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      Melatonin Attenuates Contrast-Induced Nephropathy in Diabetic Rats: The Role of Interleukin-33 and Oxidative Stress

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          Abstract

          Background. Inflammation and oxidative stress (OxS) contribute to the pathogenesis of diabetic kidney disease (DKD) and contrast-induced nephropathy (CIN). Patients with DKD were found to be more prone to CIN. Interleukin-33 (IL-33) is a proinflammatory cytokine, but its role in DKD and CIN is unknown. Methods. Thirty male Sprague-Dawley rats were enrolled. The first group was comprised of healthy rats (HRs), whereas the other four groups were made up of diabetic rats (DRs), diabetic rats with contrast-induced nephropathy (CIN + DRs), melatonin-treated diabetic rats (MTDRs), and melatonin-treated CIN + DRs (MTCIN + DRs). All groups except the HRs received 50 mg/kg/day streptozotocin (STZ). CIN + DRs were constituted by administrating 1.5 mg/kg of intravenous radiocontrast dye on the 35th day. MTDRs and MTCIN + DRs were given 20 mg/kg/day of intraperitoneal injection of melatonin (MT) from the 28th day for the constitutive seven days. Results. We observed increased IL-33 in the kidney tissue following induction of CIN in DRs. To determine whether MT is effective in preventing CIN, we administered MT in CIN + DRs and demonstrated that kidney tissue levels of OxS markers, inflammatory cytokines, and IL-33 were significantly diminished in MTCIN + DRs compared with other groups without MT treatment ( p < 0.05). Conclusion. Inhibition of IL-33 with MT provides therapeutic potential in DKD with CIN.

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          A simple method for clinical assay of superoxide dismutase.

          This assay for superoxide dismutase (SOD, EC 1.15.1.1) activity involves inhibition of nitroblue tetrazolium reduction, with xanthine-xanthine oxidase used as a superoxide generator. By using a reaction terminator, we can determine 40 samples within 55 min. One unit of activity of pure bovine liver Cu,ZnSOD and chicken liver MnSOD was expressed by 30 ng and 500 ng of protein, respectively. The mean concentrations of Cu,ZnSOD as measured by this method in blood from normal adults were 242 (SEM 4) mg/L in erythrocytes, 548 (SEM 20) micrograms/L in serum, and 173 (SEM 11) micrograms/L in plasma. The Cu,ZnSOD concentrations in serum and plasma of patients with cancer of the large intestine tended to be less and greater than these values, respectively, but not statistically significantly so.
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            A simple risk score for prediction of contrast-induced nephropathy after percutaneous coronary intervention: development and initial validation.

            We sought to develop a simple risk score of contrast-induced nephropathy (CIN) after percutaneous coronary intervention (PCI). Although several risk factors for CIN have been identified, the cumulative risk rendered by their combination is unknown. A total of 8,357 patients were randomly assigned to a development and a validation dataset. The baseline clinical and procedural characteristics of the 5,571 patients in the development dataset were considered as candidate univariate predictors of CIN (increase >or=25% and/or >or=0.5 mg/dl in serum creatinine at 48 h after PCI vs. baseline). Multivariate logistic regression was then used to identify independent predictors of CIN with a p value 75 years, anemia, and volume of contrast) were assigned a weighted integer; the sum of the integers was a total risk score for each patient. The overall occurrence of CIN in the development set was 13.1% (range 7.5% to 57.3% for a low [ or=16] risk score, respectively); the rate of CIN increased exponentially with increasing risk score (Cochran Armitage chi-square, p < 0.0001). In the 2,786 patients of the validation dataset, the model demonstrated good discriminative power (c statistic = 0.67); the increasing risk score was again strongly associated with CIN (range 8.4% to 55.9% for a low and high risk score, respectively). The risk of CIN after PCI can be simply assessed using readily available information. This risk score can be used for both clinical and investigational purposes.
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              Economic costs of diabetes in the U.S. In 2007.

