By Vasilis Marmarelis, Georgios Mitsis
This contributed quantity provides computational versions of diabetes that quantify the dynamic interrelationships between key physiological variables implicated within the underlying body structure below quite a few metabolic and behavioral stipulations. those variables contain for instance blood glucose focus and numerous hormones similar to insulin, glucagon, epinephrine, norepinephrine in addition to cortisol. The awarded types offer a strong diagnostic software yet can also permit therapy through long term glucose legislation in diabetics via closed-look model-reference regulate utilizing common insulin infusions, that are administered through implanted programmable micro-pumps. This learn quantity goals at featuring cutting-edge examine in this topic and demonstrating the aptitude functions of modeling to the prognosis and remedy of diabetes. the objective viewers essentially contains study and specialists within the box however the ebook can also be priceless for graduate students.
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Insulin-dependent) glucose component Gc(t) in a dynamic and nonlinear fashion. e. glucose-dependent) insulin component Ic(t). Each of these nonlinear dynamic operators can be described by a Volterraequivalent model (such as the PDM models of Figs. 10 and 12) that is estimated directly from spontaneous input-output data. The model residuals of the two operators (Figs. g. g. ). These disturbance signals are added to the causally linked 32 G. D. Mitsis and V. Z. Marmarelis Fig. 14 The closed-loop configuration representing the plasma insulin-glucose dynamic interactions (glucose-to-insulin and insulin-to-glucose models, B and A respectively).
However, they are not based on physiological considerations; therefore this flexibility should be exploited carefully and their complexity should be selected judiciously in order to assure that they are accurately describing the underlying phenomena without overfitting to particular data sets. 2 Insulin-Glucose Dynamics in a Fasting Dog There are four salient issues that deserve further elaboration and discussion: (1) the validity and utility of the obtained models; (2) the notion of ‘‘indirect effects’’ captured by the model of a closed-loop system; (3) the physiological interpretation of the obtained PDMs and (4) the pivotal role of internal ‘‘disturbance’’ signals in closed-loop systems.
7, 8, 9, 10, 12) may prove very beneficial, since they facilitate meaningful physiological interpretations relative to the general Volterra formulation. , the dynamics of the linear filters and the characteristics of the nonlinearities) may also be associated to clinical indices that describe insulin action and its efficiency in specific subjects. Existing insulin-glucose models can be divided in two broad categories; the first includes models that describe these dynamics in a simplified manner by using a limited number of parameters of diagnostic importance, in order to make these parameters identifiable from relatively simple experimental protocols (IVGTT, OGTT, EHC) and the second category includes more complicated models that capture the complexity of the underlying physiology under more general operating conditions.