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8,285 results for “cardiac;”
A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling
<p>Collection of auxiliary data to reproduce the results from "<strong>A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling</strong>". Source code is available at: https://github.com/saezlab/reheat2_pub<br><br>We provide processed data to facilitate access to the results, for the original count data, please see the associated manuscript for references to the original datasets. </p> <p> </p> <p> </p>
A software benchmark for cardiac elastodynamics
<p>Data used in the article:<br>"A software benchmark for cardiac elastodynamics" by Arostica et al, Computer Methods in Applied Mechanics and Engineering. The DOI of the article was not available at the moment of publishing this data set.</p>
Increased Cardiac Pi/PCr in the Diabetic Heart Observed Using Phosphorus Magnetic Resonance Spectroscopy at 7T
<p>The uploaded data relate to the work of Valkovič et al. entitled "Increased Cardiac Pi/PCr in the Diabetic Heart Observed Using Phosphorus Magnetic Resonance Spectroscopy at 7T." submitted in 2022<br> The data consist of the anonymised in vivo data of healthy volunteers and T2DM patients, in Siemens DICOM format, acquired as described in the manuscript. The uploaded data also include all analysed STEAM data results with visualised MR spectra and fitted values of PCr and Pi, used for Pi/PCr calculation.</p>
Data From: Emulation of Cardiac Mechanics using Graph Neural Networks
<p>Contains simulation results of the forward displacement from beginning to end-diastole for approximately 3000 synthetically generated left ventricle geometries.</p> <p>The simulation results are split into training, validation and test data.</p> <p>The data is described in detail in a forthcoming publication in <em>Computer Methods in Applied Mechanics and Engineering</em> - further information will be provided upon publication. A GitHub repository will also be made available, with code for processing the simulation data and training a Graph Neural Network emulator.</p>
Strain gauge platforms: Time-lapse microscopy dataset of engineered cardiac microbundles
<p>This dataset is a "part I" extension of the "<a href="https://doi.org/10.5061/dryad.5x69p8d8g">Engineered cardiac microbundle time-lapse microscopy image dataset</a>" and contains 732 experimental time-lapse image sequences of beating hiPSC-based cardiac microbundles using microbundle strain gauge platforms [1] ("Type1"). In the "part II" extension, we included 808 experimental time-lapse image sequences of beating hiPSC-based cardiac microbundles using FibroTUG platforms [2] ("Type2"). </p> <p>References:</p> <p>[1] Zhang, K., Cloonan, P. E., Sundaram, S., Liu, F., Das, S. L., Ewoldt, J. K., ... & Chen, C. S. (2021). Plakophilin-2 truncating variants impair cardiac contractility by disrupting sarcomere stability and organization. <em>Science Advances</em>, <em>7</em>(42), eabh3995.</p> <p>[2] DePalma, S. J., Davidson, C. D., Stis, A. E., Helms, A. S., & Baker, B. M. (2021). Microenvironmental determinants of organized iPSC-cardiomyocyte tissues on synthetic fibrous matrices. <em>Biomaterials science</em>, <em>9</em>(1), 93-107.</p>
Effect of psychoactive substances on cardiac and locomotory activity-Procambarus virginalis-DATA
<p>This Effect of psychoactive substances on cardiac and locomotory activity<em>-Procambarus virginalis</em></p> <p>The associated dataset investigates the impact of environmentally relevant levels of psychoactive compounds on the early developmental stages of marbled crayfish (Procambarus virginalis). The compounds analyzed include sertraline, methamphetamine, and a mixture of citalopram, oxazepam, sertraline, tramadol, venlafaxine, and methamphetamine, each at a concentration of 1 μg L-1. The dataset assesses cardiac and locomotory activity on day four and day eight of exposure, respectively.</p> <p>it is a condition of the CENAKVA RI financing provider to record accesses to open data. Data can be requested at the following email: <strong>dubova@frov.jcu.cz</strong></p> <p>or visit the CENAKVA RI website:<br>https://www.frov.jcu.cz/en/faculty/faculty-parts/south-bohemian-research-centre-for-aquaculture-and-biodiversity-of-hydrocenoses-cenakva/large-research-infrastructure-cenakva</p>
Data from: In vitro to in vivo extrapolation from three-dimensional hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrythmia risk assessment
<p>Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. <em>In vitro</em> human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing <em>in vitro</em> and <em>in vivo</em> models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established three-dimensional human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted <em>in vitro</em> to <em>in vivo</em> therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for two physiologically relevant metrics of proarrhythmic risk (<em>i.e.</em>, action potential duration and triangulation assessed by optical mapping) across concentrations spanning three orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of four compounds with a range of known proarrhythmic risk profiles (<em>i.e., </em>quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human induced pluripotent stem cell-based three-dimensional cardiac tissue models as new approach methodologies that enable <em>in vitro</em> proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.</p>
Fig. 1 in High Trypanosoma cruzi infection prevalence associated with minimal cardiac pathology among wild carnivores in central Texas
Fig. 1. Spatial occurrence and distribution of T. cruzi infected, hunter-harvested wildlife, 2014. Number of infected over total number of that species tested are shown by county.
