154 research outputs found

    Utilidad de las escalas diagnósticas de meningitis en niños en los servicios de urgencias

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    Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Pediatría: Fecha de lectura: 11/07/201

    Synthesis of polysaccharide-b-PEG block copolymers by oxime click

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    Nanocarriers based on interpolyelectrolyte complexation of Sulphated polysaccharide-b- PEG diblock copolymers and PLL

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    Publicado em "Journal of Tissue Engeneering and Regenerative Medicine", vol. 7, supp. 1 (2013)Glycosaminoglycans (GAGs) are integral part of the closest cellular environment: they can be found on the cells surface and in the extracellular matrix, where they interact with different proteins acting as local regulator of their activity. The use of GAGs in the preparation of protein delivery nanosystems is, therefore, prominent but so far, underexploited mainly because of the heterogeneity (composition and molecular weights) of natural glycans and the multistep procedures needed to obtain GAGs’ synthetic analogues and diblock copolymers.1 Recently, we have shown that oxime click reaction can be applied as a straightforward methodology for the synthesis of poly(ethylene glycol) (PEG)- hyaluronic acid (HA) diblock copolymers.2 These copolymers formed nanosized interpolyelectrolyte complexes (45 to 150 nm) by interaction with poly- L -lysine (PLL).3 Unfortunately, these complexes are not stable at physiological ionic strength. Herein, we describe a strategy to overcome this drawback; chondroitin sulphate-b-PEG diblock copolymers (CS-b-PEG) were obtained using the same oxime click reaction. The stronger negative charge of sulphate groups (versus the carboxilic groups present in HA) resulted in the complexes with higher stability: interpolyelectrolyte complexes between PLL and (CS-b-PEG) are stable up to 260 mM ionic strenght. Because carbohydrates do not activate Tcells, we believe that the reported herein complexes have an enormous potential in both drug delivery and vaccination fields

    Chitosan hydrophobic domains are favoured at low degree of acetylation and molecular weight

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    The aggregation of chitosan (CS) has been studied as a function of concentration, degree of acetylation (DA), and degree of polymerization (DP) by means of pyrene fluorescence and rheology. Fluorescence experiments show that aggregation of CS involves hydrophobic domains (HD) which are more favoured as lower the DA and DP. Consistent with these results, the viscosity of CS solutions decreases continuously on increasing DA, in the whole range of DP. These results, which rule out the participation of the acetyl groups in the HD, have been interpreted by the theory of hydrophobic polyelectrolytes in terms of the electrostatic energy of the aggregatesThis work was financially supported by the Spanish Government (CTQ2009-10963 and CTQ2009-14146-C02-02) and the Xunta de Galicia (10CSA209021PR and CN2011/037)S

    Disclosing an NMR-Invisible Fraction in Chitosan and PEGylated Copolymers and Its Role on the Determination of Degrees of Substitution

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    An unexpected 1H NMR invisible fraction (IF) for chitosan (CS) and CS-g-PEG is reported. The presence of this IF is remarkable considering that solution NMR is recognized as the method of choice for studying structural modifications in CS, including the degrees of acetylation (DA) and substitution (DS). In spite of IF figures as high as 50%, this IF does not interfere in the correct determination of the DA by 1H NMR, pointing to a homogeneous distribution of acetyl groups along the visible and invisible fractions. Quite in contrast, the IF negatively biases the determination of the DS in CS-g-PEG, with relative errors as high as 150% in a broad range of temperatures, pH values, and concentrations. This fact raises concerns about the accuracy of previously reported DS data for CS-g-PEG and many other CS copolymers. Efficient user-friendly conditions have been developed for the correct determination of the DS of CS-g-PEG by depolymerization by nitrous acidThis work was financially supported by the Spanish Government (CTQ2009-10963, CTQ2012-34790, CTQ2009-14146-C02-02, and CTQ2012-33436) and the Xunta de Galicia (10CSA209021PR and CN2011/037)S

    GATG Dendrimers and PEGylated Block Copolymers: from Synthesis to Bioapplications

