Univariate regression analysis revealed that ezFMD significantly correlated with age (r = -0.42, P < 0.0001), body mass index (r = -0.13, P = 0.028), systolic blood pressure (r = -0.15, P = 0.009), diastolic blood pressure (r = -0.14, P = 0.011), fasting glucose level (r = -0.27, P = 0.006), smoking (r = -0.21, P = 0.007) and baseline pulse wave amplitude (r = -0.51, P < 0.0001). ezFMD significantly correlated with conventional FMD (r = 0.34, P < 0.0001). Multiple regression analysis revealed that age (P = 0.002), body mass index (P = 0.013), systolic blood pressure (P = 0.009), smoking (P = 0.004) and baseline PF-03084014 pulse wave amplitude (P

< 0.001) were MLN2238 in vitro independent predictors of ezFMD.\n\nConclusions: These findings suggest that measurement of ezFMD, a novel noninvasive and simple method, may be useful

for determination of vascular diameter response to reactive hyperemia. Since ezFMD is automatically measured by a device with an oscillometric method, measurement of ezFMD is easier and less biased than that of conventional FMD. (C) 2013 Elsevier Ireland Ltd. All rights reserved.”
“The availability of the human genome sequence has allowed identification of disease-causing mutations in many Mendelian disorders, and detection of significant associations of nucleotide polymorphisms to complex diseases and traits. Despite these progresses, finding the causative variations for most of the common diseases remains a complex task. Several studies have shown gene expression analyses provide a quite unbiased way to investigate complex traits and common disorders’ pathogenesis. Therefore, whole-transcriptome analysis this website is increasingly acquiring a key role in the knowledge of mechanisms responsible for complex diseases. Hybridization- and tag-based technologies have elucidated the involvement of multiple genes and pathways in pathological conditions, providing insights into the expression of thousand

of coding and noncoding RNAs, such as microRNAs. However, the introduction of Next-Generation Sequencing, particularly of RNA-Seq, has overcome some drawbacks of previously used technologies. Identifying, in a single experiment, potentially novel genes/exons and splice isoforms, RNA editing, fusion transcripts and allele-specific expression are some of its advantages. RNA-Seq has been fruitfully applied to study cancer and host-pathogens interactions, and it is taking first steps for studying neurodegenerative diseases (ND) as well as neuropsychiatric diseases. In addition, it is emerging as a very powerful tool to study quantitative trait loci associated with gene expression in complex diseases.