Kozo Shinagawa

Nitric oxide generation in aqueous solutions of cigarette smoke and approaches to its origin.

Abstract By using the ESR spin trapping technique with the NmethylDglucamine dithiocarbamate (MGD)2-Fe(II) complex, the generation of nitric oxide (NO), a gaseous free radical, was observed in NO spin trapping solution bubbled with the filtered mainstream of cigarette smoke. The ESR signal with a threeline spectrum characteristic of an NO radical, which was not observed immediately after bubbling of smoke, started rapidly increasing with time up to around 25 min after the last addition of ferrous ions Fe(II), and then slowly approached a peak value dependent on the burned cigarette mass and on the smoking speed. The production of NO was, however, much affected by air oxidation and enhanced by the addition of ascorbic acid. A certain concentration of sodium nitrite (NaNO2) solution, in which nitrite NO2 is assumed as the main origin of the NO, mimicked closely the time course of NO generation resulting from the smoke of one cigarette. The cigarette smoke that was passed through alkaline pyrogallol solution as a deoxidizer; however, it exhibited an unchanged intensity of NO signal throughout the measurement. These results strongly suggest that NO would be gradually reproduced from NO2 in the reductive aqueous solution containing excess Fe(II) through NO2, which is initially formed and is concomitantly oxidized from NO in cigarette smoke.

2704 Nonlinear responses of nerve membranes by ferroelectric model

HIROKI NAKAE Oscillation of neural networks may play a role in various neural functions because it has been observed during development, perception, and memory. The network oscillation has been pointed out to depend on the oscillation of intracellular conditions. In order to explain macroscopic oscillation, however, some mechanisms other than the oscillation of individual neurons must be assumed. In this study, a neural network consisting of excitatory and inhibitory neurons was modeled by connecting neighboring cells. Each cell was designed to have properties of temporal summation and spontaneous firing. As a result of simulation under asynchronous conditions, the network showed an oscillatory behavior, although the frequency was not strictly constant. It was not observed with neurons without the property of temporal summation or under synchronous conditions. These results suggest that collaboration of excitatory and inhibitory neurons could be a cause of neural network oscillation.