Effects of heterogeneous structure and diffusion permeability of body tissues on decompression gas bubble dynamics
1Department of Extravehicular Activity, Institute of Biomedical Problems, Moscow, Russia. viknik@mmcc.ibmp.rssi.ru
Abstract:
To gain insight into the special nature of gas bubbles that may form in astronauts, aviators and divers, we developed a mathematical model which describes the following: 1) the dynamics of extravascular bubbles formed in intercellular cavities of a hypothetical tissue undergoing decompression; and 2) the dynamics of nitrogen tension in a thin layer of intercellular fluid and in a thick layer of cells surrounding the bubbles. This model is based on the assumption that, due to limited cellular membrane permeability for gas, a value of effective nitrogen diffusivity in the massive layer of cells in the radial direction is essentially lower compared to conventionally accepted values of nitrogen diffusivity in water and body tissues. Due to rather high nitrogen diffusivity in intercellular fluid, a bubble formed just at completion of fast one-stage reduction of ambient pressure almost instantly grows to the size determined by the initial volume of the intercellular cavity, surface tension of the fluid, the initial nitrogen tension in the tissue, and the level of final pressure. The rate of further bubble growth and maximum bubble size depend on comparatively low effective nitrogen diffusivity in the cell layer, the tissue perfusion rate, the initial nitrogen tension in the tissue, and the final ambient pressure. The tissue deformation pressure performs its conservative action on bubble dynamics only in a limited volume of tissue (at a high density of formed bubbles). Our model is completely consistent with the available data concerning the random latency times to the onset of decompression sickness (DCS) symptoms associated with hypobaric decompressions simulating extravehicular activity. We believe that this model could be used as a theoretical basis for development of more adequate methods for the DCS risk prediction.
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