The SARS-CoV-2 main protease induces capillary cell death by cleaving NEMO in brain endothelial cells

Background: Several lines of evidence suggest that neurological symptoms in patients suffering from Coronavirus disease 2019 (COVID-19) occur partially due to damage to small vessels in the brain. However, the potential mechanisms underlying this pathology are unclear. Aim: Here, we describe a novel pathway by which SARSCoV- 2 affects the brain vasculature and thereby potentially induces neurocognitive impairment in patients. Method: We examined brain tissue of deceased COVID- 19 patients and different animal models of this disease for microvascular pathology. Using several techniques like mass spectrometry, high resolution microscopy, transgenic animals, and AAV-mediated gene transfer, we investigated the effect of the SARS-CoV-2 main protease (Mpro) on brain endothelial cells. Results/Conclusions: In brains of SARS-CoV-2-infected individuals and animal models, we found an increased number of empty basement membrane tubes, so-called string vessels representing remnants of lost capillaries. We obtained evidence that brain endothelial cells are infected, and that Mpro cleaves NEMO, the essential modulator of nuclear factor-jB. By ablating NEMO, Mpro induces the death of human brain endothelial cells and the occurrence of string vessels in mice. Deletion of RIPK3, a mediator of regulated cell death, blocks the vessel rarefaction and disruption of the blood-brain barrier due to NEMO ablation. Importantly, a pharmacological inhibitor of RIPK signaling prevented the Mpro-induced microvascular pathology. These data suggest a novel mechanism by which SARS-CoV-2 affects the brain vasculature and a potential therapeutic option to interfere with the neurological consequences of COVID-19.

A Discrete SIR Model with Spatial Distribution on a Torus for COVID-19 Analysis using Local Neighborhood Properties

The ongoing COVID-19 pandemic threatens the health of humans, causes great economic losses and may disturb the stability of the societies. Mathematical models can be used to understand aspects of the dynamics of epidemics and to increase the chances of control strategies. We propose a SIR graph network model, in which each node represents an individual and the edges represent contacts between individuals. For this purpose, we use the healthy S (susceptible) population without immune behavior, two I-compartments (infectious) and two R-compartments (recovered) as a SIR model. The time steps can be interpreted as days and the spatial spread (limited in distance for a singe step) shell take place on a 200 by 200 torus, which should simulate 40 thousand individuals. The disease propagation form S to the I-compartment should be possible on a k by k square (k=5 in order to be in small world network) with different time periods and strength of propagation probability in the two I compartments. After the infection, an immunity of different lengths is to be modeled in the two R compartments. The incoming constants should be chosen so that realistic scenarios can arise. With a random distribution and a low case number of diseases at the beginning of the simulation, almost periodic patterns similar to diffusion processes can arise over the years. Mean value operators and the Laplace operator on the torus and its eigenfunctions and eigenvalues are relevant reference points. The torus with five compartments is well suited for visualization. Realistic neighborhood relationships can be viewed with a inhomogeneous graph theoretic approach, but they are more difficult to visualize. Superspreaders naturally arise in inhomogeneous graphs: there are different numbers of edges adjacent to the nodes and should therefore be examined in an inhomogeneous graph theoretical model. The expected effect of corona control strategies can be evaluated by comparing the results with various constants used in simulations. The decisive benefit of the models results from the long-term observation of the consequences of the assumptions made, which can differ significantly from the primarily expected effects, as is already known from classic predator-prey models.

A debate about implementing immersive technology for higher education: Pre-study examining the usability of virtual reality for lectures

BACKGROUND: As the COVID-pandemic has shown, the need for innovative (digitalized) solutions is in high demand across almost every field of interest. The implementation of advanced technologies in higher education provides an intriguing opportunity to expand its scope by reaching new audiences as well as ensuring a high quality of learning outcome. OBJECTIVE: In this article we tried to examine if virtual reality can be a suitable option by placing lectures into a virtual setup. METHODS: First, we explored the theoretical background if and how virtual reality has been adapted for usage in higher education. We then asked five lecturers from the IMC University of Applied Sciences Krems to test a virtual environment (Mozilla Hubs) and evaluate the platform for their teaching purposes. RESULTS: Among one of the results was, that 80 percent would recommend using the platform for lectures to their colleagues. Due to the small sample size the findings need to be further evaluated. CONCLUSIONS: In the foreseeable future virtual reality will become a valuable teaching assistance in higher education. Findings show that the response rate when training with virtual reality applications is much higher than to common studying methods. © 2020 - IOS Press and the authors. All rights reserved.