Astragalus Polysaccharide Enhances Voriconazole Metabolism under Inflammatory Conditions through the Gut Microbiota

Background and Aims:Voriconazole(VRC),a widely used antifungal drug,often causes hepatotoxicity,which presents a significant clinical challenge.Previous studies demonstrated that Astragalus polysaccharide(APS)can regulate VRC metabolism,thereby potentially mitigating its hepatotoxic effects.In this study,we aimed to explore the mechanism by which APS regulates VRC metabolism.Methods:First,we assessed the association of abnormal VRC metabolism with hepatotoxicity using the Roussel Uclaf Causality Assessment Method scale.Second,we conducted a series of basic experiments to verify the promotive effect of APS on VRC metabolism.Various in vitro and in vivo assays,including cytokine profiling,immunohistochemistry,quantitative polymerase chain reaction,metabolite analysis,and drug concentration measurements,were performed using a lipopolysaccharideinduced rat inflammation model.Finally,experiments such as intestinal biodiversity analysis,intestinal clearance assessments,and Bifidobacterium bifidum replenishment were performed to examine the ability of B.bifidum to regulate the expression of the VRC-metabolizing enzyme CYP2C19 through the gut–liver axis.Results:The results indicated that APS does not have a direct effect on hepatocytes.However,the assessment of gut microbiota function revealed that APS significantly increases the abundance of B.bifidum,which could lead to an anti-inflammatory response in the liver and indirectly enhance VRC metabolism.The dual-luciferase reporter gene assay revealed that APS can hinder the secretion of pro-inflammatory mediators and reduce the inhibitory effect on CYP2C19 transcription through the nuclear factor-B signaling pathway.Conclusions:The study offers valuable insights into the mechanism by which APS alleviates VRC-induced liver damage,highlighting its immunomodulatory influence on hepatic tissues and its indirect regulatory control of VRC-metabolizing enzymes within hepatocytes.

An Intrusion Detection Method for Advanced Metering Infrastructure System Based on Federated Learning

An advanced metering infrastructure(AMI)system plays a key role in the smart grid(SG),but it is vulnerable to cyberattacks.Current detection methods for AMI cyberattacks mainly focus on the data center or a distributed independent node.On one hand,it is difficult to train an excellent detection intrusion model on a self-learning independent node.On the other hand,large amounts of data are shared over the network and uploaded to a central node for training.These processes may compromise data privacy,cause communication delay,and incur high communication costs.With these limitations,we propose an intrusion detection method for AMI system based on federated learning(FL).The intrusion detection system is deployed in the data concentrators for training,and only its model parameters are communicated to the data center.Furthermore,the data center distributes the learning to each data concentrator through aggregation and weight assignments for collaborative learning.An optimized deep neural network(DNN)is exploited for this proposed method,and extensive experiments based on the NSL-KDD dataset are carried out.From the results,this proposed method improves detection performance and reduces computation costs,communication delays,and communication overheads while guaranteeing data privacy.