DNetDB: The human disease network database based on dysfunctional regulation mechanism.

Disease similarity study provides new insights into disease taxonomy, pathogenesis, which plays a guiding role in diagnosis and treatment. The early studies were limited to estimate disease similarities based on clinical manifestations, disease-related genes, medical vocabulary concepts or registry data, which were inevitably biased to well-studied diseases and offered small chance of discovering novel findings in disease relationships. In other words, genome-scale expression data give us another angle to address this problem since simultaneous measurement of the expression of thousands of genes allows for the exploration of gene transcriptional regulation, which is believed to be crucial to biological functions. Although differential expression analysis based methods have the potential to explore new disease relationships, it is difficult to unravel the upstream dysregulation mechanisms of diseases. We therefore estimated disease similarities based on gene expression data by using differential coexpression analysis, a recently emerging method, which has been proved to be more potential to capture dysfunctional regulation mechanisms than differential expression analysis. A total of 1,326 disease relationships among 108 diseases were identified, and the relevant information constituted the human disease network database (DNetDB). Benefiting from the use of differential coexpression analysis, the potential common dysfunctional regulation mechanisms shared by disease pairs (i.e. disease relationships) were extracted and presented. Statistical indicators, common disease-related genes and drugs shared by disease pairs were also included in DNetDB. In total, 1,326 disease relationships among 108 diseases, 5,598 pathways, 7,357 disease-related genes and 342 disease drugs are recorded in DNetDB, among which 3,762 genes and 148 drugs are shared by at least two diseases. DNetDB is the first database focusing on disease similarity from the viewpoint of gene regulation mechanism. It provides an easy-to-use web interface to search and browse the disease relationships and thus helps to systematically investigate etiology and pathogenesis, perform drug repositioning, and design novel therapeutic interventions.Database URL: http://app.scbit.org/DNetDB/ #.

The human disease network in terms of dysfunctional regulatory mechanisms.

BACKGROUND: Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. In pioneer studies, disease similarity was commonly estimated according to clinical manifestation. Subsequently, scientists started to investigate disease similarity based on gene-phenotype knowledge, which were inevitably biased to well-studied diseases. In recent years, estimating disease similarity according to transcriptomic behavior significantly enhances the probability of finding novel disease relationships, while the currently available studies usually mine expression data through differential expression analysis that has been considered to have little chance of unraveling dysfunctional regulatory relationships, the causal pathogenesis of diseases. METHODS: We developed a computational approach to measure human disease similarity based on expression data. Differential coexpression analysis, instead of differential expression analysis, was employed to calculate differential coexpression level of every gene for each disease, which was then summarized to the pathway level. Disease similarity was eventually calculated as the partial correlation coefficients of pathways' differential coexpression values between any two diseases. The significance of disease relationships were evaluated by permutation test. RESULTS: Based on mRNA expression data and a differential coexpression analysis based method, we built a human disease network involving 1326 significant Disease-Disease links among 108 diseases. Compared with disease relationships captured by differential expression analysis based method, our disease links shared known disease genes and drugs more significantly. Some novel disease relationships were discovered, for example, Obesity and cancer, Obesity and Psoriasis, lung adenocarcinoma and S. pneumonia, which had been commonly regarded as unrelated to each other, but recently found to share similar molecular mechanisms. Additionally, it was found that both the type of disease and the type of affected tissue influenced the degree of disease similarity. A sub-network including Allergic asthma, Type 2 diabetes and Chronic kidney disease was extracted to demonstrate the exploration of their common pathogenesis. CONCLUSION: The present study produces a global view of human diseasome for the first time from the viewpoint of regulation mechanisms, which therefore could provide insightful clues to etiology and pathogenesis, and help to perform drug repositioning and design novel therapeutic interventions.

DSviaDRM: an R package for estimating disease similarity via dysfunctional regulation mechanism.

UNLABELLED: Elucidation of human disease similarities has provided new insights into etiology, disease classification and drug repositioning. Since dysfunctional regulation would be manifested as the decoupling of expression correlation, disease similarity (DS) in terms of dysfunctional regulation mechanism (DRM) could be estimated by using a differential coexpression based approach, which is described in a companion paper. Due to the lack of tools for estimating DS from the viewpoint of DRM in public domain, we implemented an R package 'DSviaDRM' to identify significant DS via DRM based on transcriptomic data. DSviaDRM contains five easy-to-use functions, DCEA, DCpathway, DS, comDCGL and comDCGLplot, for identifying disease relationships and showing common differential regulation information shared by similar diseases. AVAILABILITY AND IMPLEMENTATION: DSviaDRM is available as an R package, with a user's guide and source code, at http://cran.r-project.org/web/packages/DSviaDRM/index.html. CONTACT: yyli@scbit.org or yxli@scbit.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Optimization of a quasi-solid-state dye-sensitized solar cell employing a nanocrystal-polymer composite electrolyte modified with water and ethanol.

A quasi-solid-state dye-sensitized solar cell employing a poly(ethylene oxide)-poly(vinylidene fluoride) (PEO-PVDF)/TiO2 gel electrolyte modified by various concentrations of water and ethanol is described. It is shown that the introduction of water and ethanol prevents the crystallization of the polymer matrix, and enhances the free I(-)/I(3)(-) concentration and the networks for ion transportation in the electrolyte, thus leading to an improvement in conductivity. A high energy conversion efficiency of about 5.8% is achieved by controlling the additive concentration in the electrolyte. Optimization of the additive-modified electrolyte performance has been obtained by studying the cross-linking behavior of water and ethanol with Fourier transform infrared (FTIR), differential scanning calorimetry (DSC) and viscosity measurements, and the electrical conduction behavior of the electrolyte with impedance spectra measurements.

[A preliminary study on the role of anti-atrial fibrillation pacemaker in the prevention of atrial tachyarrhythmia].

OBJECTIVE: To investigate the effect of anti-atrial fibrillation of Philos DDDR pacemaker on atrial tachyarrhythmia. METHODS: Thirty-eight patients with sick sinus syndrome and paroxysmal atrial fibrillation (AF) were implanted with Philos DDDR pacemaker. After implantation, auto-Mode-Switch (AMS) function was switched "on" and AF preventive algorithms were "off" in all cases. The number of AMS, atrial premature beats, heart rate and the percentage of atrial and ventricular pacing were recorded by pacemaker diagnostic function for one-month after procedure. AF preventive algorithms function with "middle" (approx 8 bpm) was then switched on and the same parameters as above from the database of pacemaker diagnostic function were collected for additional one month. RESULTS: The symptoms of dizziness, dyspnoea, and palpitation in the majority of patients were dramatically improved regardless of whether the AF preventive algorithms function was switched "on" or "off" after pacemaker implantation. There were no significant clinical changes in most patients when AF preventive algorithms were "on". However, 5 cases (13.2%) had palpitations and short of breath. These symptoms were relieved by changing the algorithms from "middle to slight (approx 4 bpm)". When AF preventive algorithms were switched on, atrial premature beats were reduced significantly (P < 0.05) with a dramatic increase in atrial pacing percentage and heart rate (P < 0.05). However, there was no significant difference in AMS (P > 0.05) between the two groups of AF preventive algorithms function switching "on" and "of", indicating that atrial tachyarrhythmias were not inhibited by anti-atrial fibrillation pacemaker. CONCLUSION: This study suggested that atrial fibrillation and atrial tachycardia were not reduced by implantation of an anti-atrial fibrillation Philos DDDR pacemaker, although atrial premature beats decreased significantly with increasing atrial pacing percentage when AF preventive algorithms were in "middle" and "slight".