Name: In Silico Clinical Trials for Cardiovascular Disease (Video) | JoVE Journal
Uploaded: 2022-05-27T14:00:00.000+00:00
Duration: 9 min 9 s
Description: Explore in silico clinical trials for cardiovascular disease using the Silico FCM platform with patient-specific ultrasound images and electromechanical modeling. This computational method reduces animal experiments while optimizing therapeutic outcomes.

ai_agent_IP

ai_agent

maintenance

instantTrialDurationMinutes

isBiopharma

enableNewMyJoveSectionDesign

educationFeatures

seoMetaApi

ai_copilot_institution_blacklist

ai_chat_institution_blacklist

freeAccessWindow

ssoRoleCapture

scheduledMaintenanceBanner

joveLabsFeatures

disable_autoplay_for_bots

extended_trial_duration

5 minutes

This study is supported by the European Union&#39;s Horizon 2020 research and innovation program under grant agreement SILICOFCM 777204 and the Ministry of Education, Science and Technological Development of the Republic of Serbia through Contracts No. 451-03-68/2022-14/200107. This article reflects only the authors&#39; views. The European Commission is not responsible for any use that may be made of the information the article contains.

The protocol in this study was approved by the UK National Health Service Health Research Authority North East-Tyne &#38; Wear South Research Ethics Committee with the reference number 18/NE/0318 on 6 February 2019 and was adopted by the Institutional Review Board of each participating center. The study was conducted within the principles of Good Clinical Practice and following the Declaration of Helsinki. Informed consent was obtained from all subjects involved in the study. The patient information is kept anonymous.
1. Workflow for ultrasound M-mode or Apical view DICOM image analysis and LV parameter extraction
NOTE: To begin this protocol, the user must log in to the SILICOFCM platform8 and choose the appropriate workflow (i.e., ultrasound image analysis using M-mode or Apical view). The workflow for ultrasound M-mode DICOM image analysis and LV parameter extraction, for example, involves several steps. The first step is template matching. Templates to be matched include all the relevant necessary borders. It should be emphasized that, for every new dataset, the template should be extracted manually just once for each dataset that corresponds to a specific ultrasound machine. The workflow diagram is presented in Figure 1. The area matching the template will be extracted from the analyzed image after Felsenszwalb&#39;s efficient graph-based image segmentation algorithm finds &#34;strong&#34; boundaries that correspond to the borders of the septum and the wall of the LV. Based on these borders and the places where the heart diameters are the largest (corresponds to diameter in diastole) and the smallest (corresponds to diameter in systole), various LV dimensions will be calculated. The user should define whether the view is M-mode or Apical view.
<ol>
	<li>Log in to the platform with a username and password. Under Virtual Population Module, choose ultrasound M-mode or Apical view workflow.</li>
	<li>In the list of available workflows, select the ultrasound-merged workflow.</li>
	<li>In the File Upload section, upload images and DICOM files stored locally on the user&#39;s computer (Figure 2). 
	NOTE: Inputs to the module M-mode view DICOM image analysis from a specific patient and LV parameter extraction are the testing image (provided by the user in DICOM format representing M-mode or Apical view) and static files (Figure 1). The static files comprise a) the input.txt file, which is a template file used to overwrite the calculated output measures from the module (new output files are created, in turn, from this file), and b) the template image file, which is used for template matching (i.e., template_dicom_GEMS.jpg).</li>
	<li>Select either Private or Public folders as the Destination Folder for the files. Type the desired comment or note in the Comments section before starting the workflow. Select the Apical view and M-mode LV ultrasound images and DICOM files uploaded previously (Step 1.3.) (Figure 3). 
	NOTE: Ultrasound Heart Segmentation tool consists of two sub-modules: the apical view and the M-mode view. Both modules are accessed via the platform. The analysis includes the calculation of the characteristic parameters visible in corresponding cross-sections. In both cases, the necessary values are written to a file that is taken as input to the parametric heart model from the user side (see the discussion for details).</li>
	<li>Click on the Execute button. The interactive platform notifies the user when the running workflow is finished.</li>
	<li>Visualize the created geometry of LV directly on the platform (a 3D model will appear automatically on the screen, which can be rotated using the mouse). Available options include Shaded and Wireframe models for visualization. 
	&#8203;NOTE: After the LV dimensions are extracted, based on mesh options, the mesh of finite elements is generated, and finite element analysis simulation is run with user-prescribed inlet and outlet velocities (see below).</li>
</ol>
2. PAK finite element solver for fluid-structure (FS) simulations
NOTE: This tool can be used for the finite element analysis of the coupled solid-fluid problems. It supports both strong and loose coupling between solid and fluid. Elements can be hexahedrons or tetrahedrons, with or without an additional node at the center of the element. This solver has built-in material models such as the Holzapfel model, hunter muscle model, etc. The PAK-FS information flow diagram is depicted in Figure 4. It starts from the input file and PAK preprocessor. The PAK preprocessor tool outputs a DAT file, which will serve as an input file for the finite element solver. The final output from the solver is VTK files that include the results of the finite element simulation: velocities, pressures, strains, and stresses in the left ventricle.
