The aim was to investigate the clinical characteristics of lipid metabolism and the effect of apolipoprotein E (ApoE) gene polymorphism on lipid metabolism in hemodialysis patients. The serum levels of total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC), ApoA1, ApoB, ApoE and lipoprotein Lp(a) were detected by polymerase chain reaction-restriction fragment length (PCR-RFLP). The level of serum TG was significantly increased and the level of HDLC was significantly decreased in hemodialysis patients. Serum TG level was 33% higher than normal, and HDLC was 10.4% less than normal. The correlation analysis showed that TG level was significantly correlated with serum albumin level and extracorporeal circulation blood flow during dialysis. HDLC was significantly correlated with KT/V. The incidence of hypertension in hemodialysis patients was 73.6% and cardiovascular disease was 25%. The level of TG in the cardiovascular disease group was significantly higher than that in the non-cardiovascular disease group, and there was no significant difference between the hypertensive group and the non-hypertensive group. ApoE gene polymorphism test showed that the frequency of ApoE genotype ε3/3 and allele ε3 was the highest in hemodialysis patients, and the levels of TC, TG and LDLC were higher in ApoE genotype ε3/4 + ε4/4. The levels of serum TG and ApoB were significantly increased in patients with hemodialysis, and HDLC and other indexes were significantly decreased. The level of TG in patients with cardiovascular complications was significantly higher than in patients without complications. TG level was significantly correlated with serum albumin level and extracorporeal circulation blood flow during dialysis. HDLC was significantly correlated with KT/V. Hemodialysis patients who had ApoE allele ε4 are prone to lipid metabolism disorders.

Excessive or insufficient business hall resources may result in unreasonable resource allocation, adversely affecting the value of an entity business hall. Therefore, proper characteristic parameters are the key factors for analyzing the business hall, which strongly affect the final analysis results. In this study, a characteristic analysis method for the economic operation of a business hall is developed and the feature engineering is established. Because of its simplicity and versatility, the -means algorithm has been widely used since it was first proposed around 50 years ago. However, the classical -means algorithm has poor stability and accuracy. In particular, it is difficult to achieve a suitable balance between of the centroid initialization and the clustering number . We propose a new initialization (LSH--means) algorithm for -means clustering. This algorithms is mainly based on locality-sensitive hashing (LSH) as an index for computing the initial cluster centroids, and it reduces the range of the clustering number. Furthermore, an empirical study is conducted. According to the load intensity and time change of the business hall, an index system reflecting the optimization analysis of the business hall is established, and the LSH--means algorithm is used to analyze the economic operation of the business hall. The results of the empirical study show that the LSH--means that the clustering method outperforms the direct prediction method, provides expected analysis results as well as decision optimization recommendations for the business hall, and serves as a basis for the optimal layout of the business hall.