposure to the virus in high-dense areas where social distancing might not be well-practiced. The proposed routing approach recommends pedestrians' route in a real-time manner while considering other devices' mobility. First, the IoT devices are clustered into communities according to two SIoT relations that consider the devices' locations and the friendship levels among their owners. Accordingly, the city map roads are assigned weights representing their safety levels. Afterward, a navigation algorithm, namely the Dijkstra algorithm, is applied to recommend the safest route to follow. Simulation results applied on a real-world IoT data set have shown the ability of the proposed approach in achieving trade-offs between both safest and shortest paths according to the pedestrian preference.