Atherosclerosis (AS) is a lipid-driven chronic inflammatory disease occurring at the arterial subendothelial space. Macrophages play a critical role in the initiation and development of AS. Herein, targeted codelivery of anti-miR 155 and anti-inflammatory baicalein is exploited to polarize macrophages toward M2 phenotype, inhibit inflammation and treat AS. The codelivery system consists of a carrier-free strategy (drug-delivering-drug, DDD), fabricated by loading anti-miR155 on baicalein nanocrystals, named as baicalein nanorods (BNRs), followed by sialic acid coating to target macrophages. The codelivery system, with a diameter of 150 nm, enables efficient intracellular delivery of anti-miR155 and polarizes M1 to M2, while markedly lowers the level of inflammatory factors in vitro and in vivo . In particular, intracellular fate assay reveals that the codelivery system allows for sustained drug release over time after internalization. Moreover, due to prolonged blood circulation and improved accumulation at the AS plaque, the codelivery system significantly alleviates AS in animal model by increasing the artery lumen diameter, reducing blood pressure, promoting M2 polarization, inhibiting secretion of inflammatory factors and decreasing blood lipids. Taken together, the codelivery could potentially be used to treat vascular inflammation.

In order to avoid safety problems caused by foreign bodies such as mice that may appear in the power distribution room and by demarcating the electronic fence area for key monitoring in the video surveillance screen, a foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network is proposed. To optimize the detection effects, the YOLOv4 algorithm is improved from the aspects of network structure, frame detection, and loss function. At the same time, the channel pruning algorithm is used to prune the model to simplify the model structure. The experimental results show the effectiveness of the improved YOLOv4 deep learning network, which has high detection accuracy, fast detection speed, and takes up less space after pruning.