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One of the many unresolved obstacles in the field of cardiovascular research is an uncompromising in vitro cardiac model. While primary cell sources from animal models offer both advantages and disadvantages, efforts over the past half-century have aimed to reduce their use. Additionally, obtaining a sufficient quantity of human primary cardiomyocytes faces ethical and legal challenges. As the practically unlimited source of human cardiomyocytes from induced pluripotent stem cells (hiPSC-CM) is now mostly resolved, there are great efforts to improve their quality and applicability by overcoming their intrinsic limitations. The greatest bottleneck in the field is the in vitro ageing of hiPSC-CMs to reach a maturity status that closely resembles that of the adult heart, thereby allowing for more appropriate drug developmental procedures as there is a clear correlation between ageing and developing cardiovascular diseases. Here, we review the current state-of-the-art techniques in the most realistic heart models used in disease modelling and toxicity evaluations from hiPSC-CM maturation through heart-on-a-chip platforms and in silico models to the in vitro models of certain cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmias are a major cause of cardiovascular mortality worldwide. Many arrhythmias are caused by reentry, a phenomenon where excitation waves circulate in the heart. Optical mapping techniques have revealed the role of reentry in arrhythmia initiation and fibrillation transition, but the underlying biophysical mechanisms are still difficult to investigate in intact hearts. Tissue engineering models of cardiac tissue can mimic the structure and function of native cardiac tissue and enable interactive observation of reentry formation and wave propagation. This review will present various approaches to constructing cardiac tissue models for reentry studies, using the authors' work as examples. The review will highlight the evolution of tissue engineering designs based on different substrates, cell types, and structural parameters. A new approach using polymer materials and cellular reprogramming to create biomimetic cardiac tissues will be introduced. The review will also show how computational modeling of cardiac tissue can complement experimental data and how such models can be applied in the biomimetics of cardiac tissue. [ABSTRACT FROM AUTHOR]

Cardiac mechanics models are developed to represent a high level of detail, including refined anatomies, accurate cell mechanics models, and platforms to link microscale physiology to whole-organ function. However, cardiac biomechanics models still have limited clinical translation. In this review, we provide a picture of cardiac mechanics models, focusing on their clinical translation. We review the main experimental and clinical data used in cardiac models, as well as the steps followed in the literature to generate anatomical meshes ready for simulations. We describe the main models in active and passive mechanics and the different lumped parameter models to represent the circulatory system. Lastly, we provide a summary of the state-of-the-art in terms of ventricular, atrial, and four-chamber cardiac biomechanics models. We discuss the steps that may facilitate clinical translation of the biomechanics models we describe. A well-established software to simulate cardiac biomechanics is lacking, with all available platforms involving different levels of documentation, learning curves, accessibility, and cost. Furthermore, there is no regulatory framework that clearly outlines the verification and validation requirements a model has to satisfy in order to be reliably used in applications. Finally, better integration with increasingly rich clinical and/or experimental datasets as well as machine learning techniques to reduce computational costs might increase model reliability at feasible resources. Cardiac biomechanics models provide excellent opportunities to be integrated into clinical workflows, but more refinement and careful validation against clinical data are needed to improve their credibility. In addition, in each context of use, model complexity must be balanced with the associated high computational cost of running these models.

The drug discovery and development process is still long, costly, and highly risky. The principal attrition factor is undetected toxicity, with hepatic and cardiac toxicities playing a critical role and being the main responsible of safetyrelated drug withdrawals from the market. Multi Organs-on-Chip (MOoC) represent a disruptive solution to study drug-related effects on several organs simultaneously and to efficiently predict drug toxicity in preclinical trials. Specifically focusing on drug safety, different technological features are applied here to develop versatile MOoC platforms encompassing two culture chambers for generating and controlling the type of communication between a metabolically competent liver model and a functional 3D heart model. The administration of the drug Terfenadine, a cardiotoxic compound liver-metabolized into the noncardiotoxic Fexofenadine, proves that liver metabolism and a fine control over drug diffusion are fundamental to elicit a physio-pathological cardiac response. From these results, an optimized LivHeart platform is developed to house a liver model and a cardiac construct that can be mechanically trained to achieve a beating microtissue, whose electrophysiology can be directly recorded in vitro. The platform is proved able to predict off-target cardiotoxicity of Terfenadine after liver metabolism both in terms of cell viability and functionality. [ABSTRACT FROM AUTHOR]

