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Physiomodel - an integrative physiology in Modelica
Abstract
Physiomodel (http://www.physiomodel.org) is our reimplementation and extension of an integrative physiological model called HumMod 1.6 (http://www.hummod.org) using our Physiolibrary (http://www.physiolibrary.org). The computer language Modelica is well-suited to exactly formalize integrative physiology. Modelica is an equation-based, and object-oriented language for hybrid ordinary differential equations (http:// www.modelica.org). Almost every physiological term can be defined as a class in this language and can be instantiated as many times as it occurs in the body. Each class has a graphical icon for use in diagrams. These diagrams are self-describing; the Modelica code generated from them is the full representation of the underlying mathematical model. Special Modelica constructs of physical connectors from Physiolibrary allow us to create diagrams that are analogies of electrical circuits with Kirchhoff's laws. As electric currents and electric potentials are connected in electrical domain, so are molar flows and concentrations in the chemical domain; volumetric flows and pressures in the hydraulic domain; flows of heat energy and temperatures in the thermal domain; and changes and amounts of members in the population domain.
... Dlouhá léta se výzkumně zabýváme problematikou modelování fyziologických systémů (viz např. [14][15][16][17]) a využitím teoretických výsledků matematického modelování ve výuce. ...
... Z aplikačních knihoven se pak model sestavuje jako z "legové stavebnice". Naším příspěvkem k rozvoji jazyka je vytvoření knihoven pro vývoj modelů pro oblast fyziologie, které byly na mezinárodních kongresech Modelica v letech 2014 a 2015 oceněny jako nejlepší aplikační knihovny roku [15][16][17]40,41]. Pomocí těchto knihoven jsme v jazyce Modelica implementovali rozsáhlé modely integrativní fyziologie [14,16,[42][43][44][45][46]. ...
... Naším příspěvkem k rozvoji jazyka je vytvoření knihoven pro vývoj modelů pro oblast fyziologie, které byly na mezinárodních kongresech Modelica v letech 2014 a 2015 oceněny jako nejlepší aplikační knihovny roku [15][16][17]40,41]. Pomocí těchto knihoven jsme v jazyce Modelica implementovali rozsáhlé modely integrativní fyziologie [14,16,[42][43][44][45][46]. V rámci konsorcia Open Source Modelica nyní např. ...
Univerzity jsou přirozeným zdrojem mezioborové spolupráce a mezioborová spolupráce je významným zdrojem inovací, jejichž rozvoj potencuje zakládání malých inovačních firem při univerzitách a spolupráce s vývojovými podniky. Mezifakultní a meziuniverzitní spolupráce v České republice je však zatím nedostatečná a neodpovídá potenciálním možnostem, které univerzity mají. Obrazně tak připomínají spícího obra, který čeká na probuzení. Vzbudit ho ale může pouze aktivita zdola. Nikdo jiný, než učitelé a studenti našich Alma Mater to za ně neudělá.
... Good examples are models in [14,15,16], which define the glucose regulation mechanism in patients with Type 1 Diabetes Mellitus, and the Hypothalamic-Pituitary-Gonadal (HPG) axis model presented in [6], which is specifically focused on hormones related to the human menstrual cycle. Finally, at the top level we find models of the whole human body, for example the Glucose-Insulin Model [17,18], HumMod [19], and Physiomodel [20]. ...
... In our model-based setting, we have to face with complex Virtual Physiological Human (VPH) models, e.g., HumMod [19], Physiomodel [20], and GynCycle [6] defined through highly non-linear differential equations modelling the underlying biological mechanisms (e.g., inhibitory and stimulatory effects). As outlined in Section 2.1, such VPH models are hybrid systems that can be defined by systems of Ordinary Differential Equations (ODEs) (see, e.g., [58,32,33]) whose inputs are discrete event sequences (see, e.g., [41,44]). ...
In Silico Clinical Trials (ISTC), i.e., clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation--based experimental campaigns (ISTC) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine). e show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.
... Available SBML simulators do not fully support the integration, within open-standard simulation ecosystems, of SBML models with models defined using other languages. This severely hinders the possibility to co-simulate and integrate SBML models within large model networks comprising biochemical as well as other kinds of models, possibly at different levels of abstraction (multi-scale model networks, see, e.g., de Bono and Hunter, 2012), and applying standard systems engineering For example, the interconnection of quantitative models of the human physiology (e.g., Physiomodel, Mateják and Kofránek, 2015), drugs pharmacokinetics/pharmacodynamics (e.g., Open Systems Pharmacology Suite, Eissing et al., 2011), (possibly semi-autonomous) biomedical devices, pharmacological protocol guidelines or treatment schemes, enables the set-up of in silico clinical trials for the (model-based) safety and efficacy pre-clinical assessment of such drugs, protocols, treatments, devices, using standard system engineering approaches to perform their simulation-based analysis at system level (see, e.g., Kanade et al., 2009;Mancini et al., 2013Mancini et al., , 2014Zuliani et al., 2013;Zuliani, 2015;Mancini et al., 2016aMancini et al., , 2017. Works in this direction include, e.g., (Schaller et al., 2016;Messori et al., 2018), where a model-based verification activity of a sensor-augmented insulin pump is conducted against a model of the human glucose metabolism in patients with diabetes mellitus, (Madec et al., 2019), where a model of a penicillin bio-sensor (integrating biochemistry, electrochemistry, and electronics models) is simulated to compute a first dimensioning of the sensor, and (Tronci et al., 2014;Mancini et al., 2015), where representative populations of virtual patients are generated from parametric models of the human physiology, a key step to enable in silico clinical trials (see, e.g., Mancini et al., 2018). ...
... Modelica is widespread in application domains as diverse as mechanical, electrical, electronic, hydraulic, thermal, control, electric engineering, but also physiology and pharmacology (see, e.g., Mateják and Kofránek, 2015), and several efficient and highly-configurable simulators are currently available: proprietary (e.g., Dymola and Wolfram System Modeler) as well as open-source (e.g., OpenModelica and JModelica). ...
