Myocarditis, characterized by inflammation of the heart muscle, has seen a notable 62.2% increase in incidence over the past three decades, leading to 324,490 deaths in 2019. Despite advancements in understanding its etiology, several critical questions remain unresolved, highlighting the need for improved patient treatment strategies. Computational methods play a crucial role in unraveling the complex interactions between pathogens and the immune system. This study explores the use of Physics-Informed Neural Networks (PINNs) to accelerate a newly developed model for myocardial edema formation in acute infectious myocarditis. The model describes the concentrations of pathogens and leukocytes using a nonlinear system of ordinary differential equations (ODEs). Our findings demonstrate a mean computational speedup of 5.93, while maintaining a Root Mean Squared Error of 0.0015. These results indicate that PINNs can accurately replicate the ODE system’s solutions, offering substantial computational efficiency for this application.

Cardiac in silico clinical trials can virtually assess the safety and efficacy of therapies using human-based modelling and simulation. These technologies can provide mechanistic explanations for clinically observed pathological behaviour. Designing virtual cohorts for in silico trials requires exploiting clinical data to capture the physiological variability in the human population. The clinical characterisation of ventricular activation and the Purkinje network is challenging, especially non-invasively. Our study aims to present a novel digital twinning pipeline that can efficiently generate and integrate Purkinje networks into human multiscale biventricular models based on subject-specific clinical 12-lead electrocardiogram and magnetic resonance recordings. Essential novel features of the pipeline are the human-based Purkinje network generation method, personalisation considering ECG R wave progression as well as QRS morphology, and translation from reduced-order Eikonal models to equivalent biophysically-detailed monodomain ones. We demonstrate ECG simulations in line with clinical data with clinical image-based multiscale models with Purkinje in four control subjects and two hypertrophic cardiomyopathy patients (simulated and clinical QRS complexes with Pearson's correlation coefficients > 0.7). Our methods also considered possible differences in the density of Purkinje myocardial junctions in the Eikonal-based inference as regional conduction velocities. These differences translated into regional coupling effects between Purkinje and myocardial models in the monodomain formulation. In summary, we demonstrate a digital twin pipeline enabling simulations yielding clinically consistent ECGs with clinical CMR image-based biventricular multiscale models, including personalised Purkinje in healthy and cardiac disease conditions.

Fibrosis, a pathological increase in extracellular matrix proteins, is a significant health issue that hinders the function of many organs in the body, in some cases fatally. In the heart, fibrosis impacts on electrical propagation in a complex and poorly predictable fashion, potentially serving as a substrate for dangerous arrhythmias. Individual risk depends on the spatial manifestation of fibrotic tissue, and learning the spatial arrangement on the fine scale in order to predict these impacts still relies upon invasive ex vivo procedures. As a result, the effects of spatial variability on the symptomatic impact of cardiac fibrosis remain poorly understood. In this work, we address the issue of availability of such imaging data via a computational methodology for generating new realisations of cardiac fibrosis microstructure. Using the Perlin noise technique from computer graphics, together with an automated calibration process that requires only a single training image, we demonstrate successful capture of collagen texturing in four types of fibrosis microstructure observed in histological sections. We then use this generator to quantitatively analyse the conductive properties of these different types of cardiac fibrosis, as well as produce three-dimensional realisations of histologically-observed patterning. Owing to the generator's flexibility and automated calibration process, we also anticipate that it might be useful in producing additional realisations of other physiological structures.

About seven months after the start of the COVID-19 pandemic, the first vaccine against the disease was approved for emergency use. Since then, twenty-three more vaccines have been approved, while more than three hundred are in development. Despite being one of the fastest vaccines ever created, several questions about it remain open. Computational models can be useful to answer some of these questions. This paper aims to evaluate whether a computer model previously used to reproduce the effects of the yellow fever vaccine in the body is also capable of reproducing the effects of a distinct vaccine: ChAdOx1 nCoV-19. Preliminary results show that the model is a promising tool to achieve this goal since it was able to reproduce the antibody curves observed in individuals vaccinated with ChAdOx1 nCoV-19.

The human immune system (HIS) is responsible for the defense of the organism against pathogens. Mathematical models can be a useful tool to study different aspects of HIS through in silico trials. In this work, we investigate both primary and secondary responses to the Yellow Fever virus (YFV) using mathematical models. Uncertainty quantification and sensitivity analysis are performed via Monte Carlo and polynomial chaos expansion methods. It was possible to reproduce key aspects of antibody dynamics in a secondary response to the YFV and quantity the influence of model parameters in relevant phases of the immune response.

