Comparison between two different cardiovascular models during a hemorrhagic shock scenario.

Hemorrhagic shock is a form of hypovolemic shock determined by rapid and large loss of intravascular blood volume and represents the first cause of death in the world, whether on the battlefield or in civilian traumatology. For this, the ability to prevent hemorrhagic shock remains one of the greatest challenges in the medical and engineering fields. The use of mathematical models of the cardiocirculatory system has improved the capacity, on one hand, to predict the risk of hemorrhagic shock and, on the other, to determine efficient treatment strategies. In this paper, a comparison between two mathematical models that simulate several hemorrhagic scenarios is presented. The models considered are the Guyton and the Zenker model. In the vast panorama of existing cardiovascular mathematical models, we decided to compare these two models because they seem to be at the extremes as regards the complexity and the detail of information that they analyze. The Guyton model is a complex and highly structured model that represents a milestone in the study of the cardiovascular system; the Zenker model is a more recent one, developed in 2007, that is relatively simple and easy to implement. The comparison between the two models offers new prospects for the improvement of mathematical models of the cardiovascular system that may prove more effective in the study of hemorrhagic shock.

A Simple, Realistic Stochastic Model of Gastric Emptying.

Several models of Gastric Emptying (GE) have been employed in the past to represent the rate of delivery of stomach contents to the duodenum and jejunum. These models have all used a deterministic form (algebraic equations or ordinary differential equations), considering GE as a continuous, smooth process in time. However, GE is known to occur as a sequence of spurts, irregular both in size and in timing. Hence, we formulate a simple stochastic process model, able to represent the irregular decrements of gastric contents after a meal. The model is calibrated on existing literature data and provides consistent predictions of the observed variability in the emptying trajectories. This approach may be useful in metabolic modeling, since it describes well and explains the apparently heterogeneous GE experimental results in situations where common gastric mechanics across subjects would be expected.