Neurotransmitters Functional Balance in Neurodegenerative Disease Management:recent Advances

The recent developments in neurobiology have rendered new prominence and potential to study about the structure and function of brain and related disorders. Human behaviour is the net result of neural control of the communication between brain cells. Neurotransmitters are chemicals that are used to relay, amplify and modulate electrical signals between neurons and/or another cell. It mediates rapid intercellular communication through the nervous system by interacting with cell surface receptors. These receptors often trigger second messenger signaling pathways that regulate the activity of ion channels. The functional balance of different neurotransmitters such as Acetylcholine (Ach), Dopamine (DA), Serotonin (5-HT), Norepinephrine (NE), Epinephrine (EPI), Glutamate and Gamma amino butyric acid (GABA) regulates the growth, division and other vital functions of a normal cell / organism (Sudha, 1998). Any change in neurotransmitters' functional balance will result in the failure of cell function and may lead to the occurrence of diseases. Abnormalities in the production or functioning of neurotransmitters have been implicated in a number of neurological disorders like Schizophrenia, Alzheimer's, Epilepsy, Depression and Parkinson's disease. Changes in central and peripheral neuronal signaling system is also noted in diabetes, cancer, cell proliferation, alcoholism and aging. Elucidation of neurotransmitters receptor interaction pathways and gene expression regulation by second messengers and transcriptional factors in health and disease conditions can lead to new small molecules for development of therapeutic agents to improve neurological disease conditions. Increased awareness of the global effects of neurological disorders should help health care planners and the neurological community set appropriate priorities in research, prevention, and management of these diseases.

Enhanced dopamine D2 receptor function in hypothalamus and corpus striatum: their role in liver, plasma and in vitro hepatocyte ALDH regulation in ethahol treated rats

Dopamine D2 receptors are involved in ethanol self- administration behavior and also suggested to mediate the onset and offset of ethanol drinking. In the present study, we investigated dopamine (DA) content and Dopamine D2 (DA D2) receptors in the hypothalamus and corpus striatum of ethanol treated rats and aldehyde dehydrogenase (ALDH) activity in the liver and plasma of ethanol treated rats and in vitro hepatocyte cultures. Hypothalamic and corpus striatal DA content decreased significantly (P\0.05, P\0.001 respectively) and homovanillic acid/ dopamine (HVA/DA) ratio increased significantly (P\0.001) in ethanol treated rats when compared to control. Scatchard analysis of [3H] YM-09151-2 binding to DA D2 receptors in hypothalamus showed a significant increase (P\0.001) in Bmax without any change in Kd in ethanol treated rats compared to control. The Kd of DA D2 receptors significantly decreased (P\0.05) in the corpus striatum of ethanol treated rats when compared to control. DA D2 receptor affinity in the hypothalamus and corpus striatum of control and ethanol treated rats fitted to a single site model with unity as Hill slope value. The in vitro studies on hepatocyte cultures showed that 10-5 M and 10-7 M DA can reverse the increased ALDH activity in 10% ethanol treated cells to near control level. Sulpiride, an antagonist of DA D2, reversed the effect of dopamine on 10% ethanol induced ALDH activity in hepatocytes. Our results showed a decreased dopamine concentration with enhanced DA D2 receptors in the hypothalamus and corpus striatum of ethanol treated rats. Also, increased ALDH was observed in the plasma and liver of ethanol treated rats and in vitro hepatocyte cultures with 10% ethanol as a compensatory mechanism for increased aldehyde production due to increased dopamine metabolism. A decrease in dopamine concentration in major brain regions is coupled with an increase in ALDH activity in liver and plasma, which contributes to the tendency for alcoholism. Since the administration of 10-5 M and 10-7 M DA can reverse the increased ALDH activity in ethanol treated cells to near control level, this has therapeutic application to correct ethanol addicts from addiction due to allergic reaction observed in aldehyde accumulation.

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.