Impacto da variabilidade de peso no controle matebólico de pacientes transplantados cardíacos

Heart transplantation (HT) represents one of the greatest advances in medicine over the last decades. It is indicated for patients with severe heart disease unresponsive to clinical treatment and conventional surgery, poor short-term prognosis and a 1- year mortality rate over 40%. HT has improved survival worldwide (80% in the first year, 70% in five years and 60% in ten years). However, the procedure has been associated with weight change and increased risk of secondary conditions such as diabetes, hypertension, dyslipidemia and obesity due to immunosuppressive therapy following transplantation. The objective of this study was to determine the impact of weight change on the metabolic stability of HT patients. The study was retrospective with data collected from the records of 82 adult patients (83% male; average age 45.06±12.04 years) submitted to HT between October 1997 and December 2005 at a transplantation service in Ceará (Brazil). The selected outcome variables (biopathological profile, weight and body mass index―BMI) were related to biochemical and metabolic change. The results were expressed in terms of frequency, measures of central tendency, Student s t test and Pearson s correlation coefficients. The analysis showed that following HT the average global BMI increased from 23.77±3.68kg/m2 to 25.48±3.92kg/m2 in the first year and to 28.38±4.97kg/m2 in the fifth. Overweight/obese patients (BMI ≥ 25 kg/m2) had higher average levels of glucose, total cholesterol, low-density lipoprotein and triglycerides than patients with eutrophy/malnutrition (BMI < 25 kg/m2). In conclusion, overweight/obese patients were likely to present higher average levels of glucose, triglycerides, total cholesterol and fractions than patients with eutrophy/malnutrition, indicating a direct and significant relation between nutritional status and weight change in the metabolic profile of HT patients

Modelagem da transferência de massa na remoção do óleo da água produzida por flotação em coluna com uso de tensoativos de origem vegetal

The treatment of oil produced water and its implications are continually under investigation and several questions are related to this subject. In the Northeast Region Brazil, the onshore reservoirs are, in its majority, mature oil fields with high production of water. As this oil produced water has high levels of oil, it cannot be directly discarded into the environment because it represents a risk for contamination of soil, water, and groundwater, or even may cause harm to living bodies. Currently, polyelectrolytes that promote the coalescence of the oil droplets are used to remove the dispersed oil phase, enhancing the effectiveness of the flotation process. The non-biodegradability and high cost of polyelectrolytes are limiting factors for its application. On this context, it is necessary to develop studies for the search of more environmentally friendly products to apply in the flotation process. In this work it is proposed the modeling of the flotation process, in a glass column, using surfactants derived from vegetal oils to replace the polyelectrolytes, as well as to obtain a model that represents the experimental data. In addition, it was made a comparative study between the models described in the literature and the one developed in this research. The obtained results showed that the developed model presented high correlation coefficients when fitting the experimental data (R2 > 0.98), thus proving its efficiency in modeling the experimental data.

Equilíbrio líquido-líquido para o sistema biodiesel de óleo de semente de melão (Cucumis melo l.) + metanol + glicerina

Rio Grande do Norte, northeast state from Brazil, it is the greatest producer and exporter of yellow melon, well known as Spanish melon. Despite the consumption of this fruit to be mainly its pulp, melon seeds are an important source of lipids considered an industrial residue it has been discharge product. The use of oilseeds in order to produce biodiesel establishes an important raw material and the increase of its production promotes the national development of the agriculture. In this background, the aim of this work has been to use oil from seeds of yellow melon to produce biodiesel and to accomplish a study of the phase equilibrium of the system evolving biodiesel, methanol and glycerin. The biodiesel was obtained by oil transesterification through methylic route with molar ratio 1:9.7 (oil:alcohol) and with a mass of NaOH of 0.5% from the oil mass; the reaction time was 73 minutes at 55 °C. A yield of 84.94% in biodiesel was achieved. The equilibria data present a well-characterized behavior with a great region of two phases. The tie lines indicate that methanol has a best solubility in the phase that is rich in glycerin. Consistency of the experimental data was made based on Othmer-Tobias and Hand correlations which values above 0.99 were found to correlation coefficients, this fact confers a good thermodynamic consistency to the experimental data. NRTL and UNIQUAC models were employed to predict liquid-liquid equilibrium of this system. It was observed a better concordance of the results when NRTL was applied (standard deviation 1.25%) although the UNIQUAC model has presented a quite satisfactory result either (standard deviation 2.70%). The NRTL and UNIQUAC models were also used to evaluate the effect of temperature in the range of 328 K to 358 K, in which a little change in solubility with respect to the data obtained at 298 K was observed, thus being considered negligible the effect of temperature