              (2008)
              The prevalence of diabetes continues to grow, with the number of people in the U.S. with diagnosed diabetes now reaching 17.5 million. The objectives of this study are to quantify the economic burden of diabetes caused by increased health resource use and lost productivity, and to provide a detailed breakdown of the costs attributed to diabetes. This study uses a prevalence-based approach that combines the demographics of the population in 2007 with diabetes prevalence rates and other epidemiological data, health care costs, and economic data into a Cost of Diabetes Model. Health resource use and associated medical costs are analyzed by age, sex, type of medical condition, and health resource category. Data sources include national surveys and claims databases, as well as a proprietary database that contains annual medical claims for 16.3 million people in 2006. The total estimated cost of diabetes in 2007 is $174 billion, including $116 billion in excess medical expenditures and $58 billion in reduced national productivity. Medical costs attributed to diabetes include $27 billion for care to directly treat diabetes, $58 billion to treat the portion of diabetes-related chronic complications that are attributed to diabetes, and $31 billon in excess general medical costs. The largest components of medical expenditures attributed to diabetes are hospital inpatient care (50% of total cost), diabetes medication and supplies (12%), retail prescriptions to treat complications of diabetes (11%), and physician office visits (9%). People with diagnosed diabetes incur average expenditures of $11,744 per year, of which $6,649 is attributed to diabetes. People with diagnosed diabetes, on average, have medical expenditures that are approximately 2.3 times higher than what expenditures would be in the absence of diabetes. For the cost categories analyzed, approximately $1 in $5 health care dollars in the U.S. is spent caring for someone with diagnosed diabetes, while approximately $1 in $10 health care dollars is attributed to diabetes. Indirect costs include increased absenteeism ($2.6 billion) and reduced productivity while at work ($20.0 billion) for the employed population, reduced productivity for those not in the labor force ($0.8 billion), unemployment from disease-related disability ($7.9 billion), and lost productive capacity due to early mortality ($26.9 billion). The actual national burden of diabetes is likely to exceed the $174 billion estimate because it omits the social cost of intangibles such as pain and suffering, care provided by nonpaid caregivers, excess medical costs associated with undiagnosed diabetes, and diabetes-attributed costs for health care expenditures categories omitted from this study. Omitted from this analysis are expenditure categories such as health care system administrative costs, over-the-counter medications, clinician training programs, and research and infrastructure development. The burden of diabetes is imposed on all sectors of society-higher insurance premiums paid by employees and employers, reduced earnings through productivity loss, and reduced overall quality of life for people with diabetes and their families and friends.
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                Author and article information

                Journal
                Mediators Inflamm
                Mediators Inflamm
                MI
                Mediators of Inflammation
                Hindawi Publishing Corporation
                0962-9351
                1466-1861
                2016
                18 February 2016
                : 2016
                : 9050828
                Affiliations
                1Department of Anesthesiology and Reanimation, Faculty of Medicine, Erzincan University, Erzincan, Turkey
                2Department of Cardiovascular Surgery, Faculty of Medicine, Erzincan University, Erzincan, Turkey
                3Division of Nephrology, Department of Internal Medicine, Meram School of Medicine, Necmettin Erbakan University, Meram Tıp Fakültesi No. 215, Meram, 42090 Konya, Turkey
                4Department of Biochemistry, Faculty of Veterinary, Ataturk University, Erzurum, Turkey
                5Department of Anesthesiology and Reanimation, Medeniyet University, Goztepe Training and Research Hospital, Istanbul, Turkey
                6Department of Histology and Embryology, Faculty of Medicine, Ataturk University, Erzurum, Turkey
                7Department of Cardiology, Faculty of Medicine, Erzincan University, Erzincan, Turkey
                Author notes
                *Kultigin Turkmen: ucmdkt@ 123456gmail.com

                Academic Editor: Muzamil Ahmad

                Article
                10.1155/2016/9050828
                4775802
                26989334
                77559bc8-8fea-423f-9f85-7809bd728292
                Copyright © 2016 Didem Onk et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 November 2015
                : 21 January 2016
                : 27 January 2016
                Categories
                Research Article

                Immunology
                Immunology

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