6 resultados para Tissue expansion

em Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco


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[EN]Rumenic acid (cis9,trans11-18:2) is the main natural isomer of conjugated linoleic acid (CLA). Rumenic acid has many purported health benefits, but effects of most other CLA isomers are unknown. Typically trans7,cis9-18:2 is the second most abundant CLA isomer, but it co-elutes with rumenic acid on conventional polar gas chromatography (GC) columns, requiring complimentary analysis with silver-ion high performance liquid chromatography (Ag(+)-HPLC). Herein we report a rapid method for analyzing rumenic acid and trans7,cis9-18:2 using a 30 m ionic-liquid GC column. Optimal resolution of the two CLA isomers was at 145 degrees C and analysis of backfat from barley-fed cattle compared well with GC/Ag(+)-HPLC (y =0.978x - 0.031, r =0.985, P <0.001).

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Background: A remarkable range of biological functions have been ascribed to resveratrol. Recently, this polyphenol has been shown to have body fat lowering effects. The aim of the present study was to assess some of the potential underlying mechanisms of action which take place in adipose tissue. Methods: Sixteen male Sprague-Dawley rats were randomly divided into two groups: control and treated with 30 mg resveratrol/kg body weight/d. All rats were fed an obesogenic diet and after six weeks of treatment white adipose tissues were dissected. Lipoprotein lipase activity was assessed by fluorimetry, acetyl-CoA carboxylase by radiometry, and malic enzyme, glucose-6P-dehydrogenase and fatty acid synthase by spectrophotometry. Gene expression levels of acetyl-CoA carboxylase, fatty acid synthase, lipoprotein lipase, hormone-sensitive lipase, adipose triglyceride lipase, PPAR-gamma, SREBP-1c and perilipin were assessed by Real time RT-PCR. The amount of resveratrol metabolites in adipose tissue was measured by chromatography. Results: There was no difference in the final body weight of the rats; however, adipose tissues were significantly decreased in the resveratrol-treated group. Resveratrol reduced the activity of lipogenic enzymes, as well as that of heparin-releasable lipoprotein lipase. Moreover, a significant reduction was induced by this polyphenol in hormone-sensitive lipase mRNA levels. No significant changes were observed in other genes. Total amount of resveratrol metabolites in adipose tissue was 2.66 +/- 0.55 nmol/g tissue. Conclusions: It can be proposed that the body fat-lowering effect of resveratrol is mediated, at least in part, by a reduction in fatty acid uptake from circulating triacylglycerols and also in de novo lipogenesis.

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Myotonic dystrophy type 1 (DM1 or Steinert's disease) and type 2 (DM2) are multisystem disorders of genetic origin. Progressive muscular weakness, atrophy and myotonia are the most prominent neuromuscular features of these diseases, while other clinical manifestations such as cardiomyopathy, insulin resistance and cataracts are also common. From a clinical perspective, most DM symptoms are interpreted as a result of an accelerated aging (cataracts, muscular weakness and atrophy, cognitive decline, metabolic dysfunction, etc.), including an increased risk of developing tumors. From this point of view, DM1 could be described as a progeroid syndrome since a notable age dependent dysfunction of all systems occurs. The underlying molecular disorder in DM1 consists of the existence of a pathological (CTG) triplet expansion in the 3' untranslated region (UTR) of the Dystrophia ll/Iyotonica Protein Kinase (DMPK) gene, whereas (CCTG)n repeats in the first intron of the Cellular Nucleic acid Binding Protein/Zinc Finger Protein 9 (CNBP/ZNF9) gene cause DM2. The expansions are transcribed into (CUG)n and (CCUG)n-containing RNA, respectively, which form secondary structures and sequester RNA binding proteins, such as the splicing factor muscleblind-like protein (MBNL), forming nuclear aggregates known as foci. Other splicing factors, such as CUGBP, are also disrupted, leading to a spliceopathy of a large number of downstream genes linked to the clinical features of these diseases. Skeletal muscle regeneration relies on muscle progenitor cells, known as satellite cells, which are activated after muscle damage, and which proliferate and differentiate to muscle cells, thus regenerating the damaged tissue. Satellite cell dysfunction seems to be a common feature of both age-dependent muscle degeneration (sarcopenia) and muscle wasting in DM and other muscle degenerative diseases. This review aims to describe the cellular, molecular and macrostructural processes involved in the muscular degeneration seen in DM patients, highlighting the similarities found with muscle aging.

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We review the appropriateness of using SNIa observations to detect potential signatures of anisotropic expansion in the Universe. We focus on Union2 and SNLS3 SNIa datasets and use the hemispherical comparison method to detect possible anisotropic features. Unlike some previous works where nondiagonal elements of the covariance matrix were neglected, we use the full covariance matrix of the SNIa data, thus obtaining more realistic and not underestimated errors. As a matter of fact, the significance of previously claimed detections of a preferred direction in the Union2 dataset completely disappears once we include the effects of using the full covariance matrix. Moreover, we also find that such apreferred direction is aligned with the orthogonal direction of the SDSS observational plane and this suggests a clear indication that the SDSS subsample of the Union2 dataset introduces a significant bias, making the detected preferred direction unphysical. We thus find that current SNIa surveys are inappropriate to test anisotropic features due to their highly non-homogeneous angular distribution in the sky. In addition, after removal of the highest in homogeneous sub-samples, the number of SNIa is too low. Finally, we take advantage of the particular distribution of SNLS SNIa sub- sample in the SNLS3 data set, in which the observations were taken along four different directions. We fit each direction independently and find consistent results at the 1 sigma level. Although the likelihoods peak at relatively different values of Omega(m), the low number of data along each direction gives rise to large errors so that the likelihoods are sufficiently broad as to overlap within 1 sigma. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons. org/licenses/by/4.0/).