Neuronal degeneration in streptozotocin induced diabetic rats: Effect of Aegle marmelose and pyridoxine in pancreatic cell proliferation and neuronal Surviva

This thesis Entitled Neuronal degeneration in streptozotocin induced diabetic rats: effect of aegle marmelose and pyridoxine in pancreatic B cell proliferation and neuronal survival. Diabetes mellitus, a chronic metabolic disorder results in neurological dysfunctions and structural changes in the CNS. Antioxidant therapy is a challenging but necessary dimension in the management of diabetes and neurodegenerative changes associated with it. Our results showed regional variation and imbalance in the expression pattern of dopaminergic receptor subtypes in diabetes and its role in imbalanced insulin signaling and glucose regulation. Disrupted dopaminergic signaling and increased hyperglycemic stress in diabetes contributed to the neuronal loss. Neuronal loss in diabetic rats mediated through the expression of pattern of GLUT-3, CREB, IGF-1, Akt-1, NF,B, second messengers- cAMP, cGMP, IP3 and activation of apoptotic factors factors- TNF-a,caspase-8. Disrupted dopaminergic receptor expressions and its signaling in pancreas contributed defective insulin secretion in diabetes. Activation of apoptotic factors- TNF- a,caspase-8 and defective functioning of neuronal survival factors, disrupted second messenger signaling modulated neuronal viability in diabetes. Hyperglycemic stress activated the expression of TNF-a,caspase-8, BAX and differential expression of anti oxidant enzymes- SOD and GPx in liver lead to apoptosis. Treatment of diabetic rats with insulin, Aegle marmelose and pyridoxine significantly reversed the altered dopaminergic neurotransmission, GLUT3, GLUT2, IGF-1 and second messenger signaling. Antihyperglycemic and antioxidant activity of Aegle marmelose and pyridoxine enhanced pancreatic B cell proliferation, increased insulin synthesis and secretion in diabetic rats. Thus our results conclude the neuroprotective and regenerating ability of Aegle marmelose and pyridoxine which in turn has a novel therapeutic role in the management of diabetes.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.