Evaluation of endothelial dysfunction, lipid metabolism in women with polycystic ovary syndrome: relationship of paraoxonase 1 activity, malondialdehyde levels, low-density lipoprotein subfractions, and endothelial dysfunction

The aim of this study was to assess relationship of insulin resistance, oxidant-antioxidant status, endothelial dysfunction, lipid metabolism, and their contribution to the risks of cardiovascular disease in women with polycystic ovary syndrome (PCOS). Forty-five women with PCOS and 17 healthy women were included in this study. Nitric oxide (NO), endothelin-1 (ET-1), malondialdehyde (MDA), Apo A1, Apo B, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglyceride, small, dense LDL cholesterol (sdLDL-C), large buoyant LDL cholesterol (LbLDL-C) levels, and paraoxonase 1 (PON1) activity were measured in serum/plasma obtained from study groups. Insulin resistance [homeostasis model assessment (HOMA) index] and serum sex hormone binding globulin (SHBG), total testosterone (tT), free testosterone (fT), androstenedione, and dehydroepiandrosteronsulfate (DHEAS) levels were also evaluated. Significantly decreased SHBG, NO, HDL-C levels, and PON1 activities, but increased tT, fT, androstenedione, DHEAS, HOMA index, MDA, ET-1, LDL-C, sdLDL-C, and LbLDL-C values were found in PCOS patients compared with those of controls. There was a positive correlation between MDA and fT levels; and a negative correlation between PON1 activity and fT. Our data show that insulin resistance, dyslipidemia, endothelial dysfunction, and oxidative stress might contribute to the excess risk of cardiovascular disease reported in PCOS patients.     

Algorithms for effective querying of compound graph-based pathway databases

Background  

Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.     

PATIKAmad: putting microarray data into pathway context

High-throughput experiments, most significantly DNA microarrays, provide us with system-scale profiles. Connecting these data with existing biological networks poses a formidable challenge to uncover facts about a cell's proteome. Studies and tools with this purpose are limited to networks with simple structure, such as protein-protein interaction graphs, or do not go much beyond than simply displaying values on the network. We have built a microarray data analysis tool, named PATIKAmad, which can be used to associate microarray data with the pathway models in mechanistic detail, and provides facilities for visualization, clustering, querying, and navigation of biological graphs related with loaded microarray experiments. PATIKAmad is freely available to noncommercial users as a new module of PATIKAweb at http://web.patika.org.     

Antibiotic susceptibility patterns among respiratory isolates of Gram-negative bacilli in a Turkish university hospital

Background  

Gram-negative bacteria cause most nosocomial respiratory infections. At the University of Cumhuriyet, we examined 328 respiratory isolates of Enterobacteriaceae and Acinetobacter baumanii organisms in Sivas, Turkey over 3 years. We used disk diffusion or standardized microdilution to test the isolates against 18 antibiotics.