There is growing evidence of the position of microRNAs (miRs) in Alzheimer's disease (AD), thus our objective was to discuss the impact of miR-129-5p regulating nerve injury and inflammatory response in AD rats by modulating SOX6 expression. The AD rat model was established by injecting Aβ 25-35 into the brain. The pathological changes, ultrastructure, number of neurons, cell degeneration and apoptosis of hippocampal tissue were observed in vivo. MiR-129-5p, SOX6, IL-1β, TNF-α, Bcl-2 and Bax expression in serum and hippocampal tissues were detected by ELISA, RT-qPCR or western blot analysis. The successfully modeled hippocampal neuronal cells of AD were transfected with miR-129-5p mimic, SOX6-siRNA or their controls to figure out their roles in proliferation, apoptosis and inflammatory reaction in vitro. Low expression of SOX6 and high expression of miR-129-5p in vivo of rats would shorten the escape latent period and increase the times of crossing platforms, alleviate the pathological injury, inhibit neuronal apoptosis and reduce the inflammatory reaction. Up-regulation of miR-129-5p and down-regulation of SOX6 promoted proliferation, suppressed apoptosis and degraded the inflammatory reaction of neuronal cells in vitro. Up-regulation of SOX6 reversed the expression of miR-129-5p to reduce the damage and inflammatory response of the cell model of AD. Our study presents that upregulation of miR-129-5p or down-regulation of SOX6 can reduce nerve injury and inflammatory response in rats with AD. Thus, miR-129-5p may be a potential candidate for the treatment of AD.

The explosive growth of medical data has dramatically increased the demand for computing power, resulting in insufficient spectrum resources and communication overload. Hospitals need to invest much money to expand computing resources. Various diseases require varying degrees of multi-sensor and continuous monitoring. Take venous thromboembolism (VTE) patients in the intensive care unit (ICU) as an example, enlargement of the right heart, widening of the pulmonary artery, and abnormal results of myocardial enzyme examination maybe lead to sudden death within a short time in the ICU inpatient ward. Steady and dynamic health monitoring is essential. Patients’ immediate risk perception can significantly improve medical efficiency and reduce adverse consequences. How to provide a more efficient and secure full-time monitoring scheme, dynamically adjust the workload, and allocate computing tasks and requests reasonably is a practical problem to be solved urgently. First, this paper defines a task similarity to measure the similarity between different task packages and determine the priority of tasks to avoid forwarding highly similar task packages and reduce energy consumption. Second, the edge gateway caching mechanism with a self-attention mechanism is constructed, which changes the centralized scheduling mode of traditional cloud computing, devolves the coordination function to the edge, and divides the network into multiple local sub-networks. The central node of the sub-network determines the scheduling scheme. The experimental results show that the system can ensure the quality of service and use the edge’s limited computing resources, effectively shield the inefficient data transmission requirements, reduce the use cost and medical quality, and has a specific theoretical and practical value.

The harsh environment of the battlefield challenges the delay and reliability of the cloud computing system composed of soldier terminals and BeiDou satellites. Based on this, this paper focuses on common problems in computational crowdsourcing under multi-agent and proposes a task distribution strategy optimization model based on battlefield edge computing. The process introduces the concept of flow pressure to solve these issues, load balancing and cascading congestion. Flow pressure means multiple servers can communicate and partially offload tasks that exceed the computational load to other servers. The computational overflow problem can be solved by task offloading based on flow pressure. Several different mainstream task allocation strategies are compared through experiments to demonstrate the model’s performance. The experimental results show that the model has lower latency and failure rate and reasonable computational resource occupation, which has a particular theoretical value and reference significance.

Background: Tumor classification is important for accurate diagnosis and personalized treatment and has recently received great attention. Analysis of gene expression profile has shown relevant biological significance and thus has become a research hotspot and a new challenge for bio-data mining. In the research methods, some algorithms can identify few genes but with great time complexity, some algorithms can get small time complex methods but with unsatisfactory classification accuracy, this article proposed a new extraction method for gene expression profile. Methods: In this paper, we propose a classification method for tumor subtypes based on the Minimum- Redundancy Maximum-Relevancy (MRMR) of maximum compatibility center. First, we performed a fuzzy clustering of gene expression profiles based on the compatibility relation. Next, we used the sparse representation coefficient to assess the importance of the gene for the category, extracted the top-ranked genes, and removed the uncorrelated genes. Finally, the MRMR search strategy was used to select the characteristic gene, reject the redundant gene, and obtain the final subset of characteristic genes. Results: Our method and four others were tested on four different datasets to verify its effectiveness. Results show that the classification accuracy and standard deviation of our method are better than those of other methods. Conclusion: Our proposed method is robust, adaptable, and superior in classification. This method can help us discover the susceptibility genes associated with complex diseases and understand the interaction between these genes. Our technique provides a new way of thinking and is important to understand the pathogenesis of complex diseases and prevent diseases, diagnosis and treatment.