SMART: Spatial Modeling Algorithms for Reaction and Transport

Recent advances in microscopy and 3D reconstruction methods have allowed for characterization of cellular morphology in unprecedented detail, including the irregular geometries of intracellular subcompartments such as membrane-bound organelles. These geometries are now compatible with predictive modeling of cellular function. Biological cells respond to stimuli through sequences of chemical reactions generally referred to as cell signaling pathways. The propagation and reaction of chemical substances in cell signaling pathways can be represented by coupled nonlinear systems of reaction-transport equations. These reaction pathways include numerous chemical species that react across boundaries or interfaces (e.g., the cell membrane and membranes of organelles within the cell) and domains (e.g., the bulk cell volume and the interior of organelles). Such systems of multi-dimensional partial differential equations (PDEs) are notoriously difficult to solve because of their high dimensionality, non-linearities, strong coupling, stiffness, and potential instabilities. In this work, we describe Spatial Modeling Algorithms for Reactions and Transport (SMART), a high-performance finite-element-based simulation package for model specification and numerical simulation of spatially-varying reaction-transport processes. SMART is based on the FEniCS finite element library, provides a symbolic representation framework for specifying reaction pathways, and supports geometries in 2D and 3D including large and irregular cell geometries obtained from modern ultrastructural characterization methods.Comment: 5 pages, 2 figures, submitted to the Journal of Open Source Software (JOSS), code available at https://github.com/RangamaniLabUCSD/smar

Wavelet Techniques in Medical Imaging : Classification of UltraSound Images using the Windowed Scattering Transform

In this thesis we will study wavelet techniques for image classification in ultrasound(US) images. The aim is to develop a method for classifying the degree of inflammation in finger-joints.We develop and apply the techniques of the windowed scattering transform. This is a wavelet-based technique which is proven to be very efficient in image classification problems. Both theoretical and numerical sides have been considered. We also discuss other possible techniques for classification of US images, in particular a method based on the area of inflammation

Patient-Specific Computational Modeling of Cardiac Mechanics

Computational models are an absolutely necessary tool in many engineering disciplines. For example, computational models are used to predict tomorrow’s weather, to optimize the aerodynamics of new aircraft, and to ensure buildings and bridges are safe. The use of computational models in field of biomedical engineering is emerging, but is still limited to the research level. This limitation is mainly due to the complexity and multi-scale nature of the underlying physiological processes inside the human body. Nevertheless, advances in medical imaging techniques now provide a wealth of information about structure and kinematic, that could potentially be used to parameterize these mathematical models in such a way that it is possible to create a virtual representation of an organ of the individual. With such a calibrated model at hand, we can estimate features that are impossible to measure with medical imaging, and such a model would therefore be useful for diagnostic purposes. Furthermore, we could potentially use this model predict the outcome of different treatment strategies and use it to design and optimize treatment. However, some on the challenges in the creation of such models lies in the lack of methods to accurately and efficiently estimating model parameters that best describes the measured observations. In this thesis we have developed a framework to effectively build a virtual heart of the individual patient, so that measurements made in the clinic can be incorporated into the underlying mathematical model. Such virtual hearts have been used to study the mechanics of the heart in different patient groups. Furthermore, we evaluated different biomarkers that may have potential clinical value, and evaluated the performance of the method. These simulations can be performed on a regular laptop in just a few hours, which means that this framework can potentially be included as a diagnostic toolbox in the clinic

Estimating cardiac contraction through high resolution data assimilation of a personalized mechanical model

Cardiac computational models, individually personalized, can provide clinicians with useful diagnosticinformation and aid in treatment planning. A major bottleneck in this process can be determining modelparameters to fit created models to individual patient data. However, adjoint-based data assimilationtechniques can now rapidly estimate high dimensional parameter sets. This method is used on a cohort ofheart failure patients, capturing cardiac mechanical information and comparing it with a healthy controlgroup. Excellent fit (R2≥ 0.95) to systolic strains is obtained, and analysis shows a significant differencein estimated contractility between the two groups

ComputationalPhysiology/simcardems: c2023.7.1

<h2>What's Changed</h2> <ul> <li>Add pseudo-ecg and add niederer benchmark demo by @finsberg in https://github.com/ComputationalPhysiology/simcardems/pull/200</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/ComputationalPhysiology/simcardems/compare/v2023.7.0...v2023.7.1</p&gt

High-resolution data assimilation of cardiac mechanics applied to a dyssynchronous ventricle

