Endocrine and Diabetes Center

Vishākhapatnam, India

Endocrine and Diabetes Center

Vishākhapatnam, India
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Bhramaramba R.,VNR VJIET | Allam A.R.,Jawaharlal Nehru Technological University Kakinada | Kumar V.V.,Godavari Institute of Engineering and Technology | Sridhar G.R.,Endocrine and Diabetes Center
International Journal of Diabetes in Developing Countries | Year: 2011

Genomic Data is growing very rapidly with the sequencing of genomes of various forms of life. To understand the overwhelming data and to obtain meaningful information, Data Mining techniques such as Principal Component Analysis and Discriminant Analysis are used for the purpose. Data Mining is basically used when the data is vast and there is need to extract the hidden knowledge in the form of useful patterns. The data set taken into consideration is protein data pertaining to diabetes mellitus obtained from a database. The task at hand was to find out in which species most of the diabetes related proteins exist. It so happened that most of these proteins were prevalent in Human Beings, House Mice and Norway Rat as they are all mammals and Human Beings have orthologs as House Mice and Norway Rat. Both these techniques prove that human beings show a variation from those of House Mice and Norway Rat which are similar in terms of the variation of protein attributes. This can also be inferred from statistical analysis by using histograms and bivariate plots. Other Data Mining Techniques such as Regression and Clustering can be used to further explore the above inference. © 2011 Research Society for Study of Diabetes in India.

Rao A.A.,Jawaharlal Nehru Technological University Kakinada | Rao Dattatreya A.V.,Acharya Nagarjuna University | Sridhar G.R.,Endocrine and Diabetes Center
Journal of Computer Science | Year: 2010

Problem statement: The k-means method is one of the most widely used clustering techniques for various applications. However, the k-means often converges to local optimum and the result depends on the initial seeds. Inappropriate choice of initial seeds may yield poor results. kmeans++ is a way of initializing k-means by choosing initial seeds with specific probabilities. Due to the random selection of first seed and the minimum probable distance, the k-means++ also results different clusters in different runs in different number of iterations. Approach: In this study we proposed a method called Single Pass Seed Selection (SPSS) algorithm as modification to k-means++ to initialize first seed and probable distance for k-means++ based on the point which was close to more number of other points in the data set. Result: We evaluated its performance by applying on various datasets and compare with k-means++. The SPSS algorithm was a single pass algorithm yielding unique solution in less number of iterations when compared to k-means++. Experimental results on real data sets (4-60 dimensions, 27-10945 objects and 2-10 clusters) from UCI demonstrated the effectiveness of the SPSS in producing consistent clustering results. Conclusion: SPSS performed well on high dimensional data sets. Its efficiency increased with the increase of features in the data set; particularly when number of features greater than 10 we suggested the proposed method. © 2010 Science Publications.

Rao M.R.N.,Vignan Institute of Technology and Science | Sridhar G.R.,Endocrine and Diabetes Center | Madhu K.,Andhra University | Rao A.A.,Jawaharlal Nehru Technological University Kakinada
Diabetes and Metabolic Syndrome: Clinical Research and Reviews | Year: 2010

Background: In diabetes mellitus, quality of life is recognized to be an integral outcome measure of management. We have developed a neural network system which is trained to predict the measurements of quality of life in diabetes, using data generated in real life. Methods: We developed a multi-layer perceptron neural network (NN) model, which had been trained by back propagation algorithm. Data was obtained from a cohort of 241 individuals with diabetes, which has been published. We used age, gender, weight, fasting plasma glucose as a set of inputs and predicted measures of quality of life (satisfaction, impact, social and diabetes worry). Results: Using the NN model, men reported significantly higher levels of satisfaction with the treatment being provided to them than women. Women had greater social and diabetes worry. The results have been considered based on the observation of the normalized system error (NSE) values of the neural network and are consistent with results obtained from traditional statistical methods. Conclusion: We have developed a prototype neural network model to measure the quality of life in diabetes, when biological or biographical variables are given as inputs. © 2008 Diabetes India.

