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Dataset results
579 results for “Microvascular”
Genetic Determinants of the Coronary Microvascular Obstruction in PCI
ClinicalTrials.gov study NCT05355532. IPD Sharing: NO. Countries: 1. Publications: 1.
Microvascular Blood Flow in Sickle Cell Anemia
ClinicalTrials.gov study NCT01566890. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Role of Endothelin in Microvascular Dysfunction Following PCI for NSTEMI
ClinicalTrials.gov study NCT00586820. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Microvascular Assessment of Ranolazine in Non-Obstructive Atherosclerosis (MARINA)
ClinicalTrials.gov study NCT02147067. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Impact of Microvascular Inflammation on Kidney Allograft Outcome
ClinicalTrials.gov study NCT06496269. IPD Sharing: UNDECIDED. Countries: 5. Publications: 1.
Safety and Potential Bioactivity of CLBS16 in Patients With Coronary Microvascular Dysfunction and Without Obstructive Coronary Artery Disease
ClinicalTrials.gov study NCT03508609. IPD Sharing: NO. Countries: 1. Publications: 2.
Ranolazine and Microvascular Angina by PET in the Emergency Department
ClinicalTrials.gov study NCT02052011. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Counter-Regulatory Impairment and the Effect of Microvascular Insulin Transfer in Type 1 Diabetes Mellitus
ClinicalTrials.gov study NCT00943787. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Risk Factors for Microvascular Obstruction Post-Emergency PCI in AMI Patients
ClinicalTrials.gov study NCT07042321. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Monitoring microvascular changes over time with a repositionable 3D ultrasonic capacitive micromachined row-column sensor
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Data from: Microvascular rarefaction in the sinoatrial node
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Human dermal microvascular arterial and venous blood endothelial cells and their use in bioengineered dermo-epidermal skin substitutes in vitro and in vivo
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Superb microvascular imaging (SMI) in the evaluation of musculoskeletal disorders: a systematic review
<p><strong>Objectives: </strong>To systematically review the current literature concerning the role of superb microvascular imaging (SMI), a novel Doppler technique that enables detection of fine vessels and slow blood flow, in the evaluation of musculoskeletal disorders.</p> <p><strong>Methods: </strong>An online search of the literature was conducted for the period 2013 to April 2019 and included original articles written in English language. A data analysis was performed at the end of the literature search.</p> <p><strong>Results: </strong>Eight original articles with prospective design and one with retrospective design were included in this review: 4 studies focused on rheumatoid arthritis, 2 on rheumatoid and other arthritides, 1 on lateral epicondylosis and 2 on carpal tunnel syndrome. Sample size ranged from 26 to 83 patients. Despite some methodological differences, all studies compared the performance of SMI with that of a conventional Doppler technique such as power and color Doppler and found an improvement in vascularity detection with SMI. The main variations were in sample size, evaluated parameters and vascularity interpretation methods. Inter-observer agreement for SMI ranged from moderate to excellent.</p> <p><strong>Conclusions: </strong>SMI is a promising tool for the diagnosis and treatment planning of different musculoskeletal disorders. Future investigations should include larger samples of patients with long-term follow-up.</p>
Arterial specification precedes microvascular restitution in the periinfarct cortex that is driven by small microvessels
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Dataset related to: Endothelial Glycocalyx of Peritubular Capillaries in Experimental Diabetic Nephropathy: A Target of ACE Inhibitor-Induced Kidney Microvascular Protection
<p>The files contain all the dataset included in the manuscript divided by figures.</p> <p> </p> <p>Abstract: Peritubular capillary rarefaction is a recurrent aspect of progressive nephropathies. We previously found that peritubular capillary density was reduced in BTBR <em>ob</em>/<em>ob</em> mice with type 2 diabetic nephropathy. In this model, we searched for abnormalities in the ultrastructure of peritubular capillaries, with a specific focus on the endothelial glycocalyx, and evaluated the impact of treatment with an angiotensin-converting enzyme inhibitor (ACEi). Mice were intracardially perfused with lanthanum to visualise the glycocalyx. Transmission electron microscopy analysis revealed endothelial cell abnormalities and basement membrane thickening in the peritubular capillaries of BTBR <em>ob</em>/<em>ob</em> mice compared to wild-type mice. Remodelling and focal loss of glycocalyx was observed in lanthanum-stained diabetic kidneys, associated with a reduction in glycocalyx components, including sialic acids, as detected through specific lectins. ACEi treatment preserved the endothelial glycocalyx and attenuated the ultrastructural abnormalities of peritubular capillaries. In diabetic mice, peritubular capillary damage was associated with an enhanced tubular expression of heparanase, which degrades heparan sulfate residues of the glycocalyx. Heparanase was also detected in renal interstitial macrophages that expressed tumor necrosis factor-α. All these abnormalities were mitigated by ACEi. Our findings suggest that, in experimental diabetic nephropathy, preserving the endothelial glycocalyx is important in order to protect peritubular capillaries from damage and loss.</p>
Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for multi-capillary dilation scenarios mimicking pericyte ablation
<p>Documentation to reproduce in silico analyses related to the manuscript<br> <strong>Pericyte remodelling is deficient in the aged brain and contributes to impaired capillary flow and structure</strong></p> <p>by</p> <p>Andrée-Anne Berthiaume, Franca Schmid, Stefan Stamenkovic, Vanessa Coelho-Santos, Cara D. Nielson, Bruno Weber, Mark W. Majesky and Andy Y. Shih</p> <p>Published in<br> Nature Communications (doi: 10.1038/s41467-022-33464-w)</p> <p>All simulations are performed based on the in silico blood flow model with discrete red blood cell (RBC) tracking as described in Schmid et al., 2017, PLoS Comp Biol (doi: <a href="https://doi.org/10.1371/journal.pcbi.1005392">10.1371/journal.pcbi.1005392</a>). The bi-phasic blood flow simulations have been performed in two realistic microvascular networks from the somatosensory cortex of the mouse first published in Blinder et al., 2013, Nature Neuroscience (doi: 10.1038/nn.3426). </p> <p>For further information and instructions please contact Franca Schmid (franca.schmid@unibe.ch, orcid.org/0000-0002-0689-9366).</p> <p><br> <strong>Simulation results:</strong></p> <p>All time-averaged simulation results are saved as vascular graphs building on the python library igraph and stored as python pickle files (Python 2.7). For each simulation two files are available: <em>verticesDict.pkl</em> and <em>edgesDict.pkl</em>containing all vertex and edge specific data, respectively. A summary of the vertex and edge attributes is provided below. The folder <em>Baseline</em> contains the simulation results for microvascular network 1 (MVN1) and MVN2 for the reference simulation, i.e. without any dilation. Folder <em>Dilated</em> contains the simulation results mimicking the four pericyte ablation scenarios. Subfolders <em>dc_x.x</em> contain the simulation results for the different diameter changes. Note that, folder <em>dc_0.0</em> contains no new simulation results but is a dummy folder containing the information about the vessels to be dilated for the different dilation scenarios (namely edge attribute: <em>toDilate</em> and <em>base_capillary</em>). </p> <p> </p> <p><strong>Reproducing figure 8:</strong></p> <p>Panels a-c: created by illustrating the simulation results with the open source software Paraview (v5.7.0).<br> Panels d-f & h: can be generated by executing make_all_figures.py in Python 2.7 within the provided folder structure.<br> Panel g: can be generated by executing make_figure_8g.py after installation of the the vgm-framework (further information see below). </p> <p><br> Output: All created Figures are saved in the folder <em>Figures</em>. The associated source data is available in Excel format in the folder <em>SourceData</em>.