Diagnostic Accuracy and Applicability of a PCR System for the Detection of Schistosoma mansoni DNA in Human Urine Samples from an Endemic Area

Schistosomiasis caused by Schistosoma mansoni, one of the most neglected human parasitoses in Latin America and Africa, is routinely confirmed by microscopic visualization of eggs in stool. The main limitation of this diagnostic approach is its lack of sensitivity in detecting individual low worm burdens and consequently data on infection rates in low transmission settings are little reliable. According to the scientific literature, PCR assays are characterized by high sensitivity and specificity in detecting parasite DNA in biological samples. A simple and cost effective extraction method for DNA of Schistosoma mansoni from urine samples in combination with a conventional PCR assay was developed and applied in an endemic area. This urine based PCR system was tested for diagnostic accuracy among a population of a small village in an endemic area, comparing it to a reference test composed of three different parasitological techniques. The diagnostic parameters revealed a sensitivity of 100%, a specificity of 91.20%, positive and negative predictive values of 86.25% and 100%, respectively, and a test accuracy of 94.33%. Further statistical analysis showed a k index of 0.8806, indicating an excellent agreement between the reference test and the PCR system. Data obtained from the mouse model indicate the infection can be detected one week after cercariae penetration, opening a new perspective for early detection and patient management during this stage of the disease. The data indicate that this innovative PCR system provides a simple to handle and robust diagnostic tool for the detection of S. mansoni DNA from urine samples and a promising approach to overcome the diagnostic obstacles in low transmission settings. Furthermore the principals of this molecular technique, based on the examination of human urine samples may be useful for the diagnosis of other neglected tropical diseases that can be detected by trans-renal DNA

Structural studies of T4S systems by electron microscopy

Abstract: Type IV secretion (T4S) systems are large dynamic nanomachines that transport DNA and/or proteins through the membranes of bacteria. Analysis of T4S system architecture is an extremely challenging task taking into account their multi protein organisation and lack of overall global symmetry. Nonetheless the last decade demonstrated an amazing progress achieved by X-ray crystallography and cryo-electron microscopy. In this review we present a structural analysis of this dynamic complex based on recent advances in biochemical, biophysical and structural studies

Novel role for the innate immune receptor toll-like receptor 4 (TLR4) in the regulation of the wnt signaling pathway and photoreceptor apoptosis

Recent evidence has implicated innate immunity in regulating neuronal survival in the brain during stroke and other neurodegenerations. Photoreceptors are specialized light-detecting neurons in the retina that are essential for vision. In this study, we investigated the role of the innate immunity receptor TLR4 in photoreceptors. TLR4 activation by lipopolysaccharide (LPS) significantly reduced the survival of cultured mouse photoreceptors exposed to oxidative stress. With respect to mechanism, TLR4 suppressed Wnt signaling, decreased phosphorylation and activation of the Wnt receptor LRP6, and blocked the protective effect of the Wnt3a ligand. Paradoxically, TLR4 activation prior to oxidative injury protected photoreceptors, in a phenomenon known as preconditioning. Expression of TNFα and its receptors TNFR1 and TNFR2 decreased during preconditioning, and preconditioning was mimicked by TNFα antagonists, but was independent of Wnt signaling. Therefore, TLR4 is a novel regulator of photoreceptor survival that acts through the Wnt and TNFα pathways. © 2012 Yi et al

Characterization of complex networks: A survey of measurements

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of measurements for inclusion are welcomed by the author