Doganchen6781

1, 14.8 and 11.5 mmHg (p = 0.25). Portocaval gradients measured after right portal vein clamping in the 3 patients were respectively of 10, 11 and 7 mmHg while the simulated gradients were of 9.9, 11.6 and 8.3 mmHg (p = 0.5). INTERPRETATION We succeeded to predict portal vein pressures and portocaval gradients after RAPID. This promising report demonstrates that 0D simulation could be a useful tool for human decision-making. Moreover, such a patient-specific model could be of importance if we transpose RAPID experience to hepatocellular carcinoma bearing cirrhotics, a population with high probability of portal hypertension after RAPID. Shuganjieyu capsule (Shugan) is a combined extract of Hypericum perforatum (HP) and Eleutherococcus senticosus (ES). Both HP and ES have been proven effective in the treatment of depression and impaired cognition. However, for mild to moderate depression (MMD), the treatment effect and underlying mechanism by combining both HP and ES are largely unknown. Protoporphyrin IX chemical structure Here, we aim to evaluate the therapeutic effects on impaired cognition using Shugan, a combined medication of HP and ES. Resting-state magnetic resonance imaging (MRI) data and cognitive assessment have been collected from 54 healthy controls and 55 MMD patients that have been undergoing 8-week Shugan-treatment. The functional connectivity (FC) and brain region volume changes of the basal ganglia seeded circuit have been measured, and their relation with the cognitive assessment score was calculated. After that, a literature-based pathway analysis has been conducted to explore the biological relations among Shugan, brain regions, and depression. Compared to healthy controls, MMD patients demonstrated a significantly higher FC (P= 0.0025) between right ventral caudate (vCa) and left orbitofrontal cortex (OFC), which was decreased after the treatment (P  less then  0.001). A volume of the right caudate, which is increased in MMD, has also been reduced by Shugan treatment (P= 0.017). Importantly, the cognitive scores were strongly correlated with both Shugan treatment and the FC between vCa and OFC (r= 0.321, P= 0.02). Besides, we identified multiple signaling pathways, through which Shugan might improve the cognition of MMD patients. Our results support the therapeutic effects of Shugan on cognition in MMD, which may be realized partly through the regulation within two brain regions, vCa and OFC. Deformable image registration is a fundamental problem in the field of medical image analysis. During the last years, we have witnessed the advent of deep learning-based image registration methods which achieve state-of-the-art performance, and drastically reduce the required computational time. However, little work has been done regarding how can we encourage our models to produce not only accurate, but also anatomically plausible results, which is still an open question in the field. In this work, we argue that incorporating anatomical priors in the form of global constraints into the learning process of these models, will further improve their performance and boost the realism of the warped images after registration. We learn global non-linear representations of image anatomy using segmentation masks, and employ them to constraint the registration process. The proposed AC-RegNet architecture is evaluated in the context of chest X-ray image registration using three different datasets, where the high anatomical variability makes the task extremely challenging. Our experiments show that the proposed anatomically constrained registration model produces more realistic and accurate results than state-of-the-art methods, demonstrating the potential of this approach. BACKGROUND Mental disorders, according to the definition of World Health Organization, consist of a wide range of signs, which are generally specified by a combination of unusual thoughts, feelings, behavior, and relationships with others. Social anxiety disorder (SAD) is one of the most prevalent mental disorders, described as permanent and severe fear or feeling of embarrassment in social situations. Considering the imprecise nature of SAD symptoms, the main objective of this study was to generate an intelligent decision support system for SAD diagnosis, using Adaptive neuro-fuzzy inference system (ANFIS) technique and to conduct an evaluation method, using sensitivity, specificity and accuracy metrics. METHOD In this study, a real-world dataset with the sample size of 214 was selected and used to generate the model. The method comprised a multi-stage procedure named preprocessing, classification, and evaluation. The preprocessing stage, itself, consists of three steps called normalization, feature selection, and anomaly detection, using the Self-Organizing Map (SOM) clustering method. The ANFIS technique with 5-fold cross-validation was used for the classification of social anxiety disorder. RESULTS AND CONCLUSION The preprocessed dataset with seven input features were used to train the ANFIS model. The hybrid optimization learning algorithm and 41 epochs were used as optimal learning parameters. The accuracy, sensitivity, and specificity metrics were reported 98.67%, 97.14%, and 100%, respectively. The results revealed that the proposed model was quite appropriate for SAD diagnosis and in line with findings of other studies. Further research study addressing the design of a decision support system for diagnosing the severity of SAD is recommended. V.Principal component analysis (PCA) is a popular statistical tool. However, despite numerous advantages, the good practice of imputing missing data before PCA is not common. In the present work, we evaluated the hypothesis that the expectation-maximization (EM) algorithm for missing data imputation is a reliable and advantageous procedure when using PCA to derive biomarker profiles and dietary patterns. To this aim, we used numerical simulations aimed to mimic real data commonly observed in nutritional research. Finally, we showed the advantages and pitfalls of the EM algorithm for missing data imputation applied to plasma fatty acid concentrations and nutrient intakes from real data sets deriving from the US National Health and Nutrition Examination Survey. PCA applied to simulated data having missing values resulted in biased eigenvalues with respect to the original data set without missing values. The bias between the eigenvalues from the original set of data and from the data set with missing values increased with number of missing values and appeared as independent with respect to the correlation structure among variables.