Avaliação da capacidade de caminhar do portador de obesidade mórbida utilizando teste de caminhada de 6 minutos

Introduction: The ability to walk is impaired in obese by anthropometric factors (BMI and height), musculoskeletal pain and level of inactivity. Little is known about the influence of body adiposity and the acute response of the cardiovascular system during whole the 6-minute walk test (6mWT). Objective: To evaluate the effect of anthropometric measures (BMI and WHR waist-to-hip ratio), the effort heart and inactivity in ability to walk the morbidly obese. Materials and Methods: a total 36 morbidly obese (36.23 + 11.82 years old, BMI 49.16 kg/m2) were recruited from outpatient department of treatment of obesity and bariatric surgery in University Hospital Onofre Lopes and anthropometric measurements of obesity (BMI and WHR), pulmonary function, pattern habitual physical activity (Baecke Questionnaire) and walking capacity (6mWT). The patient was checking to measure: heart rate (HR), breathing frequency (BF), peripheral oxygen saturation, level of perceived exertion, systemic arterial pressure and duplo-produto (DP), moreover the average speed development and total distance walking. The data were analysed between gender and pattern of body adiposity, measuring the behavior minute by minute of walking. The Pearson and Spearmam correlation coefficients were calculated, and stepwise multiple Regression examined the predictors of walking capacity. All analyses were performed en software Statistic 6.0. Results: 20 obese patients had abdominal adiposity (WHR = 1.01), waist circumference was 135.8 cm in women (25) and 139.8 cm in men (10). Walked to the end of 6mWT 412.43 m, with no differences between gender and adiposity. The total distance walked by obesity alone was explained by BMI (45%), HR in the sixth minute (43%), the Baecke (24%) and fatigue (-23%). 88.6% of obese (31) performed the test above 60% of maximal HR, while the peak HR achieved at 5-minute of 6mWT. Systemic arterial pressure and DP rised after walking, but with no differences between gender and adiposity. Conclusion: The walk of obese didn´t suffers influence of gender or the pattern of body adiposity. The final distance walked is attributed to excess body weight, stress heart, the feeling of effort required by physical activity and level of sedentary to obese. With a minute of walking, the obeses achieved a range of intensity cardiovascular trainning

Espectroscopia no infravermelho próximo e métodos de calibração multivariada aplicados à determinação simultânea de parâmetros bioquímicos em plasma sanguíneo

In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma

Avaliação da aplicabilidade de correlações matemáticas e redes neurais na predição de parâmetros de especificação do diesel

Diesel fuel is one of leading petroleum products marketed in Brazil, and has its quality monitored by specialized laboratories linked to the National Agency of Petroleum, Natural Gas and Biofuels - ANP. The main trial evaluating physicochemical properties of diesel are listed in the resolutions ANP Nº 65 of December 9th, 2011 and Nº 45 of December 20th, 2012 that determine the specification limits for each parameter and methodologies of analysis that should be adopted. However the methods used although quite consolidated, require dedicated equipment with high cost of acquisition and maintenance, as well as technical expertise for completion of these trials. Studies for development of more rapid alternative methods and lower cost have been the focus of many researchers. In this same perspective, this work conducted an assessment of the applicability of existing specialized literature on mathematical equations and artificial neural networks (ANN) for the determination of parameters of specification diesel fuel. 162 samples of diesel with a maximum sulfur content of 50, 500 and 1800 ppm, which were analyzed in a specialized laboratory using ASTM methods recommended by the ANP, with a total of 810 trials were used for this study. Experimental results atmospheric distillation (ASTM D86), and density (ASTM D4052) of diesel samples were used as basic input variables to the equations evaluated. The RNAs were applied to predict the flash point, cetane number and sulfur content (S50, S500, S1800), in which were tested network architectures feed-forward backpropagation and generalized regression varying the parameters of the matrix input in order to determine the set of variables and the best type of network for the prediction of variables of interest. The results obtained by the equations and RNAs were compared with experimental results using the nonparametric Wilcoxon test and Student's t test, at a significance level of 5%, as well as the coefficient of determination and percentage error, an error which was obtained 27, 61% for the flash point using a specific equation. The cetane number was obtained by three equations, and both showed good correlation coefficients, especially equation based on aniline point, with the lowest error of 0,816%. ANNs for predicting the flash point and the index cetane showed quite superior results to those observed with the mathematical equations, respectively, with errors of 2,55% and 0,23%. Among the samples with different sulfur contents, the RNAs were better able to predict the S1800 with error of 1,557%. Generally, networks of the type feedforward proved superior to generalized regression.