New insights regarding HCV-NS5A structure/function and indication of genotypic differences

Abstract Background HCV is prevalent throughout the world. It is a major cause of chronic liver disease. There is no effective vaccine and the most common therapy, based on Peginterferon, has a success rate of ~50%. The mechanisms underlying viral resistance have not been elucidated but it has been suggested that both host and virus contribute to therapy outcome. Non-structural 5A (NS5A) protein, a critical virus component, is involved in cellular and viral processes. Methods The present study analyzed structural and functional features of 345 sequences of HCV-NS5A genotypes 1 or 3, using in silico tools. Results There was residue type composition and secondary structure differences between the genotypes. In addition, second structural variance were statistical different for each response group in genotype 3. A motif search indicated conserved glycosylation, phosphorylation and myristoylation sites that could be important in structural stabilization and function. Furthermore, a highly conserved integrin ligation site was identified, and could be linked to nuclear forms of NS5A. ProtFun indicated NS5A to have diverse enzymatic and nonenzymatic activities, participating in a great range of cell functions, with statistical difference between genotypes. Conclusion This study presents new insights into the HCV-NS5A. It is the first study that using bioinformatics tools, suggests differences between genotypes and response to therapy that can be related to NS5A protein features. Therefore, it emphasizes the importance of using bioinformatics tools in viral studies. Data acquired herein will aid in clarifying the structure/function of this protein and in the development of antiviral agents.

An analysis of Taguchi's on-line quality monitoring procedure for variables based on the results of a sequence of inspections

In this paper, a procedure for the on-line process control of variables is proposed. This procedure consists of inspecting the m-th item from every m produced items and deciding, at each inspection, whether the process is out-of-control. Two sets of limits, warning (µ0 ± W) and control (µ0 ± C), are used. If the value of the monitored statistic falls beyond the control limits or if a sequence of h observations falls between the warning limits and the control limits, the production is stopped for adjustment; otherwise, production goes on. The properties of an ergodic Markov chain are used to obtain an expression for the average cost per item. The parameters (the sampling interval m, the widths of the warning, the control limits W and C(W < C), and the sequence length (h) are optimized by minimizing the cost function. A numerical example illustrates the proposed procedure.

Neonatally induced mild diabetes: influence on development, behavior and reproductive function of female Wistar rats

Abstract Background Neonatal STZ treatment induces a state of mild hyperglycemia in adult rats that disrupts metabolism and maternal/fetal interactions. The aim of this study was investigate the effect of neonatal STZ treatment on the physical development, behavior, and reproductive function of female Wistar rats from infancy to adulthood. Methods At birth, litters were assigned either to a Control (subcutaneous (s.c.) citrate buffer, n = 10) or STZ group, (streptozotocin (STZ) - 100 mg/kg-sc, n = 6). Blood glucose levels were measured on postnatal days (PND) 35, 84 and 120. In Experiment 1 body weight, length and the appearance of developmental milestones such as eye and vaginal opening were monitored. To assess the relative contribution of the initial and long term effects of STZ treatment this group was subdivided based on blood glucose levels recorded on PND 120: STZ hyperglycemic (between 120 and 300 mg/dl) and STZ normoglycemic (under 120 mg/dl). Behavioral activity was assessed in an open field on PND 21 and 75. In Experiment 2 estrous cyclicity, sexual behavior and circulating gonadotropin, ovarian steroid, and insulin levels were compared between control and STZ-hyperglycemic rats. In all measures the litter was the experimental unit. Parametric data were analyzed using one-way or, where appropriate, two-way ANOVA and significant effects were investigated using Tukey’s post hoc test. Fisher’s exact test was employed when data did not satisfy the assumption of normality e.g. presence of urine and fecal boli on the open field between groups. Statistical significance was set at p < 0.05 for all data. Results As expected neonatal STZ treatment caused hyperglycemia and hypoinsulinemia in adulthood. STZ-treated pups also showed a temporary reduction in growth rate that probably reflected the early loss of circulating insulin. Hyperglycemic rats also exhibited a reduction in locomotor and exploratory behavior in the open field. Mild hyperglycemia did not impair gonadotropin levels or estrous cylicity but ovarian steroid concentrations were altered. Conclusions In female Wistar rats, neonatal STZ treatment impairs growth in infancy and results in mild hyperglycemia/hypoinsulinemia in adulthood that is associated with changes in the response to a novel environment and altered ovarian steroid hormone levels.

In search of mechanisms associated with mesenchymal stem cell-based therapies for acute kidney injury

Acute kidney injury (AKI) is classically described as a rapid loss of kidney function. AKI affects more than 15% of all hospital admissions and is associated with elevated mortality rates. Although many advances have occurred, intermittent or continuous renal replacement therapies are still considered the best options for reversing mild and severe AKI syndrome. For this reason, it is essential that innovative and effective therapies, without side effects and complications, be developed to treat AKI and the end-stages of renal disease. Mesenchymal stem cell (MSC) based therapies have numerous advantages in helping to repair inflamed and damaged tissues and are being considered as a new alternative for treating kidney injuries. Numerous experimental models have shown that MSCs can act via differentiation-independent mechanisms to help renal recovery. Essentially, MSCs can secrete a pool of cytokines, growth factors and chemokines, express enzymes, interact via cell-to-cell contacts and release bioagents such as microvesicles to orchestrate renal protection. In this review, we propose seven distinct properties of MSCs which explain how renoprotection may be conferred: 1) anti-inflammatory; 2) pro-angiogenic; 3) stimulation of endogenous progenitor cells; 4) anti-apoptotic; 5) anti-fibrotic; 6) anti-oxidant; and 7) promotion of cellular reprogramming. In this context, these mechanisms, either individually or synergically, could induce renal protection and functional recovery. This review summarises the most important effects and benefits associated with MSC-based therapies in experimental renal disease models and attempts to clarify the mechanisms behind the MSC-related renoprotection. MSCs may prove to be an effective, innovative and affordable treatment for moderate and severe AKI. However, more studies need to be performed to provide a more comprehensive global understanding of MSC-related therapies and to ensure their safety for future clinical applications.

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.

A semi-supervised classification technique based on interacting forces

Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed technique achieves good classification results on artificial data sets and is comparable to well-known semi-supervised techniques using benchmark data sets.