996 resultados para product transfer


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Thesis dissertation presented to obtain a PhD degree in Biochemistry at Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

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Dissertation presented to obtain a Ph.D. degree in Sciences of Engineering and Technology, Cell Technology, at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

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BACKGROUND: Some studies have reported an inverse association between dairy product (DP) consumption and weight or fat mass loss. OBJECTIVES: The objective of our study was to assess the association between DP intake and abdominal obesity (AO) among Azorean adolescents. SUBJECTS/METHODS: This study was a cross-sectional analysis. A total of 903 adolescents (370 boys) aged 15--16 years was evaluated. Anthropometric measurements were collected (weight, height and waist circumference (WC)) and McCarthy’s cut-points were used to categorize WC. AO was defined when WC was X90th percentile. Adolescent food intake was assessed using a self-administered semiquantitative food frequency questionnaire and DP intake was categorized in o2 and X2 servings/day. Data were analyzed separately for girls and boys, and logistical regression was used to estimate the association between DPs and AO adjusting for potential confounders. RESULTS: The prevalence of AO was 54.9% (boys: 32.1% and girls: 70.7%, Po0.001). For boys and girls, DP consumption was 2.3±1.9 and 2.1±1.6 servings/day (P¼0.185), respectively. In both genders, the proportion of adolescents with WC o90th percentile was higher among individuals who reported a dairy intake of X2 servings/day compared with those with an intake o2 servings/day (boys: 71% vs 65% and girls: 36% vs 24%, Po0.05). After adjustments for confounders, two or more DP servings per day were a negative predictor of AO (odds ratio, 0.217; 95% confidence interval, 0.075 -- 0.633) only in boys. CONCLUSION: We found a protective association between DP intake and AO only in boys.

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Dissertação para obtenção do Grau de Doutor em Engenharia Física

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Hindawi Publishing Corporation Bioinorganic Chemistry and Applications Volume 2010, Article ID 634597, 8 pages

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The purpose of this work was to develop a reliable alternative method for the determination of the dithiocarbamate pesticide mancozeb (MCZ) in formulations. Furthermore, a method for the analysis of MCZ's major degradation product, ethylenethiourea (ETU), was also proposed. Cyclic voltammetry was used to characterize the electrochemical behavior of MCZ and ETU, and square-wave adsorptive stripping voltammetry (SWAdSV) was employed for MCZ quantification in commercial formulations. It was found that both MCZ and ETU are irreversibly reduced (− 0.6 V and − 0.5 V vs Ag/AgCl, respectively) at the surface of a glassy carbon electrode in a mainly diffusion-controlled process, presenting maximum peak current intensities at pH 7.0 (in phosphate buffered saline electrolyte). Several parameters of the SWAdSV technique were optimized and linear relationships between concentration and peak current intensity were established between 10–90 μmol L− 1 and 10–110 μmol L− 1 for MCZ and ETU, respectively. The limits of detection were 7.0 μmol L− 1 for MCZ and 7.8 μmol L− 1 for ETU. The optimized method for MCZ was successfully applied to the quantification of this pesticide in two commercial formulations. The developed procedures provided accurate and precise results and could be interesting alternatives to the established methods for quality control of the studied products, as well as for analysis of MCZ and ETU in environmental samples.

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HIV+ patients often develop alterations of the plasma lipids that may implicate in development of premature coronary artery disease. High-density lipoprotein (HDL) has an important role in preventing atherogenesis and the aim of this study was to investigate aspects of HDL function in HIV+ patients. HIV+ patients (n = 48) and healthy control subjects (n = 45) of both sexes with similar age were studied. Twenty-five were not being treated with antiretroviral agents, 13 were under reverse transcriptase inhibitor nucleosidic and non-nucleosidic (NRTI+NNRTI) and 10 were under NRTI + protease inhibitors (NRTI+PI) treatment. Paraoxonase 1 (PON1) activity and the transfer of free and esterified cholesterol, tryglicerides and phospholipids from a lipidic nanoemulsion to HDL were analyzed. In comparison with healthy controls, HIV+ patients presented low PON-1 activity and diminished transfer of free cholesterol and tryglicerides. In contrast, phospholipid transfer was increased in those patients, whereas the transfer of cholesteryl esters was unchanged. NRTI+NNRTI increases the transfer of cholesteryl esters and triglycerides but in NRTI+PI there was no difference in respect to non-treated HIV+ patients. HDL from HIV+ patients has smaller antioxidant properties, as shown by lower PON-1 activity, and the transfer of lipids to this lipoprotein fraction is also altered, suggesting that HDL function is defective in those patients.

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Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

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High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

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Dissertação de Mestrado em Solicitaria

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J Biol Inorg Chem (2011) 16:1241–1254 DOI 10.1007/s00775-011-0812-9

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Biochem. J. (2011) 438,485–494 doi:10.1042/BJ20110836

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J Biol Inorg Chem (2011) 16:881–888 DOI 10.1007/s00775-011-0785-8

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Biochemistry, 2011, 50 (20), pp 4251–4262 DOI: 10.1021/bi101605p