20 research outputs found

    Materials for Diabetes Therapeutics

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    This review is focused on the materials and methods used to fabricate closed-loop systems for type 1 diabetes therapy. Herein, we give a brief overview of current methods used for patient care and discuss two types of possible treatments and the materials used for these therapies–(i) artificial pancreases, comprised of insulin producing cells embedded in a polymeric biomaterial, and (ii) totally synthetic pancreases formulated by integrating continuous glucose monitors with controlled insulin release through degradable polymers and glucose-responsive polymer systems. Both the artificial and the completely synthetic pancreas have two major design requirements: the device must be both biocompatible and be permeable to small molecules and proteins, such as insulin. Several polymers and fabrication methods of artificial pancreases are discussed: microencapsulation, conformal coatings, and planar sheets. We also review the two components of a completely synthetic pancreas. Several types of glucose sensing systems (including materials used for electrochemical, optical, and chemical sensing platforms) are discussed, in addition to various polymer-based release systems (including ethylene-vinyl acetate, polyanhydrides, and phenylboronic acid containing hydrogels).Juvenile Diabetes Research Foundation International (17-2007-1063)Leona M. and Harry B. Helmsley Charitable Trust (09PG-T1D027)United States. National Institutes of Health (F32 EB011580-01

    Complexes between linoleate and native or aggregated β-lactoglobulin: Interaction parameters and in vitro cytotoxic effect.

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    peer-reviewedIron is essential for human health, but it sometimes causes an unpleasant taste, rusty colour and a decrease in the stability of food products. Previously, we found that ethanol-treated yeast (ETY) cells could remove iron from wine and juice, and reduce the fishy aftertaste induced by iron in wine–seafood pairings. However, the mechanism of iron sorption by ETY cells is undefined; thus, there is no indicator that can be used to estimate the iron sorption capacity of these cells. In this study, we showed that cell wall components are not mainly associated with iron sorption by investigating ETY cells with the cell wall removed. Moreover, plasma membrane permeability was correlated with the iron sorbing capacity of the cells. Microscopic analysis showed that iron accumulated within ETY cells. Proteinase-treated ETY cells had no iron sorbing capacity. On the basis of these results, we conclude that intracellular proteins are involved in iron sorption by ETY cells.S. Le Maux is currently supported by a Teagasc Walsh Fellowship and the Department of Agriculture, Fisheries and Food (FIRM project 08/RD/TMFRC/650). We also acknowledge funding from IRCSET-Ulysses Travel Grant

    Active Learning for Convenient Annotation and Classification of Secondary Ion Mass Spectrometry Images

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    Digital staining for the automated annotation of mass spectrometry imaging (MSI) data has previously been achieved using state-of-the-art classifiers such as random forests or support vector machines (SVMs). However, the training of such classifiers requires an expert to label exemplary data in advance. This process is time-consuming and hence costly, especially if the tissue is heterogeneous. In theory, it may be sufficient to only label a few highly representative pixels of an MS image, but it is not known a priori which pixels to select. This motivates <i>active learning</i> strategies in which the algorithm itself queries the expert by automatically suggesting promising candidate pixels of an MS image for labeling. Given a suitable querying strategy, the number of required training labels can be significantly reduced while maintaining classification accuracy. In this work, we propose active learning for convenient annotation of MSI data. We generalize a recently proposed active learning method to the multiclass case and combine it with the random forest classifier. Its superior performance over random sampling is demonstrated on secondary ion mass spectrometry data, making it an interesting approach for the classification of MS images

    The HBZ factor of human T-cell leukemia virus type I dimerizes with transcription factors JunB and c-Jun and modulates their transcriptional activity.

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    The human T-cell leukemia virus type I (HTLV-I)-encoded Tax protein activates transcription from the viral promoter via association with the cellular basic leucine zipper factor cAMP-response element-binding protein-2. Tax is also able to induce cellular transformation of T lymphocytes probably by modulating transcriptional activity of cellular factors, including nuclear factor-kappaB, E2F, activator protein-1 (AP-1), and p53. Recently, we characterized in HTLV-I-infected cells the presence of a novel viral protein, HBZ, encoded by the complementary strand of the HTLV-I RNA genome (Gaudray, G., Gachon, F., Basbous, J., Biard-Piechaczyk, M., Devaux, C., and Mesnard, J.-M. (2002) J. Virol. 76, 12813-12822). HBZ is a nuclear basic leucine zipper protein that down-regulates Tax-dependent viral transcription by inhibiting the binding of cAMP-response element-binding protein-2 to the HTLV-I promoter. In searching for other cellular targets of HBZ, we identified two members of the Jun family, JunB and c-Jun. Co-immunoprecipitation and cellular colocalization confirmed that HBZ interacts in vivo with JunB and c-Jun. When transiently introduced into CEM cells with a reporter gene containing the AP-1 site from the collagenase promoter, HBZ suppressed transactivation by c-Jun. On the other hand, the combination of HBZ with Jun-B had higher transcriptional activity than JunB alone. Consistent with the structure of its basic domain, we demonstrate that HBZ decreases the DNA-binding activity of c-Jun and JunB. Last, we show that c-Jun is no longer capable of activating the basal expression of the HTLV-I promoter in the presence of HBZ in vivo. Our results support the hypothesis that HBZ could be a negative modulator of the Tax effect by controlling Tax expression at the transcriptional level and by attenuating activation of AP-1 by Tax
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