ABSTRACT
Instructional design is a major concern in TELE (Technology Enhanced Learning Environments) research, especially since the beginning of the Covid-19 health crisis. Since the beginning of this crisis, emergency remote teaching has been widely used. Accordingly, the primary objective in these circumstances is not to re-create a robust educational ecosystem, but rather to provide adapted access to instructional support, learning materials, services and objects. However, design connectedness in such environments is still required regarding the emergence of IoT (Internet of things) and CPS (Cyber Physical Systems) in everyday life and thus in educational environments. In this paper, we propose a model-driven engineering method for the design of Educational Cyber Physical Systems (ECPS). Our method deals with the separation of concerns when it comes to considering a Platform Independent Model (educational aspect) and a Platform Description Model (connected aspect). This practice could then be adopted in order to design further environments by adapting the required models. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
ABSTRACT
PURPOSE: Our aim was to study the association between abnormal findings on chest and brain imaging in patients with coronavirus disease 2019 (COVID-19) and neurologic symptoms. MATERIALS AND METHODS: In this retrospective, international multicenter study, we reviewed the electronic medical records and imaging of hospitalized patients with COVID-19 from March 3, 2020, to June 25, 2020. Our inclusion criteria were patients diagnosed with Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infection with acute neurologic manifestations and available chest CT and brain imaging. The 5 lobes of the lungs were individually scored on a scale of 0-5 (0 corresponded to no involvement and 5 corresponded to >75% involvement). A CT lung severity score was determined as the sum of lung involvement, ranging from 0 (no involvement) to 25 (maximum involvement). RESULTS: A total of 135 patients met the inclusion criteria with 132 brain CT, 36 brain MR imaging, 7 MRA of the head and neck, and 135 chest CT studies. Compared with 86 (64%) patients without acute abnormal findings on neuroimaging, 49 (36%) patients with these findings had a significantly higher mean CT lung severity score (9.9 versus 5.8, P < .001). These patients were more likely to present with ischemic stroke (40 [82%] versus 11 [13%], P < .0001) and were more likely to have either ground-glass opacities or consolidation (46 [94%] versus 73 [84%], P = .01) in the lungs. A threshold of the CT lung severity score of >8 was found to be 74% sensitive and 65% specific for acute abnormal findings on neuroimaging. The neuroimaging hallmarks of these patients were acute ischemic infarct (28%), intracranial hemorrhage (10%) including microhemorrhages (19%), and leukoencephalopathy with and/or without restricted diffusion (11%). The predominant CT chest findings were peripheral ground-glass opacities with or without consolidation. CONCLUSIONS: The CT lung disease severity score may be predictive of acute abnormalities on neuroimaging in patients with COVID-19 with neurologic manifestations. This can be used as a predictive tool in patient management to improve clinical outcome.