Risks of central nervous system infections and related mortality in patients undergoing dialysis.

INTRODUCTION: This study aimed to investigate the risks of central nervous system (CNS) infections and related mortality in patients with end-stage renal disease (ESRD) undergoing dialysis. METHODS: Incident dialysis patients were identified from 2000 to 2013. The risks of CNS infection and related mortality were analyzed. RESULTS: The adjusted hazard ratio (HR) of CNS infection in the ESRD group compared with the control group was 3.46 (95% confidence interval [CI] 2.75-4.35). The adjusted odds ratio (OR) of 90-day mortality following CNS infections in the ESRD group in comparison with the control group was 5.99 (95% CI 2.78-12.9). The adjusted HR of overall CNS infection for the peritoneal dialysis (PD) group in comparison with the hemodialysis (HD) group was 1.07 (95% CI 0.63-1.82). Influenza vaccination was associated with a lower risks of CNS infection in dialysis patients (adjusted HR: 0.38, 95% CI 0.30-0.48). The adjusted OR of 90-day mortality following CNS infection for the PD group in comparison with the HD group was 1.01 (95% CI 0.55-1.87). CONCLUSIONS: The risks of CNS infections and related mortality were remarkably high in dialysis patients with no significant difference between patients with ESRD under HD and PD treatment.

Genetic diseases mimicking multiple sclerosis.

Multiple sclerosis (MS) is an inflammatory neurodegenerative disorder manifesting as gradual or progressive loss of neurological functions. Most patients present with relapsing-remitting disease courses. Extensive research over recent decades has expounded our insights into the presentations and diagnostic features of MS. Groups of genetic diseases, CADASIL and leukodystrophies, for example, have been frequently misdiagnosed with MS due to some overlapping clinical and radiological features. The delayed identification of these diseases in late adulthood can lead to severe neurological complications. Herein we discuss genetic diseases that have the potential to mimic multiple sclerosis, with highlights on clinical identification and practicing pearls that may aid physicians in recognizing MS-mimics with genetic background in clinical settings.

Genotypes of cancer stem cells characterized by epithelial-to-mesenchymal transition and proliferation related functions.

Cancer stem cells (CSCs), or cancer cells with stem cell-like properties, generally exhibit drug resistance and have highly potent cancer inducing capabilities. Genome-wide expression data collected at public repositories over the last few years provide excellent material for studies that can lead to insights concerning the molecular and functional characteristics of CSCs. Here, we conducted functional genomic studies of CSC based on fourteen PCA-screened high quality public CSC whole genome gene expression datasets and, as control, four high quality non-stem-like cancer cell and non-cancerous stem cell datasets from the Gene Expression Omnibus database. A total of 6,002 molecular signatures were taken from the Molecular Signatures Database and used to characterize the datasets, which, under two-way hierarchical clustering, formed three genotypes. Type 1, consisting of mainly glia CSCs, had significantly enhanced proliferation, and significantly suppressed epithelial-mesenchymal transition (EMT), related functions. Type 2, mainly breast CSCs, had significantly enhanced EMT, but not proliferation, related functions. Type 3, composed of ovarian, prostate, and colon CSCs, had significantly suppressed proliferation related functions and mixed expressions on EMT related functions.

Gene-Set Local Hierarchical Clustering (GSLHC)--A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups.

Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC) for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases.

Method designed to respect molecular heterogeneity can profoundly correct present data interpretations for genome-wide expression analysis.

Although genome-wide expression analysis has become a routine tool for gaining insight into molecular mechanisms, extraction of information remains a major challenge. It has been unclear why standard statistical methods, such as the t-test and ANOVA, often lead to low levels of reproducibility, how likely applying fold-change cutoffs to enhance reproducibility is to miss key signals, and how adversely using such methods has affected data interpretations. We broadly examined expression data to investigate the reproducibility problem and discovered that molecular heterogeneity, a biological property of genetically different samples, has been improperly handled by the statistical methods. Here we give a mathematical description of the discovery and report the development of a statistical method, named HTA, for better handling molecular heterogeneity. We broadly demonstrate the improved sensitivity and specificity of HTA over the conventional methods and show that using fold-change cutoffs has lost much information. We illustrate the especial usefulness of HTA for heterogeneous diseases, by applying it to existing data sets of schizophrenia, bipolar disorder and Parkinson's disease, and show it can abundantly and reproducibly uncover disease signatures not previously detectable. Based on 156 biological data sets, we estimate that the methodological issue has affected over 96% of expression studies and that HTA can profoundly correct 86% of the affected data interpretations. The methodological advancement can better facilitate systems understandings of biological processes, render biological inferences that are more reliable than they have hitherto been and engender translational medical applications, such as identifying diagnostic biomarkers and drug prediction, which are more robust.