Human body is continuously exposed to different types of agents that results in the production of reactive species called as free radicals (ROS/RNS) which by the transfer of their free unpaired electron causes the oxidation of cellular machinery. In order to encounter the deleterious effects of such species, body has got endogenous antioxidant systems or it obtains exogenous antioxidants from diet that neutralizes such species and keeps the homeostasis of body. Any imbalance between the RS and antioxidants leads to produce a condition known as "oxidative stress" that results in the development of pathological condition among which one is diabetes. Most of the studies reveal the inference of oxidative stress in diabetes pathogenesis by the alteration in enzymatic systems, lipid peroxidation, impaired Glutathione metabolism and decreased Vitamin C levels. Lipids, proteins, DNA damage, Glutathione, catalane and superoxide dismutase are various biomarkers of oxidative stress in diabetes mellitus. Oxidative stress induced complications of diabetes may include stroke, neuropathy, retinopathy and nephropathy. The basic aim of this review was to summarize the basics of oxidative stress in diabetes mellitus.

SummaryA relationship has been reported between trace elements and diabetes mellitus. This study evaluated the role of such a relationship in 83 patients with non-insulin dependent diabetes mellitus (40 men and 43 women), with a mean duration of diabetes of 3.9 ± 3.6 years. Patients with nephropathy were excluded. Thirty healthy nondiabetic subjects were studied for comparative analysis. Subjects were subdivided into obese and non-obese. Diabetic subjects were also subdivided into controlied and uncontrolled groups; control was based on fasting blood glucose and serum fructosamine levels. Plasma copper, zinc and magnesium levels were analysed using a GBC 902 double beam atomic absorption spectrophotometer. Plasma zinc and magnesium levels were comparable between diabetic and nondiabetic subjects, while copper levels were significantly elevated (p<0.01) in diabetic patients. Age, sex, duration and control of diabetes did not influence copper, zinc, or magnesium concentrations. We conclude that zinc and magnesium levels are not altered in diabetes mellitus, but the increased copper levels found in diabetics in our study may merit further investigation of the relationship between copper and non-insulin dependent diabetes mellitus.

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Introduction and Aim:The increase in the life expectancy does not necessarily correlate with a higher quality of life. The objective of this study was to determine the influences of social factors to the quality of life of the elderly in Malaysia.Methodology: This cross sectional study was conducted in Penang, Malaysia among 2005 randomly sampled elderly using the WHOQOL-BREF scale. The sample was randomly collected from a list of residents of the state who are aged 60 years and older who receive the special aid provided by the Penang state government to all elderly residing in Penang irrespective of their socio and economic status.Results: Regression analysis showed that after controlling for demographic factors which include age, sex, race, marital status, education and employment; living with spouse and family members and being socially active were significantly associated with increased quality of life scores and being dependent on partner and children as compared to being selfdependent on mobility and having poor and moderate support as compared to good social support were significantly associated with decreased quality of life scores. Conclusion:The quality of life of the elderly is very much influenced by social factors.

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Alterations in trace elements and mineral homeostasis have been documented both in insulin-dependent diabetes mellitus and non-insulin dependent diabetes mellitus. No data are available about trace elements in fibrocalculous pancreatic diabetes, a unique form of secondary diabetes mellitus. This study evaluated the plasma concentrations of copper, zinc and magnesium in this form of diabetes. Twenty-five patients (9 men and 16 women) with fibrocalculous pancreatic diabetes and 25 healthy non-diabetic subjects (16 men and 9 women) were studied. Patients with overt nephropathy were excluded. Plasma copper, zinc, and magnesium levels were analyzed using a GBC 902 double beam absorption spectrophotometer. The effect of glycemic control, microalbuminuria, sex and modality of treatment received on the plasma levels of copper, zinc and magnesium was assessed. Results of the study revealed that plasma copper, zinc, and magnesium levels were comparable between patients with fibrocalculous pancreatic diabetes and control subjects. Plasma copper levels were significantly higher in patients with controlled diabetes (16.15 +/- 0.67 micromol L(-1)) as compared to those with uncontrolled diabetes (13.75 +/- 0.61 micromol L(-1)) and healthy controls (13.91 +/- 0.55 micromol L(-1)). This merits further investigation. Microalbuminuria, modality of treatment received and sex did not influence the levels of these elements in fibrocalculous pancreatic diabetes.

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Efficient monitoring of cardiac patients can save tremendous amount of lives. Cardiac disease prediction and classification has gained utmost significance in this regard during the past few years. This paper presents a predictive model for classification of arrhythmias. The model works by selecting best features using wrapper algorithm around random forest, followed by implementing various machine learning classifiers on the selected features. Cardiac arrhythmia dataset from University of California, Irvine (UCI) machine learning repository has been used for the experimental purpose. After normalizing the data, repeated cross validation with 10 folds is applied on support vector machine (SVM), K nearest neighbor (KNN), Naïve Bayes, random forest, and Multi-Layer perceptron (MLP). The experimental results demonstrate that MLP beats other classifiers by achieving an average accuracy of 78.26%, while accuracies calculated for KNN and SVM are 76.6% and 74.4% respectively, outperforming the accuracies of previous models.