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Analysis on acupoint selection rules of acupuncture for Alzheimer’s disease based on complex network


To explore the acupoint selection rules of acupuncture for Alzheimer’s disease (AD) in modern clinical practice by complex network technology.

The relevant articles of clinical trials were retrieved from CNKI published before December 2017. Using Microsoft Excel 2010, the database was established. Using Gephi 0.8.2 software, the complex network mode was built and its topological structure was analyzed.

Finally, 81 articles were eligible and 114 acupoint prescriptions were extracted. The constructed complex network of acupoint prescriptions for AD was characteristics as small world effect and scale-free property, the crucial acupoints included Baihui (GV 20), Sishencong (EX-HN 1), Fengchi (GB 20), Yintang (GV 29), Shenmen (HT 7), Shenting (GV 24), Zusanli (ST 36), Fenglong (ST 40) and Taichong (LR 3). In acupoint combination, Baihui (GV 20), Neiguan (PC 6), Shenmen (HT 7) and Sanyinjiao (SP 6) were the most common, and the combination of the distal and nearby points was predominant. Using k-core for acupoint optimization, 29 core acupoints were screened and they were mostly located on the governor vessel and the head and neck, with the highest use frequency. 82.76% of acupoints were specific acupoints and the influential points were dominant. Using community structure partition, these acupoints were classified into two groups, i.e. deficiency syndrome and excess syndrome.

The selection of local acupoints is the first choice in acupuncture treatment for AD. The combination of distal and nearby points is the most common and the special points are the core. In clinical practice, the great consideration is provided on mind regulation, integration of disease and symptoms, the mutual treatment of the primary and the secondary as well as the deficiency and the excess.

Author: Yu Z, Xu JC, Qin QJ, Ni DY, Zhang YL.

Published in: Zhongguo Zhen Jiu. 2019 May 12;39(5):551-5. doi: 10.13703/j.0255-2930.2019.05.025.

Quelle: www.ncbi.nlm.nih.gov

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