Effect of lipid on inert gas kinetics

Abstract

A Monte Carlo simulation of inert gas transit through skeletal muscle has been extended to include regions of increased gas solubility to simulate regions of high lipid content. Position of the regions within the simulation module was varied, as was the muscle-lipid partition coefficient (lambda). The volume percentage of the lipid regions (alpha) was varied from 0 to 25% while lambda covered the range from 1 to 50. The effects of alpha and lambda on mean transit time and on relative dispersion (RD; ratio of SD to the mean) were examined for a single lipid volume and compared with expected values under the assumption that the tissue is composed of two well-stirred compartments. Mean transit times varied from approximately 0.80 to 1.20 times the values predicted by a simple parallel two-compartment model, whereas RD varied from 0.9 to 3.6. For fixed lambda, RD as a function of lipid fraction passes through a maximum that is shifted and was also smaller than expected from a simple two-compartment model. For fixed alpha, RD approaches an asymptotic value for large lambda, but the asymptote is smaller than that expected from the two-compartment model. When lipid is distributed in only two regions, RD decreases with increasing separation of the regions and with increasing surface area of the fat regions. A model of two well-stirred compartments that allows mixing between the compartments yields results similar to those from the simulation.

Probabilistic Modelling for Estimating Gas Kinetics and Decompression Sickness Risk in Pigs During H2 Biochemical Decompression

“…This approach was followed in a previous analysis of H 2 biochemical decompression in a pig model , from which a single τ for H 2 uptake and elimination of 0.7-2 min was derived, depending on model assumptions. Although experiments have shown that more complex models are required to describe real tissues (Novotny et al, 1990;Himm et al, 1994), the simplicity of a single time constant for estimating the time when tissues reach equilibrium with the ambient pressure is appealing. Years of experience with the maximum likelihood technique have shown that a large number of experiments are necessary to solve problems such as differences in gas potencies for inducing DCS (Lillo, 1988;Lillo and MacCallum, 1991), or asymmetrical exchange kinetics for compression and decompression (Thalmann et al, 1997;Lillo and Parker, 2000).…”

“…Recent models have been more successful at predicting DCS outcomes by computing two or more estimates of τ . These have included estimating τ for compression separately from decompression (Lillo et al, 1997;Lillo and Parker, 2000); estimating separate values of τ for two different gases in a breathing mixture (Parker et al, 1998); and hypothesizing two or more compartments within dive subjects, with each compartment distinguished by its own τ (Tikuisis et al, 1991;Himm et al, 1994).…”

“…Therefore, Fahlman et al, (2001) used one exponential time constant to describe the kinetics, which satisfactorily explained the observed DCS incidence. However, since other DCS modelling efforts (Tikuisis et al, 1991;Himm et al, 1994;Lillo et al, 1997;Parker et al, 1998;Lillo and Parker, 2000) and studies using direct physiological or physical measurements of gas fluxes in animals (D'aoust et al, 1976;Novotny et al, 1990) suggest that the uptake and elimination of gases are asymmetrical, we wanted to perform a critical evaluation of our previous model in an attempt to gain further understanding of gas fluxes in hyperbaria. Studying gas kinetics and DCS risk in this data set is particularly interesting because of the combination of gas fluxes occurring during conventional decompression and fluxes due to active metabolism of gas during biochemical decompression.…”

A model of extravascular bubble evolution: effect of changes in breathing gas composition

“…The experimental work of Novotny et al (25) showed that xenon kinetics were not explained by a model of parallel compartments and that other factors, such as CCE or mixing between compartments, are important. The simulation work of Himm et al (14) proposed that the relative dispersion in tissue transit times in rat muscle may be explained by assuming that there is mixing of inert gas between compartments. However, because data on the kinetics of gas washout in all these tissues are not available, we will use the simpler, singleexponential model.]…”

“…On the other hand, the tissue is assumed to be well stirred because of the convective flow of the blood. In addition, not only has it been shown experimentally (25) and via simulation (14) that inert gas kinetics in muscle are not simply described, this assumption also precludes the inclusion of potentially important geometry-specific information, e.g., the distance from the bubble to the nearest blood vessels. A more complete treatment of the tissue might be attempted, but there are too many unknowns regarding the interaction of a bubble with the tissue.…”