Myocardium consists of cardiac cells that interact with their environment through physical, biochemical, and electrical stimulations. The physiology, function, and metabolism of cardiac tissue are affected by this dynamic structure. Within the myocardium, cardiomyocytes' orientations are parallel, creating a dominant orientation. Additionally, local alignments of fibers, along with a helical organization, become evident at the macroscopic level. For the successful development of a reliable in vitro cardiac model, evaluation of cardiac cells' behavior in a dynamic microenvironment, as well as their spatial architecture, is mandatory. In this study, we hypothesize that complex interactions between long-term contraction boundary conditions and cyclic mechanical stimulation may provide a physiological mechanism to generate off-axis alignments in the preferred mechanical stretch direction. This off-axis alignment can be engineered in vitro and, most importantly, mirrors the helical arrangements observed in vivo. For this purpose, uniaxial mechanical stretching of dECM-fibrin hydrogels was performed on pre-aligned 3D cultures of cardiac cells. In view of the potential development of helical structures similar to those in native hearts, the possibility of generating oblique alignments ranging between 0° and 90° was explored. Indeed, our investigations of cell alignment in 3D, employing both mechanical stimulation and groove constraint, provide a reliable mechanism for the generation of helicoidal structures in the myocardium. By combining cyclic stretch and geometric alignment in grooves, an intermediate angle toward favored direction can be achieved experimentally: while cyclic stretch produces a perpendicular orientation, geometric alignment is associated with a parallel one. In our 2D and 3D culture conditions, nonlinear cellular addition of the strains and strain avoidance concept reliably predicted the preferred cellular alignment. The 3D dECM-fibrin model system in this study shows that cyclical stretching supports cell survival and development. Using mechanical stimulation of pre-aligned heart cells, maturation markers are augmented in neonatal cardiomyocytes, while the beating culture period is prolonged, indicating an improved model function. We propose a simplified theoretical model based on numerical simulation and nonlinear strain avoidance by cells to explain oblique alignment angles. Thus, this work lays a possible rational basis for understanding and engineering oblique cellular alignments, such as the helicoidal layout of the heart, using approaches that simultaneously enhance maturation and function. [ABSTRACT FROM AUTHOR]

Roughly 75% of normal myocardial tissue volume is comprised of myocytes, however, fibroblasts by number are the most predominant cells in cardiac tissue. Previous studies have shown distinctive differences in cellular electrophysiology and excitability between myocytes and fibroblasts. However, it is still unclear how the electrical coupling between the two and the increased population of fibroblasts affects the electromechanical dynamics of cardiac tissue. This paper focuses on investigating effects of fibroblastmyocyte electrical coupling (FMEC) and fibroblast population on atrial electrical conduction and mechanical contractility by using a twodimensional Discrete Element Method (DEM) model of cardiac tissue that is different to finite element method (FEM). In the model, the electro-mechanics of atrial cells are modelled by a biophysically detailed model for atrial electrical action potentials and myofilament kinetics, and the atrial fibroblasts are modelled by an active model that considers four active membrane ionic channel currents. Our simulation results show that the FMEC impairs myocytes' electrical action potential and mechanical contractibility, manifested by reduced upstroke velocity, amplitude and duration of action potentials, as well as cell length shortening. At the tissue level, the FMEC slows down the conduction of excitation waves, and reduces strain of the tissue produced during a contraction course. These findings provide new insights into understandings of how FMEC impairs cardiac electrical and mechanical dynamics of the heart. [ABSTRACT FROM AUTHOR]

