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8,285 results for “cardiac;”
Four-Chamber Human Heart Model for the Simulation of Cardiac Electrophysiology and Cardiac Mechanics
<p><strong>Changes in version 1.1 compared to version 1.0:</strong></p> <ul> <li>Ventricular fiber orientation changed to 66° on the endocardial and −41° on the epicardial surface</li> <li>Electrophysiology mesh was resampled</li> <li>Updated material tags in EP mesh</li> <li>Further details can be found in the <a href="https://github.com/KIT-IBT/CardioMechanics/tree/main">CardioMechanics GitHub repository</a> new reference paper:</li> </ul> <blockquote> <p>Gerach, T.; Loewe, A. Differential effects of mechano-electric feedback mechanisms on whole-heart activation, repolarization, and tension. <em>The Journal of Physiology</em> <strong>2024. </strong>https://doi.org/10.1113/JP285022 </p> </blockquote> <p>This repository contains a four-chamber model of the human heart which is ready to use for simulations of cardiac electrophysiology and cardiac mechanics problems. When using this dataset, please also cite the accompanying paper</p> <blockquote> <p>Gerach, T.; Schuler, S.; Fröhlich, J.; Lindner, L.; Kovacheva, E.; Moss, R.; Wülfers, E.M.; Seemann, G.; Wieners, C.; Loewe, A. Electro-Mechanical Whole-Heart Digital Twins: A Fully Coupled Multi-Physics Approach. <em>Mathematics</em> <strong>2021</strong>, <em>9</em>, 1247. https://doi.org/10.3390/math9111247</p> </blockquote> <p>The cardiac anatomy was manually segmented from magnetic resonance imaging (MRI) data of a 33 year old male volunteer. The volunteer provided informed consent and the study was approved by the IRB of Heidelberg University Hospital (Fritz et al., 2014).<br>The MRI data were acquired using a 1.5 T MR tomography system and consist of a static whole heart image stack taken during diastasis as well as time-resolved images in several long and short axis slices. Based on the segmentation, we first labeled the atria and the ventricles. The geometry was extended by a representation of the mitral valve, the tricuspid valve, the aortic valve and the pulmonary valve. Additionally, we closed the endo- and epicardial surfaces of the atria and added truncated pulmonary veins, vena cavae as well as the ascending aorta and pulmonary artery. Furthermore, we added a concentric layer of tissue around the entire heart which phenomenologically represents the influence of the pericardium and the surrounding tissue.</p> <p>Two tetrahedral meshes were created using Gmsh (Geuzaine et al., 2009): (1) the mechanical reference domain (<strong>M.vtu</strong>) with 128,976 elements (on average 3.17 mm edge length) and (2) the electrophysiological reference domain (<strong>EP.vtu</strong>) as a subset of M with 50,058,295 elements (on average 0.4 mm edge length).<br>We used rule-based methods to assign the myofiber orientation on EP: Wachter et al. (2015) was used for the atria and Bayer et al. (2012) for the ventricles. The fiber angle in the ventricles was chosen as +60° and -60° on the endocardial and epicardial surface, respectively. The sheet angle was set to -65° on the endocardium and 25° on the epicardium. Github repositories to these fiber generation tools are given in the sidebar. All geometry files are given in millimeter (mm).</p> <ul> <li><strong>Data:</strong><br>We provide time resolved data evaluated from cine MRI data, which can be used for model calibration. <ul> <li>Wall thickening (<strong>17AHA_WT.txt</strong>) / fractional wall thickening (<strong>17AHA_fractionalWT.txt</strong>) in the 17 AHA segments of the left ventricle</li> <li>Atrioventricular plane displacement (<strong>AVPD.txt</strong>) and velocity (<strong>AVPV.txt</strong>) as well as the displacement of all tracked points used for AVPD calculation (<strong>AVPD_trackedPoints.txt</strong>)</li> <li>Left and right ventricular volume (<strong>Volume_LV_RV.txt</strong>). RV volume is only available for end-diastole and end systole.</li> </ul> </li> <li><strong>Surfaces:</strong><br>This directory contains *.stl files with triangulated surfaces on which boundary conditions can be applied. <ul> <li><strong>cavityXX.stl</strong>: blood volume of the LV, RV, LA, RA</li> <li><strong>epicard.stl</strong>: the whole epicardium</li> <li><strong>outerPeri.stl</strong> and <strong>outerTrunks.stl</strong>: surfaces for Dirichlet boundary conditions</li> <li><strong>master.stl </strong>and <strong>slave.stl</strong>: surfaces used for the frictionless contact problem described in Fritz et al. (2014)</li> </ul> </li> <li><strong>TetGen:</strong><br>Contains the geometry <strong>M.vtu</strong> in the TetGen file format. T4 mesh with 4 node tetrahedrons and 3 node triangles. <ul> <li>.bases: fiber, sheet, and normal orientation at quadrature points of all elements</li> <li>.node: vertex coordinates</li> <li>.ele: list of tetrahedra</li> <li>.sur: list of triangles</li> </ul> </li> <li><strong>EP.vtu:</strong><br>Contains the cell arrays Fiber and Material.</li> <li><strong>M.vtu:</strong><br>Contains the cell arrays Fiber, Sheet, Sheetnormal, Material, and Label.</li> <li><strong>LabelIDs.txt:</strong><br>List of Label and Material identification numbers and corresponding anatomical structures.</li> </ul> <p> </p>
The influence of heart rate variability biofeedback on cardiac regulation and functional brain connectivity
Open the record for dataset details and reuse information.