Process Flow Diagram of Cardiac Surgery Pathway
<p><em><strong>This image is supplementary material for:</strong></em></p> <p>Currie, C. S.M. Monks, T. (2018) <em>Modelling Diseases: Prevention, Cure, and Management.</em> In M. Rabe, A. Juan, N. Mustafee, A. Skoogh, & B. Johansson (Eds.). Proceedings of the Winter Simulation Conference 2018. Gothenburg, Sweden.</p> <p><em>Relevant to:</em></p> <p><strong>Section 4.1.4. Spend some Time Observing How the Operations of the Disease Pathway are Managed</strong></p> <p>The description of the disease pathway obtained in the initial meeting may be idealized. It is advised that modelers spend at least a few hours in the one or more services in the real pathway to observe how it operates and to interview on-the-ground workforce. Observations of the system under pressure are useful to understand the complexities of the pathway and will reveal aspects that have been missed or that operate differently from described. This Figure illustrates a cardiac surgery pathway at a large hospital that operated on patients from a network of smaller hospitals in the UK. The diagram itself was primarily constructed in an initial meeting with a group of cardiac surgeons. Observation of the system and shadowing of specialist cardiac nurses revealed two differences from the idealized model. Firstly, patients could deteriorate and ‘bounce back’ from post-operative care to higher levels of care. Secondly, in times of pressure, the post operative beds were used flexibly to accommodate patients waiting for surgery and those who had already undergone surgery.</p>
Supplemental Raw Data for Imboden et al., High-speed mechano-active multielectrode array for investigating rapid stretch effects on cardiac tissue
<p>The file contains the central raw data underlying Figure 5 and, in the supplementary material, Figure 13 of the article 'High-speed mechano-active multielectrode array for investigating rapid stretch effects on cardiac tissue'.</p>
The impact of severity of acute respiratory distress syndrome following cardiac arrest on neurologic outcomes
<p>Overall survival rates at 28 days in patients with and without ARDS. ARDS = acute respiratory distress syndrome</p>
Figure 1 in Cardiotoxic effects of enrofloxacin on electrophysiological activity, cardiac markers, oxidative stress, and haematological findings in rabbits
Figure 1. Histological examination of rabbit heart in the negative control (A) shows normal morphology. Histological examination of rabbit heart in group 1 (B) and group 2 (C) shows normal morphology except for some minor abnormalities including hyperaemia in some areas (H&E, 400×).
A groupwise registration and tractography framework for cardiac myofiber architecture description by diffusion MRI : an application to the ventriclar junctions
<p>Data and materials regarding the submission of the paper. See Data Avaibility section and https://github.com/valeryozenne/Cardiac-Structure-Database</p> <p> </p>
A characterization of cardiac-induced noise in R2* maps of the brain
<p>A subset of data used in the original publication for computations of the results. These data contain 5D k-space data (kx/ky/kz/cardiac phase/TE) from one of the participants and can be used to perform the cardiac-induced noise characterization and retrieve the results from the paper.</p>
Database used in : Analysis of intermunicipal journeys for cardiac surgery in Brazilian Unified Health System (SUS): an approach based on network theory
<p>Data and scripts referring to the results generated in the article entitled: <strong>Analysis of intermunicipal journeys for cardiac surgery in Brazilian Unified Health System (SUS): an approach based on network theory.</strong></p> <p> </p> <p>To obtain the results of the work the following sequence of database treatment was performed:</p> <p> </p> <p>DATASUS --> BASE_PER_YEAR --> EDGES_BASE --> EDGES_VC_BASE</p> <p>The bases were downloaded from the DATASUS site (link: https://datasus.saude.gov.br/transferencia-de-arquivos/#) in .dbc format separated by month and year; using Tabwin we joined the bases generating a base for each year in csv format. The reformatted bases are gathered together in the file PER_YEAR_BASE.ZIP; from the bases for each year we manually built the files in csv format with the list of the edges with the following fields: "Source", "Target", "Type", "Id", "Label" and "Weight". The "Source" column was filled with data from MUNIC_RES and the "Target" column with data from MUNIC_MOV. The "ID" and "Weight" fields were filled in automatically using Gephi, where the "Weight" column represents the sum of the edge, defined by the pair of Source and Target columns, were repeated throughout the year. This generated the bases containing the list of edges that are grouped in the file EDGES_BASE.ZIP. Each base was filtered to contain only edges related to the city "Vitória da Conquista" and grouped in the file EDGES_VC_BASE.zip</p> <p>INDE BASE --> NODES_BASE</p> <p>To build the list of nodes containing the list of municipalities with their respective geographical locations (in UTM), we used the database of the INDE (available on the link: https://visualizador.inde.gov.br/). The file in shape format was treated in the ArqGis program and the database with the network nodes was created (file NODES_BASE.csv).</p> <p>EDGES_VC_BASE and NODES_BASE --> NETWORK</p> <p>Using the program Gephi we joined the bases referring to the edges (EDGES_VC_BASE) and those referring to the nodes of the network (NODES_BASE) and built the networks for each year studied for the city of "Vitória da Conquista". All networks are in gephi format and compressed in the NETWORKS.zip file.</p> <p>EDGES_VC_BASE and NODES_BASE --> INDICES</p> <p>Using the R script "distance.R" and using as input the files of edges (EDGES_VC_BASE) and nodes (NODES_BASE) we generate files in csv format with the columns: dist_med_in , dist_med_out, Flow_in and flow_out. The indexes dist_med_in and dist_med_out represent the average distance traveled in meters to enter and leave the municipality, respectively; the indexes flow_in and flow_out estimate the quantity of people that entered and left the municipality. All index files are grouped in the compressed file INDICES.zip.</p> <p>The last two digits at the end of all file names represent the year of analysis. </p>
Echo from noise: synthetically generated cardiac ultrasound data using semantic diffusion models
<p>This is the data repository for the paper: "Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation", available at: https://arxiv.org/abs/2305.05424. The corresponding code is available at: https://github.com/david-stojanovski/echo_from_noise</p> <p> </p> <p>This is the first work to utilize Denoising Diffusion Probabilistic Models (DDPMs) for generating medical images using semantic label maps as a source image for conditioning the generated image.</p> <p>Each of the 400+50 CAMUS patients contributes with 4 labelled frames (ED and ES for 2 chamber and 4 chamber), totalling 1800 initial semantic maps, to which we added the sector label. These semantic maps then had five random deformations applied (a combination of random affine and elastic deformation) to produce, 9000 transformed semantic maps (8000 for training and 1000 for validation). </p> <p>Affine transformation ranges for rotation degrees, translate, scale and shear were: (-5, 5), (0, 0.05), (0.8, 1.05) and 5 respectively. This was implemented using the torchvision python package. Elastic deformation was implemented using the TorchIO package. The settings for number of control points and max displacement were (10, 10, 4) and (0, 30, 30) respectively.</p> <p>Using these 9000 semantic maps as input to the generative models, we produced 9000 synthetic ultrasound images.</p> <p>Each echo view folder contains 3 folders:</p> <p>1) annotations: augmented labels, with no sector label and no clipping due to sector</p> <p>2) images: semantic diffusion model inferenced images</p> <p>3) sector_annotations: label maps which contain ultrasound cone sector, which were used to generate corresponding semantic diffusion model images</p> <p>ema_0.9999_050000_2ch_ed_256.pt and ema_0.9999_050000_4ch_ed_256.pt are the saved checkpoints for the 2 and 4 chamber diffusion models respectively.</p> <p>The pretrained segmentation networks are provided within the <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/final_models.zip">final_models.zip</a> file.</p> <p>A diagram of image numbers is shown in <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/Data%20diagram.png">Data diagram.png</a></p>
Delving into the relationship between regular physical exercise and cardiac interoception in two cross-sectional studies.