Personalized computational cardiac models are considered to be a unique and powerful tool in modern cardiology, integrating the knowledge of physiology, pathology and fundamental laws of mechanics in one framework. They have the potential to improve risk prediction in cardiac patients and assist in the development of new treatments. However, in order to use these models for clinical decision support, it is important that both the impact of model parameter perturbations on the predicted quantities of interest as well as the uncertainty of parameter estimation are properly quantified, where the first task is a priori in nature (meaning independent of any specific clinical data), while the second task is carried out a posteriori (meaning after specific clinical data have been obtained). The present study addresses these challenges for a widely used constitutive law of passive myocardium (the Holzapfel-Ogden model), using global sensitivity analysis (SA) to address the first challenge, and inverse-uncertainty quantification (I-UQ) for the second challenge. The SA is carried out on a range of different input parameters to a left ventricle (LV) model, making use of computationally efficient Gaussian process (GP) surrogate models in place of the numerical forward simulator. The results of the SA are then used to inform a low-order reparametrization of the constitutive law for passive myocardium under consideration. The quality of this parameterization in the context of an inverse problem having observed noisy experimental data is then quantified with an I-UQ study, which again makes use of GP surrogate models. The I-UQ is carried out in a Bayesian manner using Markov Chain Monte Carlo, which allows for full uncertainty quantification of the material parameter estimates. Our study reveals insights into the relation between SA and I-UQ, elucidates the dependence of parameter sensitivity and estimation uncertainty on external factors, like LV cavity pressure, and sheds new light on cardio-mechanic model formulation, with particular focus on the Holzapfel-Ogden myocardial model. [ABSTRACT FROM AUTHOR]

Roughly 75% of normal myocardial tissue volume is comprised of myocytes, however, fibroblasts by number are the most predominant cells in cardiac tissue. Previous studies have shown distinctive differences in cellular electrophysiology and excitability between myocytes and fibroblasts. However, it is still unclear how the electrical coupling between the two and the increased population of fibroblasts affects the electromechanical dynamics of cardiac tissue. This paper focuses on investigating effects of fibroblast-myocyte electrical coupling (FMEC) and fibroblast population on atrial electrical conduction and mechanical contractility by using a two-dimensional Discrete Element Method (DEM) model of cardiac tissue that is different to finite element method (FEM). In the model, the electro-mechanics of atrial cells are modelled by a biophysically detailed model for atrial electrical action potentials and myofilament kinetics, and the atrial fibroblasts are modelled by an active model that considers four active membrane ionic channel currents. Our simulation results show that the FMEC impairs myocytes’ electrical action potential and mechanical contractibility, manifested by reduced upstroke velocity, amplitude and duration of action potentials, as well as cell length shortening. At the tissue level, the FMEC slows down the conduction of excitation waves, and reduces strain of the tissue produced during a contraction course. These findings provide new insights into understandings of how FMEC impairs cardiac electrical and mechanical dynamics of the heart.

Understanding the spatial gene expression and regulation in the heart is key to uncovering its developmental and physiological processes, during homeostasis and disease. Numerous techniques exist to gain gene expression and regulation information in organs such as the heart, but few utilize intuitive true-to-life three-dimensional representations to analyze and visualise results. Here we combined transcriptomics with 3D-modelling to interrogate spatial gene expression in the mammalian heart. For this, we microdissected and sequenced transcriptome-wide 18 anatomical sections of the adult mouse heart. Our study has unveiled known and novel genes that display complex spatial expression in the heart sub-compartments. We have also created 3D-cardiomics, an interface for spatial transcriptome analysis and visualization that allows the easy exploration of these data in a 3D model of the heart. 3D-cardiomics is accessible from http://3d-cardiomics.erc.monash.edu/. [Display omitted] • Transcriptome-wide profiling of the murine heart reveals complex spatial patterns. • Identification of novel markers for cardiac subsections. • 3D-cardiomics allows for interrogation of gene expression in the adult heart. See https://3d-cardiomics.erc.monash.edu.au/. • Users can run a standalone 3D-cardiomics version for custom visualization. See https://github.com/Ramialison-Lab/3DCardiomics. [ABSTRACT FROM AUTHOR]