3C : Cardiac-CT-Covid19
<p>The 3C (Cardiac-CT-COVID-19) database offers CT scans of patients diagnosed with COVID-19, encompassing both individuals with and without cardiac complications (49 females, 58 males, aged between 8 and 89 years). Image interpretation was carried out by two experienced radiologists. The dataset also provides information on the severity of each cardiac condition, making it a valuable resource for examining the impact of COVID-19 on the cardiovascular system.</p>
Dataset for "Analysis of cardiac arrhythmia sources using Feynman diagrams"
<p>This archive contains the numerical methods presented in the publication "Analysis of cardiac arrhythmia sources using Feynman diagrams" as well as the data sets these methods have been applied on. The Python module for Ithildin contains the actual Python source code of those methods. Additional Python scripts have been used to generate the figures in the paper (.py files). The optical voltage mapping data (optical_*) has been slightly pre-processed (noise reduction, re-scaling, etc). The other files contain simulation results from several finite differences simulations of the mono-domain model.</p> <p>Please cite this paper when using the implementation: Arno L, Kabus D, Dierckx H (2023) Analysis of cardiac arrhythmia sources using Feynman diagrams.<a href="https://doi.org/10.48550/arXiv.2307.01508">https://doi.org/10.48550/arXiv.2307.01508</a></p>
Dataset for "Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns"
<p>This archive contains the source code and data sets presented in the publication "Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns".</p> <p>Kabus, D., De Coster, T., de Vries, A. A., Pijnappels, D. A., & Dierckx, H. (2024). Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns. <em>Computers in Biology and Medicine</em>, 107949. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.compbiomed.2024.107949" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.compbiomed.2024.107949</span></a></p>
EACTS Adult Cardiac Database (ACD)
<div> <h2>EACTS is the home for the global cardiothoracic surgical community.</h2> </div> <p>We exist to improve outcomes for patients with heart and lung conditions by supporting the global surgical cardiothoracic community and informing best practice with first-class education, cutting-edge learning opportunities, world-renowned journals and publications and pioneering research.</p> <p><a href="https://www.eacts.org/" rel="nofollow">https://www.eacts.org/</a></p> <div> <h3>About the ACD</h3> </div> <p>The EACTS Adult Cardiac Database (ACD) is a collaborative registry and benchmarking tool of cardiac surgical data for centres on an international scale, giving surgical teams the advanced data tools and insights to continually improve outcomes for patients.</p> <div> <h3>Register your interest</h3> </div> <p><a href="https://airtable.com/appRF0GOfjFh6RtRS/pagHKi67AmcK76r2R/form">https://airtable.com/appRF0GOfjFh6RtRS/pagHKi67AmcK76r2R/form</a></p> <p> </p>
Impedance Reconstruction of Non-transmural Cardiac Fibrosis
<div>In this dataset we can find geometrical setups that served as an input to carry simulations with openCARP and EIDORS. The setup consists of a Lasso with point electrodes placed on patch of tissue and embedded in a box of blood. The tissue simulates a myocardium that can be either fully healthy, fully scarred, or healthy with a 8-mm width line of scar tissue.</div> <div>In addition, the voltage and local impedance maps obtained as results are included.</div> <div>Two kinds of simulations were performed: reconstruction the forward injection of a 5µA current at 14.6kHz with EIDORS, and the electrical wave propagation with openCARP.</div> <div>For the impedance reconstruction, a four electrode circuit was always used, meaning that two electrodes were part of the injecting pattern and a potential difference was measured between another two. The stimulating pair were sequentially changed among all pairs of neighbouring electrodes, whereas the rest of them contributed to the measurement.</div> <div>The voltage maps were computed taking the recovered EGMs from the electrode positions.</div> <div> </div> <h2>Data structure</h2> <div> <ul> <li>inputGeometries: 24 vtk files representing the input setup for simulations. The file names are of the form imageXX_append_cleanToGrid_LOCATION, where XX is a number among 06, 11, 52, and 55, and LOCATION refers to the position in z-axis (transmural, endo-, midmyo-, epicardio).