Computational models of cardiac mechanics, personalized to a patient, offer access to mechanical information above and beyond direct medical imaging. Additionally, such models can be used to optimize and plan therapies in-silico, thereby reducing risks and improving patient outcome. Model personalization has traditionally been achieved by data assimilation, which is the tuning or optimization of model parameters to match patient observations. Current data assimilation procedures for cardiac mechanics are limited in their ability to efficiently handle high-dimensional parameters. This restricts parameter spatial resolution, and thereby the ability of a personalized model to account for heterogeneities that are often present in a diseased or injured heart. In this paper, we address this limitation by proposing an adjoint gradient–based data assimilation method that can efficiently handle high-dimensional parameters. We test this procedure on a synthetic data set and provide a clinical example with a dyssynchronous left ventricle with highly irregular motion. Our results show that the method efficiently handles a high-dimensional optimization parameter and produces an excellent agreement for personalized models to both synthetic and clinical data

ComputationalPhysiology/simcardems: v2023.6.1

What's Changed Bump version of base image in docker image Full Changelog: https://github.com/ComputationalPhysiology/simcardems/compare/v2023.6.0...v2023.6.

A computational study of right ventricular mechanics in a rat model of pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) presents a significant challenge to right ventricular (RV) function due to progressive pressure overload, necessitating adaptive remodeling in the form of increased wall thickness, enhanced myocardial contractility and stiffness to maintain cardiac performance. However, the impact of these remodeling mechanisms on RV mechanics in not clearly understood. In addition, there is a lack of quantitative understanding of how each mechanism individually influences RV mechanics. Utilizing experimental data from a rat model of PAH at three distinct time points, we developed biventricular finite element models to investigate how RV stress and strain evolved with PAH progression. The finite element models were fitted to hemodynamic and morphological data to represent different disease stages and used to analyze the impact of RV remodeling as well as the altered RV pressure. Furthermore, we performed a number of theoretical simulation studies with different combinations of morphological and physiological remodeling, to assess and quantify their individual impact on overall RV load and function. Our findings revealed a substantial 4-fold increase in RV stiffness and a transient 2-fold rise in contractility, which returned to baseline by week 12. These changes in RV material properties in addition to the 2-fold increase in wall thickness significantly mitigated the increase in wall stress and strain caused by the progressive increase in RV afterload. Despite the PAH-induced cases showing increased wall stress and strain at end-diastole and end-systole compared to the control, our simulations suggest that without the observed remodeling mechanisms, the increase in stress and strain would have been much more pronounced. Our model analysis also indicated that while changes in the RV's material properties-particularly increased RV stiffness - have a notable effect on its mechanics, the primary compensatory factor limiting the stress and strain increase in the early stages of PAH was the significant increase in wall thickness. These findings underscore the importance of RV remodeling in managing the mechanical burden on the right ventricle due to pressure overload

Efficient estimation of personalized biventricular mechanical function employing gradient-based optimization

10.1002/cnm.2982INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING34

In vitro safety “clinical trial” of the cardiac liability of drug polytherapy

Abstract Only a handful of US Food and Drug Administration (FDA) Emergency Use Authorizations exist for drug and biologic therapeutics that treat severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) infection. Potential therapeutics include repurposed drugs, some with cardiac liabilities. We report on a chronic preclinical drug screening platform, a cardiac microphysiological system (MPS), to assess cardiotoxicity associated with repurposed hydroxychloroquine (HCQ) and azithromycin (AZM) polytherapy in a mock phase I safety clinical trial. The MPS contained human heart muscle derived from induced pluripotent stem cells. The effect of drug response was measured using outputs that correlate with clinical measurements, such as QT interval (action potential duration) and drug‐biomarker pairing. Chronic exposure (10 days) of heart muscle to HCQ alone elicited early afterdepolarizations and increased QT interval past 5 days. AZM alone elicited an increase in QT interval from day 7 onward, and arrhythmias were observed at days 8 and 10. Monotherapy results mimicked clinical trial outcomes. Upon chronic exposure to HCQ and AZM polytherapy, we observed an increase in QT interval on days 4–8. Interestingly, a decrease in arrhythmias and instabilities was observed in polytherapy relative to monotherapy, in concordance with published clinical trials. Biomarkers, most of them measurable in patients’ serum, were identified for negative effects of monotherapy or polytherapy on tissue contractile function, morphology, and antioxidant protection. The cardiac MPS correctly predicted clinical arrhythmias associated with QT prolongation and rhythm instabilities. This high content system can help clinicians design their trials, rapidly project cardiac outcomes, and define new monitoring biomarkers to accelerate access of patients to safe coronavirus disease 2019 (COVID‐19) therapeutics