Correlation between glycocalyx and diseases, and progress in its degradation mechanism

With the burgeoning development of glycobiology, a growing body of research shows a significant relationship between the development of various diseases and polysaccharides. Glycocalyx, an important component of the vascular endothelium, has a villi-like structure and plays a highly crucial role in maintaining vascular homeostasis. In-depth multidisciplinary studies have further revealed that the biological functions of glycocalyx are not only limited to vascular homeostasis, but are also closely related to various diseases in vivo. Foundations of glycocalyx composition and biological function, this paper reviews the latest research of glycocalyx biodegradation mechanism from the perspective of biological relevance of glycocalyx main components [heparan sulfate (HS), chondroitin sulfate (CS), hyaluronic acid (HA) and core protein] to cancer, corona virus disease 2019 (COVID-19), trauma surgery and other diseases by visualization and molecular biology experimental methods, and intends to provide new thoughts for clinical development of novel diagnostic methods and therapeutic targets.

Scenario deduction and analysis for spillover risks of international sports events under influence of COVID-19

In order to improve risk prevention and control capabilities for international sports events under the background of COVID-19, case data of 23 international sports since the pandemic outbreak were collected, and an evolutionary network model with COVID-19 as risk source was established. Then, risk analysis on the model was carried out based on in-and-out degree, number of sub-net nodes, the shortest path and average path of complex network theory, key risk event nodes were identified, and preventive measures were put forward. Finally, critical chains were obtained by analyzing causal mechanism and types of risk chains, and countermeasures and suggestions for chain disconnection and disaster mitigation were put forward. The results show that severe epidemic situation and rising risk of virus transmission in host cities are the key nodes in evolutionary network, and cycle chain of political relations and public opinion is the most destructive one. Therefore, it is necessary to promote the development of a public opinion monitoring system and strengthen positive publicity of sports events. © 2020 China Safety Science Journal. All rights reserved.

Scenario deduction and analysis for spillover risks of international sports events under influence of COVID-19

In order to improve risk prevention and control capabilities for international sports events under the background of COVID-19, case data of 23 international sports since the pandemic outbreak were collected, and an evolutionary network model with COVID-19 as risk source was established. Then, risk analysis on the model was carried out based on in-and-out degree, number of sub-net nodes, the shortest path and average path of complex network theory, key risk event nodes were identified, and preventive measures were put forward. Finally, critical chains were obtained by analyzing causal mechanism and types of risk chains, and countermeasures and suggestions for chain disconnection and disaster mitigation were put forward. The results show that severe epidemic situation and rising risk of virus transmission in host cities are the key nodes in evolutionary network, and cycle chain of political relations and public opinion is the most destructive one. Therefore, it is necessary to promote the development of a public opinion monitoring system and strengthen positive publicity of sports events. © 2020 China Safety Science Journal. All rights reserved.

A Pulmonary Parenchyma Segmentation Algorithm Aiding in COVID-19 Detection

As a rapidly evolving pandemic, COVID-19 has caused severe health and economic impact. In the diagnosis of COVID-19, the extraction of pulmonary parenchyma in chest X-ray images plays an important role. A U-Net-based pulmonary parenchyma segmentation algorithm using the encoding and decoding mode is proposed. The algorithm applies the idea of feature fusion to the construction of an A-Block to fully learn the semantic information of deep features. The attention mechanism is introduced into the deep convolutional neural network by adding a Dense Atrous Convolution (DAC) module and a Residual Multi-kernel Pooling (RMP) module in order to extend the receptive field of the convolution and to extract the contextual feature information. By improving the deformable convolution and the segmentation loss function, the generalization ability and the robustness of the network model are enhanced. Experimental results show that the segmentation accuracy, Dice coefficient, sensitivity and Jaccard index of this algorithm are 98. 16%, 98. 32%, 98. 13% and 98. 54% respectively. The algorithm can effectively implement pulmonary parenchyma segmentation. © 2021, Editorial Office of Computer Engineering. All rights reserved.