Development of cardiac multiphysics models has progressed significantly over the decades and simulations combining multiple physics interactions have become increasingly common. In this review, we summarise the progress in this field focusing on various approaches of integrating ventricular structures. electrophysiological properties, myocardial mechanics, as well as incorporating blood hemodynamics and the circulatory system. Common coupling approaches are discussed and compared, including the advantages and shortcomings of each. Currently used strategies for patient-specific implementations are highlighted and potential future improvements considered. [ABSTRACT FROM AUTHOR]

Salmon pancreas disease virus (SPDV) has been affecting the salmon farming industry for over 30 years, but despite the substantial amount of studies, there are still a number of recognized knowledge gaps, for example in the transmission of the virus. In this work, an ultrastructural morphological approach was used to describe observations after infection by SPDV of an ex vivo cardiac model generated from Atlantic salmon embryos. The observations in this study and those available on previous ultrastructural work on SPDV are compared and contrasted with the current knowledge on terrestrial mammalian and insect alphaviral replication cycles, which is deeper than that of SPDV both morphologically and mechanistically. Despite their limitations, morphological descriptions remain an excellent way to generate novel hypotheses, and this has been the aim of this work. This study has used a target host, ex vivo model and resulted in some previously undescribed features, including filopodial membrane projections, cytoplasmic stress granules or putative intracytoplasmic budding. The latter suggests a new hypothesis that warrants further mechanistic research: SPDV in salmon may have retained the capacity for non‐cytolytic (persistent) infections by intracellular budding, similar to that noted in arthropod vectors of other alphaviruses. In the notable absence of a known intermediate host for SPDV, the presence of this pattern suggests that both cytopathic and persistent infections may coexist in the same host. It is our hope that the ultrastructural comparison presented here stimulates new research that brings the knowledge on SPDV replication cycle up to a similar level to that of terrestrial alphaviruses. [ABSTRACT FROM AUTHOR]

Computer modeling and simulation is fast-moving towards clinical applications to advance cardiac diagnosis and aid treatment planning. As cardiac models become more popular and useful, the need to tailor these models for individual subjects has grown. Therefore, model identification becomes an important step of cardiac modeling. Cardiac model identification is a challenging task, particularly for simulation in high organizational scales such as tissue and organ scales, as the models are nonlinear and involve hidden ion channel gating variables. In this study, we proposed a two-stage model calibration algorithm to estimate the parameters of cardiac tissue model using membrane potential data. Specifically, an ensemble Kalman Filter (EnKF) is used in the first stage to track the membrane potentials and the hidden ion channel gating variables; the outputs from the EnKF are used as the initial values to calculate forward prediction in the second stage. The optimization algorithm minimizes the sum of squared errors in both stages through a coarse and a fine optimization. The proposed method is validated through multiple simulation designs, which shows good accuracy and efficiency.

Myocardium consists of cardiac cells that interact with their environment through physical, biochemical, and electrical stimulations. The physiology, function, and metabolism of cardiac tissue are affected by this dynamic structure. Within the myocardium, cardiomyocytes’ orientations are parallel, creating a dominant orientation. Additionally, local alignments of fibers, along with a helical organization, become evident at the macroscopic level. For the successful development of a reliable in vitro cardiac model, evaluation of cardiac cells’ behavior in a dynamic microenvironment, as well as their spatial architecture, is mandatory. In this study, we hypothesize that complex interactions between long-term contraction boundary conditions and cyclic mechanical stimulation may provide a physiological mechanism to generate off-axis alignments in the preferred mechanical stretch direction. This off-axis alignment can be engineered in vitro and, most importantly, mirrors the helical arrangements observed in vivo. For this purpose, uniaxial mechanical stretching of dECM-fibrin hydrogels was performed on pre-aligned 3D cultures of cardiac cells. In view of the potential development of helical structures similar to those in native hearts, the possibility of generating oblique alignments ranging between 0° and 90° was explored. Indeed, our investigations of cell alignment in 3D, employing both mechanical stimulation and groove constraint, provide a reliable mechanism for the generation of helicoidal structures in the myocardium. By combining cyclic stretch and geometric alignment in grooves, an intermediate angle toward favored direction can be achieved experimentally: while cyclic stretch produces a perpendicular orientation, geometric alignment is associated with a parallel one. In our 2D and 3D culture conditions, nonlinear cellular addition of the strains and strain avoidance concept reliably predicted the preferred cellular alignment. The 3D dECM-fibrin model system in this study shows that cyclical stretching supports cell survival and development. Using mechanical stimulation of pre-aligned heart cells, maturation markers are augmented in neonatal cardiomyocytes, while the beating culture period is prolonged, indicating an improved model function. We propose a simplified theoretical model based on numerical simulation and nonlinear strain avoidance by cells to explain oblique alignment angles. Thus, this work lays a possible rational basis for understanding and engineering oblique cellular alignments, such as the helicoidal layout of the heart, using approaches that simultaneously enhance maturation and function.