Pasala S.K.,Andhra University | Rao A.A.,Jawaharlal Nehru Technological University Kakinada | Sridhar G.R.,Endocrine and Diabetes Center
International Journal of Diabetes in Developing Countries | Year: 2010

Development of type 2 diabetes mellitus is influenced by built environment, which is, ′the environments that are modified by humans, including homes, schools, workplaces, highways, urban sprawls, accessibility to amenities, leisure, and pollution.′ Built environment contributes to diabetes through access to physical activity and through stress, by affecting the sleep cycle. With globalization, there is a possibility that western environmental models may be replicated in developing countries such as India, where the underlying genetic predisposition makes them particularly susceptible to diabetes. Here we review published information on the relationship between built environment and diabetes, so that appropriate modifications can be incorporated to reduce the risk of developing diabetes mellitus.

Carlsson B.-M.,SAS Hospital Organization | Carlsson B.-M.,Gothenburg University | Attvall S.,Gothenburg University | Attvall S.,Sahlgrenska University Hospital | And 6 more authors.
Diabetes Technology and Therapeutics | Year: 2013

Aim: This study examined long-term effects of continuous subcutaneous insulin infusion (CSII) in clinical practice on glycemic control in patients with type 1 diabetes. Subjects and Methods: We evaluated all type 1 diabetes patients at 10 diabetes outpatient clinics in Sweden who had been treated with CSII for at least 5.5 years and had valid glycated hemoglobin (HbA1c) data before starting pump use and at 5 years±6 months. Controls treated with multiple daily insulin injections (MDI) over a time-matched period were also evaluated. Results: There were 331 patients treated with CSII at least 5.5 years at the 10 clinics. Of these, 272 (82%) fulfilled the inclusion criteria. Patients treated with CSII were younger than those treated with MDI (mean age, 38.6 vs. 45.6 years; P<0.001), more were women (56% vs. 43%; P<0.001), and diabetes duration was shorter (mean, 15.1 years vs. 20.1 years; P<0.001). After adjusting for variables differing at baseline and influencing the change in HbA1c over the study period, the reduction in HbA1c remained statistically significant at 5 years and was estimated to be 0.20% (95% confidence interval [CI] 0.07-0.32) (2.17 mmol/mol [95% CI 0.81-3.53]) (P=0.002). The corresponding adjusted reduction at years 1 and 2 was 0.42% (95% CI 0.31-0.53) (4.59 mmol/mol [95% CI 3.41-5.77]) (P<0.001) and 0.43% (95% CI 0.31-0.55) (4.71 mmol/mol [95% CI 3.38-6.04]) (P<0.001), respectively. The effect of insulin pump use versus controls on HbA1c decreased significantly with time (P<0.001). Conclusions: Use of CSII in clinical practice in Sweden is associated with an approximately 0.2% (2 mmol/mol) reduction in HbA1c after 5 years. © Mary Ann Liebert, Inc.

Sridhar G.R.,Endocrine and Diabetes Center | Putcha V.,AccoStats Solutions | Lakshmi G.,Kasturba Medical College
Journal of Association of Physicians of India | Year: 2010

Objective: To assess the time trends in the prevalence of diabetes at our Centre from 1994-2004 (N: 19,072 individuals) on the following parameters: age group, sex, rural or urban area and individuals with freshly diagnosed diabetes versus known diabetes Study Design and Setting: Analysis of data from electronic medical records at a referral Endocrine and Diabetes Centre in Southern India Methods: We have employed the period prevalence method and person-time risk to express the results. The concept of person-time risk can be estimated as the actual time-at-risk in years that all persons contributed to a study. The person-time can be estimated for each patient when a patient changed from diabetic free to diabetic patient. This can be captured for each patient from the variable onset of first diagnoses as a diabetic patient. Thus person-time is employed to derive information on the rate at which people acquire the disease. Results: Between 1994 and 2004 however there is an increasing trend in the number of individuals in the young, particularly the 18-34 year group. Similarly there is a steadily increasing pattern in both urban and rural areas; the number from rural areas tended to increase compared to urban areas. The number of women with diabetes tended to increase over the 10-year period Conclusion: Between 1994 and 2004 among persons with diabetes who presented at our Centre, there was a trend toward more number of younger persons, particularly women from rural areas. © JAPI.

