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41 results for “cardiovascular modelling”
In-vitro Major Arterial Cardiovascular Simulator: Benchmark Data Set for in-silico Model Validation
<p><strong>Background</strong><br> <br> The data described here supplements the paper "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation" (to be submitted). It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper M. Wisotzki, A. Mair, P. Schlett, B. Lindner, M. Oberhardt, S. Bernhard, In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation (2022), Data 7(11), DOI: 10.3390/data7110145 and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset Structure</strong></p> <p>Each mat-File describes a different stenosis degree at the popliteal artery of the in-vitro simulator MACSim (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The different stenosis degrees for each degree are:<br> ScenarioI: 100 % Area fraction (no stenosis)<br> ScenarioII: 37,5 % Area fraction<br> ScenarioIII: 23,4 % Area fraction<br> ScenarioIV: 6,56 % Area fraction</p> <p><strong>Data fields for each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>name of the scenario according to the paper, corresponds to filename</td> </tr> <tr> <td>configuration</td> <td>parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the paper (node numbering, not sensor numbers) or in the software SISCA (https://gitlab.com/agbernhard.lse.thm/sisca) in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q' </td> </tr> <tr> <td>anatomicalPosition</td> <td> <p>name of the corresponding anatomical position</p> </td> </tr> </tbody> </table>
Data for: Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH Cardiovascular Magnetic Resonance
<p>Magnetic Resonance Imaging measurement data used in our paper about model-based myocardial T1 mapping with sparsity constraints. The data was obtained using a single-short inversion-recovery radial FLASH sequence and is provided in a file format used by the BART toolbox (<a href="http://doi.org/10.5281/zenodo.592960">DOI: 10.5281/zenodo.592960</a>).</p>
Data associated with the study, "Non-Invasive Biomarkers for Detecting Progression Toward Hypovolemic Cardiovascular Instability In A Lower Body Negative Pressure Model".
<p>Raw data associated with the study entitled "Non-Invasive Biomarkers for Detecting Progression Toward Hypovolemic Cardiovascular Instability In A Lower Body Negative Pressure Model". There were 16 subjects with 1. electrocardiogram (ECG), 2. mean arterial pressure (MAP), 3) photoplethysmography (pleth), 4) Bioimpedance Cardiography measured via a Cheetah (Startling/Medtronic) system referred to as cheetah, 5) electrical impedance from a Sentec electrical impedance tomography system, referred to as ST, and electrical impedance from a sciospec impedance analyzer, referred to as SS. Each subject underwent a lower body negative pressure (LBNP) procedure, where the LBNP was increased modeling a small hemorrhage by drawing blood to their lower extremeties. The dataset contains 5 mat (MATLAB data files), with time reported in minutes on the day of the study, i.e.10 am = 600 minutes. The file details are as follows:</p><ul><li><strong>LBNP data</strong>: LBNP_level_times.mat. The data contains 1 structure (LBNPinf) of length 16 (corresponding to each subject) with the following fields<ul><li>ts: time vector in minutes</li><li>lbnp: LBNP value at each noted time</li></ul></li><li><strong>Vital Sign data</strong>: raw_labchart_data.mat. The data contains 1 structure array (Labchart) of length 16 (corresponding to each subject) with the following fields<ul><li>ts: time vector in minutes</li><li>ecgs: ECG data</li><li>MAP: MAP data</li><li>pleth: pleth data recorded from a single channel (V)</li></ul></li><li><strong>Sentec EIT data</strong>: raw_av_ST_impedance_data.mat. The data contains 1 structure array (STout) of length 16 (corresponding to each subject) with the following fields<ul><li>t_thx: time vector in minutes corresponding to thorax data</li><li>Z_thx: average impedance data at each time over the thorax</li><li>t_spl: time vector in minutes corresponding to abdomen data</li><li>Z_spl: average impedance data at each time over the abdomen</li></ul></li><li><strong>Sciospec EIS data</strong>: raw_sciospec_dat.mat. The data contains 1 cell array & 1 structure array (sciodat) of length 16 (corresponding to each subject).<ul><li>Cell array: Locations of the Sciospec measurements: 'Thoracic', 'Abdominal', 'Arm'</li><li>Sciodat fields:<ul><li>sciodat structure array of length 3 corresponding to the 'Thoracic', 'Abdominal', 'Arm' locations, respectively. Each component has the following fields<ul><li>tvec: time vector in minutes</li><li>fs: frequencies that the impedance is recorded over (Hz)</li><li>Zmat: matrix of impedance data size time versus frequency</li><li>erflg: not used</li></ul></li></ul></li></ul></li><li><strong>Bioimpedance cardiography data</strong>: raw_cheetah_bioimpedance.mat. The data contains a cell array of column labels (col_labs, 1x11) and a matrix (cheetah_db) of the BC data.</li></ul><p> </p><p> </p><p> </p>
Prediction Model of Cardiac Risk for Dental Extraction in Elderly Patients With Cardiovascular Diseases
ClinicalTrials.gov study NCT03211312. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Difficult Airway Incidence in Cardiovascular Surgery and a Prediction Model Development
ClinicalTrials.gov study NCT06986187. IPD Sharing: NO. Countries: 1. Publications: 2.