<ol>
	<li>Download the template files for the boundary conditions of inlet and outlet velocities using the buttons in the bottom section (Figure 3); if patient-specific flow boundary conditions are available, download and use these files. Download the mesh options by clicking on the corresponding button in the bottom section. Save these files into either Private or Public folders.</li>
	<li>Upload these files in a similar way as uploading images (Figure 3). The prescribed inlet and outlet velocities simulate the drug condition, while the mesh options control the density of the finite element mesh. For simulating patient-specific conditions, modify the default values of pressure, flow, material properties, and calcium function.</li>
	<li>Click on the Execute button. A new running workflow will appear in the list. If any of the sections of the workflow are not clear, click on the Help File button (Figure 3) to view detailed instructions on how to use this workflow, as well as to interpret the results. 
	NOTE: If everything executes without errors, the status of the workflow will change from &#34;running&#34; to &#34;finished ok&#34;.</li>
	<li>View the results with several options. Alternatively, download the results; the results folder contains VTK files, CSV files, and animations.
	<ol>
		<li>Click on the eye button to view ejection fraction and global work efficiency values, as well as diagrams of pressure vs. volume, pressure vs. strain, and myocardial work vs. time.</li>
		<li>Click on the camera button to preview and play animations of the displacement, pressure, shear stresses, and velocity fields.</li>
		<li>Click on the 3D visualization button to visualize the outputs online in ParaView Glance.
		<ol>
			<li>Load multiple VTK files previously downloaded as results. See several parameters of interest and change the field, for instance, to velocity for visualization.</li>
			<li>Rotate the model or change the color scheme. Choose Surface with edges or Wireframe for the representation of the surface. Apply the same methodology to every loaded VTK file.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. Determination of ventricular activation sequence from ECG measurement
NOTE: A modified FitzHugh-Nagumo model of the cardiac cell was implemented. The precordial leads were modeled with the standard six electrodes. The potential of the heart was optimized with inverse ECG. Starting from activation in the sinoatrial node (which is a function of time), with heterogeneous action potential through the heart and torso, the user can get electrical activity on the total torso model. The PAK-TORSO information flow diagram is given in Figure 5. The user provides scaling of the torso model in all directions (x,y,z) and ECG signal function. Then the scaled model is created, and its behavior is simulated using the PAK-FS solver. The user provides these input values in a text file. The output of the simulation is a VTK file with the electrical activity of the heart in the torso-embedded environment.
<ol>
	<li>On the homepage, go to Execute Workflow and then choose torse-cwl in the list of available workflows. Add a comment or note in the Comments section before executing the workflow.</li>
	<li>Click on the Input Template File button and save the content shown on the webpage as input.txt file, which will be used for the torso model. In the Input File field, select the downloaded input.txt file. After the file is imported, click on the Execute button to start the calculation. 
	NOTE: A new workflow will appear in the bottom left, with its status shown as &#34;running&#34;; the calculation lasts about 0.5 h, and then the status will change to &#34;finished ok&#34;.</li>
	<li>Click on the eye or camera buttons in the bottom left corner to visualize the available simulation reports or animations directly on the platform. 
	NOTE: The results include distributions of the electrical field, velocity, pressure, shear stress, and deformation at each time step. Animations for each of these distributions are possible to view for the total heart cycle.</li>
	<li>Alternatively, click on the 3D visualization button to visualize the outputs online in ParaView Glance.
	<ol>
		<li>Select the OPEN A FILE button, go to the GIRDER tab, enter user credentials if prompted, and open the Private folder.</li>
		<li>On the next page, select the workflow-outputs folder, and open the torso-cwl folder. Open the first folder on the list.</li>
		<li>See the list of VTK files that represent the simulation results. Choose one or more files and click on the SELECT button to load the file in ParaView Glance.</li>
		<li>Manipulate the model geometry (i.e., move, rotate, zoom in or out, etc.) using the mouse.</li>
		<li>Select different options for model representation as follows.
		<ol>
			<li>Choose the Wireframe option to see the interior of the torso with a heart incorporated within the torso. Choose the Points option to display a dotted representation of the torso model with full heart mesh.</li>
			<li>Adjust the Point Size value to change the display results. Adjust the Opacity value to see the interior of the torso and display results inside the heart mesh.</li>
			<li>Click on the Color By drop-down menu and choose the desired option, for instance, electric potential. Change the default color scale to any of the listed options.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