</li> </ul> </div> <div> <ul> <li>results: <ul> <li>BidomainSimulations: the extracellular potentials of each electrical propagation simulation using the Courtemanche model are saved in each of the 16 folders. All the corresponding outputs from a pseudobidomain openCARP simulation are located in each folder. <ul> <li>2023-04-16_image06_endo_00</li> <li>2023-04-16_image06_epi_00</li> <li>2023-04-16_image06_mid_00</li> <li>2023-04-16_image06_transmural_00</li> <li>2023-04-16_image11_endo_00</li> <li>2023-04-16_image11_epi_00</li> <li>2023-04-16_image11_mid_00</li> <li>2023-04-16_image11_transmural_00</li> <li>2023-04-16_image52_endo_00</li> <li>2023-04-16_image52_epi_00</li> <li>2023-04-16_image52_mid_00</li> <li>2023-04-16_image52_transmural_00</li> <li>2023-04-16_image55_endo_00</li> <li>2023-04-16_image55_epi_00</li> <li>2023-04-16_image55_mid_00</li> <li>2023-04-16_image55_transmural_00</li> </ul> </li> <li>ImpedanceSimulations: mat and vtk files corresponding to either local electrical impedance reconstruction simulation. The file names are of the form imageXX_append_cleanToGrid_LOCATION, where XX is a number among 06, 11, 52, and 55, and LOCATION refers to the position in z-axis (transmural, endo-, midmyo-, epicardio). </li> </ul> </li> </ul> </div>
Eye tracking videos and raw data of breathing recognition attempts in simulated out-of-hospital cardiac arrest
<div> <div> <div> <p>This dataset comprises eye tracking videos and raw data documenting attempts to recognize breathing in simulated out-of-hospital cardiac arrest scenarios.</p> <p>The data were recorded using an Ergoneers Dikablis head-mounted eye tracker.</p> <p>Our analysis of this data resulted in the publication of two studies: Study 1, available at <a href="https://doi.org/10.1097/SIH.0000000000000617" target="_blank" rel="noopener">https://doi.org/10.1097/SIH.0000000000000617</a>, and Study 2, accessible at <a href="https://doi.org/10.25894/ijfae.2307" target="_blank" rel="noopener">https://doi.org/10.25894/ijfae.2307</a></p> <p> </p> <p>Version 2 is up-to-date.</p> <p>In Version 1:</p> <ul> <li>the doi for Study 2 was incorrect</li> <li>data for participant #51 of Study 1 were missing</li> </ul> </div> </div> </div>
Synthetic Dataset of Cardiac Microbundles
<p>The synthetic microbundle dataset consists of 60 200x256x256 ".tif" files generated based on textures extracted from real data and warped according to experimentally-informed Finite Element (FE) simulations. More details about generating this dataset can be found on the dedicated <a href="https://github.com/HibaKob/SyntheticMicroBundle">GitHub repository</a>.</p>
Single-cell transcriptomic profiling unveils dysregulation of cardiac progenitor cells and cardiomyocytes in a mouse model of maternal hyperglycemia
<p>Congenital heart disease (CHD) is the most prevalent structural malformations of the heart affecting ∼1% of live births. To date, both damaging genetic variations and adverse environmental exposure such as maternal diabetes have been found to cause CHD. Clinical studies show ∼fivefold higher risk of CHD in the offspring of mothers with pregestational diabetes. Maternal pregestational diabetes affects the gene regulatory networks key to proper cardiac development in the fetus. However, the cell-type specificity of these gene regulatory responses to maternal diabetes and their association with the observed cardiac defects in the fetuses remains unknown. To uncover the transcriptional responses to maternal diabetes in the early embryonic heart, we used an established murine model of pregestational diabetes. In this model, we have previously demonstrated an increased incidence of CHD. Here, we show maternal hyperglycemia (matHG) elicits diverse cellular responses during heart development by single-cell RNA-sequencing in embryonic hearts exposed to control and matHG environment. Through differential gene-expression and pseudotime trajectory analyses of this data, we identified changes in lineage specifying transcription factors, predominantly affecting Isl1+ second heart field progenitors and Tnnt2+cardiomyocytes with matHG. Using in vivo cell-lineage tracing studies, we confirmed that matHG exposure leads to impaired second heart field-derived cardiomyocyte differentiation. Finally, this work identifies matHG-mediated transcriptional determinants in cardiac cell lineages elevate CHD risk and show perturbations in Isl1-dependent gene-regulatory network (Isl1-GRN) affect cardiomyocyte differentiation. Functional analysis of this GRN in cardiac progenitor cells will provide further mechanistic insights into matHG-induced severity of CHD associated with diabetic pregnancies.</p>
Task 3 Dataset for Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models
Open the record for dataset details and reuse information.
