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Impact of Depression in Diabetes The comorbidity of depression and diabetes can have substantial and debilitating effects on patients health This may occur either directly via physiological routes or indirectly through alterations in self-care Lustman, Grif th, and Clouse (1997) developed an empirically based model in which depression has direct and indirect links to glucose dysregulation and risk of diabetes complications In this model, depression is directly associated with obesity, physical inactivity, and treatment noncompliance These factors lead to the risk of diabetes complications Depression is also directly related to diabetes complications as well as to speci c behavioral factors, such as smoking and substance abuse, that have been found to increase the risk of disease complications According to this model, smoking cessation treatment and weight loss programs would aid in the reduction of diabetes complications.

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Unfortunately, however, depressed patients are generally more resistant to such treatment approaches and thus continue to compromise their diabetes management In further support of the mechanisms inherent in this model, the presence of concomitant depressive symptoms among older diabetic Mexican Americans was found to be associated with signi cantly increased health burden (eg, myocardial infarction, increased health service use; Black, 1999) Thus, treating depression in patients with diabetes is particularly important in preventing or delaying diabetes complications, stabilizing metabolic control, and decreasing health service utilization Other studies have focused on the relationship between depressive symptoms and medical outcomes Results of a meta-analysis including 24 studies in which research participants had either type 1 or type 2 diabetes indicate that depression is signi cantly associated with hyperglycemia (Lustman, Anderson, et al, 2000) Similar effect sizes were found in studies of patients with both types of diabetes.

However, results differed depending on the assessment methods utilized To elucidate, larger effect sizes were found when standardized interviews and diagnostic criteria were employed to assess depression in comparison to self-report questionnaires (eg, BDI; Beck, Ward, & Mendelson, 1961) According to the authors, it is possible that one of the reasons for these results is the decreased speci city of selfreport measures that capture not only depression but also anxiety, general emotional distress, or medical illness Nonetheless, the authors assert that future research is needed to determine the cause and effect relationship between depression and hyperglycemia as well as the effect of depression treatment on glycemic control and the continuous course of diabetes In addition, Gary, Crum, Cooper-Patrick,.

Methods for the detection, identi cation, and quanti cation of GMOs have become widely used by enforcement laboratories, and the number of published systems has increased considerably in recent years. Nowadays, a wide range of methods is available, which can generate confusion for nal users. In order to determine the suitability of each method for providing reliable analytical data, every method should be validated (Anklam et al. 2002). The concept of validation implies that the application of a given method must provide similar analytical results in different laboratories using different reagents and operators. For this reason, the validation process must involve several laboratories, which have to be coordinated by a principal one, usually different from the laboratory that developed the method. In Europe, there are institutes that coordinate GMO validation studies, such as the Federal Institute for Health Protection of Consumers and Veterinary Medicine (BgVV) in Germany, or the Community Reference Laboratory (CRL) through the Biotechnology and GMOs Unit of the Joint Research Centre (JRC) in Italy (http://gmo-crl.jrc.it). The Gipsa Grain Inspection, Packers and Stockyards Administration (GIPSA) of the U.S. Department of Agriculture (USDA) and

(See Section 2.4.2) and placed in the HARV bioreactor. The biochamber is secured on its motor mount and the entire apparatus is placed in the incubator at 37 C in 5% CO2 . The rotational speed is set a 62 rpm. The coculture period is for a total of 72 h, after which the aggregate is retrieved from the biochamber and washed (3 ) with NT2M by resuspension and settling. The SNAC tissue construct is now ready for transplantation or assay.

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