Oestrogen(E), oestrogen receptor (ER) and oestrogen induced proteins form a cellular signal transduction pathway. As a member of this pathway, PS2 protein has close relations with gastric cancer. To elucidate its role in the course of tumourigenesis and advancing may contribute to the insight into the tumourigenic mechanisms and signal-transduction therapy of the gastric cancer.

According to the characteristics of general surgery class teaching,methods and experiences of the application of associative approach to general surgery class teaching were analyzed,such as word,picture,contrast,comprehensive,analogical and association,which is advantageous to the improvement of class teaching methods,effects and students' study interests and ideation.

Objective To analyze the distribution and influential factors of exam results in medical students' general surgery theory course.Methods 171 medical students were selected as subjects by cluster sampling,and the distribution and influential factors of exam results were analyzed.SPSS 17.0 software was used for statistical analysis,measurement data with (-x) ± s,and normality test with Kolmogorov-Smirnov test.Those quantitative data which do not meet the normal distribution were compared with Mann-Whitney U and Kruskal-Wallis H test.Rank transformation univariate multi-factor variance of LSD (Levene test equal error variance between groups) or Tamhane method (Levene test range error variance between groups) were compared between two groups(3-4) and the influence factors of whether the grade was good was analyzed by single factor and multi factor non conditional Logistic regression model,with the test level of alpha=0.05.Results The distribution of total exam results was normal.77 score was outlier,and the scores of female students were higher than those of male students.Scores of total exam results,multiple-choice questions and essay questions in different classes were significantly different.Data from multivariate logistic regression analysis showed that male students(OR=0.212,95％CI:0.077-0.584) were unfavorable factor for good exam results,while higher scores in noun explanation (OR=12.160,95％CI:1.985-74.495),multiple-choice questions (OR=9.887,95％CI:2.997-32.617),essay questions(OR=18.323,95％CI:6.593-50.928) were favorable factors.Conclusion The cause analysis of score's outlier and sex difference should be strengthened,and the influence of examination items on score should be emphasized.

The effects of various media, pH, temperature and time course on mycelial growth (MG) of and oxalic acid accumulation (OA) by Sclerotinia sclerotiorum in the culture were studied. It was shown in an orthogonal experiment design (L_(16)4~3?2~6) that OA were significantly different (p

BACKGROUND: The concern over the use of electroencephalogram (EEG)diagnosis and biofeedback training as a viable option for children with attention deficit hyperactivity disorder(ADHD) has grown gradually.OBJECTIVE: To evaluate the diagnostic accuracy and therapeutic efficacy of A620 EEG system to ADHD children.DESIGN: Prospective observation and comparative study on ADHD children.SETTING: Psychological department of a municipal hospital.PARTICIPANTS: From October 2003 to May 2004, 113 outpatient children in Psychology Hyperactivity Department of the Central Hospital of Anshan City were selected. Inclusion criteria: 6 to 14-year-old outpatient children in Hyperactivity Department. Exclusion criteria: nervous system organic diseases, pervasive developmental disorder(PDD), mental retardation, epilepsy, psychotic disorder, acoustical and visual abnormalities,etc. Of all the ADHD children, 88 were male and 25 were female, with the mean age of(10 ± 3) years.METHODS: A620 EEG system was applied to examine the 113 participants. According to the Diagnostic Criteria for ADHD in Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-Ⅳ), published by American Psychiatry Association, ADHD children were diagnosed and then the EEG diagnostic accuracy and specificity were calculated. The System was also used for biofeedback treatment on 27 ADHD children. Fluctuation of EEG θ/β value was measured both before and after treatment, and the change of hyperactivity index was investigated by Conners Parent Symptom Questionnaire as well.MAIN OUTCOME MEASURES: EEG θ/β value, Conners index of hyperactivity (CIH).RESULTS: The diagnostic sensitivity of EEG on ADHD was 83.58%, and the specificity of it was 82.61%; EEB θ/β value of post-treatment was 8.26 ±4.05, which decreased significantly compared with pre-treatment value (12.38 ± 7.96) ( t = 2. 39, P ＜ 0.05); CIH was also dramatically declined from(24. 73 ± 10. 96) (pre-treatment) to( 12.05 ± 8.63)(post-treatment) ( t= 4.72, P ＜ 0. 01 ).CONCLUSION: EEG can be used as an accessory method in ADHD diagnosis due to its high sensitivity and specificity. Abnormalities of brainwave and hyperactivities of ADHD patients can be greatly ameliorated by EEGbiofeedback treatment.

