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Orvosi Hetilap
Article
Data sources: UnpayWall
Orvosi Hetilap
Article . 2016 . Peer-reviewed
Data sources: Crossref
Orvosi Hetilap
Article . 2016
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In silico diabetológia

Authors: DiĂł, MihĂĄly; Deutsch, Tibor; MĂŠszĂĄros, Judit;

In silico diabetológia

Abstract

Introduction: Simulation models can contribute substantially to our understanding and ability to control the dynamic processes underlying impaired glucose metabolism in diabetic patients. Aim: The aim of this paper is to outline a new comprehensive, physiologically-based dynamic model of glucose homeostasis incorporating up-to-date quantitative knowledge about glucose metabolism and its control by insulin and glucagon. Method: The model is composed of three submodels for glucose, insulin, and glucagon. Results: The glucose submodel specifies the dynamics of glucose absorption following meals, hepatic glucose production and uptake, peripheral glucose uptake, kidney excretion, and insulin-independent uptake of glucose in the brain and red blood cells. The insulin submodel includes equations for insulin absorption, pancreatic insulin release and insulin clearance. The glucagon model specifies the hormone secretion and elimination kinetics. Algebraic equations are used to specify (i) how the hormones affect glucose production and utilisation in various compartments such as liver, muscle and fat tissues, and (ii) how glucose levels modify insulin and glucagon release from the pancreas. Setting the values of various model parameters is used to generate virtual individual patients. Conclusions: The model allows the simulation of 24-hour blood glucose profiles for both insulin-dependent non-insulin dependent diabetic patients. Orv. Hetil., 2016, 157(6), 219–223.

Country
Hungary
Keywords

Glucagon, Models, Biological, Kinetics, User-Computer Interface, Diabetes Mellitus, Type 1, Glucose, R1 Medicine (General) / orvostudomány általában, Diabetes Mellitus, Type 2, Diabetes Mellitus, Humans, Insulin, Computer Simulation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Average
bronze