Motivation: SBML is the most widespread language for the definition of biochemical models. Although dozens of SBML simulators are available, there is a general lack of support to the integration of SBML models within open-standard general-purpose simulation ecosystems. This hinders co-simulation and integration of SBML models within larger model networks, in order to, e.g. enable in silico clinical trials of drugs, pharmacological protocols, or engineering artefacts such as biomedical devices against Virtual Physiological Human models. Modelica is one of the most popular existing open-standard general-purpose simulation languages, supported by many simulators. Modelica models are especially suited for the definition of complex networks of heterogeneous models from virtually all application domains. Models written in Modelica (and in 100+ other languages) can be readily exported into black-box Functional Mock-Up Units (FMUs), and seamlessly co-simulated and integrated into larger model networks within open-standard language-independent simulation ecosystems. Results: In order to enable SBML model integration within heterogeneous model networks, we present SBML2Modelica, a software system translating SBML models into well-structured, user-intelligible, easily modifiable Modelica models. SBML2Modelica is SBML Level 3 Version 2-compliant and succeeds on 96.47% of the SBML Test Suite Core (with a few rare, intricate and easily avoidable combinations of constructs unsupported and cleanly signalled to the user). Our experimental campaign on 613 models from the BioModels database (with up to 5438 variables) shows that the major open-source (general-purpose) Modelica and FMU simulators achieve performance comparable to state-of-the-art specialized SBML simulators. Availability and implementation: https://bitbucket.org/mclab/sbml2modelica
... Good examples are models in [14,15,16], which define the glucose regulation mechanism in patients with Type 1 Diabetes Mellitus, and the Hypothalamic-Pituitary-Gonadal (HPG) axis model presented in [6], which is specifically focused on hormones related to the human menstrual cycle. Finally, at the top level we find models of the whole human body, for example the Glucose-Insulin Model [17,18], HumMod [19], and Physiomodel [20]. ...
... In our model-based setting, we have to face with complex Virtual Physiological Human (VPH) models, e.g., HumMod [19], Physiomodel [20], and GynCycle [6] defined through highly non-linear differential equations modelling the underlying biological mechanisms (e.g., inhibitory and stimulatory effects). As outlined in Section 2.1, such VPH models are hybrid systems that can be defined by systems of Ordinary Differential Equations (ODEs) (see, e.g., [58,32,33]) whose inputs are discrete event sequences (see, e.g., [41,44]). ...
In Silico Clinical Trials (ISCT), i.e., clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation-based experimental campaigns (ISCT) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine). We show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.
... Such an environment is e.g. Physiomodel [15], SimEdu [16], and T1DMS [6]. Nevertheless, they do not provide a simulation of the insulin-pump software stack. ...
... Physiomodel [15] and SimEdu [16] provide a number of models for different human body systems. Nevertheless, no official regulatory body recognizes them. ...
Diabetes is a widespread civilization disease. It manifests with an elevated blood glucose level. In the long-term, elevated blood glucose level continuously damages organs. In the short-term, hypo- and hyperglycemia are acute complications. Insulin lowers blood glucose level by promoting its utilization. At basal rate, insulin pump delivers insulin to subcutaneous tissue to control blood glucose level. In addition, patient doses insulin boluses in accordance with estimated carbohydrate content of consumed meal. Control algorithm of the pump considers the boluses, when calculating the basal rate. In our previous work, we have proposed a parallel-architecture for the next-generation of glucose monitoring - SmartCGMS. It unifies the source-code base and the glucose-monitoring-and-control paradigm across real, simulated and prototyped devices. As the development continues, especially towards the pump-control algorithms, we face a problem of reducing the SmartCGMS requirements when considering a low-power hardware. In this paper, we present the modifications that lead to a reduced number of threads, while implementing the closed-loop feedback between a glucose sensor and insulin pump to conduct FDA accepted in-silico pre-clinical trials.
... For example, the interconnection of quantitative models of the human physiology (e.g., Physiomodel, Mateják and Kofránek, 2015), drugs pharmacokinetics/pharmacodynamics (e.g., Open Systems Pharmacology Suite, Eissing et al., 2011), (possibly semi-autonomous) biomedical devices, pharmacological protocol guidelines or treatment schemes, enables the set-up of in silico clinical trials for the (model-based) safety and efficacy pre-clinical assessment of such drugs, protocols, treatments, devices, using standard system engineering approaches to perform their simulation-based analysis at system level (see, e.g., Kanade et al., 2009;Mancini et al., 2013Mancini et al., , 2014Zuliani et al., 2013;Zuliani, 2015;Mancini et al., 2016aMancini et al., , 2017. Works in this direction include, e.g., (Schaller et al., 2016;Messori et al., 2018), where a model-based verification activity of a sensor-augmented insulin pump is conducted against a model of the human glucose metabolism in patients with diabetes mellitus, (Madec et al., 2019), where a model of a penicillin bio-sensor (integrating biochemistry, electrochemistry, and electronics models) is simulated to compute a first dimensioning of the sensor, and (Tronci et al., 2014;Mancini et al., 2015), where representative populations of virtual patients are generated from parametric models of the human physiology, a key step to enable in silico clinical trials (see, e.g., Mancini et al., 2018). ...
... Modelica is widespread in application domains as diverse as mechanical, electrical, electronic, hydraulic, thermal, control, electric engineering, but also physiology and pharmacology (see, e.g., Mateják and Kofránek, 2015), and several efficient and highly-configurable simulators are currently available: proprietary (e.g., Dymola and Wolfram System Modeler) as well as open-source (e.g., OpenModelica and JModelica). ...
Motivation:
SBML is the most widespread language for the definition of biochemical models. Although dozens of SBML simulators are available, there is a general lack of support to the integration of SBML models within open-standard general-purpose simulation ecosystems. This hinders co-simulation and integration of SBML models within larger model networks, in order to, e.g., enable in-silico clinical trials of drugs, pharmacological protocols, or engineering artefacts such as biomedical devices against Virtual Physiological Human models.
Modelica is one of the most popular existing open-standard general-purpose simulation languages, supported by many simulators. Modelica models are especially suited for the definition of complex networks of heterogeneous models from virtually all application domains. Models written in Modelica (and in 100+ other languages) can be readily exported into black-box Functional Mock-Up Units (FMUs), and seamlessly co-simulated and integrated into larger model networks within open-standard language-independent simulation ecosystems.
