Previous hospital admissions and disease severity predict the use of antipsychotic combination treatment in patients with schizophrenia

<p>Abstract</p> <p>Background</p> <p>Although not recommended in treatment guidelines, previous studies have shown a frequent use of more than one antipsychotic agent among patients with schizophrenia. The main aims of the present study were to explore the antipsychotic treatment regimen among patients with schizophrenia in a catchment area-based sample and to investigate clinical characteristics associated with antipsychotic combination treatment.</p> <p>Methods</p> <p>The study included 329 patients diagnosed with schizophrenia using antipsychotic medication. Patients were recruited from all psychiatric hospitals in Oslo. Diagnoses were obtained by use of the Structured Clinical Interview for DSM-IV Axis I disorders (SCID-I). Additionally, Global Assessment of Functioning (GAF), Positive and Negative Syndrome Scale (PANSS) and number of hospitalisations and pharmacological treatment were assessed.</p> <p>Results</p> <p>Multiple hospital admissions, low GAF scores and high PANSS scores, were significantly associated with the prescription of combination treatment with two or more antipsychotics. The use of combination treatment increased significantly from the second hospital admission. Combination therapy was not significantly associated with age or gender. Regression models confirmed that an increasing number of hospital admission was the strongest predictor of the use of two or more antipsychotics.</p> <p>Conclusions</p> <p>Previous hospital admissions and disease severity measured by high PANSS scores and low GAF scores, predict the use of antipsychotic combination treatment in patients with schizophrenia. Future studies should further explore the use of antipsychotic drug treatment in clinical practice and partly based on such data establish more robust treatment guidelines for patients with persistently high symptom load.</p

Lymphotoxin-Beta Receptor Blockade Reduces CXCL13 in Lacrimal Glands and Improves Corneal Integrity in the NOD Model of Sjögren\u27s Syndrome

In Sjögren\u27s syndrome, keratoconjunctivitis sicca (dry eye) is associated with infiltration of lacrimal glands by leukocytes and consequent losses of tear-fluid production and the integrity of the ocular surface. We investigated the effect of blockade of the lymphotoxin-beta receptor (LTBR) pathway on lacrimal-gland pathology in the NOD mouse model of Sjögren\u27s syndrome

A multigroup radiation diffusion test problem: Comparison of code results with analytic solution

We consider a 1D, slab-symmetric test problem for the multigroup radiation diffusion and matter energy balance equations. The test simulates diffusion of energy from a hot central region. Opacities vary with the cube of the frequency and radiation emission is given by a Wien spectrum. We compare results from two LLNL codes, Raptor and Lasnex, with tabular data that define the analytic solution

Lymphotoxin-beta receptor blockade reduces CXCL13 in lacrimal glands and improves corneal integrity in the NOD model of Sjögren's syndrome

Introduction: In Sjögren’s syndrome, keratoconjunctivitis sicca (dry eye) is associated with infiltration of lacrimal glands by leukocytes and consequent losses of tear-fluid production and the integrity of the ocular surface. We investigated the effect of blockade of the lymphotoxin-beta receptor (LTBR) pathway on lacrimal-gland pathology in the NOD mouse model of Sjögren’s syndrome. Methods: Male NOD mice were treated for up to ten weeks with an antagonist, LTBR-Ig, or control mouse antibody MOPC-21. Extra-orbital lacrimal glands were analyzed by immunohistochemistry for high endothelial venules (HEV), by Affymetrix gene-array analysis and real-time PCR for differential gene expression, and by ELISA for CXCL13 protein. Leukocytes from lacrimal glands were analyzed by flow-cytometry. Tear-fluid secretion-rates were measured and the integrity of the ocular surface was scored using slit-lamp microscopy and fluorescein isothiocyanate (FITC) staining. The chemokine CXCL13 was measured by ELISA in sera from Sjögren’s syndrome patients (n = 27) and healthy controls (n = 30). Statistical analysis was by the two-tailed, unpaired T-test, or the Mann-Whitney-test for ocular integrity scores. Results: LTBR blockade for eight weeks reduced B-cell accumulation (approximately 5-fold), eliminated HEV in lacrimal glands, and reduced the entry rate of lymphocytes into lacrimal glands. Affymetrix-chip analysis revealed numerous changes in mRNA expression due to LTBR blockade, including reduction of homeostatic chemokine expression. The reduction of CXCL13, CCL21, CCL19 mRNA and the HEV-associated gene GLYCAM-1 was confirmed by PCR analysis. CXCL13 protein increased with disease progression in lacrimal-gland homogenates, but after LTBR blockade for 8 weeks, CXCL13 was reduced approximately 6-fold to 8.4 pg/mg (+/- 2.7) from 51 pg/mg (+/-5.3) in lacrimal glands of 16 week old control mice. Mice given LTBR blockade exhibited an approximately two-fold greater tear-fluid secretion than control mice (P = 0.001), and had a significantly improved ocular surface integrity score (P = 0.005). The mean CXCL13 concentration in sera from Sjögren’s patients (n = 27) was 170 pg/ml, compared to 92.0 pg/ml for sera from (n = 30) healthy controls (P = 0.01). Conclusions: Blockade of LTBR pathways may have therapeutic potential for treatment of Sjögren’s syndrome

