Effects of multiple-dose ponesimod, a selective SIP1 receptor modulator, on lymphocyte subsets in healthy humans

This study investigated the effects of ponesimod, a selective SIP1 receptor modulator, on T lymphocyte subsets in 16 healthy subjects. Lymphocyte subset proportions and absolute numbers were determined at baseline and on Day 10, after once-daily administration of ponesimod (10 mg, 20 mg, and 40 mg each consecutively for 3 days) or placebo (ratio 3: 1). The overall change from baseline in lymphocyte count was -1,292 +/- 340x10(6) cells/L and 275 +/- 486x10(6) cells/L in ponesimod- and placebo-treated subjects, respectively. This included a decrease in both T and B lymphocytes following ponesimod treatment. A decrease in naive CD4(+) T cells (CD45RA(+)CCR7(+)) from baseline was observed only after ponesimod treatment (-113 +/- 98x10(6) cells/L, placebo: 0 +/- 18x10(6) cells/L). The number of T-cytotoxic (CD3(+)CD8(+)) and T-helper (CD3(+)CD4(+)) cells was significantly altered following ponesimod treatment compared with placebo. Furthermore, ponesimod treatment resulted in marked decreases in CD4(+) T-central memory (CD45RA(-)CCR7(+)) cells (-437 +/- 164x10(6) cells/L) and CD4(+) T-effector memory (CD45RA(-)CCR7(-)) cells (-131 +/- 57x10(6) cells/L). In addition, ponesimod treatment led to a decrease of -228 +/- 90x10(6) cells/L of gut-homing T cells (CLA(-)integrin beta 7(+)). In contrast, when compared with placebo, CD8(+) T-effector memory and natural killer (NK) cells were not significantly reduced following multiple-dose administration of ponesimod. In summary, ponesimod treatment led to a marked reduction in overall T and B cells. Further investigations revealed that the number of CD4(+) cells was dramatically reduced, whereas CD8(+) and NK cells were less affected, allowing the body to preserve critical viral-clearing functions

Reconnaissance of Suspected Old Novae

Several of the \ blank fields\ in the novae atlas by Duerbeck were imaged at the WIYN 3.5 m telescope during technical engineering and commissioning activities in 1994-1995. Several old novae have been recovered utilizing CCD photometry. Multiobject spectroscopy with the Hydra/MOS instrumentation at WIYN was also used on random stars in the fields to search for a cataclysmic variable. The old novae candidates identified include SV Ari, V465 Cyg, SS LMi, V2104 Oph, GR Ori, V529 Ori, UW Per, and UW Tri

Exploring out-of-equilibrium quantum magnetism and thermalization in a spin-3 many-body dipolar lattice system

Understanding quantum thermalization through entanglement build-up in isolated quantum systems addresses fundamental questions on how unitary dynamics connects to statistical physics. Here, we study the spin dynamics and approach towards local thermal equilibrium of a macroscopic ensemble of S = 3 spins prepared in a pure coherent spin state, tilted compared to the magnetic field, under the effect of magnetic dipole-dipole interactions. The experiment uses a unit filled array of 104 chromium atoms in a three dimensional optical lattice, realizing the spin-3 XXZ Heisenberg model. The buildup of quantum correlation during the dynamics, especially as the angle approaches pi/2, is supported by comparison with an improved numerical quantum phase-space method and further confirmed by the observation that our isolated system thermalizes under its own dynamics, reaching a steady state consistent with the one extracted from a thermal ensemble with a temperature dictated from the system's energy. This indicates a scenario of quantum thermalization which is tied to the growth of entanglement entropy. Although direct experimental measurements of the Renyi entropy in our macroscopic system are unfeasible, the excellent agreement with the theory, which can compute this entropy, does indicate entanglement build-up.Comment: 12 figure

Regularized gene selection in cancer microarray meta-analysis

<p>Abstract</p> <p>Background</p> <p>In cancer studies, it is common that multiple microarray experiments are conducted to measure the same clinical outcome and expressions of the same set of genes. An important goal of such experiments is to identify a subset of genes that can potentially serve as predictive markers for cancer development and progression. Analyses of individual experiments may lead to unreliable gene selection results because of the small sample sizes. Meta analysis can be used to pool multiple experiments, increase statistical power, and achieve more reliable gene selection. The meta analysis of cancer microarray data is challenging because of the high dimensionality of gene expressions and the differences in experimental settings amongst different experiments.</p> <p>Results</p> <p>We propose a Meta Threshold Gradient Descent Regularization (MTGDR) approach for gene selection in the meta analysis of cancer microarray data. The MTGDR has many advantages over existing approaches. It allows different experiments to have different experimental settings. It can account for the joint effects of multiple genes on cancer, and it can select the same set of cancer-associated genes across multiple experiments. Simulation studies and analyses of multiple pancreatic and liver cancer experiments demonstrate the superior performance of the MTGDR.</p> <p>Conclusion</p> <p>The MTGDR provides an effective way of analyzing multiple cancer microarray studies and selecting reliable cancer-associated genes.</p

Pancreatic Expression database: a generic model for the organization, integration and mining of complex cancer datasets

