Treatment of psychotic symptoms in bipolar disorder with aripiprazole monotherapy: A meta-analysis

Background: We present a systematic review and meta-analysis of the available clinical trials concerning the usefulness of aripiprazole in the treatment of the psychotic symptoms in bipolar disorder.Methods: A systematic MEDLINE and repository search concerning clinical trials for aripiprazole in bipolar disorder was conducted.Results: The meta-analysis of four randomised controlled trials (RCTs) on acute mania suggests that the effect size of aripiprazole versus placebo was equal to 0.14 but a more reliable and accurate estimation is 0.18 for the total Positive and Negative Syndrome Scale (PANSS) score. The effect was higher for the PANSS-positive subscale (0.28), PANSS-hostility subscale (0.24) and PANSS-cognitive subscale (0.20), and lower for the PANSS-negative subscale (0.12). No data on the depressive phase of bipolar illness exist, while there are some data in favour of aripiprazole concerning the maintenance phase, where at week 26 all except the total PANSS score showed a significant superiority of aripiprazole over placebo (d = 0.28 for positive, d = 0.38 for the cognitive and d = 0.71 for the hostility subscales) and at week 100 the results were similar (d = 0.42, 0.63 and 0.48, respectively).Conclusion: The data analysed for the current study support the usefulness of aripiprazole against psychotic symptoms during the acute manic and maintenance phases of bipolar illness. © 2009 Fountoulakis et al; licensee BioMed Central Ltd

A Measurement of Rb using a Double Tagging Method

The fraction of Z to bbbar events in hadronic Z decays has been measured by the OPAL experiment using the data collected at LEP between 1992 and 1995. The Z to bbbar decays were tagged using displaced secondary vertices, and high momentum electrons and muons. Systematic uncertainties were reduced by measuring the b-tagging efficiency using a double tagging technique. Efficiency correlations between opposite hemispheres of an event are small, and are well understood through comparisons between real and simulated data samples. A value of Rb = 0.2178 +- 0.0011 +- 0.0013 was obtained, where the first error is statistical and the second systematic. The uncertainty on Rc, the fraction of Z to ccbar events in hadronic Z decays, is not included in the errors. The dependence on Rc is Delta(Rb)/Rb = -0.056*Delta(Rc)/Rc where Delta(Rc) is the deviation of Rc from the value 0.172 predicted by the Standard Model. The result for Rb agrees with the value of 0.2155 +- 0.0003 predicted by the Standard Model.Comment: 42 pages, LaTeX, 14 eps figures included, submitted to European Physical Journal

Risk of malnutrition is associated with mental health symptoms in community living elderly men and women: The Tromsø Study

<p>Abstract</p> <p>Background</p> <p>Little research has been done on the relationship between malnutrition and mental health in community living elderly individuals. In the present study, we aimed to assess the associations between mental health (particularly anxiety and depression) and both the risk of malnutrition and body mass index (BMI, kg/m²) in a large sample of elderly men and women from Tromsø, Norway.</p> <p>Methods</p> <p>In a cross-sectional survey, with 1558 men and 1553 women aged 65 to 87 years, the risk of malnutrition was assessed by the Malnutrition Universal Screening Tool ('MUST'), and mental health was measured by the Symptoms Check List 10 (SCL-10). BMI was categorised into six groups (< 20.0, 20.0-22.4, 22.5-24.9, 25.0-27.4, 27.5-29.9, ≥ 30.0 kg/m²).</p> <p>Results</p> <p>The risk of malnutrition (combining medium and high risk) was found in 5.6% of the men and 8.6% of the women. Significant mental health symptoms were reported by 3.9% of the men and 9.1% of the women. In a model adjusted for age, marital status, smoking and education, significant mental health symptoms (SCL-10 score ≥ 1.85) were positively associated with the risk of malnutrition (odds ratio 3.9 [95% CI 1.7-8.6] in men and 2.5 [95%CI 1.3-4.9] in women), the association was positive also for subthreshold mental health symptoms. For individuals with BMI < 20.0 the adjusted odds ratio for significant mental health symptoms was 2.0 [95% CI 1.0-4.0].</p> <p>Conclusions</p> <p>Impaired mental health was strongly associated with the risk of malnutrition in community living elderly men and women and this association was also significant for subthreshold mental health symptoms.</p

PathFinder: mining signal transduction pathway segments from protein-protein interaction networks

<p>Abstract</p> <p>Background</p> <p>A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.</p> <p>Results</p> <p>In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules.</p> <p>Conclusion</p> <p>Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, <it>S. cerevisiae </it>(yeast) data is used to demonstrate the effectiveness of our method.</p

Treatment of bipolar disorder: a complex treatment for a multi-faceted disorder

Background: Manic-depression or bipolar disorder (BD) is a multi-faceted illness with an inevitably complex treatment. Methods: This article summarizes the current status of our knowledge and practice of its treatment. Results: It is widely accepted that lithium is moderately useful during all phases of bipolar illness and it might possess a specific effectiveness on suicidal prevention. Both first and second generation antipsychotics are widely used and the FDA has approved olanzapine, risperidone, quetiapine, ziprasidone and aripiprazole for the treatment of acute mania. These could also be useful in the treatment of bipolar depression, but only limited data exists so far to support the use of quetiapine monotherapy or the olanzapine-fluoxetine combination. Some, but not all, anticonvulsants possess a broad spectrum of effectiveness, including mixed dysphoric and rapid-cycling forms. Lamotrigine may be effective in the treatment of depression but not mania. Antidepressant use is controversial. Guidelines suggest their cautious use in combination with an antimanic agent, because they are supposed to induce switching to mania or hypomania, mixed episodes and rapid cycling. Conclusion: The first-line psychosocial intervention in BD is psychoeducation, followed by cognitive-behavioral therapy. Other treatment options include Electroconvulsive therapy and transcranial magnetic stimulation. There is a gap between the evidence base, which comes mostly from monotherapy trials, and clinical practice, where complex treatment regimens are the rule

Mathematical modeling of intracellular signaling pathways

Dynamic modeling and simulation of signal transduction pathways is an important topic in systems biology and is obtaining growing attention from researchers with experimental or theoretical background. Here we review attempts to analyze and model specific signaling systems. We review the structure of recurrent building blocks of signaling pathways and their integration into more comprehensive models, which enables the understanding of complex cellular processes. The variety of mechanisms found and modeling techniques used are illustrated with models of different signaling pathways. Focusing on the close interplay between experimental investigation of pathways and the mathematical representations of cellular dynamics, we discuss challenges and perspectives that emerge in studies of signaling systems