Razia S.,Koneru Lakshmaiah College of Engineering | Narasingarao M.R.,Koneru Lakshmaiah College of Engineering | Sridhar G.R.,Endocrine and Diabetes Center
Journal of Theoretical and Applied Information Technology | Year: 2015

Thyroid disease is a major cause of concern in medical diagnosis and the prediction/onset of which is a difficult proposition in medical research. In this research, we use two Neural Network models Multilayer perceptron (MLP) and Radial Basis Function Networks (RBF) for the prediction of onset of thyroid disease using the data generated in real life. The MLP is trained and tested with Back-propagation algorithm whereas RBF networks was trained and tested with SPSS software. Thyroid disease database which had been published and was used for empirical comparisons and the results show that MLP and RBF show the almost same kind of results in diagnosing the thyroid disease. © 2005-2015 JATIT & LLS. All rights reserved.

Sridhar G.R.,Endocrine and Diabetes Center
Advances in Experimental Medicine and Biology | Year: 2010

Glutathione S-transferases (GST) belong to the transferase group of enzymes; GST are a family of enzymes that catalyze the addition of glutathione to endogenous or xenobiotic, often toxic electrophilic chemicals, and a major group of detoxification enzymes. We used the homology modeling technique to construct the structure of Gallus gallus GST. The amino acid sequence identity between the target protein and sequence of template protein 1ML6 (Mus musculus) was 66.2%. Based on the template structure, the protein model was constructed by using the Homology program Modeller9v1, and briefly refined by energy minimization steps; it was validated by PROCHECK. In all, 94.4% of the amino acids were in allowed regions of Ramachandran plot, showing the accuracy of the model and good stereochemical quality. Our results correlated well with the experimental data reported earlier, which proved the quality of the model. This generated model can be further used for the design and development of more potent GST inhibitors. © 2010 Springer Science+Business Media, LLC.

PubMed | Endocrine and Diabetes Center
Type: Journal Article | Journal: World journal of diabetes | Year: 2015

Type 2 diabetes mellitus and Alzheimers disease are both associated with increasing age, and each increases the risk of development of the other. Epidemiological, clinical, biochemical and imaging studies have shown that elevated glucose levels and diabetes are associated with cognitive dysfunction, the most prevalent cause of which is Alzheimers disease. Cross sectional studies have clearly shown such an association, whereas longitudinal studies are equivocal, reflecting the many complex ways in which the two interact. Despite the dichotomy, common risk and etiological factors (obesity, dyslipidemia, insulin resistance, and sedentary habits) are recognized; correction of these by lifestyle changes and pharmacological agents can be expected to prevent or retard the progression of both diseases. Common pathogenic factors in both conditions span a broad sweep including chronic hyperglycemia per se, hyperinsulinemia, insulin resistance, acute hypoglycemic episodes, especially in the elderly, microvascular disease, fibrillar deposits (in brain in Alzheimers disease and in pancreas in type 2 diabetes), altered insulin processing, inflammation, obesity, dyslipidemia, altered levels of insulin like growth factor and occurrence of variant forms of the protein butyrylcholinesterase. Of interest not only do lifestyle measures have a protective effect against the development of cognitive impairment due to Alzheimers disease, but so do some of the pharmacological agents used in the treatment of diabetes such as insulin (especially when delivered intranasally), metformin, peroxisome proliferator-activated receptors agonists, glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors. Diabetes must be recognized as a risk for development of Alzheimers disease; clinicians must ensure preventive care be given to control and postpone both conditions, and to identify cognitive impairment early to manage it appropriately.

PubMed | Endocrine and Diabetes Center
Type: Review | Journal: World journal of diabetes | Year: 2016

Synchrony of biological processes with environmental cues developed over millennia to match growth, reproduction and senescence. This entails a complex interplay of genetic, metabolic, chemical, light, hormonal and hedonistic factors across life forms. Sleep is one of the most prominent rhythms where such a match is established. Over the past 100 years or so, it has been possible to disturb the synchrony between sleep-wake cycle and environmental cues. Development of electric lights, shift work and continual accessibility of the internet has disrupted this match. As a result, many non-communicable diseases such as obesity, insulin resistance, type 2 diabetes, coronary artery disease and malignancies have been attributed in part to such disruption. In this presentation a review is made of the origin and evolution of sleep studies, the pathogenic mediators for such asynchrony, clinical evidence and relevance and suggested management options to deal with the disturbances.

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