Modelling the spatial distribution of DEM Error

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Definition of the σW regulon of Bacillus subtilis in the absence of stress

Bacteria employ extracytoplasmic function (ECF) sigma factors for their responses to environmental stresses. Despite intensive research, the molecular dissection of ECF sigma factor regulons has remained a major challenge due to overlaps in the ECF sigma factor-regulated genes and the stimuli that activate the different ECF sigma factors. Here we have employed tiling arrays to single out the ECF σW regulon of the Gram-positive bacterium Bacillus subtilis from the overlapping ECF σX, σY, and σM regulons. For this purpose, we profiled the transcriptome of a B. subtilis sigW mutant under non-stress conditions to select candidate genes that are strictly σW-regulated. Under these conditions, σW exhibits a basal level of activity. Subsequently, we verified the σW-dependency of candidate genes by comparing their transcript profiles to transcriptome data obtained with the parental B. subtilis strain 168 grown under 104 different conditions, including relevant stress conditions, such as salt shock. In addition, we investigated the transcriptomes of rasP or prsW mutant strains that lack the proteases involved in the degradation of the σW anti-sigma factor RsiW and subsequent activation of the σW-regulon. Taken together, our studies identify 89 genes as being strictly σW-regulated, including several genes for non-coding RNAs. The effects of rasP or prsW mutations on the expression of σW-dependent genes were relatively mild, which implies that σW-dependent transcription under non-stress conditions is not strictly related to RasP and PrsW. Lastly, we show that the pleiotropic phenotype of rasP mutant cells, which have defects in competence development, protein secretion and membrane protein production, is not mirrored in the transcript profile of these cells. This implies that RasP is not only important for transcriptional regulation via σW, but that this membrane protease also exerts other important post-transcriptional regulatory functions

Relationship between aerodynamic roughness length and bulk sedge leaf area index in a mixed-species boreal mire complex

Leaf area index (LAI) is an important parameter in natural ecosystems, representing the seasonal development of vegetation and photosynthetic potential. However, direct measurement techniques require labor-intensive field campaigns that are usually limited in time, while remote sensing approaches often do not yield reliable estimates. Here we propose that the bulk LAI of sedges (LAI(s)) can be estimated alternatively from a micrometeorological parameter, the aerodynamic roughness length for momentum (z(0)). z(0) can be readily calculated from high-response turbulence and other meteorological data, typically measured continuously and routinely available at ecosystem research sites. The regressions of LAI versus z(0) were obtained using the data from two Finnish natural sites representative of boreal fen and bog ecosystems. LAI(s) was found to be well correlated with z(0) and sedge canopy height. Superior method performance was demonstrated in the fen ecosystem where the sedges make a bigger contribution to overall surface roughness than in bogs.Peer reviewe

Identifying differential correlation in gene/pathway combinations

<p>Abstract</p> <p>Background</p> <p>An important emerging trend in the analysis of microarray data is to incorporate known pathway information a priori. Expression level "summaries" for pathways, obtained from the expression data for the genes constituting the pathway, permit the inclusion of pathway information, reduce the high dimensionality of microarray data, and have the power to elucidate gene-interaction dependencies which are not already accounted for through known pathway identification.</p> <p>Results</p> <p>We present a novel method for the analysis of microarray data that identifies joint differential expression in gene-pathway pairs. This method takes advantage of known gene pathway memberships to compute a summary expression level for each pathway as a whole. Correlations between the pathway expression summary and the expression levels of genes not already known to be associated with the pathway provide clues to gene interaction dependencies that are not already accounted for through known pathway identification, and statistically significant differences between gene-pathway correlations in phenotypically different cells (e.g., where the expression level of a single gene and a given pathway summary correlate strongly in normal cells but weakly in tumor cells) may indicate biologically relevant gene-pathway interactions. Here, we detail the methodology and present the results of this method applied to two gene-expression datasets, identifying gene-pathway pairs which exhibit differential joint expression by phenotype.</p> <p>Conclusion</p> <p>The method described herein provides a means by which interactions between large numbers of genes may be identified by incorporating known pathway information to reduce the dimensionality of gene interactions. The method is efficient and easily applied to data sets of ~10²arrays. Application of this method to two publicly-available cancer data sets yields suggestive and promising results. This method has the potential to complement gene-at-a-time analysis techniques for microarray analysis by indicating relationships between pathways and genes that have not previously been identified and which may play a role in disease.</p