Dopamine Receptor Gene Expression In Streptozotocin Induced Diabetic Rats And Its Role In Insulin Secretion

Diabetes Mellitus is a metabolic disorder associated with insulin deficiency, which not.only affects the carbohydrate metabolism but also is associated with various central and peripheral complications. Chronic hyperglycemia during diabetes mellitus is a major initiator of diabetic microvascular complications like retinopathy, neuropathy, The central nervous system (CNS) neurotransmitters play an important role in the regulation of glucose homeostasis. These neurotransmitters mediate rapid intracellular communications not only within the central nervous system but also in the peripheral tissues. They exert their function through receptors present in both neuronal and non neuronal cell surface that trigger second messenger signaling pathways. Dopamine is a neurotransmitter that has been implicated in various central neuronal degenerative disorders like Parkinson's disease and behavioral diseases like Schizophrenia. Dopamine is synthesised from tyrosine, stored in vesicles in axon terminals and released when the neuron is depolarised. Dopamine interacts with specific membrane receptors to produce its effect. Dopamine plays an important role both centrally and peripherally. The recent identification of five dopamine receptor subtypes provides a basis for understanding dopamine's central and peripheral actions . Dopamine receptors are classified into two major groups : DA D1 like and DA D2 like. Dopamine D1 like receptors consists of DA D1 and DA D5 receptors . Dopamine D2 like receptors consists of DA D2, DA D3 and DA D4 receptors. Stimulation of the DA D1 receptor gives rise to increased production of cAMP. Dopamine D2 receptors inhibit cAMP production, but activate the inositol phosphate second messenger system . Impairment of central dopamine neurotransmission causes muscle rigidity, hormonal regulation , thought disorder and cocaine addiction. Peripheral dopamine receptors mediate changes in blood flow, glomerular filtration rate, sodium excretion and catecholamine release. The dopamine D2 receptors increased in the corpus striatum and cerebral cortex but decreased in the hypothalamus and brain stem indicating their involvement in regulating insulin secretion. Dopamine D2 receptor which has a stimulatory effecton insulin secretion decreased in the pancreatic islets during diabetes. Our in vitro studies confirmed the stimulatory role of dopamine D2 receptors in stimulation of glucose induced insulin secretion. A detailed study at the molecular level on the mechanisms involved in the role of dopamine in insulin secretion, its functional modification could lead to therapeutic interventions that will have immense clinical importance.

AN ARTIFICIAL NEURAL NETWORK FOR SONAR TARGET DETECTION

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.