ABSTRACT
OBJECTIVE：To study the improvement eff ects and its mech anism of Guiyuan decoction formula granules （GDFG） on model mice with decreased ovarian reserve （DOR）. METHODS ：Totally 42 female ICR mice whith with normal estrous cycle were randomly divided into control group ，model group ，estradiol valerate group （positive control ，0.15 mg/kg）and GDFG low-dose，medium-dose and high-dose groups （0.75，1.49，2.98 g/kg），with 7 mice in each group. Except for control group ，other groups were given cisplatin （3 mg/kg）intraperitoneally to establish DOR model. After modeling ，administration groups were given relevant medicine intragastrically；model group and control group were given normal saline intragastrically ，once a day ，for consecutive 4 weeks. After last administration ，ELISA assay was used to measure the serum levels of anti-Müllerian hormone （AMH）and follicle-stimulating hormone （FSH）in mice. Histopathological morphology of ovarian was observed by HE staining. Protein distribution of AMH receptor Ⅱ（AMHRⅡ）and Smad 4 in ovarian tissue were observed by immunohistochemistry. Protein expression of AMHR Ⅱ and Smad 4 were detected by Western blot assay. RESULTS ：Compared with control group ，theserum level of AMH ，the expression of AMHR Ⅱ and Smad 4 protein in ovarian tissue in model group were significantly decreased （P＜0.01），while the FSH level in serum was significantly increased （P＜0.01）；follicles were crumpled and lost nucleus ，ovarian interstitial were fibrosis ，luteum were loose ； AMHRⅡ and Smad 4 protein in ovarian tissue were mainly distributed in the follicle membrane and ovarian interstitial. Compared with model group ，the serum level of AMH ，the expression of AMHR Ⅱ and Smad 4 protein in ovarian tissue was increased significantly in GDFG groups （P＜0.01），while the serum level of FSH was decreased significantly （P＜0.05 or P＜0.01）；in ovarian tissue ，follicles at all levels could be found and follicle morphology was improved ，and no obvious nuclear loss and cumulus formation were found ；AMHRⅡ and Smad 4 protein were mainly distributed in the follicular nucleus （except for GDFG high-dose group) and the granular cell membrane （mainly distributed in the sinus follicles of GDFG medium-dose group ）；they were slightly distributed around the mature follicular nucleus or in corpus luteum. CONCLUSIONS ：GDFG can improve ovarian function of DOR model mice. The mechanism may be related with promoting serum level of AMH ，protein expression of AMHR Ⅱ and Smad 4，improving the distribution of AMHR Ⅱ and Smad 4 protein in ovarian granulosa cell membrane and follicular nucleus ， reducing FSH levels.
ABSTRACT
Objective To study distribution rules of TCM syndrome characteristics of menopausal syndrome; To provide references for clinical treatment and further study.Methods The articles related to menopausal syndrome in CNKI, VIP, Wanfang Data, CBM and TCMDS during Jun. 1983 to Jun. 2013 were retrieved and analyzed. The original data were input into computer and established the menopausal syndrome literature database. Methods of cluster analysis, factor analysis, and frequency analysis were combined to conduct statistical analysis.Results A total of 270 articles and 730 patients were included in the analysis, including 42 syndromes and 73 symptoms. The four diagnostic information of top 9 syndromes was analyzed by clustering and 5 kinds of basic syndrome types were obtained, including liver and kidney yin deficiency, liver depression, heart-kidney disharmony, heart and spleen deficiency, and spleen and kidney yang deficiency; the core symptoms of each syndrome were extracted by factor analysis and frequency dominance method, and the main points of syndrome differentiations were initially established. Conclusion Three methods combined with relevant expert advice were used to initially establish the syndrome diagnosis of menopause syndrome, which can lay a solid foundation for further study.
ABSTRACT
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.