969 resultados para Maladie de type Alzheimer


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Eguíluz, Federico; Merino, Raquel; Olsen, Vickie; Pajares, Eterio; Santamaría, José Miguel (eds.)

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La tesis se ha centrado en la síntesis y caracterización estructural de materiales tipo perovskita: SrLnMRuO6 (Ln=La,Pr,Nd; M=Zn,Co,Mg,Ni,Fe) y ALn2CuTi2O9 (A=Ca,Ba; Ln=La,Pr,Nd,Sm). El estudio de las estructuras de los materiales se ha realizado mediante el análisis de los patrones de difracción en polvo de rayos-X, sincrotrón y/o neutrones. En el refinamiento por el método de Rietveld de las estructuras se han sustituido las coordenadas atómicas (el método más común), por coordenadas colectivas: las amplitudes de los modos que describen la distorsión de la fase prototipo. Los resultados generales para la serie SrLnMRuO6 (Ln=La,Pr,Nd; M=Zn,Co,Mg,Ni) a temperatura ambiente se ha recogido en un diagrama en el que se han indicado las amplitudes de los modos que transforman de acuerdo a las irreps en función del factor de tolerancia, ya que todos ellos cristalizan en la misma fase monoclínica (P21/n); y a temperaturas altas se ha construido un diagrama de fase. Los materiales SrLnFeRuO6 ( Ln=La,Pr,Nd) y CaLn2CuTi2O9 cristalizan en la fase ortorrómbica Pbnm a temperatura ambiente; mientras que BaLn2CuTi2O9 tienen una estructura más simétrica, I4/mcm. A altas temperaturas se han identificado las transiciones de fase inducidas por el cambio de temperatura.A temperaturas bajas se han analizado las estructuras magnéticas de algunos de los compuestos mediante difracción de neutrones.

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DNA microarray, or DNA chip, is a technology that allows us to obtain the expression level of many genes in a single experiment. The fact that numerical expression values can be easily obtained gives us the possibility to use multiple statistical techniques of data analysis. In this project microarray data is obtained from Gene Expression Omnibus, the repository of National Center for Biotechnology Information (NCBI). Then, the noise is removed and data is normalized, also we use hypothesis tests to find the most relevant genes that may be involved in a disease and use machine learning methods like KNN, Random Forest or Kmeans. For performing the analysis we use Bioconductor, packages in R for the analysis of biological data, and we conduct a case study in Alzheimer disease. The complete code can be found in https://github.com/alberto-poncelas/ bioc-alzheimer

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The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

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Background/Aims: In diabetic ventricular myocytes, transient outward potassium current (I-to) amplitude is severely reduced because of the impaired catecholamine release that characterizes diabetic autonomic neuropathy. Sympathetic nervous system exhibits a trophic effect on I-to since incubation of myocytes with noradrenaline restores current amplitude via beta-adrenoceptor (beta AR) stimulation. Here, we investigate the intracellular signalling pathway though which incubation of diabetic cardiomyocytes with the beta AR agonist isoproterenol recovers I-to amplitude to normal values. Methods: Experiments were performed in ventricular myocytes isolated from streptozotocin-diabetic rats. I-to current was recorded by using the patch-clamp technique. Kv4 channel expression was determined by immunofluorescence. Protein-protein interaction was determined by coimmunoprecipitation. Results: Stimulation of beta AR activates first a G alpha s protein, adenylyl cyclase and Protein Kinase A. PKA-phosphorylated receptor then switches to the G alpha i protein. This leads to the activation of the beta AR-Kinase-1 and further receptor phosphorylation and arrestin dependent internalization. The internalized receptor-arrestin complex recruits and activates cSrc and the MAPK cascade, where Ras, c-Raf1 and finally ERK1/2 mediate the increase in Kv4.2 and Kv4.3 protein abundance in the plasma membrane. Conclusion: beta(2)AR stimulation activates a G alpha s and G alpha i protein dependent pathway where the ERK1/2 modulates the Ito current amplitude and the density of the Kv4.2 and Kv4.2 channels in the plasma membrane upon sympathetic stimulation in diabetic heart.