Results:
In order to enable SBML model integration within heterogeneous model networks, we present SBML2Modelica, a software system translating SBML models into well-structured, user-intelligible, easily modifiable Modelica models. SBML2Modelica is SBML Level 3 Version 2 –compliant and succeeds on 96.47% of the SBML Test Suite Core (with a few rare, intricate, and easily avoidable combinations of constructs unsupported and cleanly signalled to the user). Our experimental campaign on 613 models from the BioModels database (with up to 5438 variables) shows that the major open-source (general-purpose) Modelica and FMU simulators achieve performance comparable to state-of-the-art specialised SBML simulators.
Availability and Implementation:
SBML2Modelica is written in Java and is freely available for non-commercial use at https://bitbucket.org/mclab/sbml2modelica
... Also, VPH models of different body compartments can be integrated in larger models, eventually defining whole human models. Important attempts in this direction are given by HumMod [17] and the more recent Physiomodel [33], a complex whole-body model written in the standard Modelica language and executable by means of open-source (e.g., OpenModelica, openmodelica.org) as well as proprietary (e.g., Dymola, dymola.com) ...
... In our model-based setting, we have to face with complex Virtual Physiological Human (VPH) models, e.g., HumMod [17], Physiomodel [33] and Gy-nCycle [36] defined through highly non-linear differential equations modelling underlying biological mechanisms (e.g., inhibitory and stimulatory effects). ...
In Silico Clinical Trials (ISCT), i.e., clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present a case study aiming at quantifying, by means of a multi-arm ISCT supervised by intelligent search, the potential impact of precision medicine approaches on a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction.
... Introduction equation-based, object-oriented implementation of the pulsatile cardiovascular system over a block-oriented approach are described in detail in [12]. Therefore, an alternative implementation of the HumMod model was introduced in the object-oriented acausal Modelica language as Physiomodel [13]. Fig. 1 illustrates, that the model structure in block-based modeling language (here Simulink) corresponds rather with a computational algorithm, while the model structure in Modelica is more similar to the modeled reality itself. ...
... The possible reason that biomedical simulation has not yet fully adopted Modelica may be the lack of specialized libraries. This drawback has recently been solved by Physiolibrary [19], which originally emerged from the construction of Physiomodel, a large integrative model of human physiology [13], an extended Modelica implementation of HumMod [5]. Physiolibrary later proved to be useful for a variety of small to large models in the physiological area. ...
Purpose
The main objective is to accelerate the mathematical modeling of complex systems and offer the researchers an accessible and standardized platform for model sharing and reusing.
Methods
We describe a methodology for creating mathematical lumped models, decomposing a system into basic components represented by elementary physical laws and relationships expressed as equations. Our approach is based on Modelica, an object-oriented, equation-based, visual, non-proprietary modeling language, together with Physiolibrary, an open-source library for the domain of physiology.
Results
We demonstrate this methodology on an open implementation of a range of simple to complex cardiovascular models, with great complexity variance (simulation time from several seconds to hours). The parts of different complexity could be combined together.
Conclusions
Thanks to the equation-based nature of Modelica, a hierarchy of subsystems can be built with an appropriate connecting component. Such a structural model follows the concept of the system rather than the computational order. Such a model representation retains structural knowledge, which is important for e.g., model maintainability and reusability of the components and multidisciplinary cooperation with domain experts not familiar with modeling methods.
... Při srovnání obou obrázků je vidět, že struktura modelu v Simulinku odpovídá spíše výpočetnímu algoritmu, zatímco struktura modelu v Modelice více zobrazuje samotnou strukturu modelované reality.Naše verze modelu HumMod v Modelice (https://www. physiomodel.org/)[22,23] má přehlednou hierarchickou strukturu. ...
Jakýmsi moderním norimberským trychtýřem usnadňujícím vstřebávání znalostí ve výuce medicíny jsou dnes tzv. vysvětlující počítačové modely (explanatory models). Jejich úlohou je pomocí simulačních her podpořit porozumění příčinám klinických příznaků a pochopení diagnostiky a terapie jednotlivých onemocnění v kontextu fyziologických regulací. Jádrem lékařských simulátorů jsou matematické modely fyziologických systémů. Jejich tvorba vyžaduje multidisciplinární spolupráci vysoce kvalifikovaných odborníků. Díky rozvoji výpočetní techniky se výukové simulátory staly novým trhem. Vývojem simulátorů se dnes zabývá mnoho firem. Vývoj probíhá i v otevřených komunitách v akademickém prostředí univerzit a vývojových firem. Vytvoření a udržování komunity uživatelů a tvůrců simulátorů je zásadní. Naším příspěvkem je vytvoření metodiky pro vývoj webových simulátorů, která umožňuje vývoj elektronických učebnic propojujících text s animovanými obrázky se simulačním modelem na pozadí.
... Při srovnání obou obrázků je vidět, že struktura modelu v Simulinku odpovídá spíše výpočetnímu algoritmu, zatímco struktura modelu v Modelice více zobrazuje samotnou strukturu modelované reality.Naše verze modelu HumMod v Modelice (https://www. physiomodel.org/)[22,23] má přehlednou hierarchickou strukturu. ...
Jakýmsi moderním norimberským trychtýřem usnadňujícím vstřebávání znalostí ve výuce medicíny jsou dnes tzv. vysvětlující počítačové modely (explanatory models). Jejich úlohou je pomocí simulačních her podpořit porozumění příčinám klinických příznaků a pochopení diagnostiky a terapie jednotlivých onemocnění v kontextu fyziologických regulací. Jádrem lékařských simulátorů jsou matematické modely fyziologických systémů. Jejich tvorba vyžaduje multidisciplinární spolupráci vysoce kvalifikovaných odborníků. Díky rozvoji výpočetní techniky se výukové simulátory staly novým trhem. Vývojem simulátorů se dnes zabývá mnoho firem. Vývoj probíhá i v otevřených komunitách v akademickém prostředí univerzit a vývojových firem. Vytvoření a udržování komunity uživatelů a tvůrců simulátorů je zásadní. Naším příspěvkem je vytvoření metodiky pro vývoj webových simulátorů, která umožňuje vývoj elektronických učebnic propojujících text s animovanými obrázky se simulačním modelem na pozadí.
