Probiotics combined with atorvastatin administration in the treatment of hyperlipidemia: A randomized, double-blind, placebo-controlled clinical trial.

BACKGROUND: Hyperlipidemia is a common feature of chronic diseases. The aim of this work was designed to assess the role of probiotics (Lactobacillus casei Zhang, Bifidobactetium animalis subsp. lactis V9, and Lactobacillus plantarum P-8) in the treatment of hyperlipidemia. METHODS: Thirty three patients with hyperlipidemia were randomly divided into a probiotic group (n = 18) and a control group (n = 15). The probiotic group was administered probiotics (2 g once daily) and atorvastatin 20 mg (once daily), and the control group was administered a placebo (2 g once daily) and atorvastatin 20 mg (once daily). Serum and fecal samples were gathered for subsequent analyses. RESULTS: Time had a significant effect on the total cholesterol (TC), triglycerides (TG), and low-density lipoprotein-cholesterol (LDL-C) levels in the probiotic and control groups (P < .05). The gut microbial abundance in the probiotic group was markedly higher than that in the control group following 3-month probiotic treatment (P < .05). At the phylum level, probiotics exerted no notable effects on the relative abundance of Firmicutes, Bacteroidetes, and Actinobacteria but elevated that of Tenericutes and reduced Proteobacteria. At the genus level, probiotics increased the relative abundance of Bifidobacterium, Lactobacillus, and Akkermansia, and decreased that of Escherichia, Eggerthella, and Sutterella relative to the control group in months 1, 2, and 3 (P < .05). CONCLUSIONS: Probiotics optimize the gut microbiota structure and decrease the amount of harmful bacteria in patients with hyperlipidemia. Probiotics can influence the composition of gut microorganisms and increase their diversity and abundance in vivo. It is recommended to use probiotics combined with atorvastatin to treat patients with hyperlipidemia.

Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox.

This paper introduces a fault diagnosis method for mine scraper conveyor gearbox gears using motor current signature analysis (MCSA). This approach solves problems related to gear fault characteristics that are affected by coal flow load and power frequency, which are difficult to extract efficiently. A fault diagnosis method is proposed based on variational mode decomposition (VMD)-Hilbert spectrum and ShuffleNet-V2. Firstly, the gear current signal is decomposed into a series of intrinsic mode functions (IMF) by using VMD, and the sensitive parameters of VMD are optimized by using a genetic algorithm (GA). The Sensitive IMF algorithm judges the modal function sensitive to fault information after VMD processing. By analyzing the local Hilbert instantaneous energy spectrum for fault-sensitive IMF, an accurate expression of signal energy changing with time is obtained to generate the local Hilbert immediate energy spectrum dataset of different fault gears. Finally, ShuffleNet-V2 is used to identify the gear fault state. The experimental results show that the accuracy of the ShuffleNet-V2 neural network is 91.66% after 778 s.