942 resultados para Body Education


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Objective: To investigate the effects of rosiglitazone (RSG) on insulin sensitivity and regional adiposity (including intrahepatic fat) in patients with type 2 diabetes. Research Methods and Procedures: We examined the effect of RSG (8 mg/day, 2 divided doses) compared with placebo on insulin sensitivity and body composition in 33 type 2 diabetic patients. Measurements of insulin sensitivity (euglycemic hyperinsulinemic clamp), body fat (abdominal magnetic resonance imaging and DXA), and liver fat (magnetic resonance spectroscopy) were taken at baseline and repeated after 16 weeks of treatment. Results: There was a significant improvement in glycemic control (glycosylated hemoglobin -0.7 +/- 0.7%, p less than or equal to 0.05) and an 86% increase in insulin sensitivity in the RSG group (glucose-disposal rate change from baseline: 17.5 +/- 14.5 mumol glucose/min/kg free fat mass, P < 0.05), but no significant change in the placebo group compared with baseline. Total body weight and fat mass increased (p &LE; 0.05) with RSG (2.1 +/- 2.0 kg and 1.4 +/- 1.6 kg, respectively) with 95% of the increase in adiposity occurring in nonabdominal regions. In the abdominal region, RSG increased subcutaneous fat area by 8% (25.0 +/- 28.7 cm(2), p = 0.02), did not alter intra-abdominal fat area, and reduced intrahepatic fat levels by 45% (-6.7 +/- 9.7%, concentration relative to water). Discussion: Our data indicate that RSG greatly improves insulin sensitivity in patients with type 2 diabetes and is associated with an increase in adiposity in subcutaneous but not visceral body regions.

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Introduction Bioelectrical impedance analysis (BIA) is a useful field measure to estimate total body water (TBW). No prediction formulae have been developed or validated against a reference method in patients with pancreatic cancer. The aim of this study was to assess the agreement between three prediction equations for the estimation of TBW in cachectic patients with pancreatic cancer. Methods Resistance was measured at frequencies of 50 and 200 kHz in 18 outpatients (10 males and eight females, age 70.2 +/- 11.8 years) with pancreatic cancer from two tertiary Australian hospitals. Three published prediction formulae were used to calculate TBW - TBWs developed in surgical patients, TBWca-uw and TBWca-nw developed in underweight and normal weight patients with end-stage cancer. Results There was no significant difference in the TBW estimated by the three prediction equations - TBWs 32.9 +/- 8.3 L, TBWca-nw 36.3 +/- 7.4 L, TBWca-uw 34.6 +/- 7.6 L. At a population level, there is agreement between prediction of TBW in patients with pancreatic cancer estimated from the three equations. The best combination of low bias and narrow limits of agreement was observed when TBW was estimated from the equation developed in the underweight cancer patients relative to the normal weight cancer patients. When no established BIA prediction equation exists, practitioners should utilize an equation developed in a population with similar critical characteristics such as diagnosis, weight loss, body mass index and/or age. Conclusions Further research is required to determine the accuracy of the BIA prediction technique against a reference method in patients with pancreatic cancer.

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Numerical modeling of the eddy currents induced in the human body by the pulsed field gradients in MRI presents a difficult computational problem. It requires an efficient and accurate computational method for high spatial resolution analyses with a relatively low input frequency. In this article, a new technique is described which allows the finite difference time domain (FDTD) method to be efficiently applied over a very large frequency range, including low frequencies. This is not the case in conventional FDTD-based methods. A method of implementing streamline gradients in FDTD is presented, as well as comparative analyses which show that the correct source injection in the FDTD simulation plays a crucial rule in obtaining accurate solutions. In particular, making use of the derivative of the input source waveform is shown to provide distinct benefits in accuracy over direct source injection. In the method, no alterations to the properties of either the source or the transmission media are required. The method is essentially frequency independent and the source injection method has been verified against examples with analytical solutions. Results are presented showing the spatial distribution of gradient-induced electric fields and eddy currents in a complete body model.

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Supporting student learning can be difficult, especially within open-ended or loosely structured activities, often seen as valuable for promoting student autonomy in many curriculum areas and contexts. This paper reports an investigation into the experiences of three teachers who implemented design and technology education ideas in their primary school classrooms for the first time. The teachers did not capitalise upon many of the opportunities for scaffolding their students' learning within the open-ended activities they implemented. Limitations of the teachers' conceptual and procedural knowledge of design and technology were elements that influenced their early experiences. The study has implications for professional developers planning programs in newly introduced areas of the curriculum to support teachers in supporting learning within open-ended and loosely structured problem solving activities. (C) 2001 Elsevier Science Ltd. All rights reserved.

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Why does species richness vary so greatly across lineages? Traditionally, variation in species richness has been attributed to deterministic processes, although it is equally plausible that it may result from purely stochastic processes. We show that, based on the best available phylogenetic hypothesis, the pattern of cladogenesis among agamid lizards is not consistent with a random model, with some lineages having more species, and others fewer species, than expected by chance. We then use phylogenetic comparative methods to test six types of deterministic explanation for variation in species richness: body size, life history, sexual selection, ecological generalism, range size and latitude. Of eight variables we tested, only sexual size dimorphism and sexual dichromatism predicted species richness. Increases in species richness are associated with increases in sexual dichromatism but reductions in sexual size dimorphism. Consistent with recent comparative studies, we find no evidence that species richness is associated with small body size or high fecundity. Equally, we find no evidence that species richness covaries with ecological generalism, latitude or range size.