... It is worth mentioning that each block in the model depicted in Fig. 2 was implemented using the Modelica language (refer to [64] and the related references). The top layer to connect the model components was inspired by the Physiolibrary library in its version 2.3.1 [76][77][78]. Specifically, this simulator consists of 47 components, amounting to 1600 lines of programming code. 5 ...
The transition from fetal to newborn condition involves complex physiological adaptations for extrauterine life. A crucial event in this process is the clamping of the umbilical cord, which can be categorized as immediate or delayed. The type of clamping significantly influences the hemodynamics of the newborn. In this study, we developed a simulator based on existing cardiovascular models to better understand this practice. The simulator covers the period from late gestation to 24 h after birth and faithfully reproduces flow patterns observed in real-life situations (as evaluated by clinical specialists), considering factors such as the timing of cord clamping and the altitude of the birth location. It also reproduces blood pressure values reported in clinical data. Under similar conditions, the simulation results indicate that delayed cord clamping leads to increased oxygen concentration and improved blood volume compared to immediate cord clamping. Delayed cord clamping also had a positive impact on sustained placental respiration. Furthermore, this study provides further evidence that umbilical cord clamping should be based on physiological criteria rather than predefined time intervals.
... Modelica simulation is widely used in different simulations, including thermodynamics [1], ecological environmental protection [2], electric energy [3], Physiology [4], etc. Shi et al. [5] built a dynamic programming function mathematical model which considers the load characteristics of the units for multi-unit refrigeration optimization. Xu et al. [6] realized the control switching of refrigerators, cold storage pools, and other units by formulating switching strategies for refrigerator mode. ...
With the rapid development of information technology in China, the problem of energy conservation in data centers has become increasingly prominent, and the green data center model is inevitable. The refrigeration equipment which is the main power consumption in the data center is affected by the environment, room temperature, unit output, and other factors. This paper provides a scheme of multi-unit collaborative optimization cooling. It is mainly divided into four stages: first, a model of the data center is built through Modelica, and it is exported as a fmu file. Second, the PSO optimization strategy is used to generate the data sets which contain unit output and cooling effect at different times. Third, LSTM and TCN load forecasting models are trained. Finally, through a comparison of various optimization algorithms, the PSO-LSTM-TCN model is used to simulate the unit output and optimize the output parameters of multiple units.
... OSA has been used and validated extensively as a reliable tool for the estimation of gas exchange and acid-base parameters. [15][16][17][18][19][20][21][22][23][24][25] This study involved ARDS cases resulted from COVID-19. Previous studies had shown the effects of varying FiO2 on the P/F ratios and on the resultant ARDS classifications in classic form of ARDS. ...
Background:
The ratio of partial pressure of oxygen in arterial blood (PaO2) to the fraction of inspiratory oxygen concentration (FiO2) is an indicator of pulmonary shunt fraction. PaO2/FiO2 (P/F) ratio is used to classify severity of acute respiratory distress syndrome (ARDS). With the same shunt fraction, P/F ratio decreases with increases in FiO2 which may lead to errors in classifying severity of ARDS. The effect of FiO2 on P/F ratio has not been investigated in COVID-19 pneumonia. In this study, we estimated the best FiO2 for the calculation of P/F ratio in a sample of patients with ARDS due to COVID-19 pneumonia.
Materials and methods:
Blood gas and ventilatory data of 108 COVID-19 ARDS patients were analyzed in a cross-sectional observational study. Using Oxygen Status Algorithm the calculated shunt fraction served a basis for calculating P/F ratio for different FiO2. The severity of ARDS determined by P/F ratios at each FiO2s was compared with the shunt-based severity to find the optimum FiO2 for calculation of P/F ratio so the resulting classification has the best match with the reference classification.
Results:
A FiO2 of 1.0 for calculation of P/F ratio and ARDS classification showed the best match with shunt-based ARDS classification. A regression model was obtained with the PaO2, patient's original FiO2, Hemoglobin concentration, and SaO2 as the independent predictors of the P/F ratio for the FiO2 of 1.0.
Conclusion:
This study shows a FiO2 of 1.0 as the best value for correct calculation of P/F ratio and proper classification of ARDS.
... Modelica is a major breakthrough in the so-called field of Object Oriented Modelling of Dynamic Systems [32]. At present, there is a growing trend in developing Modelica libraries for modelling human physiology [26][27][28][29][30][33][34][35][36]. We selected OpenModelica (OpenModelica is an open source programming environment for the language Modelica. ...
There is a lack of medical simulation tools that can be understood and used, at the same time, by researchers, teachers, clinicians and students. Regarding this issue, in this work we report a virtual simulator (developed in OpenModelica) that allow to experiment with the fundamental variables of the cardiovascular and respiratory system of a neonate. We extended a long-tested lumped parameter model that represents the cardiovascular and respiratory physiology of a neonate. From this model, we implemented a physiological simulator using Modelica. The fidelity and versatility of the reported simulator were evaluated by simulating seven physiological scenarios: two of them representing a healthy infant (newborn and 6-months old) and five representing newborns affected by different heart diseases. The simulator properly and consistently represented the quantitative and qualitative behaviour of the seven physiological scenarios when compared with existing clinical data. Results allow us to consider the simulator reported here as a reliable tool for researching, training and learning. The advanced modelling features of Modelica and the friendly graphical user interface of OpenModelica make the simulator suitable to be used by a broad community of users. Furthermore, it can be easily extended to simulate many clinical scenarios.
... To do this, we follow the design patterns from Jezek et al. [6]: that is, creating a library of modular hierarchical components using the Modelica language and Physiolibrary and assembling the model from replaceable components. Modelica has been designed for modular hierarchical development of large scale multi-domain systems, especially in the automotive, energy, and aerospace industries, but it has proven advantageous for physiological systems in numerous applications [19,6,20,21,22,23]. The development of this model was performed using Dymola 2021 (Dassault Systémes) but can be run and modified in an open source Modelica environment OpenModelica 1.17 (OpenModelica Consortium). ...