This paper investigates mixed-mode oscillations (MMOs) with a three-dimensional conductance-based cardiac action potential model, which makes the heart beat in a nonrenewable way. The 3D model was entailed by utilizing voltage-dependent timescales to describe the mechanism in which MMOs are generated. As expected, motivated by geometric singular perturbation theory, our analysis explains in detail the geometric mechanisms that there is a range of parameters under which the cardiac model highlights that the presence of MMOs is induced by the intrinsical canard phenomenon. Much is currently known about the geometric mechanisms, for a folded saddle, the two singular canards perturb to maximal canards. Characteristics of the stimulus current such as frequency and duration determine which early afterdepolarizations (EADs), as a special case of MMOs bears, as well as the article compares the detailed manifold structures of original and dimensionless systems with square wave pulses by setting the pacing cycle length. An exceedingly vital technique of the analysis is the slow–fast dynamics analysis by which the system governs multiple timescale structures analytically. A more novel and successful multiple-timescale approach divides the system so that there is only one fast variable and demonstrates that the MMOs arise from canard dynamics, such as using a three-variable model in which two variables are treated as "slow" and one treated as "fast", which the layer problem and the reduced problem are considered to explain the trajectory on the critical manifold. Meanwhile, if one variable was regarded as the single slow variable, substantial bifurcation properties are discovered for slow–fast system, as well as general one-parameter bifurcation type is discussed for the whole system similarly. By focusing on the first Lyapunov coefficient of the Hopf bifurcation, which decides whether the bifurcation is supercritical or subcritical, it was shown that an unstable limit cycle can arise via a delayed subcritical Hopf bifurcation for the original system. Meanwhile, the dynamical studies of cardiac model have major implications for further elaborating the complex dynamic behaviors, such as EADs, which can lead to tissue-level arrhythmias. Ultimately, it has turned these researches into a considerable player in the signal and information transmission for underlying nervous systems. [ABSTRACT FROM AUTHOR]

Ballistic gel was investigated as a tissue-mimicking material in an anthropomorphic cardiac phantom for ultrasound imaging. The gel was tested for its acoustic properties and its compatibility with conventional plastics molding techniques. Speed of sound and attenuation were evaluated in the range 2-12 MHz. The speed of sound was 1537 ± 39 m/s, close to typical values for cardiac tissue (∼1576 m/s). The attenuation coefficient was 1.07 dB/cm·MHz, within the range of values previously reported for cardiac tissue (0.81-1.81 dB/cm·MHz). A cardiac model based on human anatomy was developed using established image segmentation processes and conventional plastic molding techniques. Key anatomic features were observed, captured and identified in the model using an intracardiac ultrasound imaging system. These favorable results along with the material's durability and processes that allow for repetitive production of detailed whole-heart models at low cost are promising. There are numerous applications for geometrically complex phantoms in research, training, device development and clinical use. [ABSTRACT FROM AUTHOR]

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions-that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here, we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes and autoregressive-movingaverage models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. [ABSTRACT FROM AUTHOR]

