755 research outputs found

    Emricasan (IDN-6556) Lowers Portal Pressure in Patients with Compensated Cirrhosis and Severe Portal Hypertension

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    Caspases play a central role in apoptosis, inflammation and fibrosis. They produce hemodynamically-active, pro-inflammatory microparticles that cause intrahepatic inflammation, vasoconstriction and extrahepatic splanchnic vasodilation. Emricasan is a pan-caspase inhibitor that lowers portal hypertension (PH) and improves survival in murine models of cirrhosis. This exploratory study assessed whether emricasan lowers PH in patients with compensated cirrhosis. This multicenter, open-label study enrolled 23 subjects with compensated cirrhosis and PH (HVPG >5 mmHg). Emricasan 25 mg BID was given for 28 days. HVPG measurements were standardized and performed before and after emricasan. A single expert read all HVPG tracings.Median age was 59 (range 49-80); 70% were male. Cirrhosis etiologies were NASH and HCV. Subjects were Child class A (87%) with median MELD score of 8 (range 6-15). Twelve had severe PH (HVPG?12mmHg). Overall, there was no significant change in HVPG after emricasan (mean [SD] -1.1[4.57] mmHg). HVPG decreased significantly (mean [SD] -3.7[4.05] mmHg; p=0.003) in those with severe PH. 4/12 had a ?20% decrease; 8/12 had a ?10% decrease; and 2/12 HVPG decreased below 12mmHg. There were no significant changes in blood pressure or heart rate. AST/ALT decreased significantly in the entire group and in severe PH. Serum cCK18 and caspase-3/7 decreased significantly. Emricasan was well-tolerated. One subject discontinued for non-serious adverse events.Emricasan administered for 28 days decreased HVPG in patients with compensated cirrhosis and severe PH. An effect upon portal venous inflow is likely and concomitant decreases in AST/ALT suggest an intrahepatic anti-inflammatory effect

    <i>Gaia</i> Data Release 1. Summary of the astrometric, photometric, and survey properties

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    Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7. Aims. A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release. Methods. The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue. Results. Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the HIPPARCOS and Tycho-2 catalogues – a realisation of the Tycho-Gaia Astrometric Solution (TGAS) – and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ∼3000 Cepheid and RR-Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr−1 for the proper motions. A systematic component of ∼0.3 mas should be added to the parallax uncertainties. For the subset of ∼94 000 HIPPARCOS stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr−1. For the secondary astrometric data set, the typical uncertainty of the positions is ∼10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ∼0.03 mag over the magnitude range 5 to 20.7. Conclusions. Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data

    Human and murine clonal CD8+ T cell expansions arise during tuberculosis because of TCR selection

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    The immune system can recognize virtually any antigen, yet T cell responses against several pathogens, including Mycobacterium tuberculosis, are restricted to a limited number of immunodominant epitopes. The host factors that affect immunodominance are incompletely understood. Whether immunodominant epitopes elicit protective CD8+ T cell responses or instead act as decoys to subvert immunity and allow pathogens to establish chronic infection is unknown. Here we show that anatomically distinct human granulomas contain clonally expanded CD8+ T cells with overlapping T cell receptor (TCR) repertoires. Similarly, the murine CD8+ T cell response against M. tuberculosis is dominated by TB10.44-11-specific T cells with extreme TCRß bias. Using a retro genic model of TB10.44-11-specific CD8+ Tcells, we show that TCR dominance can arise because of competition between clonotypes driven by differences in affinity. Finally, we demonstrate that TB10.4-specific CD8+ T cells mediate protection against tuberculosis, which requires interferon-? production and TAP1-dependent antigen presentation in vivo. Our study of how immunodominance, biased TCR repertoires, and protection are inter-related, provides a new way to measure the quality of T cell immunity, which if applied to vaccine evaluation, could enhance our understanding of how to elicit protective T cell immunity.This work was supported by the Portuguese Foundation for Science and Technology individual fellowship (CNA) www.fct.pt, a National Institutes of Health Grant R01 AI106725 (SMB) www.nih.gov, and a Center for AIDS Research Grant P30 AI 060354 (SMB) www.nih.gov. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

    Soft windowing application to improve analysis of high-throughput phenotyping data.

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    MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce \u27soft windowing\u27, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online
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