Blood flows and pressures throughout the human cardiovascular system are regulated in response to various dynamic perturbations, such as changes to peripheral demands in exercise, rapid changes in posture, or loss of blood from hemorrhage, via the coordinated action of the heart, the vasculature, and autonomic reflexes. To assess how the systemic and pulmonary arterial and venous circulation, the heart, and the baroreflex work together to effect the whole-body responses to these perturbations, we integrated an anatomically-based large-vessel arterial tree model with the TriSeg heart model, models capturing nonlinear characteristics of the large and small veins, and baroreflex-mediated regulation of control vascular tone and cardiac chronotropy and inotropy. The model was identified by matching data on Valsalva maneuver (VM), exercise, and head-up tilt (HUT). Thirty-one parameters were optimized using a custom parameter-fitting tool chain, resulting in an uniquely high-fidelity whole-body human cardiovascular systems model. Because the model captures the effects of exercise and posture changes, it can be used to simulate numerous clinical assessments, such as HUT, the VM, and cardiopulmonary exercise stress testing. The model can also be applied as a framework for representing and simulating individual patients and pathologies. Moreover, it can serve as a framework for integrating multi-scale organ-level models, such as for the heart or the kidneys, into a whole-body model. Here, the model is used to analyze the relative importance of chronotropic, inotropic, and peripheral vascular contributions to the whole-body cardiovascular response to exercise. It is predicted that in normal physiological conditions chronotropy and inotropy make roughly equal contributions to increasing cardiac output and cardiac power output during exercise. Under upright exercise conditions, the nonlinear pressure-volume relationship of the large veins and sympathetic-mediated venous vasoconstriction are both required to maintain preload to achieve physiological exercise levels. The developed modeling framework is built using the open Modelica modeling language and is freely distributed.
... Table 1 and 2 were presented in (M. Mateják & Kofránek, 2015). ...
... Je to taky klíč k personalizované medicíně, která může umožnit téměř automaticky řešit interakci i dávkování léků, a to dokonce i v závislosti na genetickém profilu pacienta [8]. Zpracování dat může být umožněno použitím simulačních modelů [9][10][11][12] vybudovaných využitím sofistikovaných softwarových knižnic [13][14][15][16][17]. Příkladem může být i využití přepočtů přenosu krevních plynů [18][19] na základě naměřených dat u pacienta v různých stavech a diagnózách [20][21]. To vše však vyžaduje nejenom vhodnou formalizaci dat a jejich relací [22], ale zároveň možnosti jak s danými daty pracovat bez toho aby se jakkoli narušilo soukromí pacienta definované jeho souhlasy. ...
Elektronizace zdravotnictví je trend, který se už nedá zastavit. Výhody automatického zpracování a poskytování zdravotních záznamů často zastiňuje fakt, že se jedná i o údaje osobní a citlivé. Tedy jejich zpracování a sdílení by mělo být řízeno zabezpečeně, a to výhradně akceptováním všech souhlasů od pacienta = vlastníka těchto zdravotních dat. V neposlední řadě by pro každou podezřelou operaci nad účtem pacienta nebo s jeho osobními a citlivými údaji mělo být vždy možné zjistit čas a identitu přistupujícího uživatele.
... V Modelice je poměrně snadné vytvářet modely cirkulačního systému nejrůznější složitosti -od jednoduchých modelů cirkulačního systému proměnné složitosti [27,28] až velmi komplexní modely integrativní fyziologie [29][30][31]. ...
Výukové modely cirkulace a přenosu krevních plynů umož-ňují názorně demonstrovat dynamické propojení regulačních smyček a jejich projevy při nejrůznějších patogenezích poruch kardiorespiračního systému. Jejich nasazení ve výuce mediků prokázalo jejich vysokou pedagogickou účinnost.
... One for the glomerulus simulator ( Figure 10) and one for the other (nephron tubules) simulators ( Figure 11). Both glomerulus and nephron tubule models utilize the PhysioLibrary (Matejak and Kofranek, 2015), which was also developed within our group. The models are considered to be in the steady state and the model does not take temporal evolution into account. ...
... 44 With respect to HumMod, Kofranek et al 44 have noted that "the model description has been divided into thousands of XML files and more than a thousand directories," and "the entire structure of the model and following links and references are not easily identifiable." However, with the reimplementation of HumMod and other models in newer, more comprehensible software simulation environments like that provided by the Modelica modeling language, [44][45][46] it is hoped that opportunities for understanding differences in equations and structures between different models will improve. ...
Recently, mathematical models of human integrative physiology, derived from Guyton's classic 1972 model of the circulation, have been used to investigate potential mechanistic abnormalities mediating salt sensitivity and salt-induced hypertension. We performed validation testing of 2 of the most evolved derivatives of Guyton's 1972 model, Quantitative Cardiovascular Physiology-2005 and HumMod-3.0.4, to determine whether the models accurately predict sodium balance and hemodynamic responses of normal subjects to increases in salt intake within the real-life range of salt intake in humans. Neither model, nor the 1972 Guyton model, accurately predicts the usual changes in sodium balance, cardiac output, and systemic vascular resistance that normally occur in response to clinically realistic increases in salt intake. Furthermore, although both contemporary models are extensions of the 1972 Guyton model, testing revealed major inconsistencies between model predictions with respect to sodium balance and hemodynamic responses of normal subjects to short-term and long-term salt loading. These results demonstrate significant limitations with the hypotheses inherent in the Guyton models regarding the usual regulation of sodium balance, cardiac output, and vascular resistance in response to increased salt intake in normal salt-resistant humans. Accurate understanding of the normal responses to salt loading is a prerequisite for accurately establishing abnormal responses to salt loading. Accordingly, the present results raise concerns about the interpretation of studies of salt sensitivity with the various Guyton models. These findings indicate a need for continuing development of alternative models that incorporate mechanistic concepts of blood pressure regulation fundamentally different from those in the 1972 Guyton model and its contemporary derivatives.
... Given the architectural flexibility, it is also possible to connect an implementation of the proposed architecture with diabetic-patient simulators such as DMMS.R [17], University of Cambridge T1D Simulator [18], and Physiomodel [19]. Such a connection will require custom terminal filters to receive patient simulator data and send the calculated output back to the patient simulator. ...
