Abstract

To explore the feasibility and application value of combination regularities of acupoint Houxi (SI3) in Chinese ancient times based on latent structure model. Relevant articles about SI3 for treating various diseases with acupuncture, moxibustion, acupoint application, etc. were mainly searched from book Chinese Medical Classics (5th edition), followed by establishment of a Database of Houxi Acupoint Recipes. The Lantern 5.0 software was used to construct and analyze the latent structure model of high-frequently-used acupoints. A total of 46 high frequently-used acupoints contained in 240 articles of 26 medical books were collected. The top 7 acupoints are Shenmai (BL62), Hegu (LI4), Qiangu (SI2), Fengchi (GB20), Jianshi (PC5), Wangu (SI4) and Quchi (LI11) in sequence. After modeling the 46 high-frequently used adjunct acupoints, 12 latent variables (Y0-Y11) and 24 latent classes were obtained by setting the cumulative coverage threshold ratio to be 95%. According to the Bayesian information criterion (BIC) measure, the model score was -2 170.68 points. Seven comprehensive clustering models were summarized up according to the latent structure. Compared with the yin meridians, the yang meridians played a more significant role. The multiple combinations of SI3 with specific acupoints provided a reference for clinical practice. The supplementary acupoints mainly distribute in the upper and lower limbs, head, face, neck, etc. and the SI3 acupoint recipes function mainly in dredging and activating meridians and collaterals, clearing away pathologic heat and wind, improving eyesight, and relieving swelling and pain. The latent structure model is applicable in analysis of the regularities of SI3 acupoint combination for treating some diseases. Comprehensive clustering is employed to determine the primary acupoint SI3 and adjunct acupoint matching, revealing the common regularity and logical progressive relationship between the primary and secondary points, which may be helpful for teaching, clinical and scientific research.

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