A randomized, double-blind, placebo-controlled trial to assess the efficacy of topiramate in the treatment of post-traumatic stress disorder

<p>Abstract</p> <p>Background</p> <p>Topiramate might be effective in the treatment of posttraumatic stress disorder (PTSD) because of its antikindling effect and its action in both inhibitory and excitatory neurotransmitters. Open-label studies and few controlled trials have suggested that this anticonvulsant may have therapeutic potential in PTSD. This 12-week randomized, double-blind, placebo-controlled clinical trial will compare the efficacy of topiramate with placebo and study the tolerability of topiramate in the treatment of PTSD.</p> <p>Methods and design</p> <p>Seventy-two adult outpatients with DSM-IV-diagnosed PTSD will be recruited from the violence program of Federal University of São Paulo Hospital (UNIFESP). After informed consent, screening, and a one week period of wash out, subjects will be randomized to either placebo or topiramate for 12 weeks. The primary efficacy endpoint will be the change in the Clinician-administered PTSD scale (CAPS) total score from baseline to the final visit at 12 weeks.</p> <p>Discussion</p> <p>The development of treatments for PTSD is challenging due to the complexity of the symptoms and psychiatric comorbidities. The selective serotonin reuptake inhibitors (SSRIs) are the mainstream treatment for PTSD, but many patients do not have a satisfactory response to antidepressants. Although there are limited clinical studies available to assess the efficacy of topiramate for PTSD, the findings of prior trials suggest this anticonvulsant may be promising in the management of these patients.</p> <p>Trial Registration</p> <p>NCT 00725920</p

Effects of adenosine A2A receptor activation and alanyl-glutamine in Clostridium difficile toxin-induced ileitis in rabbits and cecitis in mice

<p>Abstract</p> <p>Background</p> <p>Severe <it>Clostridium difficile </it>toxin-induced enteritis is characterized by exuberant intestinal tissue inflammation, epithelial disruption and diarrhea. Adenosine, through its action on the adenosine A_2Areceptor, prevents neutrophillic adhesion and oxidative burst and inhibits inflammatory cytokine production. Alanyl-glutamine enhances intestinal mucosal repair and decreases apoptosis of enterocytes. This study investigates the protection from enteritis by combination therapy with ATL 370, an adenosine A_2Areceptor agonist, and alanyl-glutamine in a rabbit and murine intestinal loop models of <it>C. difficile </it>toxin A-induced epithelial injury.</p> <p>Methods</p> <p>Toxin A with or without alanyl-glutamine was administered intraluminally to rabbit ileal or murine cecal loops. Animals were also given either PBS or ATL 370 parenterally. Ileal tissues were examined for secretion, histopathology, apoptosis, Cxcl1/KC and IL-10.</p> <p>Results</p> <p>ATL 370 decreased ileal secretion and histopathologic changes in loops treated with Toxin A. These effects were reversed by the A_2Areceptor antagonist, SCH 58261, in a dose-dependent manner. The combination of ATL 370 and alanyl-glutamine significantly further decreased ileal secretion, mucosal injury and apoptosis more than loops treated with either drug alone. ATL 370 and alanyl-glutamine also decreased intestinal tissue KC and IL-10.</p> <p>Conclusions</p> <p>Combination therapy with an adenosine A_2Areceptor agonist and alanyl-glutamine is effective in reversing <it>C. difficile </it>toxin A-induced epithelial injury, inflammation, secretion and apoptosis in animals and has therapeutic potential for the management of <it>C. difficile </it>infection.</p

An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae

Background: Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings: We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance: YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.This work was supported by grants from the N.S.F. (IIS-0325116, EIA-0219061), N.I.H. (GM06779-01,GM076536-01), Welch (F-1515), and a Packard Fellowship (EMM). These agencies were not involved in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.Cellular and Molecular Biolog

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes