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31
datasets available to search
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Dataset results
31 results for “Patient-specific modeling”
Repository of IVD Patient-Specific FE Models
<p>Free repository of 169 PP FE models of the IVD. Resulting cohort from a morphing process as a free-access repository to further empower the scientific community. This initiative underlines our commitment to promoting standardization and facilitating a more comprehensive understanding of the mechanisms underlying IVD degeneration.</p>
Dataset related to aticle "Additive Fabrication of a Vascular 3D Phantom for Stereotactic Radiosurgery of Arteriovenous Malformations"The database contains 3D models in STL file format of a patient-specific brain arteriovenous malformation phantom reconstructed from computed tomography scans.
<p><em>The database contains 3D models in STL file format of a patient-specific brain arteriovenous malformation phantom reconstructed from computed tomography scans.</em></p>
Raw Data for the article: Patient-Specific Analysis of Ascending Thoracic Aortic Aneurysm with the Living Heart Human Model
<p>In ascending thoracic aortic aneurysms (ATAAs), aneurysm kinematics are driven by ventricular traction occurring every heartbeat, increasing the stress level of dilated aortic wall. Aortic elongation due to heart motion and aortic length are emerging as potential indicators of adverse events in ATAAs; however, simulation of ATAA that takes into account the cardiac mechanics is technically challenging. The objective of this study was to adapt the realistic Living Heart Human Model (LHHM) to the anatomy and physiology of a patient with ATAA to assess the role of cardiac motion on aortic wall stress distribution. Patient-specific segmentation and material parameter estimation were done using preoperative computed tomography angiography (CTA) and ex vivo biaxial testing of the harvested tissue collected during surgery. The lumped-parameter model of systemic circulation implemented in the LHHM was refined using clinical and echocardiographic data. The results showed that the longitudinal stress was highest in the major curvature of the aneurysm, with specific aortic quadrants having stress levels change from tensile to compressive in a transmural direction. This study revealed the key role of heart motion that stretches the aortic root and increases ATAA wall tension. The ATAA LHHM is a realistic cardiovascular platform where patient-specific information can be easily integrated to assess the aneurysm biomechanics and potentially support the clinical management of patients with ATAAs.</p>
High-throughput behavioural phenotyping of 25 C. elegans disease models including patient-specific mutations
<p>This repository contains: all code, phenomic data, extracted features, calculated stats, normalised z-scores and timerseries data for all of the disease mutant phenologs and data in our paper: High-throughput behavioural phenotyping of 25 C. elegans disease models including patient-specific mutations.</p>
Model-informed Patient-specific Rehabilitation Using Robotics and Neuromuscular Modeling
ClinicalTrials.gov study NCT06008743. IPD Sharing: NO. Countries: 1. Publications: 1.
Patient-specific models of dyssynchronous heart failure for assessment of regional work in patients undergoing cardiac resynchronization therapy
Open the record for dataset details and reuse information.
Patient-specific genome-scale metabolic models reconstructed for 8 TCGA tumor types
<p>TCGA_reconstructedGEMs: 3,599 cancer patient-specific GEMs for 8 different tumor types reconstructed using the TCGA (The Cancer Genome Atlas) RNA-seq data and generic human GEM 'Recon 2M.2'</p>
29 Atrial Models created with a Patient-specific Augmented Atrial model Generation Tool (AugmentA)
<p>This dataset is part of the publication "AugmentA: Patient-specific Augmented Atrial model Generation Tool" (L. Azzolin et al., Preprint: <a href="https://doi.org/10.1101/2022.02.13.22270835">doi:10.1101/2022.02.13.22270835</a>). It consists of 29 statistical shape model instances derived using a non-rigid fitting algorithm to 29 magnetic resonance imaging segmentations from the University Heart Center Freiburg-Bad Krozingen. Our Patient-specific Augmented Atrial model Generation Tool (AugmentA) provided bilayer atrial models resampled with an average edge length of 0.4 mm and augmented with anatomical labels and fiber orientation so that they are ready to use for electrophysiological simulations.</p> <p> </p>
Files for: Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling
<p>This repository includes the files necessary to reproduce the digital anatomies, cardiac and aortic sleeves, and multi-material 3D-printed valves from the "Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling" article.</p>
Educating Brain Tumor Patients Using Patient-specific Actual-size Three-dimensional Printed Models
ClinicalTrials.gov study NCT04970615. IPD Sharing: NO. Countries: 1. Publications: 7.
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.
Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models
<p><strong>Abstract</strong></p> <p><strong>Background: </strong>Current ablation therapy for atrial fibrillation is sub-optimal and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more inter-individual variability.</p> <p><strong>Methods: </strong>Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, 16 long-standing persistent), undergoing first ablation. Patients were followed for 1-year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fibre orientation maps, electrical properties and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were post-processed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging and atrial fibrillation simulation metrics.</p> <p><strong>Results: </strong>We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging and simulation stress tests (average ten-fold cross-validation area under the curve 0.85 ± 0.09, recall 0.80 ± 0.13, precision 0.74 ± 0.13) outperformed those trained to history and imaging (area under the curve 0.66 ± 0.17), or history alone (area under the curve 0.61 ± 0.14). </p> <p><strong>Conclusion: </strong>A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalise selection for atrial fibrillation ablation.</p> <p><strong>Dataset Description: </strong>We include surface meshes in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the endocardial and epicardial fibre fields defined on the elements. </p> <p>We also include universal atrial coordinate fields alpha and beta, which are a lateral-septal coordinate and posterior-anterior coordinate for the LA. More details on the coordinate construction are given in our manuscript and <a href="https://www.ncbi.nlm.nih.gov/pubmed/31026761">https://www.ncbi.nlm.nih.gov/pubmed/31026761</a>. These coordinates can be used for registering datasets. </p> <p><strong>Publication</strong>: https://pubmed.ncbi.nlm.nih.gov/35089057/</p>
Reproducibility of PD patient-specific midbrain organoid data for in vitro disease modelling (passages)
GEO Series GSE287566. Homo sapiens. 48 samples. Type: Expression profiling by high throughput sequencing.
Modeling Diabetic Endothelial Dysfunction with Patient-Specific Induced Pluripotent Stem Cells
GEO Series GSE236430. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Modeling and drug targeting of a myeloid neoplasm with atypical 3q26/MECOM rearrangement using patient-specific iPSCs [RNA-seq]
GEO Series GSE248239. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Advanced patient-specific microglia cell models for pre-clinical studies in Alzheimer’s disease
GEO Series GSE255718. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Podocyte Twin in Culture: Patient-specific ex vivo Model of Genetic FSGS caused by an INF2 Mutation
GEO Series GSE254563. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
Perceptions of Orthopedic Prespecialists on Patient-Specific 3D Models.
ClinicalTrials.gov study NCT04430270. IPD Sharing: YES. Countries: 1. Publications: 0.
Patient-Specific Computational Walking Models in Improving Surgical and Rehabilitation Treatment in Patients With Pelvic Sarcomas
ClinicalTrials.gov study NCT05054335. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Surgical Planning With Patient-specific Pancreaticobiliary Disease With 3D Models
ClinicalTrials.gov study NCT04410640. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.