Myocardial slices, also known as "cardiac tissue slices" or "organotypic heart slices," are ultrathin (100–400 μm) slices of living adult ventricular myocardium prepared using a high-precision vibratome. They are a model of intermediate complexity as they retain the native multicellularity, architecture, and physiology of the heart, while their thinness ensures adequate oxygen and metabolic substrate diffusion in vitro. Myocardial slices can be produced from a variety of animal models and human biopsies, thus providing a representative human in vitro platform for translational cardiovascular research. In this review, we compare myocardial slices to other in vitro models and highlight some of the unique advantages provided by this platform. Additionally, we discuss the work performed in our laboratory to optimize myocardial slice preparation methodology, which resulted in highly viable myocardial slices from both large and small mammalian hearts with only 2–3% cardiomyocyte damage and preserved structure and function. Applications of myocardial slices span both basic and translational cardiovascular science. Our laboratory has utilized myocardial slices for the investigation of cardiac multicellularity, visualizing 3D collagen distribution and micro/macrovascular networks using tissue clearing protocols and investigating the effects of novel conductive biomaterials on cardiac physiology. Myocardial slices have been widely used for pharmacological testing. Finally, the current challenges and future directions for the technology are discussed. [ABSTRACT FROM AUTHOR]

Cardiac mechanics models are developed to represent a high level of detail, including refined anatomies, accurate cell mechanics models, and platforms to link microscale physiology to whole-organ function. However, cardiac biomechanics models still have limited clinical translation. In this review, we provide a picture of cardiac mechanics models, focusing on their clinical translation. We review the main experimental and clinical data used in cardiac models, as well as the steps followed in the literature to generate anatomical meshes ready for simulations. We describe the main models in active and passive mechanics and the different lumped parameter models to represent the circulatory system. Lastly, we provide a summary of the state-of-the-art in terms of ventricular, atrial, and four-chamber cardiac biomechanics models. We discuss the steps that may facilitate clinical translation of the biomechanics models we describe. A well-established software to simulate cardiac biomechanics is lacking, with all available platforms involving different levels of documentation, learning curves, accessibility, and cost. Furthermore, there is no regulatory framework that clearly outlines the verification and validation requirements a model has to satisfy in order to be reliably used in applications. Finally, better integration with increasingly rich clinical and/or experimental datasets as well as machine learning techniques to reduce computational costs might increase model reliability at feasible resources. Cardiac biomechanics models provide excellent opportunities to be integrated into clinical workflows, but more refinement and careful validation against clinical data are needed to improve their credibility. In addition, in each context of use, model complexity must be balanced with the associated high computational cost of running these models., Competing Interests: Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

In this work we propose an adaptive parallel genetic algorithm, which is able to automatically choose which loops should run in parallel in order to balance the workload sharing and the extra cost to manage threads' forks and joins. Our genetic algorithm finds parameters to our cardiac simulator based on mass-spring systems and cellular automata, in order to reproduce a cycle of contraction and relaxation of the human left ventricle. The ventricle geometry was obtained by extracting information from MRI data. Our multilevel parallelism scheme decreased the execution time up to 12 % and the GA adjusts the cardiac model so that it reproduced a cardiac cycle. The advantages and drawbacks of our method are discussed as well as its limitations. [ABSTRACT FROM AUTHOR]

Medical imaging and clinical studies have proven that the heart pumps by means of minor outer volume changes and back-and-forth longitudinal movements in the atrioventricular (AV) region. The magnitude of AV-plane displacement has also shown to be a reliable index for diagnosis of heart failure. Despite this, AV-plane displacement is usually omitted from cardiovascular modelling. We present a lumped-parameter cardiac model in which the heart is described as a displacement pump with the AV plane functioning as a piston unit (AV piston). This unit is constructed of different upper and lower areas analogous with the difference in the atrial and ventricular cross-sections. The model output reproduces normal physiology, with a left ventricular pressure in the range of 8–130 mmHg, an atrial pressure of approximatly 9 mmHg, and an arterial pressure change between 75 mmHg and 130 mmHg. In addition, the model reproduces the direction of the main systolic and diastolic movements of the AV piston with realistic velocity magnitude (∼10 cm/s). Moreover, changes in the simulated systolic ventricular-contraction force influence diastolic filling, emphasizing the coupling between cardiac systolic and diastolic functions. The agreement between the simulation and normal physiology highlights the importance of myocardial longitudinal movements and of atrioventricular interactions in cardiac pumping. [ABSTRACT FROM AUTHOR]

