NLRP3-dependent pyroptosis is required for HIV-1 gp120- induced neuropathology

The human immunodeficiency virus-1(HIV-1)envelope protein gp120 is the major contributor to the pathogenesis of HIVassociated neurocognitive disorder(HAND).Neuroinflammation plays a pivotal role in gp120-induced neuropathology,but how gp120 triggers neuroinflammatory processes and subsequent neuronal death remains unknown.Here,we provide evidence that NLRP3 is required for gp120-induced neuroinflammation and neuropathy.Our results showed that gp120-induced NLRP3-dependent pyroptosis and IL-1βproduction in microglia.Inhibition of microglial NLRP3 inflammasome activation alleviated gp120-mediated neuroinflammatory factor release and neuronal injury.Importantly,we showed that chronic administration of MCC950,a novel selective NLRP3 inhibitor,to gp120 transgenic mice not only attenuated neuroinflammation and neuronal death but also promoted neuronal regeneration and restored the impaired neurocognitive function.In conclusion,our data revealed that the NLRP3 inflammasome is important for gp120-induced neuroinflammation and neuropathology and suggest that NLRP3 is a potential novel target for the treatment of HAND.

RIS-SWIPT for Batteryless Users in Disaster Areas

For trapped users in disaster areas,the available energy of affected user equipment(UE)is limited due to the breakdown of the ground power system.When complex geographical condition prevents ground emergency vehicles from reaching disaster-stricken areas,unmanned aerial vehicle(UAV)can effectively work as a temporary aerial base station for serving terrestrial trapped users.Simultaneous wireless information and power transfer(SWIPT)system is intriguing for distributed batteryless users(BUs)by transferring data and energy simultaneously.However,how to achieve the maximum energy efficiency(EE)and energy transfer efficiency(ETE)for distributed BUs in UAV-enabled SWIPT systems is not very clear.In this paper,we develop three novel reconfigurable intelligent surface(RIS)-based SWIPT algorithms to solve this nonconvex joint optimization problem using deep reinforcement learning(RL)algorithms.Through the deployment of RIS-assisted UAVs,we aim to maximize the EE along with the ETE via jointly designing the UAV trajectory,the phase matrix,and the power splitting ratio within strict time and energy constraints.The obtained numerical results show that our developed RL-based algorithms can effectively improve the cost time,the average charging rate,data rate,and the EE/ETE performance of the RIS-assisted SWIPT systems as compared with benchmark solutions.