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.

Isolation and Characterization of Agglutinin in the Hemolymph of Penaeus indicus H. Milne Edwards

The present study is an attempt to find out the ralation between RNA/DNA ratio, protein,percentage growth rate and specific growth rate of prawn,Penaeus indicus with respect to Nervous system, Eyestalk and Muscle tissues during ontogenesis. We have isolated and purified a natural agglutinin in the hemolymph of P.indicus with antigenecity, agglutinating, hemolytic and antibacterial properties. The influence of growth and environmental parameters on the level of agglutinin in the hemolymph was studied. Agglutinin concentration during normal growth process was compared. The agglutinin concentration in the hemolymph was quantified through developing ELISA, which is useful in health monitoring studies of individual species. Complete amino acid composition of both the subunits of P.indicus agglutinin were analysed. P.indicus agglutinin showed similarity to those proteins having antigenecity,hemolytic and agglutinating properties.Hence, agglutinin was considered as a natural defence protein in the hemolymph of P.indicus responsible for immune surveillance. The humoral defence mechanism of agglutinin was a co-operative effort with hemocytes and complement system. The composition of isolated agglutinin of P.indicus amino acids will be helpful in the synthesis of new antibacterial analogues which can be used against disease causing organisms.

Detection of Flock Movement in Spatio-Temporal Database using Clustering Techniques - an Experience

In this paper, moving flock patterns are mined from spatio- temporal datasets by incorporating a clustering algorithm. A flock is defined as the set of data that move together for a certain continuous amount of time. Finding out moving flock patterns using clustering algorithms is a potential method to find out frequent patterns of movement in large trajectory datasets. In this approach, SPatial clusteRing algoRithm thrOugh sWarm intelligence (SPARROW) is the clustering algorithm used. The advantage of using SPARROW algorithm is that it can effectively discover clusters of widely varying sizes and shapes from large databases. Variations of the proposed method are addressed and also the experimental results show that the problem of scalability and duplicate pattern formation is addressed. This method also reduces the number of patterns produced