Diabetes is a widespread disease. Elevated blood glucose levels continuously damage multiple organs in the long-term. In the short-term, hypo- and hyperglycemic shocks are acute risks. Diabetes patients monitor their glucose level using continuous glucose monitoring systems. Based on their measured glucose level, the patient take insulin to lower their blood glucose level. With the advances in mobile computing, an increasing number of diabetes patients engage in self-built systems. They read their glucose levels from glucose-monitoring systems and calculate their insulin dosage based on the measured levels. The self-built nature of such a system raises a number of medical and software engineering concerns. Therefore, we propose a software architecture for the next generation of glucose monitoring. The proposed architecture builds on the principles of the high-level architecture. We decompose the entire glucose monitoring system to basic elements, which are either real or simulated. This opens the proposed architecture to software engineering, simulation, and fault-tolerance research. As a proof of concept, we present an illustrative configuration of the implemented software architecture that predicts future blood glucose levels 15 minutes in advance for type-1 diabetes patients. All relative errors are in the A+B zones of Clarke and Parkes error grids, with almost 95% of errors in the safest A-zones of both grids.
... The current model has been greatly simplified by omitting oxygen, dead space in airways, solubility of CO2 in other body compartments and in water contained in snow and also rising breath work, which produces more CO 2 . Thanks to Modelica implementation, it is planned to connect it directly with the most extensive open model of human physiology, the Physiomodel (Mateják and Kofránek, 2015) . ...
... Kofranek et al. implemented Guyton's 1972 model using the MATLAB ® Simulink [25]. However, the complexity of the model increased in the transition from Guyton's model to HumMod's, and it became too complicated to keep the model up to date using block oriented tools, therefore an alternative implementation of the HumMod model was introduced in the object-oriented acausal Modelica language [26,27] and recently as Physiomodel [28]. ...
Not published work, rejected by reviewers no time to rewrite and republish. Several models of a cardiovascular system, integrated with short-term and long-term control mechanisms, are pre-
sented with a methodology of an object-oriented modeling technique in Modelica language.
... Mnohé namerané hodnoty pacienta majú medzi sebou vzťahy, pomocou ktorých je možné popisovať i odhadovať (ne)funkčnosť jednotlivých fyziologických systémov [13][14][15][16][17]. Tým vzniká obrovský priestor, ako formálne zapisovať a dokonca simulovať správanie konkrétneho jedinca [18][19][20][21] a to až na úrovni jednotlivých chemických procesov [22]. ...
... Archived results of genetic testing can be reused because inherited DNA code remains almost the same throughout a subject's life. As the usage of this information can be improved to the level of complex simulation, such as Physiomodel [12,13,14] or HumMod, it is much better to store the names of the identified alleles rather than only the codes of a few categories such as in the mentioned case of the enzyme's rates. Today, format and data media for storing these genetic patient's health records can almost be completely accessible such as QR codes named as "Safety-code" [21], mobile phones, a USB disk, a smart card, an ambulant computer, a network server and so on. ...
Personalized medicine that is based on genetic testing opens new possibilities for better pharmacological treatments of a patient, even for physicians or pharmacists who are not familiar with the interpretation of genetic data. Genetic data can be automatic processes that use knowledge from many already accessible databases, collecting results from many scientific studies. This paper presents the idea of a system for the personalized optimization of drug dosing using the integration of pharmacogenomics, pharmacokinetics and pharmacodynamics. For such a system, it is necessary to store the genetic data of the patient as a set of the patient's alleles of selected genes.
The free open-source library called Physiolibrary (https://github.com/MarekMatejak/Physiolibrary) in version 3.0 recast components from physiological domains such as hydraulic (cardiovascular), thermal, osmotic and chemical into Modelica Standard Library (MSL) concept of Fluid/Media. Components are expanded to include gases transports, acid-base, electrolytes, nutrients delivery and endocrines by simple selecting pre-packaged media. They can be connected directly (the same medium) or across membranes (different media), allowing small physiological models to be easily coupled within more quantitative ones with minimal effort.
The free open-source Physiolibrary version 3.0 (https://github.com/MarekMatejak/Physiolibrary) has transformed components from physiological domains such as hydraulic (cardiovascular), thermal, osmotic, and chemical into the Modelica Standard Library (MSL) concept of Fluid/Media and Chemical library. Components are extended to include gas transports, acids-bases, electrolytes, nutrient delivery, and endocrines by simply selecting pre-made media. They can be connected directly (same medium) or across membranes (different media), allowing small physiological models to be integrated into more quantitative models with minimal effort.
Nové webové (HTML 5, WebAssembly, JavaScript, Web Components) a modelovací (FMI) standardy otevírají možnosti vytváření webových simulátorů propojujících simulační modely, grafiku, hypertext a multimédia, které lze spouštět na jakémkoliv zařízení s internetovým prohlížečem. Na těchto standardech je založena i naše technologie BodyLight.js.
When modeling body fluids using physical chemistry, we en-countered a contradiction. We proceeded from the erroneous assumption that the molar amount of water in an aqueous so-lution is the molar amount of H2O molecules (mass divided by the mass of one H2O molecule). Thus, in one kilogram of pure water, we calculated 55.508 moles of water because the molar mass of H2O is 18.01528 g / mol. When calculating the molar fractions as the molar amount of the substance divided by the solution's total molar amount, we thus obtained numerically completely different values than for molalities or molarities. According to the theory, these values should be substitutable. However, it turned out that using these values in the calculati-ons of the solubility of gases in aqueous solutions showed us an error of about 55 mol/kg. Similar errors began to be reported for chemical processes with different numbers of reactants and products (at the same number, the error is annulled algebrai-cally). So is the water molality really about 55 mol/kg? No. This is because water forms bonds with each other, which cluster more H2O molecules into larger particles. From the required molar amount of water, we derived the dissociation constant and enthalpy of this bond. The results are compatible with data from the National Institute of Standards and Technology (NIST) and the data of formation energies of individual substances. Using these constants, it is possible to derive the molar amount of water in aqueous solutions and subsequently make calculati-ons over molar fractions, the results of which begin to coincide with the measured and published experiments.
In order to teach different modeling techniques we demonstrate equation-based, block-schema based, compartment and component-based modeling using acausal and object-oriented modeling language - Modelica. Hands-on implementation using all these techniques and comparing them towards same system (in our case glucose-insulin regulation) we teach pros and cons of each technique. Equation-based or block-schema based may be rapidly implemented from literature. However, compartment based or component-based models brings better understanding of modeled reality. When students have such experience, they tend to assess published papers more critically and do more complex system analysis.