<ol><li>Gibbons Kroeker, C. A., Adeeb, S., Tyberg, J. V., Shrive, N. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+2D+FE+model+of+the+heart+demonstrates+the+role+of+the+pericardium+in+ventricular+deformation%5BTitle%5D)+AND+%22American+Journal+of+Physiology%22%5BJournal%5D)">A 2D FE model of the heart demonstrates the role of the pericardium in ventricular deformation.</a> American Journal of Physiology. 291 (5), 2229-2236 (2006).</li>
<li>Pullan, A. J., Buist, M. L., Cheng, L. K. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aPullan%2C+AJ+%22Mathematically+Modelling+the+Electrical+Activity+of+the+Heart+-+From+Cell+To+Body+Surface+and+Back+Again%22">Mathematically Modelling the Electrical Activity of the Heart - From Cell To Body Surface and Back Again</a>. , World Scientific. (2005).</li>
<li>Trudel, M. -C., Dub´e, B., Potse, M., Gulrajani, R. M., Leon, L. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Simulation+of+QRST+integral+maps+with+a+membrane+based+computer+heart+model+employing+parallel+processing%5BTitle%5D)+AND+%22IEEE+Transactions+on+Biomedical+Engineering%22%5BJournal%5D)">Simulation of QRST integral maps with a membrane based computer heart model employing parallel processing.</a> IEEE Transactions on Biomedical Engineering. 51 (8), 1319-1329 (2004).</li>
<li>Kojic, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Smeared+multiscale+finite+element+models+for+mass+transport+and+electrophysiology+coupled+to+muscle+mechanics%5BTitle%5D)+AND+%22Frontiers+in+Bioengineering+and+Biotechnology%22%5BJournal%5D)">Smeared multiscale finite element models for mass transport and electrophysiology coupled to muscle mechanics.</a> Frontiers in Bioengineering and Biotechnology. 7, 381(2019).</li>
<li>Baillargeon, B., Rebelo, N., Fox, D. D., Taylor, R. L., Kuhl, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+Living+Heart+Project%3A+A+robust+and+integrative+simulator+for+human+heart+function%5BTitle%5D)+AND+%22European+Journal+of+Mechanics+-+A%2FSolids%22%5BJournal%5D)">The Living Heart Project: A robust and integrative simulator for human heart function.</a> European Journal of Mechanics - A/Solids. 48, 38-47 (2014).</li>
<li>Guccione, J. M., McCulloch, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mechanics+of+active+contraction+in+cardiac+muscle%3A+Part+I--Constitutive+relations+for+fiber+stress+that+describe+deactivation%5BTitle%5D)+AND+%22TheJournal+of+Biomechanical+Engineering%22%5BJournal%5D)">Mechanics of active contraction in cardiac muscle: Part I--Constitutive relations for fiber stress that describe deactivation.</a> TheJournal of Biomechanical Engineering. 115, 72-81 (1993).</li>
