OBJECTIVE: To analyze the rules of moxibustion for low back pain by ZHOU Meisheng by using data mining and knowledge graph technology. METHODS: Taking the medical cases of moxibustion for low back pain from ZHOU Meisheng's legacy manuscripts and existing works as the research objects, information on disease types, symptoms, tongue manifestations, pulse conditions, syndrome patterns, moxibustion methods and acupoints were collected. Frequency statistics and community analysis were conducted by the ancient and modern medical record cloud platform V 2.3.7, cluster analysis of high-frequency acupoints was performed by SPSS26.0, association rule analysis of high-frequency acupoints was performed by SPSS Modeler 18.0, and the generated linked data were imported into Cytoscape 3.9.1 for complex network analysis. Knowledge graph of moxibustion for low back pain by ZHOU Meisheng was constructed based on the results of data mining. The data storage and display of knowledge graph were realized through the Neo4j 3.5.25 graph database, and the Cypher query language was used for knowledge graph retrieval and discovery. RESULTS: A total of 219 medical cases were collected, involving 14 disease types, 85 related clinical symptoms, 5 related TCM syndrome types, and 6 moxibustion methods. The acupoints were mostly attributed to the governor vessel, the bladder meridian of foot-taiyang, non-meridian and non-acupoint areas. The core prescription of acupoints derived from complex network analysis included tender points, Shenshu (BL23), Jinsuo (GV8), Yinjiao (CV7), Yaoyangguan (GV3), Yanglingquan (GB34), which were largely coincides with high-frequency acupoints. Cluster analysis obtained 4 cluster combinations. Community analysis yielded 6 communities, each corresponding to different acupoints.The constructed knowledge graph contained 187 nodes and 696 relationships, by retrieving clinical elements related to low back pain, the disease-moxibustion association graph, disease-acupoint association graph, accompanying symptom-acupoint association graph and syndrome type-matching point association graph were obtained. CONCLUSION When treating low back pain with moxibustion, ZHOU Meisheng adopts the principle of promoting circulation, distinguishing diseases to determine the treatment, selecting acupoints according to the diseases, and matching points according to the symptoms.And taking tender points, Shenshu (BL23), Jinsuo (GV8), Yinjiao (CV7), Yaoyangguan (GV3), Yanglingquan (GB34) as core acupoints, combined with tenderness point selection, acupoint selection based on meridian and zangfu syndrome differentiation, "sunshine area" acupoint selection, yin-yang acupoint matching. Additionally, he skillfully employs special points, such as Zhongzhu (KI15) and ear tips, pays attention to the reform of moxibustion tools, and innovates the moxibustion techniques, using distinctive moxibustion tools and methods to treat low back pain.
Moxibustion/methods* ; Humans ; Data Mining ; Low Back Pain/history* ; Acupuncture Points ; History, Ancient ; Female ; China ; Male ; Adult ; Middle Aged

Objective:To explore the therapeutic law of moxibustion in Professor Zhou Meisheng's medical manuscripts for epidemic hemorrhagic fever (EHF) based on data mining and knowledge map technology.Methods:The manuscript data of Professor Zhou Meisheng's moxibustion treatment of EHFwere collected from Infectious Diseases Department of Dangshan County People's Hospital from December 16, 1985 to December 25, 1987. Graphpad Grism 8.0 software was used for descriptive analysis. PHP 5.4 program code was used for association rule analysis. SPSS Statistics 26.0 was used for clustering analysis. Neo4j Community 3.5.25 database was used to analyze the syndrome-weight graph.Results:205 prescriptions were included. There were 21 symptoms with frequency>40, in which the frequency of aversion to cold, fever, rash and irritability was 100%. The main types of moxibustion methods used in the treatment included moxibustion frame fumigation moxibustion, Wanying acupoint moxibustion pen moxibustion, and fire needle instead of moxibustion. There were 29 acupoints with a frequency of >25, including Zhongwan (CV12), Shenshu (BL23) and Mingmen (DU4), etc. Association rules showed that Sanyinjiao (SP6)-Zhongwan (CV12)-Feishu (BL13)-Shenshu (BL23)-Zhiyang (DU9) had the highest correlation. Six effective clustering combinations of moxibustion for EHF were summarized by clustering analysis. The weight graph can obtained the first 30 relationships with high correlation of target syndromes.Conclusions:Professor Zhou applied the idea of "moxibustion for heat syndrome" to the treatment of EHF, and took the method of "acupoint selection according to symptoms" as the main acupoint selection idea for moxibustion treatment of EHF. In clinical practice, moxibustion combined with auxiliary operation of TCM is often used to treat EHF, which can achieve good results.