V poslední době se objevily nové elektronické učebnice, propo-jující hypertext, simulační modely a interaktivní grafiku (řízenou modelem na pozadí), které přinášejí zcela nové možnosti pro vysvětlování složitě propojených regulačních vztahů zejmé-na v medicíně. Jsou to většinou aplikace typu client-server, kdy celá aplikace běží na serveru a uživatel se k ní připojuje většinou pomocí internetového prohlížeče či jiného dediko-vaného rozhraní. Existují také aplikace které pracují lokálně na klientském počítači nebo tabletu. My jsme vyvinuli technologii Bodylight.js, která umožňuje tvorbu obdobných výukových aplikací s interaktivními simulátory spustitelnými přímo v inter-netovém prohlížeči na jakékoli platformě či operačním systému (notebooku, tabletu či chytrém telefonu), o níž jsme referovali v loňském ročníku MEDSOFT, V tomto sdělení tuto technologii popíšeme podrobněji a ukážeme i první aplikace.
Při modelování tělesných tekutin pomocí fyzikální chemie jsme narazili na rozpor. Vycházeli jsme přitom z mylného předpokla-du, že molární množství vody ve vodném roztoku je molárním množstvím molekul H2O (hmotnost dělená hmotností jedné molekuly H2O). V jednom kilogramu čisté vody jsme tedy počítali s 55.508 mol vody, protože molární hmotnost H20 je 18.01528 g/mol. Při počítání molárních frakcí jako molární množství látky dělené celkovým molárním množstvím roztoku jsme tak dostávali numericky úplně jiné hodnoty než při mola-litách nebo při molaritách. Přitom podle teorie by měly být tyto hodnoty zastupitelné. Ukázalo se však, že použití těchto hodnot ve výpočtech rozpustnosti plynů ve vodních roztocích nám vy-kazovaly právě chybu cca 55 mol/kg. Podobné chyby se začaly vykazovat pro chemické procesy s různým počtem reaktantů a produktů (při stejném počtu se chyba algebraicky anuluje). Je tedy skutečně molalita vody cca 55 mol/kg? Ne. Voda totiž mezi sebou vytváří vazby, které shlukují více molekul H2O do větších částic. Z požadovaného molárního množství vody jsme odvodili disociační konstantu i entalpii této vazby tak, aby výsledky byly kompatibilní s daty z National Institute of Standards and Tech-nology (NIST) i s daty formačních energií jednotlivých substan-cí. Použitím těchto konstant je zpětně možné odvodit molární množství vody ve vodních roztocích a následně dělat výpočty přes molární frakce, jejichž výsledky se začínají shodovat s na-měřenými a publikovanými experimenty.
Resumen: El trabajo presentado en este artículo está enmarcado en el campo del desarrollo de simuladores médicos basados en modelos. La finalidad de este trabajo es acelerar la investigación interdisciplinaria y mejorar las habilidades, destrezas y experticia del personal de la salud sin prácticas invasivas sobre el paciente. Para esto proponemos un simulador educativo de la fisiología cardiovascular neonatal, implementado en Modelica con base en la analogía con un modelo hidráulico. Esta herramienta permite simular el estado normal de un recién nacido, patologías cardiacas como la coartación de la aorta y transposición de las grandes arterias. También se puede visualizar variables fisiológicas como presión, volumen, flujo sanguíneo, elastancia, resistencia vascular y relaciones como presión-volumen ventricular para evaluar el ciclo cardiaco. Este trabajo es un primer avance en el desarrollo de simuladores de la fisiología completa neonatal que permitan evaluar la evolución del paciente con la aplicación de un tratamiento. El desarrollo de estas herramientas posibilita la reducción del error humano y permite la realización de pruebas sin consecuencias para los pacientes. Palavras-chave: Simulación, Neonato, Fisiología Cardiovascular, Patología cardiaca, Enseñanza en medicina y biomédica.
Modeling and simulation are becoming increasingly important tools for teaching and researching in medicine. Medical guidelines are systematically developed statements to assist practitioners to determine appropriate health care in specific circumstances. A medical guideline or protocol can be represented, in an abstract way, as a discrete event system model. This is indeed a promising field for applying this kind of models. In this paper, we present a finite automaton model for the Neonatal Adaptation Guideline developed by the School of Pediatrics and Neonatology of the National University of Colombia. The reported model extends the original flow chart containing the guideline, in terms of understanding, consistence and level of detail.
As has been known for over a century, oxygen binding onto hemoglobin is influenced by the activity of hydrogen ions (H+), as well as the concentration of carbon dioxide (CO2). As is also known, the binding of both CO2 and H+ on terminal valine-1 residues is competitive. One-parametric situations of these hemoglobin equilibria at specific levels of H+, O2 or CO2 are also well described. However, we think interpolating or extrapolating this knowledge into an ‘empirical’ function of three independent variables has not yet been completely satisfactory. We present a model that integrates three orthogonal views of hemoglobin oxygenation, titration, and carbamination at different temperatures. The model is based only on chemical principles, Adair’s oxygenation steps and Van’t Hoff equation of temperature dependences. Our model fits the measurements of the Haldane coefficient and CO2 hemoglobin saturation. It also fits the oxygen dissociation curve influenced by simultaneous changes in H+, CO2 and O2, which makes it a strong candidate for integration into more complex models of blood acid-base with gas transport, where any combination of mentioned substances can appear.
Part I: Introduction. Chapter 1: Introduction to Modeling and Simulation. Chapter 2: A Quick Tour of Modelica. Part II: The Modelica Language. Chapter 3: Classes, Types, and Declarations. Chapter 4: Inheritance, Modifications, and Generics. Chapter 5: Components, Connectors, and Connections. Chapter 6: Literals, Operators, and Expressions. Chapter 7: Arrays. Chapter 8: Equations. Chapter 9: Algorithms and Functions. Chapter 10: Packages. Chapter 11: Annotations, Units, and Quantities. Part III: Modeling and Applications. Chapter 12: System Modeling Methodology and Continuous Model Representation. Chapter 13: Discrete Event, Hybrid, and Concurrency Modeling. Chapter 14: Basic Laws of Nature. Chapter 15: Application Examples. Chapter 16: Modelica Library Overview. Part IV: Technology and Tools. Chapter 17: A Mathematical Representation for Modelica Models. Chapter 18: Techniques and Research. Chapter 19: Environments. Appendix A: Modelica Formal Syntax. Appendix B: Mathematica-style Modelica Syntax. Appendix C: Solutions for Exercises. Appendix D: Modelica Standard Library. Appendix E: Modelica Scripting Commands. Appendix F: Related Object-Oriented Modeling Languages. Appendix G: A Modelica XML Representation. References. Index.