<p>Abstract</p> <p>Background</p> <p>Pancreatic cancer is the 5th leading cause of cancer death in both males and females. In recent years, a wealth of gene and protein expression studies have been published broadening our understanding of pancreatic cancer biology. Due to the explosive growth in publicly available data from multiple different sources it is becoming increasingly difficult for individual researchers to integrate these into their current research programmes. The Pancreatic Expression database, a generic web-based system, is aiming to close this gap by providing the research community with an open access tool, not only to mine currently available pancreatic cancer data sets but also to include their own data in the database.</p> <p>Description</p> <p>Currently, the database holds 32 datasets comprising 7636 gene expression measurements extracted from 20 different published gene or protein expression studies from various pancreatic cancer types, pancreatic precursor lesions (PanINs) and chronic pancreatitis. The pancreatic data are stored in a data management system based on the BioMart technology alongside the human genome gene and protein annotations, sequence, homologue, SNP and antibody data. Interrogation of the database can be achieved through both a web-based query interface and through web services using combined criteria from pancreatic (disease stages, regulation, differential expression, expression, platform technology, publication) and/or public data (antibodies, genomic region, gene-related accessions, ontology, expression patterns, multi-species comparisons, protein data, SNPs). Thus, our database enables connections between otherwise disparate data sources and allows relatively simple navigation between all data types and annotations.</p> <p>Conclusion</p> <p>The database structure and content provides a powerful and high-speed data-mining tool for cancer research. It can be used for target discovery i.e. of biomarkers from body fluids, identification and analysis of genes associated with the progression of cancer, cross-platform meta-analysis, SNP selection for pancreatic cancer association studies, cancer gene promoter analysis as well as mining cancer ontology information. The data model is generic and can be easily extended and applied to other types of cancer. The database is available online with no restrictions for the scientific community at <url>http://www.pancreasexpression.org/</url>.</p

Using BioMart as a framework to manage and query pancreatic cancer data

We describe the Pancreatic Expression Database (PED), the first cancer database originally designed based on the BioMart infrastructure. The PED portal brings together multidimensional pancreatic cancer data from the literature including genomic, proteomic, miRNA and gene expression profiles. Based on the BioMart 0.7 framework, the database is easily integrated with other BioMart-compliant resources, such as Ensembl and Reactome, to give access to a wide range of annotations alongside detailed experimental conditions. This article is intended to give an overview of PED, describe its data content and work through examples of how to successfully mine and integrate pancreatic cancer data sets and other BioMart resources

Antibody Therapy Targeting Cancer-Specific Cell Surface Antigen AGR2

For anterior gradient 2 (AGR2), normal cells express the intracellular form iAGR2 localized to the endoplasmic reticulum while cancer cells express the extracellular form eAGR2 localized on the cell surface and secreted. Antibodies targeting eAGR2+ cancer cells for eradication will spare normal cells. Two AGR2 monoclonal antibodies, P1G4 and P3A5, were shown to recognize specifically eAGR2+ pancreatic tumors implanted in mice. In addition, P1G4 showed enhancement in drug inhibition of tumor growth. Human:mouse chimeric antibodies of IgG1, IgG2, IgG4 were generated for both antibodies. These human IgG were shown to lyse eAGR2+ prostate cancer cells in vitro with human serum. AGR2 has an important function in distal spread of cancer cells, and is highly expressed in prostate, pancreatic, bladder metastases. Therefore, immunotherapy based on AGR2 antibody-mediated ADCC and CDC is highly promising. Cancer specificity of eAGR2 predicts possibly minimal collateral damage to healthy tissues and organs. Moreover, AGR2 therapy, once fully developed and approved, can be used to treat other solid tumors since AGR2 is an adenocarcinoma antigen found in many common malignancies

IL-2 Regulates Expression of C-MAF in Human CD4 T Cells

Blockade of IL-2R with humanized anti-CD25 Abs, such as daclizumab, inhibits Th2 responses in human T cells. Recent murine studies have shown that IL-2 also plays a significant role in regulating Th2 cell differentiation by activated STAT5. To explore the role of activated STAT5 in the Th2 differentiation of primary human T cells, we studied the mechanisms underlying IL-2 regulation of C-MAF expression. Chromatin immunoprecipitation studies revealed that IL-2 induced STAT5 binding to specific sites in the C-MAF promoter. These sites corresponded to regions enriched for markers of chromatin architectural features in both resting CD4 and differentiated Th2 cells. Unlike IL-6, IL-2 induced C-MAF expression in CD4 T cells with or without prior TCR stimulation. TCR-induced C-MAF expression was significantly inhibited by treatment with daclizumab or a JAK3 inhibitor, R333. Furthermore, IL-2 and IL-6 synergistically induced C-MAF expression in TCR-activated T cells, suggesting functional cooperation between these cytokines. Finally, both TCR-induced early IL4 mRNA expression and IL-4 cytokine expression in differentiated Th2 cells were significantly inhibited by IL-2R blockade. Thus, our findings demonstrate the importance of IL-2 in Th2 differentiation in human T cells and support the notion that IL-2R–directed therapies may have utility in the treatment of allergic disorders