<p>This repository contains raw data from two studies corresponding to the article "No evidence of a relationship between regular physical exercise and cardiac interoception" by Yoris et al. In Study I, 45 resting EEG files are included for the Active (N = 24) and Inactive (N = 21) groups, both for the eyes closed and eyes open conditions. For Study II, there are 60 resting EEG files (30 Active/30 Inactive). Data are in EEGLAB format .set/fdt. The project is publicly available for free use and can be accessed at <a href="https://osf.io/xrsgn/">https://osf.io/xrsgn/</a>.</p>
Engineered cardiac microbundle time-lapse microscopy image dataset
<p>The "Microbundle Time-lapse Dataset" contains 24 experimental time-lapse images of cardiac microbundles using three distinct types of experimental testbed of beating lab grown hiPSC-based cardiac microbundles. Of the 24 experimental time-lapse images, 23 examples are brightfield videos, and a single example is a phase contrast video. We categorize the different experimental testbeds into 3 types, where "Type 1" includes movies obtained from standard experimental microbundle platforms termed microbundle strain gauges [1,2,3]. We refer to data collected from non-standard platforms termed FibroTUGs [4] as "Type 2" data, and "Type 3" data represents a highly versatile and diverse nanofabricated experimental platform [5,6].</p> <p><strong>References:</strong></p> <p>[1] Boudou T, Legant WR, Mu A, Borochin MA, Thavandiran N, Radisic M, Zandstra PW, Epstein JA, Margulies KB, Chen CS. A microfabricated platform to measure and manipulate the mechanics of engineered cardiac microtissues. Tissue Engineering Part A. 2012 May 1;18(9-10):910-9.</p> <p>[2] Xu F, Zhao R, Liu AS, Metz T, Shi Y, Bose P, Reich DH. A microfabricated magnetic actuation device for mechanical conditioning of arrays of 3D microtissues. Lab on a Chip. 2015;15(11):2496-503.</p> <p>[3] Bielawski KS, Leonard A, Bhandari S, Murry CE, Sniadecki NJ. Real-time force and frequency analysis of engineered human heart tissue derived from induced pluripotent stem cells using magnetic sensing. Tissue Engineering Part C: Methods. 2016 Oct 1;22(10):932-40.</p> <p>[4] DePalma SJ, Davidson CD, Stis AE, Helms AS, Baker BM. Microenvironmental determinants of organized iPSC-cardiomyocyte tissues on synthetic fibrous matrices. Biomaterials science. 2021;9(1):93-107.</p> <p>[5] Jayne RK, Karakan MÇ, Zhang K, Pierce N, Michas C, Bishop DJ, Chen CS, Ekinci KL, White AE. Direct laser writing for cardiac tissue engineering: a microfluidic heart on a chip with integrated transducers. Lab on a Chip. 2021;21(9):1724-37.</p> <p>[6] Karakan MÇ. A Direct-Laser-Written Heart-on-a-Chip Platform for Generation and Stimulation of Engineered Heart Tissues (Doctoral dissertation, Boston University, 2023).</p>
ECG recordings of cardiac pacing in mice carrying TRPV1
<p>The data are ECG recordings acquired from mice whose cardiac muscle tissue carries the human TRPV1 channel. ECG was recorded with standard procedure on the limbs with disposable electrodes, with one or two electrodes. Myocardial stimulation was applied with an IR laser. Each archive contains the result of an ECG recording of one animal. Initial recordings are cut so that the unsuccessful recordings are excluded.</p><p> </p><p> </p><p>File name / Recording date / Vector type / Vector Amount </p><p>No1 / 2022.06.22 / AAV488 / 6x10-11</p><p>No2 / 2022.06.6 / AAV479 / 6x10-12</p><p>No3 / 2022.06.22 / AAV488 / 6x10-11</p><p>No4 / 2021.10.18 </p><p> </p>
A large comprehensive curated dataset of small molecules and their activities covering three cardiac ion channels: hERG, Cav1.2, and Nav1.5
<p>The compressed data folder (dataset.rar) represents a data framework for researchers in the field of drug discovery to perform in depth analyses on a very large open-access unique and comprehensive hERG, Nav1.5, and Cav1.2 cardiotoxicity integrated database of small molecules and their activities. The database is organized as follows:</p> <ul> <li>Each sub-folder represents a cardiac ion channel target: hERG, Nav1.5, and Cav1.2</li> <li>Each target sub-folder consists of 3 files in CSV format: One file containing the development set (split into training and validation sets using an 80/20 ratio for hyperparameter tuning). The other 2 files contain external evaluation sets. The first test dataset consists of compounds with a structural similarity of no more than 60% (Tanimoto similarity ≤ 0.6) to the remaining development set, while the second test dataset comprises compounds with a structural similarity of no more than 70% (Tanimoto similarity ≤ 0.7) to the remaining development set.</li> <li>Each file contains data with 7 columns: "InChl Key" as a unique identifier of the chemical structure, "SMILES" as the string format of storage and exchange of the chemical structure, "Source" as the upstream data source from which the data was retrieved, "ChEMBL ID" as the ChEMBL identifier if the compound comes from ChEMBL database, "PubChem CID" as the PubChem compound identifier if the compound comes from PubChem database, "pIC50" as the negative logarithm of the half-maximal inhibitory concentration (IC50) to describe the potency of the compound, and "USED_AS" column specifying whether the compound was used for training or validation.</li> </ul> <p><strong>Upon usage, please cite this publication:</strong></p> <ul> <li>Issar Arab, Kristof Egghe, Kris Laukens, Ke Chen, Khaled Barakat, Wout Bittremieux, <strong>Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction</strong>, <em>Journal of Chemical Information and Modeling</em>, (2023). doi:<a href="https://doi.org/10.1021/acs.jcim.3c01301">10.1021/acs.jcim.3c01301</a></li> </ul> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.