Enhanced /3-adrenergic receptors in the brain and pancreas during pancreatic regeneration in weanling rats

Adrenergic stimulation has an inyortant role in the pancreatic It-cell proliferation and insulin secretion. In the present study. we have investigaled how sympathetic system mgulales the panrrealic n I rnerui nr ht an:ilyiing I'pinephi inn 1111 ), Norepinephrinc (NE) and /1-adrenergic receptor changes in the brain as (%eli is in the I swirls. Fill and NII showed a significant decrease in the brain regions, pancreas and plasma :rt 72Ius iller partial prurcrealectonty. We observed an increase in the circulating insulin levels at 72 hrs. Scatchard analysis using I CHI propranolol showed a significant increase in the number of loth the low affinity and high affinity t-adrenergic receplors in cerebral cortex and hypothalamus of partially pancreatectornised rats during peak DNA synthesis. The affinity of the receptors decrea,ed significantly in the low and high affinity receptors of cerebral cortex and the high affinity hypothalamic receptors. In file brain stein, low affinity receptors were increased significantly during regeneration whereas there was no change in the high affinity receptors. The pancreatic ff-adrenergic receptors were also up regulated at 72 firs after partial panerealectony. In vitro studies showed that /i-adrenergic receptors are positive regulators of islet cell proliferation and insulin secretion. Thus our results suggest that the t-adrenergic receptors are functionally enhanced during pancreatic regeneration, which in turn increases pancreatic ft-cell proliferation an(hilisulin secretion in wean hug rats.

Decreased [ 3 H] YM-09151-2 binding to dopamine D2 receptors in the hypothalamus, brainstem and pancreatic islets of streptozotocin -induced diabetic rats

In the present study dopamine was measured in the hypothalamus, brainstem, pancreatic islets and plasma, using HPLC. Dopamine D2 receptor changes in the hypothalamus, brainstem and pancreatic islets were studied using [3H] YM-09151-2 in streptozotocin-induced diabetic and insulintreated diabetic rats. There was a significant decrease in dopatnine content in the hypothalamus (P<0.001), brainstem (P<0.001), pancreatic islets (P<0.001) and plasma (P<0.00I) in diabetic rats when compared to control. Scatchard analysis of [3H] YM-09151-2 in the hypothalamus of diabetic rats showed a significant decrease in Bax (P<0.001) and Kd, showing an increased affinity of D2 receptors when compared to control. Insulin treatment did not completely reverse the changes that occurred during diabetes. There was a significant decrease in B,nax (P<0.01) with decreased affinity in the brainstem of diabetic rats. The islet membrane preparation of diabetic rats showed a significant decrease (P<0.001) in the binding of [3H] YM-09 151-2 with decreased Kd (P<0.001) compared to control. The increase in affinity of D2 receptors in hypothalamus and pancreatic islets and the decreased affinity in brainstem were confirmed by competition analysis. Thus our results suggest that the decreased dopamine D, receptor function in the hypothalamus, brainstem and pancreas affects insulin secretion in diabetic rats, which has immense clinical relevance to the management of diabetes.

Decreased GABAA Receptors Functional Regulation 3 in the Cerebral Cortex and Brainstem of Hypoxic Neonatal Rats: 4 Effect of Glucose and Oxygen Supplementation

Hypoxia in neonates can lead to biochemical and molecular alterations mediated through changes in neurotransmitters resulting in permanent damage to brain. In this study, we evaluated the changes in the receptor status of GABAA in the cerebral cortex and brainstem of hypoxic neonatal rats and hypoxic rats supplemented with glucose and oxygen using binding assays and gene expression of GABAAa1 and GABAAc5. In the cerebral cortex and brainstem of hypoxic neonatal rats, a significant decrease in GABAA receptors was observed, which accounts for the respiratory inhibition. Hypoxic rats sup- plemented with glucose alone and with glucose and oxygen showed, respectively, a reversal of the GABAA receptors, andGABAAa1 and GABAAc5 gene expression to control. Glucose acts as an immediate energy source thereby reducing the ATP-depletion-induced increase in GABA and oxygenation, which helps in encountering anoxia. Resuscitation with oxygen alone was less effective in reversing the receptor alterations. Thus, the results of this study suggest that reduction in the GABAA receptors functional regulation during hypoxia plays an important role in mediating the brain damage. Glucose alone and glucose and oxygen supplementation to hypoxic neonatal rats helps in protecting the brain from severe hypoxic damage.

Scaling relations in the Lyapunov exponents of one dimensional maps

We establish numerically the validity of Huberman-Rudnick scaling relation for Lyapunov exponents during the period doubling route to chaos in one dimensional maps. We extend our studies to the context of a combination map. where the scaling index is found to be different.

Evidence for the existence of Sarkovskii ordering in a two-dimensional map

We analyse numerically the bifurcation structure of a two-dimensional noninvertible map and show that different periodic cycles are arranged in it exactly in the same order as in the case of the logistic map. We also show that this map satisfies the general criteria for the existence of Sarkovskii ordering, which supports our numerical result. Incidently, this is the first report of the existence of Sarkovskii ordering in a two-dimensional map.

Forms of Nitrogen (NO 3 - -N; NO 2 - -N and NH 2 CONH 2 -N) and their relations to A.O.U. in the Indian coastal waters of Arabian Sea

The distribution of three important dissolved forms of nitrogen, viz. nitrate, nitrite and urea in the surface and bottom water samples collected from 27 selected hydrographic profiles, in the Arabian Sea, along the west coast of India is described. Of the three forms, nitrate concentrations were the highest and comparatively higher concentrations were observed in the bottom water. Decomposition of organic matter resulting in the release of the thermodynamically stable nitrogen species, i.e. nitrate, may be the major factor resulting in higher nitrate concentrations at these depths, where the water is also characterized by low values of dissolved oxygen and temperature. The significant positive correlation between A.O.U. and nitrate of the bottom water samples emphasizes the role of oxidative decomposition of organic matter which plays an active role in reducing the oxygen concentrations below the theoretical values since at this depth ( 200 m) the net production is taken to be zero. This is also evidenced by the negative correlation of nitrate with dissolved oxygen and temperature, for the bottom samples

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.