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Abstract: Determination of the mechanical properties of the myocardium is crucial for cardiac diagnosis. Cardiac strain and strain rate imaging may enable such quantification. To further develop these methodologies, an experimental setup allowing the recording of ultrasonic deformation data in a reproducible manner is necessary. Such setup with biventricular polyvinyl alcohol heart phantoms has been built. To test this setup, segmental longitudinal, radial and circumferential displacement, velocity, strain and strain rate in the phantoms were measured using a clinical ultrasound scanner and commercially available deformation imaging algorithms (based on both tissue velocity imaging and speckle tracking). The model deformation was close to that observed in the human left ventricular wall and was highly reproducible (e.g., the average peak longitudinal strain for the mid- and apical phantom segments equals −15.32 ± 0.53% and −19 ± 6% for the ventricle wall). The experimental setup is a valuable source of data for the development of algorithms for deformation estimation. (E-mail: b.lesniak-plewinska@mchtr.pw.edu.pl) [Copyright &y& Elsevier]

The electrical dynamics in the heart is modeled by a two-component PDE. Using geometric singular perturbation theory, it is shown that a traveling pulse solution, which corresponds to a single heartbeat, exists. One key aspect of the proof involves tracking the solution near a point on the slow manifold that is not normally hyperbolic. This is achieved by desingularizing the vector field using a blow-up technique. This feature is relevant because it distinguishes cardiac impulses from, for example, nerve impulses. Stability of the pulse is also shown, by computing the zeros of the Evans function. Although the spectrum of one of the fast components is only marginally stable, due to essential spectrum that accumulates at the origin, it is shown that the spectrum of the full pulse consists of an isolated eigenvalue at zero and essential spectrum that is bounded away from the imaginary axis. Thus, this model provides an example in a biological application reminiscent of a previously observed mathematical phenomenon: that connecting an unstable-in this case marginally stable-front and back can produce a stable pulse. Finally, remarks are made regarding the existence and stability of spatially periodic pulses, corresponding to successive heartbeats, and their relationship with alternans, irregular action potentials that have been linked with arrhythmia. [ABSTRACT FROM AUTHOR]

Abstract: A cardiovascular system (CVS) model has previously been validated in simulated cardiac and circulatory disease states. It has also been shown to accurately capture all main hemodynamic trends in a porcine model of pulmonary embolism. In this research, a slightly extended CVS model and parameter identification process are presented and validated in a porcine experiment of positive end-expiratory pressure (PEEP) titrations at different volemic levels. The model is extended to more physiologically represent the separation of venous and arterial circulation. Errors for the identified model are within 5% when re-simulated and compared to clinical data. All identified parameter trends match clinically expected changes. This work represents another clinical validation of the underlying fundamental CVS model, and the methods and approach of using them for cardiovascular diagnosis in critical care. [Copyright &y& Elsevier]

Abstract: A cardiovascular system model and parameter identification method have previously been validated for porcine experiments of induced pulmonary embolism and positive end-expiratory pressure (PEEP) titrations, accurately tracking all the main hemodynamic trends. In this research, the model and parameter identification process are further validated by predicting the effect of intervention. An overall population-specific rule linking specific model parameters to increases in PEEP is formulated to predict the hemodynamic effects on arterial pressure, pulmonary artery pressure and stroke volume. Hemodynamic changes are predicted for an increase from 0 to 10cmH2O with median absolute percentage errors less than 7% (systolic pressures) and 13% (stroke volume). For an increase from 10 to 20cmH2O median absolute percentage errors are less than 11% (systolic pressures) and 17% (stroke volume). These results validate the general applicability of such a rule, which is not pig-specific, but holds over for all analyzed pigs. This rule enables physiological simulation and prediction of patient response. Overall, the prediction accuracy achieved represents a further clinical validation of these models, methods and overall approach to cardiovascular diagnosis and therapy guidance. [Copyright &y& Elsevier]