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The EC (entorhinal cortex) is fundamental for cognitive and mnesic functions. Thus damage to this area appears as a key element in the progression of AD (Alzheimer's disease), resulting in memory deficits arising from neuronal and synaptic alterations as well as glial malfunction. In this paper, we have performed an in-depth analysis of astroglial morphology in the EC by measuring the surface and volume of the GFAP (glial fibrillary acidic protein) profiles in a triple transgenic mouse model of AD [3xTg-AD (triple transgenic mice of AD)]. We found significant reduction in both the surface and volume of GFAP-labelled profiles in 3xTg-AD animals from very early ages (1 month) when compared with non-Tg (non-transgenic) controls (48 and 54%, reduction respectively), which was sustained for up to 12 months (33 and 45% reduction respectively). The appearance of Lambda beta (amyloid beta-peptide) depositions at 12 months of age did not trigger astroglial hypertrophy; nor did it result in the close association of astrocytes with senile plaques. Our results suggest that the AD progressive cognitive deterioration can be associated with an early reduction of astrocytic arborization and shrinkage of the astroglial domain, which may affect synaptic connectivity within the EC and between the EC and other brain regions. In addition, the EC seems to be particularly vulnerable to AD pathology because of the absence of evident astrogliosis in response to A beta accumulation. Thus we can consider that targeting astroglial atrophy may represent a therapeutic strategy which might slow down the progression of AD.

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The formation of cerebral senile plaques composed of amyloid beta peptide (A beta) is a fundamental feature of Alzheimer's disease (AD). Glial cells and more specifically microglia become reactive in the presence of A beta. In a triple transgenic model of AD (3 x Tg-AD), we found a significant increase in activated microglia at 12 (by 111%) and 18 (by 88%) months of age when compared with non-transgenic (non-Tg) controls. This microglial activation correlated with A beta plaque formation, and the activation in microglia was closely associated with A beta plaques and smaller A beta deposits. We also found a significant increase in the area density of resting microglia in 3 x Tg-AD animals both at plaque-free stage (at 9 months by 105%) and after the development of A plaques (at 12 months by 54% and at 18 months by 131%). Our results show for the first time that the increase in the density of resting microglia precedes both plaque formation and activation of microglia by extracellular A beta accumulation. We suggest that AD pathology triggers a complex microglial reaction: at the initial stages of the disease the number of resting microglia increases, as if in preparation for the ensuing activation in an attempt to fight the extracellular A beta load that is characteristic of the terminal stages of the disease. Cell Death and Disease (2010) 1, e1; doi:10.1038/cddis.2009.2; published online 14 January 2010

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Background: Cell-surface glycoproteins play critical roles in cell-to-cell recognition, signal transduction and regulation, thus being crucial in cell proliferation and cancer etiogenesis and development. DPP IV and NEP are ubiquitous glycopeptidases closely linked to tumor pathogenesis and development, and they are used as markers in some cancers. In the present study, the activity and protein and mRNA expression of these glycoproteins were analysed in a subset of clear-cell (CCRCC) and chromophobe (ChRCC) renal cell carcinomas, and in renal oncocytomas (RO). Methods: Peptidase activities were measured by conventional enzymatic assays with fluorogen-derived substrates. Gene expression was quantitatively determined by qRT-PCR and membrane-bound protein expression and distribution analysis was performed by specific immunostaining. Results: The activity of both glycoproteins was sharply decreased in the three histological types of renal tumors. Protein and mRNA expression was strongly downregulated in tumors from distal nephron (ChRCC and RO). Moreover, soluble DPP IV activity positively correlated with the aggressiveness of CCRCCs (higher activities in high grade tumors). Conclusions: These results support the pivotal role for DPP IV and NEP in the malignant transformation pathways and point to these peptidases as potential diagnostic markers.

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A new numerical procedure is proposed to investigate cracking behaviors induced by mismatch between the matrix phase and aggregates due to matrix shrinkage in cement-based composites. This kind of failure processes is simplified in this investigation as a purely spontaneous mechanical problem, therefore, one main difficulty during simulating the phenomenon lies that no explicit external load serves as the drive to propel development of this physical process. As a result, it is different from classical mechanical problems and seems hard to be solved by using directly the classical finite element method (FEM), a typical kind of "load -> medium -> response" procedures. As a solution, the actual mismatch deformation field is decomposed into two virtual fields, both of which can be obtained by the classical FEM. Then the actual response is obtained by adding together the two virtual displacement fields based on the principle of superposition. Then, critical elements are detected successively by the event-by-event technique. The micro-structure of composites is implemented by employing the generalized beam (GB) lattice model. Numerical examples are given to show the effectiveness of the method, and detailed discussions are conducted on influences of material properties.