Search

To observe the therapeutic effect of low-dose amitriptyline (AMT) on epigastric pain syndrome (EPS) in patients with functional dyspepsia. Sixty patients with EPS were randomly divided into the following two groups for a four-week clinical trial: routine treatment with pantoprazole (RT group) and the AMT group. The RT group was treated with 40 mg of pantoprazole once daily. The AMT group received 25 mg of AMT once daily before bedtime. The Nepean Dyspepsia Index (NDI) checklist, Hamilton Rating Scale of Anxiety/Depression (HAMA/HAMD), and Pittsburgh Sleep Quality Index (PSQI) were employed to evaluate dyspepsia symptoms, psychological distress, and sleep, respectively. All items were similar between the two groups before treatment (0 week). After 4 weeks of treatment, the NDI–symptom checklist score as well as the severity and bothersomeness of EPS in the AMT group was significantly decreased compared with those in the RT group (p

Objective The aim of this study was to evaluate the predictive factors of central lymph node metastasis (CLNM) and BRAFV600E mutation in Chinese patients with papillary thyroid carcinoma (PTC). Methods A total of 943 PTC patients who underwent thyroidectomy from 2014 to 2016 at our hospital were enrolled. Those patients were divided into PTC > 10 mm and papillary thyroid microcarcinoma (PTMC) groups by tumor size. The BRAFV600E mutation was examined by quantitative real-time PCR. Univariate and multivariate analyses were used to examine risk factors associated with CLNM and the BRAFV600E mutation. Results The frequency of CLNM was 53% (505/943). Both univariate and multivariate analyses suggested that the risk factors for CLNM in PTC patients were male, younger age, and larger tumor size (P < 0.05). Coexistent Hashimoto thyroiditis (HT) was an independent protective factor against CLNM when the tumor was > 10 mm (P = 0.006). Stratified analysis revealed that male, age ≤ 30 years, and tumor size > 5 mm were independent risk factors for CLNM. The BRAFV600E mutation rate was 85%. Multivariate logistic regression analysis revealed that age (P < 0.001) and coexistent HT (P = 0.005) were independent predictive factors of BRAFV600E mutation in PTC patients. Only age was a risk factor for the BRAFV600E mutation when the tumor was > 10 mm (P = 0.004). In the PTMC group, the BRAFV600E mutation was significantly correlated with tumor size (P < 0.001) and coexistent HT (P = 0.03). Stratified analysis revealed that age > 30 years and tumor size > 5 mm were independent predictive factors of BRAFV600E mutation. Furthermore, the incidence of CLNM was significantly higher in BRAFV600E mutation-positive patients (P = 0.009) when the tumor was ≤ 5 mm. Conclusion The factors male, younger age (≤ 30 years), large tumor size (> 5 mm), and coexistent HT are independent predicative factors for CLNM. The BRAFV600E mutation is associated with both large size and without HT in PTMC patients, age > 30 years in the PTC > 10 mm group. The BRAFV600E mutation was an independent risk factor for CLNM when the tumor was ≤ 5 mm. For optimal management, these features should be comprehensively evaluated to determine the initial surgical approach for PTC patients.

In order to describe the characteristics of dynamic traffic flow and improve the robustness of its multiple applications, a dynamic traffic temporal-spatial model (DTTS) is established. With consideration of the temporal correlation, spatial correlation and historical correlation, a basic DTTS model is built. And a three-stage approach is put forward for the simplification and calibration of the basic DTTS model. Through critical sections pre-selection and critical time pre-selection, the first stage reduces the variable number of the basic DTTS model. In the second stage, variable coefficient calibration is implemented based on basic model simplification and stepwise regression analysis. Aimed at dynamic noise estimation, the characteristics of noise are summarized and an extreme learning machine is presented in the third stage. A case study based on a real-world road network in Beijing, China, is carried out to test the efficiency and applicability of proposed DTTS model and the three-stage approach.

In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm (HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages (MSA), the middle level model is solved by non-dominated sorting genetic algorithm II (NSGA II), and the upper level model is solved by genetic algorithm (GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.

The support vector classification (SVC) was employed to make a model for classification of antifungal activities of 1-(1H-1,2,4-triazole-1-yl)-2-(2,4-difluorophenyl)-3-substituted-2-propanols triazole derivatives. The compounds with high antifungal activities and those with low antifungal activities were compared on the basis of the following molecular descriptors: net atomic charge on the atom N connecting with R, dipole moment and heat of formation. By using the SVC, a mathematical model was constructed, which can predict the antifungal activities of the triazole derivatives, with an accuracy of 91% on the basis of the leave-one-out cross-validation (LOOCV) test. The results indicate that the performance of the SVC model can exceed that of the principal component analysis (PCA) and K-Nearest Neighbor (KNN) models for this real world data set.