By November 2020, the Coronavirus disease 2019 (COVID-19) has infected more than 50 million people worldwide, causing more than 1.2 million deaths. This new contagious disease is not well understood, and the scientific community is trying to comprehend better the interactions of the causative agent of the disease, SAR2-CoV-2, and the immune response to identify its weak points to develop new therapies to impair its lethal effects. Mathematical and computational tools can help in this task: the multiscale interactions among the various components of the human immune system and the pathogen are very complex. In this work, we present a simple system of five ordinary differential equations that can be used to model the immune response to SARS-CoV-2. The model parameters and initial conditions were adjusted to cohort studies that collected viremia and antibody data. The results have shown that the model was able to reproduce both viremia and antibodies dynamics successfully.

BACKGROUND: An effective yellow fever (YF) vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for a booster dose, the optimal immunisation schedule for immunocompetent, immunosuppressed, and pediatric populations, among other issues. This work aims to demonstrate that computational tools can be used to simulate different scenarios regarding YF vaccination and the immune response of individuals to this vaccine, thus assisting the response of some of these open questions. RESULTS: This work presents the computational results obtained by a mathematical model of the human immune response to vaccination against YF. Five scenarios were simulated: primovaccination in adults and children, booster dose in adult individuals, vaccination of individuals with autoimmune diseases under immunomodulatory therapy, and the immune response to different vaccine doses. Where data were available, the model was able to quantitatively replicate the levels of antibodies obtained experimentally. In addition, for those scenarios where data were not available, it was possible to qualitatively reproduce the immune response behaviours described in the literature. CONCLUSIONS: Our simulations show that the minimum dose to confer immunity against YF is half of the reference dose. The results also suggest that immunological immaturity in children limits the induction and persistence of long-lived plasma cells are related to the antibody decay observed experimentally. Finally, the decay observed in the antibody level after ten years suggests that a booster dose is necessary to keep immunity against YF.

An effective yellow fever vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for booster dose, the optimal immunization schedule for immunocompetent, immunosuppressed, and children, among other issues. The objective of this work is to demonstrate that computational tools can be used to simulate different scenarios regarding yellow fever vaccination and the immune response of the individuals to this vaccine, thus assisting the response of some of these open questions. In this context, this work presents the results of a computational model of the human immune response to vaccination against yellow fever. The model takes into account essential cells and molecules of the human immune system, such as antigen-presenting cells, B and T lymphocytes, memory cells, and antibodies. The model was able to replicate the levels of antibodies obtained experimentally in different vaccination scenarios, allowing a quantitative validation with experimental data.

Viral infections cause a large number of deaths worldwide. During the inflammatory process secondary to a viral infection, edema may appear, that is, fluid may accumulate in the interstitial tissue. Computational modeling is a tool that can be used to understand this phenomenon better. This paper proposes a first-step model to describe the formation of viral cardiac edema. In particular, this paper presents a model that describes the increase in interstitial pressure during viral infections. Preliminary computational results show that the model can qualitatively reproduce results described in the literature.

In recent years, Petri nets are being used for modeling complex biological systems. Examples include the modeling of biochemical reactions, protein interaction networks, gene regulation networks and the immune system. In this work, Petri nets are used to model the innate immune response to a bacterial infection. The objective of this work is to study how changes in macrophage phenotype impact the regulation and resolution of the immune response. From simulations performed, we have observed that the conversion between a M1 proinflammatory phenotype to a M2 regulatory phenotype during an inflammatory response is an important process to avoid a state of persistent inflammation.

BACKGROUND: Myocarditis is defined as the inflammation of the myocardium, i.e. the cardiac muscle. Among the reasons that lead to this disease, we may include infections caused by a virus, bacteria, protozoa, fungus, and others. One of the signs of the inflammation is the formation of edema, which may be a consequence of the interaction between interstitial fluid dynamics and immune response. This complex physiological process was mathematically modeled using a nonlinear system of partial differential equations (PDE) based on porous media approach. By combing a model based on Biot's poroelasticity theory with a model for the immune response we developed a new hydro-mechanical model for inflammatory edema. To verify this new computational model, T2 parametric mapping obtained by Magnetic Resonance (MR) imaging was used to identify the region of edema in a patient diagnosed with unspecific myocarditis. RESULTS: A patient-specific geometrical model was created using MRI images from the patient with myocarditis. With this model, edema formation was simulated using the proposed hydro-mechanical mathematical model in a two-dimensional domain. The computer simulations allowed us to correlate spatiotemporal dynamics of representative cells of the immune systems, such as leucocytes and the pathogen, with fluid accumulation and cardiac tissue deformation. CONCLUSIONS: This study demonstrates that the proposed mathematical model is a very promising tool to better understand edema formation in myocarditis. Simulations obtained from a patient-specific model reproduced important aspects related to the formation of cardiac edema, its area, position, and shape, and how these features are related to immune response.

Summary This work presents a resource allocation algorithm that considers the characteristics of regular applications to choose the subset of processors, accelerators, and networks that minimize their parallel execution time in a small heterogeneous cluster environment. The resource allocation algorithm uses Heterogeneous Cluster Model (HCM) to estimate the execution time of the parallel applications. The experimental results have shown that, for applications with distinct behaviors, the resource allocation algorithm has successfully chosen the set of resources that minimizes their execution time.

An effective yellow fever vaccine has been available since 1937. However, some issues regarding its use remain open, such as the minimum dose that can provide immunity against the disease. Mathematical-computational tools can be useful to assist the search for answers to some of these open issues. In this context, this study presents a simplified mathematical-computational model of the human immune response to the vaccination against yellow fever that takes into account important cells of the immune system. The model was able to qualitatively reproduce some experimental results reported in the literature, such as the amount of antibodies and viremia along time, as well as to reproduce distinct behaviors of the immune response reported in the literature. This is the first step towards an ideal scenario where it will be possible to simulate distinct situations related to the use of the yellow fever vaccine, such as its application in immunodeficient individuals, different vaccination strategies, duration of immunity and the need for a booster dose.

The Human Immune System (HIS) plays a fundamental role in the defense of the body against diseases. However, the multi-scale interactions among several of its components make the complete understanding of the mechanisms involved in the body defense a complex task. Mathematical and computational tools can help towards this goal. Lots of works use differential equations, agent-based models and cellular automaton to describe the behavior of the HIS. In this work, we use the Gillespie stochastic simulation algorithm to model the stochastic nature of the HIS response to Yellow Fever vaccine.

In recent years, there has been an increasing interest in the mathematical and computational modeling of the human immune system (HIS). Computational models of HIS dynamics may contribute to a better understanding of the relationship between complex phenomena and immune response; in addition, computational models will support the development of new drugs and therapies for different diseases. However, modeling the HIS is an extremely difficult task that demands a huge amount of work to be performed by multidisciplinary teams. In this study, our objective is to model the spatio-temporal dynamics of representative cells and molecules of the HIS during an immune response after the injection of lipopolysaccharide (LPS) into a section of tissue. LPS constitutes the cellular wall of Gram-negative bacteria, and it is a highly immunogenic molecule, which means that it has a remarkable capacity to elicit strong immune responses. We present a descriptive, mechanistic and deterministic model that is based on partial differential equations (PDE). Therefore, this model enables the understanding of how the different complex phenomena interact with structures and elements during an immune response. In addition, the model's parameters reflect physiological features of the system, which makes the model appropriate for general use.

Abstract The modeling of the electrical activity of the heart is of great medical and scientific interest, because it provides a way to get a better understanding of the related biophysical phenomena, allows the development of new techniques for diagnoses and serves as a platform for drug tests. The cardiac electrophysiology may be simulated by solving a partial differential equation coupled to a system of ordinary differential equations describing the electrical behavior of the cell membrane. The numerical solution is, however, computationally demanding because of the fine temporal and spatial sampling required. The demand for real‐time high definition 3D graphics made the new graphic processing units (GPUs) a highly parallel, multithreaded, many‐core processor with tremendous computational horsepower. It makes the use of GPUs a promising alternative to simulate the electrical activity in the heart. The aim of this work is to study the performance of GPUs for solving the equations underlying the electrical activity in a simple cardiac tissue. In tests on 2D cardiac tissues with different cell models it is shown that the GPU implementation runs 20 times faster than a parallel CPU implementation running with 4 threads on a quad–core machine, parts of the code are even accelerated by a factor of 180. Copyright © 2010 John Wiley & Sons, Ltd.

Medical imaging is becoming a key technique to visualize the internal structure of the body. Magnetic resonance imaging (MRI) is currently used to take different spatial images of organs, such as the heart. The output of such an analysis is a set of images representing different views of the body or of an organ. There exist many algorithms to pre-process and analyze medical images such as the well known Snake segmentation algorithm. An issue in medical imaging is the large size of images, that require large and efficient data stores, and the high computational power needed to process them. For these reasons, the Grid is being more and more used as an ideal environment for medical image processing. This paper presents a first experience in porting the snake algorithm on a Globus-based Grid.

Computational modeling of the heart has demonstrated to be a useful tool for the investigation and comprehension of the complex biophysical processes that underlie cardiac function. Unfortunately, large scale simulations, such as those resulting from the discretization of an entire heart, remain a computational challenge. In order to reduce simulation execution times, parallel implementations have traditionally exploited data parallelism via numerical schemes based on domain-decomposition. However, it has been verified that the parallel efficiency of these implementations severely degrades as the number of processors increases. In this work, we propose and implement a new parallel algorithm for the solution of cardiac models. By relaxing the coherence of the execution, a new level of parallelism could be identified and exploited: pipelining. A synchronous parallel algorithm that uses both pipelining and data decomposition techniques was implemented and used the MPI library for communication. Numerical tests were performed in a 8-node linux-cluster. Our preliminary results indicate that the proposed algorithm is able to increase the parallel efficiency up to 20% when compared to the traditional approach that uses pure data-level parallelism. In addition, the numerical precision was kept under control (relative errors under 4%) when the relaxed coherence execution was adopted.