Artificial Intelligence Models to Predict Clinically Relevant Cardiovascular Outcomes
ClinicalTrials.gov study NCT06847100. IPD Sharing: NO. Countries: 3. Publications: 14.
Effectiveness New Health Care Organization Model in Primary Care for Chronic Cardiovascular Disease Patients Based
ClinicalTrials.gov study NCT01826929. IPD Sharing: Not stated. Countries: 1. Publications: 21.
Generation of Marfan Syndrome and Fontan Cardiovascular Models Using Patient-specific Induced Pluripotent Stem Cells
ClinicalTrials.gov study NCT02815072. IPD Sharing: NO. Countries: 1. Publications: 2.
Proof of Concept of Model Based Cardiovascular Prediction
ClinicalTrials.gov study NCT02591940. IPD Sharing: Not stated. Countries: 0. Publications: 3.
Generation of human chambered cardiac organoids from pluripotent stem cells for improved modelling of cardiovascular diseases
GEO Series GSE168464. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Modeling SMAD2 mutations in iPSCs provides insights into cardiovascular disease pathogenesis. [ATAC-Seq]
GEO Series GSE278726. Homo sapiens. 14 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Circular RNAs in Rat Models of Cardiovascular and Renal diseases
GEO Series GSE92669. Rattus norvegicus. 12 samples. Type: Non-coding RNA profiling by array.
Restoration of branched chain amino acid catabolism improves kidney function in preclinical cardiovascular-kidney-metabolic syndrome models
GEO Series GSE263155. Rattus norvegicus. 120 samples. Type: Expression profiling by high throughput sequencing.
Modeling Radiation-induced Cardiovascular Dysfunction with Human iPSC-Derived Engineered Heart Tissues
GEO Series GSE242108. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
RNA sequencing of cardiac endothelial cells from the cardiovascular disease risk factor mouse models
GEO Series GSE145263. Mus musculus. 31 samples. Type: Expression profiling by high throughput sequencing.
Multi-Lineage Heart-Chip Models Drug Cardiotoxicity and Enhances Maturation of Human Stem Cell-Derived Cardiovascular Cells
GEO Series GSE241223. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Sinus venosus adaptation models prolonged cardiovascular disease and reveals insights into evolutionary transitions of the vertebrate heart
GEO Series GSE229821. Danio rerio. 4 samples. Type: Expression profiling by high throughput sequencing.
S1_raw_images paper "Use of dual-flow bioreactor to develop a simplified model of nervous-cardiovascular systems crosstalk: a preliminary assessment
<p>Original uncropped and unadjusted images underlying all blot or gel results reported in a submission’s figures and Supporting Information files.</p>
a Foundational Model for Cardiovascular Disease Diagnosis and Prediction
ClinicalTrials.gov study NCT06591923. IPD Sharing: NO. Countries: 1. Publications: 0.
Prediction Models for Cardiovascular and Neurocognitive Disease Risk in the General Population
ClinicalTrials.gov study NCT05951764. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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International Brain Laboratory public data
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OpenNeuro
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