Reducing cardiac-induced noise in brain maps of R2* and magnetic susceptibility
<p>This high resolution dataset contain MR images of one participant acquired with a standard linear sampling and a cartesian pseudo-spiral sampling, with 3 repetitions each. It also contain the corresponding R2* and QSM maps shown in the paper. The data are presented both as .nii and .mat files.</p>
Cardiac_Digital_Twin_Data
<p>Repository creation in progress.</p> <p>meta_data can be directly used to run the codes in https://github.com/juliacamps/Cardiac-Digital-Twin to generate and visualise digital twins and reproduce the results from "Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing" (https://doi.org/10.1016/j.media.2024.103361).</p> <p>The supplement of the publication mentioned earlier contains additional information on the code and data structure.</p> <p>The monodomain simulations were performed using the configuration and mesh files in monodomain_monoalg3D_configuration_meshes.tar and using the version of monoAlg3D that can be found at <a title="https://github.com/bergolho/monoalg3d_c/tree/t-wave-personalisation-2024" href="https://github.com/bergolho/MonoAlg3D_C/tree/t-wave-personalisation-2024" target="_blank" rel="noreferrer noopener">https://github.com/bergolho/MonoAlg3D_C/tree/t-wave-personalisation-2024</a> </p> <p>The specific custom functions that were implemented in the t-wave-personalisation-2024 branch of the monoAlg3D code to enable the simulations can be found in monodomain_monoalg3D_custom_functions.tar. </p>
Noncanonical electromechanical coupling paths in cardiac hERG potassium channel (semi-binary contact maps)
<p>Matrices of the semi-binary contact maps of the following open and closed systems: WT, A527L, A614G, L524R, L529H, L532H, T425L, T618L, W563L.</p> <p>The residue numbering is not the official one because the first residues (397) of hERG (PAS domain) were not included in our simulations so that each subunit comprizes 466 residues. Moreover, the four subunits were numbered consecutively. The official numbering of a residue can be easily recovered. The general rule is:</p> <p>official residue - 397 = our residue</p> <p>For example, the official T425 corresponds to T28 in the first subunit (425-397), T494 in the second subunit (425-397+466), T960 in the third subunit (425-397+466+466), and T1426 in the fourth subunit (425-397+466+466+466).</p>
2D Cardiac black-blood TSE MR raw data
<p>Raw data in ismrmrd format obtained with a 2D black-blood TSE sequence on a 3T Siemens Verio scanner in three different orientations. This data is used as test data for the comparison of different open-source image reconstruction packages provided here: <a href="https://github.com/ckolbPTB/OpenSourceMrRecon">OpenSourceMrRecon</a></p>
Cardiac Patient Bed Head Ticket Dataset
<p>This data set comprises the data which are manually extracted from the Bed Head Tickets(BHT) of patients who transferred from the Emergency Treatment Unit (ETU) to the cardiac ward in Teaching Hospital Karapitiya Galle. The data set contains 19 features as follows</p> <ol> <li>Systolic Blood Pressure (SBP) </li> <li>Diastolic Blood Pressure (DBP) </li> <li>Heart Rate (HR) </li> <li>Respiratory Rate (RR) </li> <li>Body temperature (BT) </li> <li>SpO<sub>2</sub> </li> <li>Age </li> <li>Gender </li> <li>Glasgow Coma Scale (GCS) </li> <li>Sodium Level (Na) </li> <li>Potassium Level (K) </li> <li>Chloride Level (Cl) </li> <li>Urea </li> <li>Creatinine </li> <li>Consumption of alcohol </li> <li>Smoking status</li> <li>Family History of Ischemic Hear Diseases (FHIHD) </li> <li>Triage Score </li> <li>Outcome</li> </ol> <p> </p>
An integrated approach including docking, MD simulations, and network analysis highlights the action mechanism of the cardiac hERG activator RPR260243
