Effects of Na +

Fibrotic remodeling, characterized by fibroblast phenotype switching, is often associated with atrial fibrillation and heart failure. This study aimed to investigate the effects on electrotonic myofibroblast-myocyte (Mfb-M) coupling on cardiac myocytes excitability and repolarization of the voltage-gated sodium channels (VGSCs) and single mechanogated channels (MGCs) in human atrial Mfbs. Mathematical modeling was developed from a combination of (1) models of the human atrial myocyte (including the stretch activated ion channel current, ISAC) and Mfb and (2) our formulation of currents through VGSCs (INa_Mfb) and MGCs (IMGC_Mfb) based upon experimental findings. The effects of changes in the intercellular coupling conductance, the number of coupled Mfbs, and the basic cycle length on the myocyte action potential were simulated. The results demonstrated that the integration of ISAC, INa_Mfb, and IMGC_Mfb reduced the amplitude of the myocyte membrane potential (Vmax) and the action potential duration (APD), increased the depolarization of the resting myocyte membrane potential (Vrest), and made it easy to trigger spontaneous excitement in myocytes. For Mfbs, significant electrotonic depolarizations were exhibited with the addition of INa_Mfb and IMGC_Mfb. Our results indicated that ISAC, INa_Mfb, and IMGC_Mfb significantly influenced myocytes and Mfbs properties and should be considered in future cardiac pathological mathematical modeling

Naltrexone and Bupropion Combination Treatment for Smoking Cessation and Weight Loss in Patients With Schizophrenia

Objective: The rates of obesity and cigarette smoking are much higher in patients with schizophrenia compared to the general population. This study was to examine whether naltrexone and bupropion combination treatment can help weight loss and smoking cessation in patients with schizophrenia.Methods: Obese male schizophrenia patients with current cigarette smoking were randomized to receive adjunctive naltrexone (25 mg/day) and bupropion (300 mg/day) combination or placebo for 24 weeks. Twenty-two patients were enrolled in the study, and 21 patients completed the study (11 in the treatment group, and 10 in the placebo group). Body weight, body mass index (BMI), fasting lipids, smoking urge, expired carbon monoxide (CO) level and cigarettes smoked per week were measured at baseline and week 24.Results: There was no significant difference between two groups in changes in weight, BMI, fasting lipids, or cigarette smoking measures (p's > 0.05)Conclusion: Naltrexone and bupropion combination treatment didn't show weight loss or smoking cessation effect in patients with schizophrenia in this pilot study.Implications for future studies were discussed.ClinicalTrials.gov identifier: NCT02736474

Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics.

Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests

Additional file 1 of Safety and effectiveness of oral medium to high dose blonanserin in patients with schizophrenia: subgroup analysis from a prospective, multicenter, post-marketing surveillance study in mainland China

Additional file 1: Table S1. Reasons for discontinuation