131,034 research outputs found
Vitamin D protects human endothelial cells from oxidative stress through the autophagic and survival pathways
No full textContext: Recently, vitamin D (VitD) has been recognized as increasingly importance in many cellular functionsof several tissues andorgansother thanbone.Inparticular,VitD showedimportantbeneficial effects in the cardiovascular system. Although the relationship among VitD, endothelium, and cardiovascular disease is well established, little is known about the antioxidant effect of VitD. Objective: Our objective was to study the intracellular pathways activated by VitD in cultured human umbilical vein endothelial cells undergoing oxidative stress. Design: Nitric oxide production, cell viability, reactive oxygen species, the mitochondrial permeability transition pore, membrane potential, and caspase-3 activity were measured during oxidative stress induced by administration of 200μM hydrogen peroxide for 20 minutes. Experiments were repeated in the presence of specific vitamin D receptor ligand ZK191784. Results: Pretreatment with VitD alone or in combination with ZK191784 is able to reduce the apoptosis-related gene expression, involving both intrinsic and extrinsic pathways. At the same time, it has been shown the activation of pro-autophagic beclin 1 and the phosphorylation of ERK1/2 and Akt, indicating a modulation between apoptosis and autophagy.Moreover, VitD alone or in combination with ZK191784 is able to prevent the loss of mitochondrial potential and the consequent cytochrome C release and caspase activation. Conclusions: The present study shows that VitD may prevent endothelial cell death through modulation of the interplay between apoptosis and autophagy. This effect is obtained by inhibiting superoxide anion generation, maintaining mitochondria function and cell viability, activating survival kinases, and inducing NO production. Copyright © 2014 by the Endocrine Society
No influence of nevirapine on vitamin D deficiency in HIV-infected patients
No full textNevirapine does not influence the levels of Vitamin D in HIV-Infected Patient
Optimal Resource Allocation of Cloud-Based Spark Applications
No full textNowadays, the big data paradigm is consolidating its central position in the industry, as well as in society at large. Lots of
applications, across disparate domains, operate on huge amounts of data and offer great advantages both for business and research.
According to analysts, cloud computing adoption is steadily increasing to support big data analyses and Spark is expected to take a
prominent market position for the next decade.
As big data applications gain more and more importance over time and given the dynamic nature of cloud resources, it is fundamental
to develop an intelligent resource management system to provide Quality of Service guarantees to end-users.
This paper presents a set of run-time optimization-based resource management policies for advanced big data analytics. Users submit
Spark applications characterized by a priority and by a hard or soft deadline. Optimization policies address two scenarios:
i) identification of the minimum capacity to run a Spark application within the deadline; ii) re-balance of the cloud resources in case
of heavy load, minimising the weighted soft deadline application tardiness. The solution relies on an initial non-linear programming
model formulation and a search space exploration based on simulation-optimization procedures. Spark application execution times are
estimated by relying on a gamut of techniques, including machine learning, approximated analyses, and simulation. The benefits of
the approach are evaluated on Microsoft Azure HDInsight and on a private cloud cluster based on POWER8 by considering the
TPC-DS industry benchmark and SparkBench. The results obtained in the first scenario demonstrate that the percentage error of the
prediction of the optimal resource usage with respect to system measurement and exhaustive search is the range 4%-29% while
literature-based techniques present an average error in the range 6%-63%. Moreover, in the second scenario, the proposed algorithms
can address complex problems like computing the optimal redistribution of resources among tens of applications in less than a minute
with an error of 8% on average. On the same considered tests, literature-based approaches obtain an average error of about 57%
Inhibitory effect of somatostatin on human T lymphocytes proliferation
No full textSomatostatin (SOM) was originally described as a growth hormone release inhibiting factor, but SOM and its specific receptors (SOM-r) have been shown to be expressed on both normal and activated T and B lymphocytes and other immunocompetent cells. In the present study we have demonstrated that SOM strongly inhibits the proliferation of human T lymphocytes when stimulated by PHA, Con A or alloantigens. However, SOM was most effective when the T cells were stimulated by an alloantigen rather than a polyclonal activator such as PHA and ConA. Moreover, SOM strongly inhibited the expression of activation markers such as CD69 and CD25 that are expressed on T lymphocytes during alloantigen stimulation. SOM also inhibited both CD28 and CD2 mediated T cell proliferation. Whereas proliferation of T cells induced by the engagement of CD3 antigen using specific mAbs was only marginally affected. Our results would support the concept that in humans SOM plays a key role in the modulation of T cell activation by interfering with the antigen-independent pathways CD2 and CD28
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Andrea Vaccaro: 'Saint Laurent'. Artemisia Gentileschi: 'Allégorie de l'Astronomie (ou de la Cosmographie)'.
No full textEssays of two important paintings by Andrea Vaccaro and Artemisia Gentilesch
Receptors for dopamine and somatostatin: formation of hetero-oligomers with enhanced functional activity on lymphocytes proliferation
No full textDown regulation of Qa gene expression on drug-modified tumor cells
No full textMouse leukemia, L1210, strongly enhances its immunogenicity following in vivo treatment with 5-(3-3'-dimethyl-1-triazeno) imidazole-4-carboxamide (DTIC). Previous experiments have shown that transformed cells elicit a cell-mediated response accountable for rejection and resistance to a subsequent injection of parental tumor into a syngeneic host. L1210 expresses classical H-2 class I molecules, and since it has been shown that DTIC treatment does not modify the expression of these molecules, this is a suitable model to study nonclassical class I antigens, such as Qa2 glycoproteins, and their potential role in tumorigenicity
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