Wireless mesh networks (WMNs) have emerged as a key technology for next generation wireless networks and provide a low-cost and convenient solution to the last-mile problem. Security and privacy issues are of paramount importance to WMNs for their wide deployment and for supporting service-oriented applications. Moreover, to support real-time services, WMNs must also be equipped with secure, reliable, and efficient routing protocols. Therefore, a number of research studies have been devoted to privacy-preserving routing protocols in WMNs. However, these studies cannot defend against inside attacks effectively, often take it for granted that every internal node is cooperative and trustworthy, and rarely consider dividing the user privacy information into different categories according to the security requirements. To address these issues, we propose a Privacy-Aware Secure Hybrid Wireless Mesh Protocol (PA-SHWMP), which combines a new dynamic reputation mechanism based on subject logic and uncertainty with the multi-level security technology. PA-SHWMP can defend against the internal attacks caused by compromised nodes and achieve stronger security and privacy protection while maintaining reasonable balances between security and performance. We analyze the PA-SHWMP protocol in terms of security, privacy, and performance. The simulation results show that the packet delivery ratio of the proposed PA-SHWMP becomes better than that of the existing HWMP and SHWMP protocols, when the number of malicious nodes and the percentage of lossy links increase. Moreover, the convergence time of PA-SHWMP is smaller than HWMP and SHWMP with any percentage of malicious mesh routers.
Background: Sepsis is a systemic inflammatory syndrome (SIRS) caused by acute microbial infection with high mortality rate. The role of tumour necrosis factor α (TNF-α)-induced necroptosis in promoting the pathophysiology of sepsis has been identified. Effective prevention of necroptosis is expected to improve the prognosis of sepsis patients. Methods: We conducted bioinformatics prediction of candidate drugs by analyzing differentially expressed genes of sepsis patients extracted from GEO database, combining library of integrated network-based cellular signatures (LINCS) L1000 perturbation database. Biological experiments based on TNF-α-induced necroptosis in cellular and mouse model were performed to verify the protection of candidate drugs from SIRS. Cell viability was measured by CellTiter‐Glo luminescent ATP assay. Effects of linifanib on necroptosis were investigated by western blotting, immunoprecipitation, and in vitro RIPK1 kinase assay. Survival curve analysis of SIRS mice treated by linifanib was performed. Results: A total of 16 candidate drugs was screened out through bioinformatics analysis. Our experiments demonstrated that linifanib effectively protected cells from necroptosis and rescued the death of SIRS mice from shock induced by TNF-α. In vitro, linifanib directly suppressed RIPK1 kinase activity. In vivo, linifanib effectively reduced the overexpressed level of IL-6, a good marker of severity during severe sepsis, in the lung of SIRS mice. Conclusion: We provide preclinical evidence for the potential clinical utility of linifanib in sepsis. Study of drug repositioning using bioinformatical predictions combined with experimental validations provides novel strategies for the development of sepsis drug. Keywords: Linifanib, drug reposition, necroptosis, SIRS, sepsis
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