<li>Guccione, J. M., Waldman, L. K., McCulloch, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mechanics+of+active+contraction+in+cardiac+muscle%3A+Part+II--Cylindrical+models+of+the+systolic+left+ventricle%5BTitle%5D)+AND+%22The+Journal+of+Biomechanical+Engineering%22%5BJournal%5D)">Mechanics of active contraction in cardiac muscle: Part II--Cylindrical models of the systolic left ventricle.</a> The Journal of Biomechanical Engineering. 115, 82-90 (1993).</li>
<li>H2020 project SILICOFCM: In Silico. trials for drug tracing the effects of sarcomeric protein mutations leading to familial cardiomyopathy. , Available from: 
 <a target="_blank" href="www.silicofcm.eu">www.silicofcm.eu</a> 2018-2022 (2022).</li>
<li>Kojic, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Smeared+multiscale+finite+element+model+for+electrophysiology+and+ionic+transport+in+biological+tissue%5BTitle%5D)+AND+%22Computers+in+Biology+and+Medicine%22%5BJournal%5D)">Smeared multiscale finite element model for electrophysiology and ionic transport in biological tissue.</a> Computers in Biology and Medicine. 108, 288-304 (2019).</li>
<li>Wang, Y., Rudy, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Application+of+the+method+of+fundamental+solutions+to+potential-based+inverse+electrocardiography%5BTitle%5D)+AND+%22Annals+of+Biomedical+Engineering%22%5BJournal%5D)">Application of the method of fundamental solutions to potential-based inverse electrocardiography.</a> Annals of Biomedical Engineering. 34 (8), 1272-1288 (2006).</li>
<li>Van Oosterom, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+use+of+the+spatial+covariance+in+computing+pericardial+potentials%5BTitle%5D)+AND+%22IEEE+Transactions+on+Biomedical+Engineering%22%5BJournal%5D)">The use of the spatial covariance in computing pericardial potentials.</a> IEEE Transactions on Biomedical Engineering. 46 (7), 778-787 (1999).</li>
<li>Van Oosterom, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+spatial+covariance+used+in+computing+the+pericardial+potential+distribution%5BTitle%5D)+AND+%22Computational+Inverse+Problems+in+Electrocardiography%22%5BJournal%5D)">The spatial covariance used in computing the pericardial potential distribution.</a> Computational Inverse Problems in Electrocardiography. , 1-50 (2001).</li>
<li>Van Oosterom, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Source+models+in+inverse+electrocardiography%5BTitle%5D)+AND+%22International+Journal+of+Bioelectromagnetism%22%5BJournal%5D)">Source models in inverse electrocardiography.</a> International Journal of Bioelectromagnetism. 5, 211-214 (2003).</li>
<li>Van Oosterom, A. The equivalent double layer: source models for repolarization. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aVan+Oosterom%2C+A+%22Comprehensive+Electrocardiology%22">Comprehensive Electrocardiology</a>. , Springer. 227-246 (2010).</li>
</ol>