<p>500 ns MD trajectories of the hERG bound states (protein and ligand) used for the analyses discussed in the paper. There are three replica for each system. The trajectories can be visualized using molecular visualization programs such as Pymol or VMD uploading the PDB and the DCD file.</p> <p>The PDB and the topology files of the hERG bound state (protein, membrane, ions and ligand) are also included.</p> <p>An example of input file used for the production step of the dynamics has been provided (production_1.conf). </p>
Data for: Cardiac and Respiratory Self-Gating in Radial MRI using an Adapted Singular Spectrum Analysis (SSA-FARY)
<p>Magnetic Resonance Imaging measurement data used in our paper about self-gating with SSA-FARY (DOI: <a href="https://doi.org/10.1109/TMI.2020.2985994">10.1109/TMI.2020.2985994</a>). Cardiac data was obtained from eight volunteers with no known illness using single-slice radial (SS), simultaneous multi-slice radial (SMS), and stack-of-stars (SoS) FLASH and bSSFP sequences and is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p>
PLOS Comput. Biol. "Biophysically detailed mathematical models of multiscale cardiac active mechanics": datasets
<p>This repository contains the data accompanying the PLOS Computational Biology paper "<em>Biophysically detailed mathematical models of multiscale cardiac active mechanics</em>", by Francesco Regazzoni, Luca Dedè and Alfio Quarteroni.</p> <p>It contains the following datasets:</p> <ul> <li><strong>steady_state.csv</strong>: steady-state active tension for constant calcium concentration and sarcomere length (Figs. 11, 12, 13 ,14).</li> <li><strong>isometric_twitches.csv</strong>: active tension transients in isometric conditions (Figs. 15, 16, 17).</li> <li><strong>force_velocity_relationship.csv</strong>: force-velocity relationship at different calcium concentrations and sarcomere lenghts (Fig. 18).</li> <li><strong>fast_transient_response.csv</strong>: tension-elongation curve after a fast step in length (Fig. 19).</li> </ul> <p>CSV headers refer to the following variables (and measure units):</p> <ul> <li><strong>Ca</strong> (<em>μM</em>): intracellular calcium concentration.</li> <li><strong>SL</strong> (<em>μm</em>): sarcomere length.</li> <li><strong>active_tension</strong> (<em>kPa</em>): active tension.</li> <li><strong>Delta_L</strong> (<em>nm/hs</em>): step length.</li> <li><strong>velocity</strong> (<em>hs/s</em>): shortening velocity.</li> <li><strong>time</strong> (<em>s</em>): time.</li> </ul>
Data from: Maximum cardiac performance of Antarctic fishes that lack haemoglobin and myoglobin: exploring the effect of warming on nature's natural knockouts
Comparisons among related species provide valuable insight into the functional consequences of natural genetic mutations. We assessed cardiac function at ambient and elevated temperatures in Antarctic notothenioids with contrasting levels of the oxygen binding proteins, haemoglobin (Hb) and myoglobin (Mb), to elucidate changes in cardiac performance that may compensate for impaired O2 transport. Notothenia coriiceps (Hb+Mb+) at 1oC had the highest maximum cardiac work rate (WC) and pressure generating capacity, but lowest relative ventricular mass and maximum cardiac output (Q̇) when compared with two icefish species, Chionodraco rastrospinosus (Hb-Mb+) and Chaenocephalus aceratus (Hb-Mb-). Cardiomegaly associated with absence of Hb generated an exceptionally large maximum stroke volume (VS) and Q̇, but a lower WC. However, C. rastrospinosus had a larger ventricle, a higher intrinsic heart rate (fH), and greater maximum VS and Q̇ than C. aceratus, suggesting that cardiac Mb has functional relevance. Warming to 4oC increased fH, but only increased maximum Q̇ in icefishes, while maximum WC and pressure development increased in N. coriiceps (both ~2.5x that of C. aceratus). The Hb+Mb+ myocardium generated considerable Q̇ against raised afterload, unlike icefish hearts. The presence of Hb and Mb enhances cardiac performance, and likely resilience to near-future ocean warming.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.