We examined the behavioral effects of i.c.v. microinfusions of different doses of selective agonists of each of the five somatostatin

receptor subtypes. Their behavioral effects were assessed in the elevated plus-maze and the forced swim apparatus, rodent models of anxiolytic and antidepressant drug effects, respectively.

Anxiety-like behavior was reduced following i.c.v. selleck compound infusions of a selective sst2 receptor agonist, but not after infusions of the other four receptor agonists. An antidepressant-like effect was observed following infusions of either sst2 or sst3 agonists.

The results add to our nascent understanding of the role of somatostatin in anxiety- and depression-like behavior and suggest a clinical role for somatostatin agonists for the simultaneous GSK621 treatment of anxiety and depression, which are often comorbid.”
“Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed

for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly

abnormal nodal centralities, including the limbic system, basal ganglia, LGX818 medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (P < 0.05). Correlation analysis between feature importance and the statistical significance of metrics was investigated, and the results revealed a strong positive correlation between them. Overall, the current study demonstrated that major depressive disorder is associated with abnormal functional brain network topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms. NeuroReport 23:1006-1011 (C) 2012 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins.”
“Coeliac disease (CD) is a malabsorptive enteropathy resulting from intolerance to gluten. Environmental factors and the microbiota are suggested to have critical roles in the onset of CD.