ABSTRACT
Corona Virus Disease 2019 (COVID-19) has caused several pandemic peaks worldwide due to its high variability and infectiousness, and COVID-19 has become a long-standing global public health problem. There is growing evidence that severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) frequently causes multi-organ injuries and more severe neurological manifestations. Therefore, increased awareness of possible neurological complications is beneficial in preventing and mitigating the impact of long-term sequelae and improving the prognostic outcome of critically ill patients with COVID-19. Here, we review the main pathways of SARS-CoV-2 neuroinvasion and the potential mechanisms causing neurological damage. We also discuss in detail neurological complications, aiming to provide cutting-edge basis for subsequent related basic research and clinical studies of diagnosis and treatment.
Subject(s)
COVID-19 , Nervous System Diseases , Humans , COVID-19/complications , SARS-CoV-2 , Nervous System Diseases/etiology , Nervous System Diseases/therapyABSTRACT
Background/purpose: Coronavirus disease 2019 (COVID-19) has led to a rapid increase in mortality worldwide. Systemic lupus erythematosus (SLE) was a high-risk factor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2) infection, Whereas the molecular mechanisms underlying SLE and CVOID-19 are not well understood. This study aims to discover the common molecular mechanisms and genetic biomarkers of SLE and COVID-19, providing new ideas for the treatment of COVID-19. Method(s): RNA sequencing data of peripheral blood mononuclear cells (PBMC) from 6 SLE datasets and 8 COVID-19 datasets were obtained from the GEO database. Highly related modular genes associated with COVID-19 and SLE were identified by weighted gene co-expression network analysis (WGCNA). The differentially expressed genes (DEGs) between patients and healthy controls (HCs) were identified by the limma package. Common shared DEGs from COVID-19 and SLE were identified. Cytoscape and MCODE plugin were utilized for exploring the protein-protein interaction network (PPI) and identifying shared hub genes. Potential biological functions and pathways were also explored from the common DEGs. For better analysis of detailed biological mechanisms, both xCell algorithm and the cMap in CLUE (https://clue.io/) were utilized for discovering immune cell infiltration and predicting potential drugs that negatively regulate the highly expressed genes. Result(s): With identified 498 up-regulated common DEGs in SLE and COVID-19 related genes, total 11 and 13 gene modules of SLE and COVID-19 were identified espectively After overlapping differential genes, the final intersection gene set contains 218 genes. The PPI, especially the functional subnet module consists of upregulated genes by MCODE showed a great deal IFN related genes involved in the regulation of immunity. GO biological processes also showed possible functions were defense response to virus and mitotic cell cycle. Moreover, changes of most immune cells were strongly consistent between SLE and COVID-19. CDK inhibitors identified may be more likely to inhibit two diseases. Conclusion(s): Our study examined in detail the common molecular mechanisms of SLE and COVID-19, in which cellular response to cytokine stimulus, like regulating IFN, which might be the key target of both diseases. CDK is associated with the progression of SLE and COVID-19, which may be the potential therapeutic drug for SLE patients with COVID-19 infection.
ABSTRACT
Affected by the COVID-19 pandemic, teleworking is becoming more popular, with the exposed attack surface of the internal network expanding. Once outsiders personate accounts or insiders conduct illegal operations, the data security in teleworking with traditional border protection will be broken. Therefore, it is necessary to implement fine-grained and dynamic access control to protect data from malicious access. Attribute-based access control (ABAC) is ideal, where authorization is performed through attributes and rules. On this basis, risk assessment, context awareness, and machine learning are supplemented for dynamic access control. However, these methods have their limitations due to the requirement of sufficient prior knowledge and massive label-classified data. Moreover, it is challenging to obtain the samples of attack behaviors, and the attack behaviors may change frequently to evade detection. In contrast, the normal behaviors are relatively stable except for the update of network services. We propose a dynamic access control model, ABAC-IntroVAE, to address the above issues. ABAC-IntroVAE judges users' requests through rule matching and behavior analysis based on the attributes of the requests. It first filters out requests against the rules by rule matching. Then, the introspective variational autoencoder (IntroVAE) is used for behavior analysis to realize dynamic access decisions. Requests classified as normal can be authorized for access. ABAC-IntroVAE only needs samples of normal requests for training, avoiding the difficult task of collecting massive and frequently changing samples of attack requests. Meanwhile, the IntroVAE model is updated through continual learning to adapt to new-style normal behaviors due to the update of network services. Our experiment study suggests that our proposed ABAC-IntroVAE can effectively perform dynamic access control. It achieves an accuracy of 97.2% in abnormal detection and maintains an accuracy of over 97% through continual learning, despite the addition of new-style user behavior patterns. © 2022 IEEE.
ABSTRACT
Background/Purpose: The 2019 outbreak of coronavirus disease COVID-19 causes immune system disruption. Recent studies reported that the decrease or depletion of regulatory T cell (Treg) may be responsible for overstimulation of the immune system and lung damage in patients with severe COVID-19. This study aims to find the molecular mechanisms and genetic biomarkers associated with Tregs in COVID-19, providing new ideas for the treatment of COVID-19. Method(s): RNA sequencing data of peripheral blood mononuclear cells (PBMC) from 252 COVID-19 infected patients and 69 healthy controls (HC) were obtained from the GEO database. The Tregs composition of COVID-19 samples was quantified using the CIBERSORT deconvolution method. The differential genes (DEGs) were identified by the limma R package. Gene co-expression network analysis (WGCNA) was used to identify the gene. Differentially expressed Tregs-related genes (DETregRGs) were obtained by intersecting DEGs with the highly related modular genes obtained in the previous step. The potential biological functions and pathways of DETregRGs were then explored. Protein-protein interaction (PPI) networks were subsequently constructed to identify hub genes. In addition, the prediction of small molecule drugs for the potential treatment of COVID-19 was made using the CMap database. Result(s): After the weighted gene co-expression network analysis (WGCNA), the turquoise module was highly correlated with Treg expression and a total of 134 DEGs was identified as DETregRGs. These genes were mainly involved in GO biological processes, such as the inflammatory response, and T cell differentiation of thymus. Then, 11 hub genes (including RPS12, RPL21, RPS3A, CD8B, CD3D, TRAT1, RPS6, CD3E, CD28, RPL3, and CD4) were ranked based on Molecular Complex Detection (MCODE) analysis. The TregRG score of COVID-19 patients showed significantly lower than HC, calculated by the 'singscore' algorithms. After the signature query of the CMap database, the KU-0063794, an mTOR inhibitor ranked second in the negative enrichment score, may restore immune system dysregulation caused by increased Th17 differentiation and decreased Treg differentiation during SARS-CoV- 2 infection. Conclusion(s): Our study examined in detail the molecular mechanisms underlying the inadequacy of Tregs in patients with COVID-19 infection. mTOR inhibitors may improve COVID-19 symptoms by expanding Tregs which may be one of the potential therapeutic methods that need further investigation. (Figure Presented).