The authors have no conflicts of interest.

The SILICOFCM project is an in silico clinical trials platform to design virtual patient populations for risk prediction, testing the effects of pharmacological treatment, and reducing animal experiments and human clinical trials. Testing the effects of pharmacological treatment was modeled with prescribed inlet/outlet boundary flow conditions, calcium function, and material wall properties. This platform integrates multiscale methods on the sarcomeric level with whole-heart performance and the functional optimi...

The rapid development of information technologies, simulation software packages, and medical devices in recent years provides the opportunity for collecting a large amount of clinical information. Creating comprehensive and detailed computational tools has, therefore, become essential to process specific information from the abundance of available data.
From the physicians&#39; point of view, it is of paramount importance to distinguish &#34;normal&#34; versus &#34;abnormal&#34; phenotypes in a specific patient to estimate disease progression, therapeutic responses, and future risks. Recent computational models have significantly improved the integrative understanding of the behavior of heart muscles in hypertrophic (HCM) and dilated (DCM) cardiomyopathies1. It is crucial to use a high-resolution, detailed, and anatomically accurate model of whole-heart electrical activity, which necessitates massive computation times, dedicated software, and supercomputers1,2,3. A methodology for a real 3D heart model has been recently developed using a linear elastic and orthotropic material model based on Holzapfel experiments, which can accurately predict the electrical signal transport and displacement field within heart4. The development of novel integrative modeling approaches could be an effective tool for distinguishing the type and severity of symptoms in patients with multigenic disorders and assessing the degree of impairment in normal physical activity.
There are, however, many new challenges for patient-specific modeling. The physical and biological properties of the human heart are not possible to fully determine. Non-invasive measurements usually include noisy data from which it is difficult to estimate specific parameters for the individual patient. Large-scale computation requires a lot of time to run, whereas the clinical time frame is limited. Patient personal data should be managed in such a way that generated metadata can be reused without compromising patient confidentiality. Despite these challenges, multi-scale heart models can include a sufficient level of detail to achieve predictions that closely follow observed transient responses, thereby providing promise for prospective clinical applications.
However, regardless of the substantial scientific effort by multiple research labs and the significant amount of grant support, currently, there is only one commercially available software package for multiscale and whole heart simulations, called SIMULIA Living Heart Model5. It includes dynamic electro-mechanical simulation, refined heart geometry, a blood flow model, and complete cardiac tissue characterization, including passive and active characteristics, fibrous nature, and electrical pathways. This model is targeted for use in personalized medicine, but the active material characterization is based on a phenomenological model introduced by Guccione et al.6,7. Therefore, SIMULIA cannot directly and accurately translate the changes in contractile protein functional characteristics observed in numerous cardiac diseases. These changes are caused by mutations and other abnormalities at the molecular and subcellular levels6. The limited use of SIMULIA software for a small number of applications in clinical practice is a great example of today&#39;s struggles in developing higher-level multiscale human heart models. On the other hand, it motivates the development of a new generation of multiscale program packages that can trace the effects of mutations from the molecular to organ scale.
The main aim of electrophysiology of the heart is to determine signal propagation inside the torso and the properties of all compartments4,5,6. The SILICOFCM8 project predicts cardiomyopathy disease development using patient-specific biological, genetic, and clinical imaging data. It is achieved with multiscale modeling of the realistic sarcomeric system, the patient&#39;s genetic profile, muscle fiber direction, fluid-structure interaction, and electrophysiology coupling. The effects of left ventricle deformation, mitral valve motion, and complex hemodynamics give detailed functional behavior of the heart conditions in a specific patient.
This article demonstrates the use of the SILICOFCM platform for a parametric model of the left ventricle (LV) generated automatically from patient-specific ultrasound images using a fluid-structure heart model with electromechanical coupling. Apical view and M-mode view analyses of LV were generated with a deep learning algorithm. Then, using the mesh generator, the finite element model was built automatically to simulate different boundary conditions of the full cycle for LV contraction9. On this platform, users can directly visualize the simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses. Input parameters from specific patients are geometry from ultrasound images, velocity profile in the input and output boundary flow conditions for LV, and specific drug therapy (e.g., entresto, digoxin, mavacamten, etc.).

<table><tbody><tr><td>SILICOFCM project</td><td></td><td>www.silicofcm.eu</td><td>open access for registered users</td></tr></tbody></table>

in silico clinical trials for cardiovascular disease

In Silico Clinical Trials for Cardiovascular Disease

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses.

As an example, the workflow for ultrasound M-mode DICOM image analysis and LV parameter extraction is presented in Figure 1. M-mode and apical view could be tested separately or one after another, depending on the parameters of interest. If tested one after another, the results are appended to one common file (separately for systole and diastole phases). If only one view is tested, the values for unknown parameters are taken from the default file input.txt (Figu...

This protocol demonstrates the workflow of the SILICOFCM platform for automatically generating a parametric model of the left ventricle from patient-specific ultrasound images by applying a multi-scale electromechanical model of the heart. This platform enables in silico clinical trials intended to reduce real clinical trials and maximize positive therapeutic outcomes.