We present a finite difference method for modeling the propagation of electrical waves in cardiac tissue using the cable equation with homogeneous Neumann boundary conditions. This method is novel in that it is derived by first discretizing the phase field method as described by Fenton et al. [Chaos, 15 (2005), p. 013502.] then taking a limit to recover a sharp interface. Our method provides for exact voltage conservation (up to floating point precision in application), can be used on arbitrary geometries, and can be implemented using only nearest neighbor coupling between grid points. [ABSTRACT FROM AUTHOR]

The paper uses electrical principles and the electrophysiology of the heart to develop an electrical model of the heart that is suitable for lectures on bioelectrical engineering. The model computes the electrocardiogram (ECG) that is in agreement with that from the normal heart. [ABSTRACT FROM AUTHOR]

A minimal cardiac model has been shown to accurately capture a wide range of cardiovascular system dynamics commonly seen in the intensive care unit (ICU). However, standard parameter identification methods for this model are highly non-linear and non-convex, hindering real-time clinical application. An integral-based identification method that transforms the problem into a linear, convex problem, has been previously developed, but was only applied on continuous simulated data with random noise. This paper extends the method to handle discrete sets of clinical data, unmodelled dynamics, a significantly reduced data set theta requires only the minimum and maximum values of the pressure in the aorta, pulmonary artery and the volumes in the ventricles. The importance of integrals in the formulation for noise reduction is illustrated by demonstrating instability in the identification using simple derivative-based approaches. The cardiovascular system (CVS) model and parameter identification method are then clinically validated on porcine data for pulmonary embolism. Errors for the identified model are within 10% when re-simulated and compared to clinical data. All identified parameter trends match clinically expected changes. This work represents the first clinical validation of these models, methods and approach to cardiovascular diagnosis in critical care. [Copyright &y& Elsevier]

Abstract: A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy selection. This paper presents an integral-based parameter identification method for fast, accurate identification of patient specific parameters using limited measured data. The integral method turns a previously non-linear and non-convex optimization problem into a linear and convex identification problem. The model includes ventricular interaction and physiological valve dynamics. A healthy human state and four disease states, valvular stenosis, pulmonary embolism, cardiogenic shock and septic shock are used to test the method. Parameters for the healthy and disease states are accurately identified using only discretized flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms through the aorta and pulmonary artery. These input values can be readily obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care. The method enables rapid identification of model parameters to match a particular patient condition in clinical real time (3–5 min) to within a mean value of 4–10% in the presence of 5–15% uniformly distributed measurement noise. The specific changes made to simulate each disease state are correctly identified in each case to within 10% without false identification of any other patient specific parameters. Clinically, the resulting patient specific model can then be used to assist medical staff in understanding, diagnosis and treatment selection. [Copyright &y& Elsevier]

Cardiac toxicity still represents a common adverse outcome causing drug attrition and post-marketing withdrawal. The development of relevant in vitro models resembling the human heart recently opened the path towards a more accurate detection of drug-induced human cardiac toxicity early in the drug development process. Organs-on-chip have been proposed as promising tools to recapitulate in vitro the key aspects of the in vivo cardiac physiology and to provide a means to directly analyze functional readouts. In this scenario, a new device capable of continuous monitoring of electrophysiological signals from functional in vitro human hearts-on-chip is here presented. The development of cardiac microtissues was achieved through a recently published method to control the mechanical environment, while the introduction of a technology consisting in micro-electrode coaxial guides allowed to conduct direct and non-destructive electrophysiology studies. The generated human cardiac microtissues exhibited synchronous spontaneous beating, as demonstrated by multi-point and continuous acquisition of cardiac field potential, and expression of relevant genes encoding for cardiac ion-channels. A proof-of-concept pharmacological validation on three drugs proved the proposed model to potentially be a powerful tool to evaluate functional cardiac toxicity., (Creative Commons Attribution license.)