This letter introduces an alternative approach to modeling the cardiovascular system with a short-term control mechanism published in Computers in Biology and Medicine Volume 47 (2014), pages 104–112. We recommend using abstract components on a distinct physical level, separating the model into hydraulic components, subsystems of the cardiovascular system and individual subsystems of the control mechanism and scenario. We recommend utilizing an acausal modeling feature of Modelica language, which allows model variables to be expressed declaratively. Furthermore, the Modelica tool identifies which are the dependent and independent variables upon compilation. An example of our approach is introduced on several elementary components representing the hydraulic resistance to fluid flow and the elastic response of the vessel, among others.
The introduced model implementation can be more reusable and understandable for the general scientific community.
Modelica is an object-oriented language, in which models can be created and graphically represented by connecting instances of classes from libraries. These connections are not only assignments of values; they can also represent acausal equality. Even more, they can model Kirchhoff 's laws of circuits. In Modelica it is possible to develop library classes which are an analogy of electrical circuit components. The result of our work in this field is Physiolibrary (www. physiolibrary.org) – a free, open-source Modelica library for human physiology. By graphical joining instances of Physiolibrary classes, user can create models of cardiovascular circulation, thermoregulation, metabolic processes, nutrient distribution, gas transport, electrolyte regulation, water distribution, hormonal regulation and pharmacological regulation. After simple setting of the parameters, the models are ready to simulate. After simulation, the user can examine variables as their values change over time. Representing the model as a diagram has also great educational advantages, because students are able to better understand physical principles when they see them modeled graphically.
Physiolibrary is a free open-source Modelica library designed for modeling human physiology. It is accessible on the Modelica Libraries web page at https://www.modelica.org/libraries. This library contains basic physical laws governing human physiology, usable for cardiovascular circulation, metabolic processes, nutrient distribution, thermoregulation, gases transport, electrolyte regulation, water distribution, hormonal regulation and pharmacological regulation.
Mathematical models and simulations are important tools in discovering key causal relationships governing physiological processes. Simulations guide and improve outcomes of medical interventions involving complex physiology. We developed HumMod, a Windows-based model of integrative human physiology. HumMod consists of 5000 variables describing cardiovascular, respiratory, renal, neural, endocrine, skeletal muscle, and metabolic physiology. The model is constructed from empirical data obtained from peer-reviewed physiological literature. All model details, including variables, parameters, and quantitative relationships, are described in Extensible Markup Language (XML) files. The executable (HumMod.exe) parses the XML and displays the results of the physiological simulations. The XML description of physiology in HumMod's modeling environment allows investigators to add detailed descriptions of human physiology to test new concepts. Additional or revised XML content is parsed and incorporated into the model. The model accurately predicts both qualitative and quantitative changes in clinical and experimental responses. The model is useful in understanding proposed physiological mechanisms and physiological interactions that are not evident, allowing one to observe higher level emergent properties of the complex physiological systems. HumMod has many uses, for instance, analysis of renal control of blood pressure, central role of the liver in creating and maintaining insulin resistance, and mechanisms causing orthostatic hypotension in astronauts. Users simulate different physiological and pathophysiological situations by interactively altering numerical parameters and viewing time-dependent responses. HumMod provides a modeling environment to understand the complex interactions of integrative physiology. HumMod can be downloaded at http://hummod.org
Input parameters for the program are the arterial pH, pCO2, and pO2 (measured by a blood gas analyzer), oxygen saturation, carboxy-, met-, and total hemoglobin (measured by a multi-wavelength spectrometer), supplemented by patient age, sex, temperature, inspired oxygen fraction, fraction of fetal hemoglobin, and ambient pressure. Output parameters are the inspired and alveolar oxygen partial pressures, pH,pCO2 and pO2 referring to the actual patient temperature, estimated shunt fraction, half-saturation tension, estimated 2,3-diphosphoglycerate concentration, oxygen content and oxygen capacity, extracellular base excess, and plasma bicarbonate concentration. Three parameters related to the blood oxygen availability are calculated: the oxygen extraction tension, concentration of extractable oxygen, and oxygen compensation factor. Calculations of the 'reverse' type may also be performed so that the effect of therapeutic measures on the oxygen status or the acid-base status can be predicted. The user may choose among several different units of measurement and two different conventions for symbols. The results are presented in a data display screen comprising all quantities together with age, sex, and temperature adjusted reference values. The program generates a 'laboratory diagnosis' of the oxygen status and the acid-base status and three graphs illustrating the oxygen status and the acid-base status of the patient: the oxygen graph, the acid-base chart and the blood gas map. A printed summary in one A4 page including a graphical display can be produced with an Epson or HP Laser compatible printer. The program is primarily intended for routine laboratories with a blood gas analyzer combined with a multi-wavelength spectrometer. Calculating the derived quantities may enhance the usefulness of the analyzers and improve patient care. The program may also be used as a teaching aid in acid-base and respiratory physiology. The program requires an IBM PC, XT, AT or similar compatible computer running under DOS version 2.11 or later. A VGA color monitor is preferred, but the program also supports EGA, CGA, and Hercules monitors. The program will be freely available at the cost of a discette and mailing expenses by courtesy of Radiometer Medical A/S, Emdrupvej 72, DK-2400 Copenhagen NV, Denmark (valid through 1991). A simplified algorithm for a programmable pocket calculator avoiding iterative calculations is given as an Appendix.
Simple models of the cardiovascular system for educational and research purposes
- T Kulhánek
- M Tribula
- J Kofránek
- M Mateják
Virtual reality in advanced simulations of intensive care scenarios
- P Privitzer
- J Kofránek
- M Tribula
- F Jezek
- T Kulhánek
- M Mateják
Simple models of the cardiovascular system for educational and research purposes
- kulhánek
Virtual reality in advanced simulations of intensive care scenarios
- privitzer