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique ...

in-silico-clinical-trials-for-cardiovascular-disease

Research

JoVE Journal

Bioengineering

We present a three-dimensional (3D) lung cancer model based on a biological collagen scaffold to study sensitivity towards non-small-cell-lung-cancer-(NSCLC)-targeted therapies. We demonstrate different read-out techniques to determine the proliferation index, apoptosis and epithelial-mesenchymal transition (EMT) status. Collected data are integrated into an in silico model for prediction of drug sensitivity.

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Here, we show the generation of human engineered heart tissue from induced pluripotent stem cells (hiPSC)-derived cardiomyocytes. We present a method to analyze contraction force and exemplary alteration of contraction pattern by the hERG channel inhibitor E-4031. This method shows high level of robustness and suitability for cardiac drug screening.

Automated Contraction Analysis of Human Engineered Heart Tissue for Cardiac Drug Safety Screening

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have emerged as a promising in vitro model for drug-induced cardiotoxicity screening and disease modeling. Here, we detail a protocol for measuring the contractility and electrophysiology of hiPSC-CMs.

Technical Applications of Microelectrode Array and Patch Clamp Recordings on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes

The present protocol describes a large animal exercise test model to assess the functional capacity of the cardiovascular system for evaluating the efficiency of new therapies in the preclinical setting. It is comparable to a clinical exercise test.

Exercise Test for Evaluation of the Functional Efficacy of the Pig Cardiovascular System

A method for seeding titanium blood-contacting biomaterials with autologous cells and testing biocompatibility is described.  This method uses endothelial progenitor cells and titanium tubes, seeded within minutes of surgical implantation into porcine venae cavae.  This technique is adaptable to many other implantable biomedical devices.

Autologous Endothelial Progenitor Cell-Seeding Technology and Biocompatibility Testing For Cardiovascular Devices in Large Animal Model

This protocol reviews Electric Cell-substrate Impedance Sensing, a method to record and analyze the impedance spectrum of adherent cells for the quantification of cell attachment, proliferation, motility, and cellular responses to pharmacological and toxic stimuli. Detection of endothelial permeability and assessment of cell-cell and cell-substrate contacts are emphasized.

Electric Cell-substrate Impedance Sensing for the Quantification of Endothelial Proliferation, Barrier Function, and Motility

Accurate, causality-based quantification of global diastolic function has been achieved by kinematic modeling-based analysis of transmitral flow via the Parametrized Diastolic Filling (PDF) formalism. PDF generates unique stiffness, relaxation, and load parameters and elucidates 'new' physiology while providing sensitive and specific indexes of dysfunction.

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

The goal of this protocol is to build up a three-dimensional full thickness skin equivalent, which resembles natural skin. With a specifically constructed automated wounding device, precise and reproducible wounds can be generated under maintenance of sterility.

Generation of a Three-dimensional Full Thickness Skin Equivalent and Automated Wounding

This manuscript describes the production, characterization and potential uses of a tissue engineered 3D esophageal construct prepared from normal primary human esophageal fibroblast and squamous epithelial cells seeded within a de-cellularized porcine scaffold. The results demonstrate the formation of a mature stratified epithelium similar to the normal human esophagus.

Production, Characterization and Potential Uses of a 3D Tissue-engineered Human Esophageal Mucosal Model

This protocol describes a platform for fabricating self-assembled tissue rings in variable sizes using a customized 3D-printed plastic mold. PDMS negatives are cured in the 3D-printed mold; then agarose is cast in the cured PDMS negatives. Cells are seeded into the resulting agarose wells where they aggregate into tissue rings.

Fabrication of Custom Agarose Wells for Cell Seeding and Tissue Ring Self-assembly Using 3D-Printed Molds

Explore in silico clinical trials for cardiovascular disease using the Silico FCM platform with patient-specific ultrasound images and electromechanical modeling. This computational method reduces animal experiments while optimizing therapeutic outcomes.

In Silico Clinical Trials for Cardiovascular Disease

Explore More Videos