Objective To investigate the association between body mass index (BMI), waist circumference (WC), waist?to?height ratio (WHtR), and the incidence risk of type 2 diabetes mellitus (T2DM). Methods In total, 20 194 participants≥18 years old were selected randomly by cluster sampling from two township (town) of the county in Henan province from July to August of 2007 and July to August of 2008 and the investigation included questionnaires, anthropometric measurements, fasting plasma glucose,and lipid profile examination were performed at baseline; 17 236 participants were enrolled in this cohort study. 14 720 (85.4%) were followed up from July to August 2013 and July to October 2014. Finally, 11 643 participants (4 301 males and 7 342 females) were included in this study. Incidence density and Cox proportional hazards regression models were used to evaluate the risk of T2DM associated with baseline BMI, WC, WHtR, and their dynamic changes. Results After average of 6.01 years following up for 11 643 participants, 613 developed T2DM and the incidence density was 0.89 per 100 person?years. After adjusted for baseline sex, age, smoking, drinking, family history of diabetes, as well as the difference of fasting plasma?glucose (FPG), total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL?C), systolic blood pressure (SBP), diastolic blood pressure (DBP) between baseline and follow?up, Cox Proportional?Hazards regression analysis indicated that T2DM risk of baseline BMI overweight group, BMI obesity group, abnormal WC group and abnormal WHtR group were significantly higher than that of the corresponding baseline normal groups , and the incidence risk of T2DM reached the highest for those whose baseline BMI, WC and WHtR were all abnormal, the corresponding HR (95%CI) were 2.05 (1.62-2.59), 3.01 (2.33-3.90), 2.34 (1.89-2.90), 2.88 (2.21-3.74), 3.32 (2.50-4.40), respectively. Whether baseline BMI/WC was normal or not, T2DM risk increased if baseline WHtR was abnormal, and the HR (95%CI) of baseline normal BMI/abnormal WHtR group, baseline abnormal BMI/abnormal WHtR group, baseline normal WC/abnormal WHtR group, baseline abnormal WC/abnormal WHtR group were 1.88 (1.29-2.74), 3.08 (2.34-4.05), 2.15 (1.53-3.00), 3.22 (2.45-4.23), respectively. The analysis for dynamic changes of BMI, WC, and WHtR indicated that in baseline normal WC or WHtR group, T2DM risk increased when baseline normal WC or WHtR developed abnormal at follow?up, and the corresponding HR (95%CI) were 1.79 (1.26-2.55), 2.12 (1.32-3.39), respectively. In baseline abnormal WC or WHtR group, T2DM risk decresed when baseline abnormal WC or WHtR reversed to normal at follow?up, and the corresponding HR (95%CI) were 2.16 (1.42-3.29), 2.62 (1.63-4.20), respectively. Conclusion BMI, WC, and WHtR were associated with increased T2DM risk. The more abnormal aggregation of BMI, WC, and WHtR presents, the higher T2DM risk was. T2DM risk could be decreased when abnormal WC or WHtR reversed to normal.

Objective To investigate the association between body mass index (BMI), waist circumference (WC), waist?to?height ratio (WHtR), and the incidence risk of type 2 diabetes mellitus (T2DM). Methods In total, 20 194 participants≥18 years old were selected randomly by cluster sampling from two township (town) of the county in Henan province from July to August of 2007 and July to August of 2008 and the investigation included questionnaires, anthropometric measurements, fasting plasma glucose,and lipid profile examination were performed at baseline; 17 236 participants were enrolled in this cohort study. 14 720 (85.4%) were followed up from July to August 2013 and July to October 2014. Finally, 11 643 participants (4 301 males and 7 342 females) were included in this study. Incidence density and Cox proportional hazards regression models were used to evaluate the risk of T2DM associated with baseline BMI, WC, WHtR, and their dynamic changes. Results After average of 6.01 years following up for 11 643 participants, 613 developed T2DM and the incidence density was 0.89 per 100 person?years. After adjusted for baseline sex, age, smoking, drinking, family history of diabetes, as well as the difference of fasting plasma?glucose (FPG), total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL?C), systolic blood pressure (SBP), diastolic blood pressure (DBP) between baseline and follow?up, Cox Proportional?Hazards regression analysis indicated that T2DM risk of baseline BMI overweight group, BMI obesity group, abnormal WC group and abnormal WHtR group were significantly higher than that of the corresponding baseline normal groups , and the incidence risk of T2DM reached the highest for those whose baseline BMI, WC and WHtR were all abnormal, the corresponding HR (95%CI) were 2.05 (1.62-2.59), 3.01 (2.33-3.90), 2.34 (1.89-2.90), 2.88 (2.21-3.74), 3.32 (2.50-4.40), respectively. Whether baseline BMI/WC was normal or not, T2DM risk increased if baseline WHtR was abnormal, and the HR (95%CI) of baseline normal BMI/abnormal WHtR group, baseline abnormal BMI/abnormal WHtR group, baseline normal WC/abnormal WHtR group, baseline abnormal WC/abnormal WHtR group were 1.88 (1.29-2.74), 3.08 (2.34-4.05), 2.15 (1.53-3.00), 3.22 (2.45-4.23), respectively. The analysis for dynamic changes of BMI, WC, and WHtR indicated that in baseline normal WC or WHtR group, T2DM risk increased when baseline normal WC or WHtR developed abnormal at follow?up, and the corresponding HR (95%CI) were 1.79 (1.26-2.55), 2.12 (1.32-3.39), respectively. In baseline abnormal WC or WHtR group, T2DM risk decresed when baseline abnormal WC or WHtR reversed to normal at follow?up, and the corresponding HR (95%CI) were 2.16 (1.42-3.29), 2.62 (1.63-4.20), respectively. Conclusion BMI, WC, and WHtR were associated with increased T2DM risk. The more abnormal aggregation of BMI, WC, and WHtR presents, the higher T2DM risk was. T2DM risk could be decreased when abnormal WC or WHtR reversed to normal.