Although some studies have reported the potential efficacy of electroconvulsive therapy (ECT) in the treatment of acute mania, there is no consensus on the matter. Therefore, we performed a meta-analysis to determine the efficacy and safety of ECT combination with medication (ECT-combo) vs. medication alone (Med-alone) in the treatment of acute mania. Randomized controlled trials (RCTs) of ECT-combo versus Med-alone in acute mania were searched in Chinese databases and English databases from their inceptions up to February 2020. Twelve RCTs (including 863 patients, n=863) met our criteria and were included into meta-analysis. The pooled results found that ECT-combo outperformed Med-alone in reducing manic symptoms from baseline to endpoint with a standardized mean difference of -3.50 (95% CI -4.57, -2.44, p less then 0.00001).  https://www.selleckchem.com/products/reparixin-repertaxin.html  The significant difference occurred after 3-5 treatments or after a 1-week treatment. ECT-combo had significantly increased memory impairment compared to Med-alone. Apart from increased memory impairment in ECT-combo group (SMD=8.33; 95% CI 2.73 to 25.45, p= 0.0002), no other statistically significant differences in side effects or drop-out rates were found between groups. The results of this meta-analysis suggest that ECT-combo was significantly superior to Med-alone in efficacy and well-tolerated as Med-alone in the acute treatment of mania. However, larger studies with randomized, double-blind design, and standardized treatment regimens are still warranted due to the high heterogeneity of studies included in the present meta-analysis.NMR relaxation dispersion experiments have been widely applied to probe important conformational exchange of macro-molecules in many biological systems. The current improvements in computational techniques as well as the theoretical breakthroughs make the quantitative data analysis of complex exchange models possible. However, the topology of a given exchange model is also one of the main factors affecting the solution of Bloch-McConnell equation. The lack of a theoretical analysis of the exchange topologies at n-site exchange hinders further progress of such data analysis. Here, using graph theory, we reveal the topological complexity of n-site exchange and present all exchange models when n is less than 6. Furthermore, we introduce an alternative way, using machine learning, to select an exchange model based on a set of relaxation dispersion data without fitting them with every individual exchange model.Magnetic resonance T1-T2* relaxation correlation is a newly emerging and powerful tool to study the structure and dynamics of materials. However, the T1-T2* of solid-like materials may consist of a linear combination of exponential decays and non-exponential decays, and the traditional methods for processing T1-T2 data would be not applicable. In this paper, a method of processing T1-T2* data with non-exponential decays was proposed. The critical idea is to decompose the data into two sub-datasets, exponential decays and non-exponential decays, employing a non-linear fitting method, and then to invert the sub-datasets and to combine the inversion results. We also introduce a related relaxation correlation measurement, T1ρ-T2*, for examination of solid-like materials. The same data processing strategy as for T1-T2* was implemented. The effectiveness of the proposed method for processing non-exponential data, Sinc Gaussian and Gaussian decay, was validated with simulation and experiment. The results showed that the proposed method recovers T1-T2* and T1ρ-T2* spectra with accurate relative signal intensities. The proposed method provides a platform for further development of MR methods applied to solid-like materials. These relaxation correlations are well suited to measuring composition of mixtures, with solid components in the mixture.Total variation (TV) minimization algorithm is an effective algorithm capable of accurately reconstructing images from sparse projection data in a variety of imaging modalities including computed tomography (CT) and electron paramagnetic resonance imaging (EPRI). The data divergence constrained, TV minimization (DDcTV) model and its Chambolle-Pock (CP) solving algorithm have been proposed for CT. However, when the DDcTV-CP algorithm is applied to 3D EPRI, it suffers from slow convergence rate or divergence. We hypothesize that this is due to the magnitude imbalance between the data fidelity term and the TV regularization term. In this work, we propose a balanced TV (bTV) model incorporating a balance parameter and demonstrate its capability to avoid convergence issues for the 3D EPRI application. Simulation and real experiments show that the DDcTV-CP algorithm cannot guarantee convergence but the bTV-CP algorithm may guarantee convergence and achieve fast convergence by use of an appropriate balance parameter. Experiments also show that underweighting the balance parameter leads to slow convergence, whereas overweighting the balance parameter leads to divergence. The iteration-behavior change-law with the variation of the balance parameter is explained by use of the data tolerance ellipse and gradient descent principle. The findings and insights gained in this work may be applied to other imaging modalities and other constrained optimization problems.Depressive Disorders are the most common psychiatric diagnoses in the general population. To estimate the frequency, costs associated with Depressive Disorders in usual clinical practice, and in the whole Spanish population, a longitudinal, retrospective, observational study was carried out using data from the BIG-PAC database®. Study population all patients aged ≥ 18 years with a diagnosis of a Depressive Disorder in 2015-2017. Prevalence was computed as the proportion of Depressive Disorder cases in the adult general population, and the incidence rate, as the number of new Depressive Disorder cases diagnosed per 1,000 person-years in the population using health services, during 2015-2017. We collected demographic variables, comorbidity, direct health costs, and indirect costs (temporary and permanent disability). Health costs related to Depressive Disorders were estimated according to the annual resource use rate (resource/patient/year). Indirect costs were calculated according to the human capital method. Using the study data and information from the Spanish National Institute of Statistics, we estimated the cost of Depressive Disorders corresponding to the Spanish adult population, including premature mortality.