Objective: To evaluate the efficacy of articular compression molding techniques in the surgery for acetabular posterior wall fracture. Methods: A retrospective study was conducted of the 28 patients (28 hips) with fracture of acetabular posterior wall plus comminuted compression of articular surface who had been treated using the articular compression molding techniques at Department of Orthopedics, The Hospital of 81 Group Army of PLA from January 2014 to January 2018. They were 25 males and 3 females, aged from 26 to 63 years (average, 49.3 years). The time from injury to surgery ranged from 4 to 12 days (average, 7.8 days). According to the Letournel-Judet classification, all were single element (simple) posterior wall fractures combined with posterior dislocation of the femoral head and articular compression of different degrees; transient sciatic nerve injury was complicated in 12 cases. The posterior hip dislocations were timely and successful reset at emergency treatment. The compression fractures of the posterior wall and hip joint were treated by open reduction and internal fixation via the acetabular posterior (K-L) approach. The articular compression was treated by 4 stabilization techniques: absorbable screwing and blocking technique in 10 cases, indwelling screwing in 12 cases, indwelling Kirschner wiring in 2 cases and die push and squeeze tamping in 4 cases. The operation time, intraoperative bleeding and complications were recorded. The therapeutic efficacy was evaluated by the modified Merle d’Aubigne & Postel scoring criteria at the final follow-up. Results: The operation time lasted from 76 to 118 minutes (average, 94.2 minutes); the intraoperative bleeding ranged from 100 to 320 mL (average,220.8 mL). No incision liquefaction, infection or other complications occurred. The 28 patients were followed up for 10 to 36 months (average, 17.6 months). According to the Matta scoring criteria, 24 cases achieved anatomical reduction but 4 dissatisfactory reduction. By the modified Merle d’Aubigne & Postel scoring criteria at the final follow-up, the efficacy was evaluated as excellent in 26 cases and as good in 2. Heterotopic ossification was observed in 3 cases and microscopic free dense shadow in the joint cavity (about 2 mm in the round ligament) in 3 cases. There were no cases of obvious traumatic arthritis or osteoarthritis, femoral head necrosis, walking pain, lameness or hip abduction weakness. The 12 patients complicated with preoperative transient sciatic nerve injury recovered within 3 months after surgery. Conclusion The articular compression molding techniques can effectively treat severely comminuted articular compression in the acetabular posterior wall fracture and improve reduction of articular surface and hip joint matching, leading to stability and good joint function.

Objective To construct and appraise a new model for predicting the prognosis of rectal cancer patients using the Lasso-Cox strategy.Methods The clinical pathological data of 599 rectal cancer patients who underwent radical resection were analyzed.Comparison between groups,Lasso and Cox regression were used to select varia-bles and construct a model,and its discrimination,consistency,and clinical benefits were appraised by the receiv-er operating characteristic(ROC),calibration curve,and decision curve analysis.Results Comparison between groups showed that age,body mass index(BMI),preoperational nutrition status,carbohydrate antigen199(CA199),preoperative chemotherapy,intraoperative blood transfusion,vascular or nerve invasion,cancer nod-ules,pathologic T,N,and TNM stages,tumor recurrence or metastasis,radiotherapy and postoperative survival time were associated with grouping of death or survival in rectal cancer patients.Among them,8 variables were se-lected by lasso and contained into the Cox regression model.Age(HR=1.04,P＜0.05),BMI(HR=0.89,P＜0.05),blood transfusion(HR=2.29,P＜0.05),postoperative chemotherapy(HR=0.16,P＜0.01),recur-rence(HR=43.67,P＜0.01),and metastasis(HR=2.75,P＜0.05)were identified as independent prognostic factors,which were used to construct a nomogram model.The area under the curve(AUC)and the 95％confi-dence interval of the receiver operating characteristic(ROC)curve of the predictive model was 0.95(0.91-0.99),P＜0.01.The predicted probability of 1-year and 3-year survival was close to the actual probability.The DCA curve of the model was far away from a decision line parallel to the X-axis and another line with a negative slope.Conclusion The newly established nomogram has good discrimination,consistency and clinical benefits,which help predict the prognosis of rectal cancer after surgery.

Objective:To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.Methods:The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-Ⅳ v2.0 (MIMIC-Ⅳ v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA).Results:A total of 3?295 patients with septic shock were included, with 2?164 surviving and 1?131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2?307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95% CI) was 0.795-0.849], 0.823 (95% CI was 0.796-0.849), and 0.810 (95% CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95% CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95% CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca 2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score Ⅲ (SAPSⅢ), the highest WBC, acute physiology score Ⅲ (APSⅢ), the lowest Na +, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95% CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APSⅢ score, SAPSⅢ score, and sequential organ failure assessment (SOFA) score] with AUC (95% CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. Conclusions:The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca 2+, Hb, WBC, SAPSⅢ score, APSⅢ score, Na +, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.