Policosanol exhibits a lipid accumulation alleviating effect, but the underlying mechanisms remains unclear. Bile acids are a significant factor in regulating cholesterol and lipid metabolism homeostasis in mammals. This study was aimed to elucidate the alleviating effect and underlying mechanisms of policosanol on hepatic lipid accumulation through bile acid (BA) metabolism. Policosanol supplementation significantly reduced hepatic triglycerides (19.29%), cholesterol (30.38%) in high fat diet (HFD) induced obese mice (P < 0.05).Furthermore, compared with the control group, HFD decreased the levels of total BAs (TBAs, 37.67%) and cholic acid (CA, 62.74%) in the serum of mice (P < 0.05). Meanwhile, compared to HFD group, policosanol also increased the level of secondary BAs (SBAs) and muricholic acids (MCAs, P < 0.05).qRT-PCR combined with protein level analysis revealed that policosanol significantly decreased sterol regulatory element-binding protein (SREBP-1c) and CD36, and increased the expression level of cytochrome P450 family 7 subfamily A member 1 (CYP7A1) and cytochrome P450 Family 27 Subfamily A Member 1 (CYP27A1, P < 0.05). Additionally, in the liver, policosanol was found downregulated the expression of farnesoid X receptor (FXR)-small heterodimer partner (SHP), and activate the Takeda G-coupled protein receptor 5 (TGR5)adenosine-monophosphate-activated protein kinase (APMK) signaling pathway (P < 0.05). Peroxisome proliferator activated receptor (PPAR)-α, hormone sensitive lipase (HSL), and carnitine palmitoyltransferase (CPT)-1α also significantly increased in HP group (P < 0.05). The aforementioned results reveal that the potential mechanism of policosanol in alleviating liver lipid accumulation is to promote BA synthesis and lipolysis through regulating the cross-talk of the 5466

The aim of the study was to investigate the regulatory effects of policosanol on hyperlipidemia, gut microbiota and metabolic status in a C57BL/6 mouse model. A total of 35 C57BL/6 mice were assigned to 3 groups, chow (n=12), high fat diet (HFD, n=12) and HFD+policosanol (n=11), then treated for 18 weeks. Policosanol supplementation significantly reduced serum triglycerides and total cholesterol, as well as the weight of brown adipose tissue (BAT) (p&lt;0.05), without affecting body weight in HFD-fed mice (p&gt;0.05). Combined 16S rRNA gene sequencing and untargeted metabolomic analysis demonstrated that policosanol had regulatory effects on gut microbiota and serum metabolism in mice. In obese mice, policosanol increased the proportion of Bacteroides, decreased the proportion of Firmicutes, and increased the ratio of Bacteroides to Firmicutes (p&lt;0.05). Policosanol promoted lipolysis and thermogenesis process, including tricarboxylic acid (TCA) cycle and pyruvate cycle, correlated with the increasing level of Bacteroides, Parasutterella, and decreasing level of Lactobacillus and Candidatus_Saccharimonas. Moreover, policosanol decreased fatty acid synthase (FAS) in the iWAT of obese mice. Policosanol also increased peroxisome proliferators-activated receptor-γ (PPARγ), uncoupling Protein-1 (UCP-1), peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) and PR domain containing 16 (PRDM16) in brown adipose tissue (BAT) obese mice (p&lt;0.05). This study presents the new insight that policosanol may inhibit the synthesis of fatty acids, and promote lipolysis, thermogenesis related gene expression and regulate gut microbiota constituents, which provides potential for policosanol as an antihyperlipidemia functional food additive and provide new evidence for whole grain food to replace refined food.

The diagnosis of sleep apnea syndrome (SAS) has important clinical significance for the prevention of hypertension, coronary heart disease, arrhythmias, stroke and other diseases. In this paper, a novel method for the detection of SAS based on single-lead Electrocardiogram (ECG) signal was proposed. Firstly, the R-peak points of ECG recordings were pre-detected to calculate RR interval series and ECG-derived respiratory signal (EDR). Then 40 time- and spectral-domain features were extracted and normalized. Finally, support vector machine (SVM) was employed to these features as a classifier to detect SAS events. The performance of the presented method was evaluated using the MIT-BIH Apnea-ECG database, results show that an accuracy of 95% in train sets and an accuracy of 88% in test sets are achievable.