Objective To investigate the relationship between PCNA and nm23 expression in breast cancer tissue and its prognosis. Methods Detected the expression of PCNA and nm23 of 278 cases of breast cancer by immunohistochemical technique S-P and analyzed it combined with the clinicopathology. Results PCNA positive rate was 37.4%(104/278),nm23 positive rate was 59.7%(166/278).PCNA and nm23 genes expression had no obvious correlation with the breast cancer pathological type,patients age and clinical stage (P > 0.05). PCNA expression had the positive correlation with the axillary lymph nodes meta-sitasis but negative to 5-year survival rates(P < 0.01 ),nm23 expression had the negative correlation with the axillary lymph nodes metasitasis but positive to 5-year survival rates(P < 0.01 ). PCNA and nm23 expression had the obvious negative correlation between them(P < 0.01 ). Conclusion Combined detection the expres-sion of PCNA and nm23 in breast cancer tissue will be important reference to forcast and evaluate the prognosis of the breast cancer patients.

Objective: To know the psychological health status of the college students with guarded experience and to supply reasonable suggestion for psychological health education and consultation.Methods: SCL-90 questionairs were applied to survey the psychological health status of the college students in grade 2 and 3.Results: The psychological characteristic of the students with guarded experience involve.(1) Compared with the national college students norm,the scores of interpersonal sensitivity,depression anxiety and horror are higher(.2) Compared with the college students without guarded experience,the scores of anxiety and horror are higher.(3) Inside the students with guarded experience,the scores of interpersonal sensitivity,depression,anxiety and horror in females are higher than those in males.The above outlined have statistical significance.Conclusion: The guarded experience has positive influence on college students,psychological health,especially on females,We should enforce psychological health education,improve the psychological quality and reduce the psychological disease.

Traditionally, Tripterygium hypoglaucum (Levl.) Hutch (THH) are widely used in Chinese folk to treat rheumatoid arthritis (RA). This study aimed to investigate whether the anti-RA effect of THH is related with the gut microbiota. The main components of prepared THH extract were identified by HPLC-MS. C57BL/6 mice with adjuvant-induced arthritis (AIA) were treated with THH extract by gavage for one month. THH extract significantly alleviated swollen ankle, joint cavity exudation, and articular cartilage destruction in AIA mice. The mRNA and protein levels of inflammatory mediators in muscles and plasma indicated that THH extract attenuated inflammatory responses in the joint by blocking TLR4/MyD88/MAPK signaling pathways. THH extract remarkably restored the dysbiosis of the gut microbiota in AIA mice, featuring the increases of Bifidobacterium, Akkermansia, and Lactobacillus and the decreases of Butyricimonas, Parabacteroides, and Anaeroplasma. Furthermore, the altered bacteria were closely correlated with physiological indices and drove metabolic changes of the intestinal microbiota. In addition, antibiotic-induced pseudo germ-free mice were employed to verify the role of the intestinal flora. Strikingly, THH treatment failed to ameliorate the arthritis symptoms and signaling pathways in pseudo germ-free mice, which validates the indispensable role of the intestinal flora. For the first time, we demonstrated that THH extract protects joint inflammation by manipulating the intestinal flora and regulating the TLR4/MyD88/MAPK signaling pathway. Therefore, THH extract may serve as a microbial modulator to recover RA in clincial practice.ver RA in clincial practice.
Mice ; Animals ; Gastrointestinal Microbiome ; Tripterygium ; Myeloid Differentiation Factor 88/genetics* ; Toll-Like Receptor 4/genetics* ; Mice, Inbred C57BL ; Arthritis, Experimental/drug therapy*

Objective    To explore the application value of machine learning models in predicting postoperative survival of patients with thoracic squamous esophageal cancer. Methods    The clinical data of 369 patients with thoracic esophageal squamous carcinoma who underwent radical esophageal cancer surgery at the Department of Thoracic Surgery of Northern Jiangsu People's Hospital from January 2014 to September 2015 were retrospectively analyzed. There were 279 (75.6%) males and 90 (24.4%) females aged 41-78 years. The patients were randomly divided into a training set (259 patients) and a test set (110 patients) with a ratio of 7 : 3. Variable screening was performed by selecting the best subset of features. Six machine learning models were constructed on this basis and validated in an independent test set. The  performance of the models' predictions was evaluated by area under the curve (AUC), accuracy and logarithmic loss, and the fit of the models was reflected by calibration curves. The best model was selected as the final model. Risk stratification was performed using X-tile, and survival analysis was performed using the Kaplan-Meier method with log-rank test. Results    The 5-year postoperative survival rate of the patients was 67.5%. All clinicopathological characteristics of patients between the two groups in the training and test sets were not statistically different (P>0.05). A total of seven variables, including hypertension, history of smoking, history of alcohol consumption, degree of tissue differentiation, pN stage, vascular invasion and nerve invasion, were included for modelling. The AUC values for each model in the independent test set were: decision tree (AUC=0.796), support vector machine (AUC=0.829), random forest (AUC=0.831), logistic regression (AUC=0.838), gradient boosting machine (AUC=0.846), and XGBoost (AUC=0.853). The XGBoost model was finally selected as the best model, and risk stratification was performed on the training and test sets. Patients in the training and test sets were divided into a low risk group, an intermediate risk group and a high risk group, respectively. In both data sets, the differences in surgical prognosis among three groups were statistically significant (P<0.001). Conclusion    Machine learning models have high value in predicting postoperative prognosis of thoracic squamous esophageal cancer. The XGBoost model outperforms common machine learning methods in predicting 5-year survival of patients with thoracic squamous esophageal cancer, and it has high utility and reliability.