Connecting cancer research and communities: Assessing barriers and facilitators to the implementation of a community scientist program

Introduction: Community Scientist (CS) programs - often referred to as Citizen Scientist programs - that facilitate direct engagement between scientific researchers and community members have emerged as effective strategies for building community trust in scientists and better informing research design and dissemination to address true community needs. While population health research has increasingly incorporated community stakeholders into the research continuum, basic and translational sciences struggle to do the same and may contribute to cancer disparities. We designed and implemented a virtual CS program at the Robert H. Lurie Comprehensive Cancer Center of Northwestern University (LCC). We report barriers, facilitators, and lessons learned. Methods: Translational scientists (TSs) were recruited from among LCC investigators, and CSs were identified for participation from among LCC community networks. We sought to recruit a CS cohort representing LCC's catchment area and a TS cohort whose research focuses on cancers most impacting LCC catchment. CS program interactions included monthly meetings between two CSs and one TS wherein the CS-TS triad discuss TS research in lay terms and work together to co-create educational infographics suitable for dissemination to the catchment and LCC scientists. Virtual attendance was tracked and meeting recordings retroactively reviewed to identify and create product development. Results: Six CSs and three TSs agreed to participate in the CS program. The CS cohort includes cancer survivors, patient advocates, community organization leaders, a nurse, and an educator, while the TS cohort includes breast, prostate, and lung cancer researchers. Currently, 11 of 18 triad meetings have been completed, with attendance averaging 97%. Barriers to program implementation have included technological difficulties, restrictions on in-person meeting, scheduling conflicts, time limitation, and language barriers, while facilitators have included small group meetings to promote comfortable group-member contribution, presence of a trained facilitator, articulation of achievable meeting goals and mission for product creation, and clear assignment of team roles. Conclusion: The COVID-19 pandemic has illuminated pre-existing needs for improved connectivity between communities impacted by cancer disparities and cancer researchers. By identifying current barriers and facilitators to successful virtual CS program implementation, our findings can be used to guide development and implementation of similar programs at LCC and other cancer centers that are aimed at mitigating cancer health disparities.

A novel coronavirus pneumonia epidemic model under the background of media information dissemination

This novel novel coronavirus pneumonia novel coronavirus pneumonia background has been limited in the context of the spread of media information. For example, the government's credibility has been reduced due to the flooding of false information, this paper proposes a novel coronavirus pneumonia epidemic model under the background of media information dissemination. The model combines a variety of advanced science and technology, such as big data technology, cloud computing technology, artificial intelligence technology, etc., these technologies are based on the Internet platform, and can provide help for the dissemination and development of media information. Based on novel coronavirus pneumonia, the model can also disseminate scientific and authoritative information, avoid panic as much as possible and reduce the speed of negative public opinion. The experimental results show that the model can reduce the speed of negative public opinion, improve the credibility of the government, and improve the efficiency of public governance. © 2020 IEEE.

Impacts of the COVID-19 pandemic on air quality in Xianyang

Using a Machine Learning Model (MLM) to decouple meteorological parameters, this paper quantified true impacts of emission reduction by pollution sources resulting from COVID-19 on air quality in Xianyang. Compared with the non-epidemic scenario, the results showed that concentrations of PM2.5, PM10, SO2, NO2, and CO in Xianyang had significantly decreased by 19.3%, 26.0%, 13.4%, 60.1% and 9.1%, respectively, with NO2 decreasing the most, SO2 and CO decreasing slightly, and O3 increased by 50.9% conversely. Under the condition that both primary emission and precursors of secondary particulate matter decreased, the concentration of PM2.5 dropped lower than expected, and O3 increased though, showing the complexity of PM2.5 and O3 control, in the meanwhile implying that the impact of operating pollution sources during the epidemic on air quality was greater than malfunctioned sources, and official regulations to restrict and suspend production in factories (similar to the impact of the pandemic) had limited improvement on air quality. In the future, emphases should be put on the treatment of operating pollution sources during the pandemic such as scattered coal and biomass combustion, heat production and supply, and crude oil processing and petroleum product manufacturing. © 2021, Editorial Board of China Environmental Science. All right reserved.

Protection and utilization of aquatic wildlife from a multi-dimensional perspective

The recent outbreak of the COVID-19 virus has raised concerns regarding the trade in aquatic wildlife. Species of aquatic wildlife are both valuable natural resources and important ecosystem constituents. Their value is reflected not only in maintaining an ecological balance but also in meeting the diverse needs of human cultivation, development and utilization. Aquatic wildlife plays an irreplaceable role in achieving sustainable socio-economic development, as well as improving and enriching human material and cultural life. However, from the perspective of valuing aquatic wildlife, there exists a certain conflict between protection and utilization. In this paper, we examine the seemingly disparate aims of aquatic wildlife protection and breeding and utilization, and consider future progress with respect to aquatic wildlife resource protection and regulation of the breeding and utilization of aquatic animals. Appeal society actively encourage and promote aquatic wild animal domestication and breeding and processing enterprise, guide the consumer right edible safety of domestic products, and expand and improve the aquatic wild animals living space and environment, realization of aquatic wildlife species gauge touch is growing stronger, finally realize the sustainable use of wildlife resources. © Chinese Academy of Fishery Sciences.