The effective constituent puerarin, from Pueraria lobata, inhibits the proliferation and inflammation of vascular smooth muscle in atherosclerosis through the miR-29b-3p/IGF1 pathway.

CONTEXT: Atherosclerosis (AS) is the main cause of cardiovascular and cerebrovascular diseases. Pueraria lobata (Willd.) Ohwi (Fabaceae) has a positive effect on improving these diseases. OBJECTIVE: The P. lobata effect on the proliferation and inflammation of vascular smooth muscle in AS and the potential mechanism were investigated. MATERIALS AND METHODS: By feeding a high-fat diet to 8-week-old apolipoprotein E knockout mice, an atherosclerosis model was created. H&E and IHC staining were used to analyse the histopathology of mice. CCK-8, TUNEL, and scratch tests were used to detect cell proliferation, apoptosis, and migration after 24 h treatment, respectively. ELISA was performed to evaluate the level of IL-6 and IL-8. The target miRNA and its downstream target gene were screened by the bioinformatics method; RT-qPCR has conducted to analyse the expression of these genes. RESULTS: In the aortic tissue and serum of AS mice, puerarin can lower the expression of α-SMA and the inflammatory proteins IL-6 and IL-8. Puerarin (200 M) decreased hVSMC proliferation, migration, and IL-6 and IL-8 secretion by more than half. The inhibitory impact of puerarin on hVSMC was decreased by overexpression of miR-29b-3p. IGF1 was miR-29b-3p's downstream target gene. IGF1 expression increased almost 3-fold in AS mice and hVSMC, but miR-29b-3p mimic inhibited it. The effect of miR-29b-3p on hVSMC was reversed when IGF1 was overexpressed. DISCUSSION AND CONCLUSIONS: Puerarin inhibits the proliferation and inflammation of vascular smooth muscle in AS through the miR-29b-3p/IGF1 pathway. Puerarin may have a beneficial effect in the treatment of atherosclerosis and offer a novel therapy option.

Blockchain and K-Means Algorithm for Edge AI Computing.

The current development of blockchain, technically speaking, still faces many key problems such as efficiency and scalability issues, and any distributed system faces the problem of how to balance consistency, availability, and fault tolerance need to be solved urgently. The advantage of blockchain is decentralization, and the most important thing in a decentralized system is how to make nodes reach a consensus quickly. This research mainly discusses the blockchain and K-means algorithm for edge AI computing. The natural pan-central distributed trustworthiness of blockchain provides new ideas for designing the framework and paradigm of edge AI computing. In edge AI computing, multiple devices running AI algorithms are scattered across the edge network. When it comes to decentralized management, blockchain is the underlying technology of the Bitcoin system. Due to its characteristics of immutability, traceability, and consensus mechanism of transaction data storage, it has recently received extensive attention. Blockchain technology is essentially a public ledger. This is done by recording data related to trust management to this ledger. To collaboratively complete artificial intelligence computing tasks or jointly make intelligent group decisions, frequent communication is required between these devices. By integrating idle computing resources in an area, a distributed edge computing platform is formed. Users obtain benefits by sharing their computing resources, and nodes in need complete computing tasks through the shared platform. In view of the identity security problems faced in the sharing process, this article introduces blockchain technology to realize the trust between users. All participants must register a secure identity in the blockchain network and conduct transactions in this security system. A K-means algorithm suitable for edge environments is proposed to identify different degradation stages of equipment operation reflected by multiple types of data. Based on the prediction of the fault state for a single type of data, the algorithm uses the historical data of multiple types of data together with the prediction data to predict the fault stage. During the research process, the average optimization energy consumption of K-means algorithm is 14.6% lower than that of GA. On the basis of designing a resource allocation scheme based on blockchain, the problem of how the participants can realize reliable resource use according to the recorded data on the chain is studied. The article implements the verification of the legality of the use of blockchain resources. In addition, a control node is introduced to master the global real-time information of the network to provide data support for the user's choice.