RESUMEN
Objective To detect the immunoregulation and clinical effect ofYupingfeng capsule combined with Seretide on patients with cough variant asthma (CVA).Methods CVA Patients were randomly divided into the Seretide control group (n=54) andYupingfeng capsule combined with Seretide group (n=54). Seretide group received inhaled Seretide. Combined traditional Chinese medicine group received Seretide and Yuping Feng capsule. Two groups were treated for 12 weeks. The IL-17, IL-10 and IL-6 expression was detected by ELISA analysis. The clinic effect rate and adverse events were compared.Results After treatment, compared with the Seretide group, the expression of IL-17 (18.72 ± 4.26 ng/mlvs. 26.17 ± 5.58 ng/ml;t=2.462,P<0.05) and IL-6 (21.58 ± 4.12 ng/mlvs. 30.66 ± 6.27 ng/ml;t=2.523,P<0.05) were significantly lower in combined traditional Chinese medicine group than that in Seretide group; and IL-10 (15.56 ± 2.74 ng/mlvs. 12.25 ± 2.81 ng/ml;t=2.244, P<0.05) was significantly higher in combined traditional Chinese medicine group. The daytime (1.12 ± 0.26 vs.1.42 ± 0.33,t=2.283) and night time cough score (1.24 ± 0.28vs. 1.52 ± 0.37,t=2.291) in combined traditional Chinese medicine group was significantly lower than that in Seretide group (P<0.05). The clinic effect rate (92.6%vs. 77.8%,χ2=2.438) in combined traditional Chinese medicine group was significantly higher than that in Seretide group (P=0.037).ConclusionYupingfengcapsule combined with Seretide can decrease IL-17 expression and increase IL-10 expression to inhibit inflammatory reaction in CVA patients, and showed significantly higher clinical effect rates.
RESUMEN
Objective To establish the optimum syndrome classification method by using the technology of modern TCM diagnosis and artificial intelligence analysis method for menopausal syndrome differentiation of TCM. Methods Diagnostic information of menopausal syndrome patients was collected and syndromes were classified according to TCM syndrome differentiation standard. Three kinds of common data mining classification algorithm, Bayesian network, K-nearest neighbors and support vector machine, were used for analysis on information data of the four methods of diagnosis of menopausal syndrome.Results The time, classification accuracy, coverage rate and margin curve of establishing TCM syndrome model by the three kinds of algorithm methods under the circumstances of same training and data. The influence of the number of training samples of 3 kinds of algorithm methods was analyzed, and the model established by the three kinds of algorithms was evaluated.Conclusion Bayesian network algorithm is better than the other two methods in the menopausal syndrome classification effect.