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    This is a post-peer-review, pre-copyedit version of an article published in The AAPS Journal. The final authenticated version is available online at: https://doi.org/10.1208/s12248-014-9642-3Dendrimers are synthetic macromolecules composed of repetitive layers of branching units that emerge from a central core. They are characterized by a tunable size and precise number of peripheral groups which determine their physicochemical properties and function. Their high multivalency, functional surface, and globular architecture with diameters in the nanometer scale makes them ideal candidates for a wide range of applications. Gallic acid-triethylene glycol (GATG) dendrimers have attracted our attention as a promising platform in the biomedical field because of their high tunability and versatility. The presence of terminal azides in GATG dendrimers and poly(ethylene glycol) (PEG)-dendritic block copolymers allows their efficient functionalization with a variety of ligands of biomedical relevance including anionic and cationic groups, carbohydrates, peptides, or imaging agents. The resulting functionalized dendrimers have found application in drug and gene delivery, as antiviral agents and for the treatment of neurodegenerative diseases, in diagnosis and as tools to study multivalent carbohydrate recognition and dendrimer dynamics. Herein, we present an account on the preparation and recent applications of GATG dendrimers in these fieldsThe authors wish to acknowledge past and present lab members who have contributed to the development of dendrimers in our group. This work was financially supported by the Spanish Government (CTQ2009-10963, CTQ2012-34790, CTQ2009-14146-C02-02, CTQ2012-33436) and the Xunta de Galicia (10CSA209021PR and CN2011/037)S

    Structure, rheology, and copper-complexation of a hyaluronan-like exopolysaccharide from Vibrio

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    MO245 exopolysaccharide (EPS) was produced in laboratory conditions from Vibrio genus microorganism isolated from bacterial mats found in Moorea Island. Its structure consists of a linear tetrasaccharide repeating unit →4)-β-D-GlcpA-(1→4)-α-D-GalpNAc-(1→3)-β-D-GlcpNAc-(1→4)-β-D-GlcpA-(1→ containing covalently-linked 5% of glucose, galactose, and rhamnose, determined by methylation analyses and NMR spectroscopy. The molecular weight, radius of gyration (Rg) and intrinsic viscosity, [η], determined by gel permeation chromatography with light scattering and viscosity detection, were 513 ± 4 kDa (PDI, 1.42 ± 0.01), 6.7 ± 0.3 dl/g and 56 ± 0.3 nm respectively. The chelation of the EPS with copper divalent ions leads to the instantaneous formation of gels. The structural similitude proposed, based in an equal ratio of GlcA to N-acetylated sugars and in the same type of glyosidic linkages present in the repeating unit (alternated 1→3 and 1→4 linkages), is translated into analogous physicochemical properties: MO245 EPS is a flexible polyelectrolyte, with scaling exponents similar to that described for HA. This similitude opens opportunities in future drug delivery, tissue engineering, and cosmetic applications.publishe

    Comparative in vitro studies on PBN loaded nanoparticles prepared by biodegradable chitosan, PLGA polymers and their PEGylated block copolymers

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    α-phenyl-N-tert-butyl nitrone (PBN) is a neuroprotective free radical scavenger however it has low in vivo stability and blood residence time. Aim. of this study is to develop a nanoparticle formulation by using different polymeric system which enhance the blood residence time and in vivo stability of PBN and characterize in terms of particle size, zeta potential, morphology, encapsulation efficiency, in vitro release profiles. Chitosan (CS), poly(D,L-lactide-co-glycolide) (PLGA) and their poly(ethylene glycol) (PEG) block co-polymers were used for comparative study. Results showed that particle sizes of CS, CS-PEG, PLGA and PLGA-PEG nanoparticles are between 142-356 nm. PLGA nanoparticles and their block copolymers' nanoparticle have greatly monodisperse distribution. CS and CS-PEG nanoparticles have zeta potential values between 17-40 mV related to amine groups, contrariwise PLGA and PLGA-PEG nanoparticles have negative zeta potential in the range of (-8)-(-19) mV. Encapsulation efficiency and loading capacity for all formulations are between 12-54 %, 9-68 %, respectively. PLGA-PEG nanoparticles are promising for further studies due to their sufficient encapsulation efficiency and in vitro release profilesAuthors would like to acknowledge that this project was financially supported by Tubitak (Scientific Research Project Number: 110S460)S

    A review on machine learning approaches and trends in drug discovery

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    Abstract: Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.Instituto de Salud Carlos III; PI17/01826Instituto de Salud Carlos III; PI17/01561Xunta de Galicia; Ref. ED431D 2017/16Xunta de Galicia; Ref. ED431D 2017/23Xunta de Galicia; Ref. ED431C 2018/4
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