</p> <p> </p> <p><strong>Edge attributes:</strong></p> <p>diameter: vessel diameter [µm]<br> mainAV: 1 if ascending venule main branch, 0 otherwise<br> connectivity: vertex tuple to define location of edge<br> flow: flow rate [µm<sup>3</sup>/ms]<br> mainDA: 1 if descending arteriole main branch, 0 otherwise<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> htt: tube hematocrit [-]<br> toDilate: 1 if vessel is dilated for the current dilation scenario, 0 otherwise<br> base_capillary: 1 if vessel is the base capillary of the current dilation scenario, 0 otherwise</p> <p> </p> <p><strong>Vertex attributes:</strong></p> <p>index: vertex index<br> pressure: pressure [mmHg]<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> coords: vertex coordinates x,y,z [µm]<br> pBC: pressure boundary conditions at inflow vertices [mmHg], None at internal nodes</p> <p> </p> <p><strong>Obtaining simulation results:</strong><br> General:</p> <ul> <li>Running bi-phasic blood flow simulations requires setting-up the vgm-framework available at: <a href="https://github.com/Franculino/vgm.git">https://github.com/Franculino/vgm.git</a> (v.1.0).</li> <li>vgm is written in Python 2.7 and builds on standard python libraries.</li> <li>vgm has been used on macOS, Ubuntu and Windows Systems.</li> <li>Installation time < 5min. Further details available within the vgm README.</li> <li>Runtime depends on the network size, the chosen blood flow model and the initial conditions (e.g. ~8hrs for a Restart simulation of MVN1 with the bi-phasic blood flow model, see Restarty.py).</li> <li>scripts/Test.py provides an example how a simulation can be initiated. A Demo case is provided (details see below).</li> <li>Output: sampledict_BackUp_xx.pkl</li> <li>The bi-phasic blood flow model can be applied on all kind of microvascular graphs.</li> </ul> <p>Specific for current application:</p> <ul> <li>Simulations are a restart on the statistical steady state of the baseline cases.</li> <li>All relevant pre-processing functions for the current study are available in scripts/find_stroke_locations.py. Further details are available from the definition of the different functions.</li> <li>The simulations are initiated with scripts/Restart.py.</li> <li>To obtain the time-averaged simulation results scripts/01_put_together_sampledicts.py and scripts/02_convergenceDiscrete.py need to be executed. This results in the file G_averaged.pkl that is used for further analyses.</li> </ul> <p>Demo:</p> <ul> <li>Contains a small hexagonal microvascular network to test the code.</li> <li>1) Run Test.py to start the simulation</li> <li>2) Run 01_put_together_sampledicts.py</li> <li>3) Run 02_convergenceDiscrete.py to obtain time-averaged results (<em>G_averaged.pkl</em>)</li> </ul>
Human dermal microvascular arterial and venous blood endothelial cells and their use in bioengineered dermo-epidermal skin substitutes in vitro and in vivo
<p>The bio-engineering of vascular networks is pivotal to create complex tissues and</p> <p>organs in vitro for regenerative medicine applications. The vascular plexus is needed for a</p> <p>sufficient and fast blood supply after transplantation, and, thus, required for the survival and</p> <p>function of the engineered tissue or organ. Hence, human endothelial cells are an attractive</p> <p>source for bio-engineering purposes, for example human dermal microvascular endothelial</p> <p>cells (HDMECs).</p> <p>So far, a discrimination between arterial and venous blood endothelial cells after</p> <p>isolation of HDMECs from skin biopsies and if arterial and/or venous capillaries are formed in</p> <p>pre-vascularized bio-engineered substitutes was not investigated.</p> <p>In this study, we investigated employedby single cell sequencing for to</p> <p>investigate/compare human arterial and venous endothelial cell markers in human fetal and</p> <p>juvenile skin. Further, we analyzed if these markers are present after isolation of human skin</p> <p>derived endothelial cells under 2D culture conditions. In additionFinally, we investigated</p> <p>assessed if human endothelial cells form distinct arterial and venous capillaries in 3D</p> <p>collagen type I hydrogels, and if these capillaries retain their identity after transplantation.</p> <p>We determinedOur results showed that arterial and venous endothelial cell markers</p> <p>such as NRP1 and NR2F2 are expressed both in fetal and juvenile skin, and are retained after</p> <p>isolation in culture. We could show demonstrate that arterial and venous endothelial cells</p> <p>maintain their differentiation status and form arterial and venous capillaries in 3D in vitro</p> <p>culture systems and that the capillaries inosculate after transplantation.</p> <p>In summary, we could show that we could bio-engineer human arterial, venous, and</p> <p>lymphatic capillaries in a human skin substitute in view of regenerative medicine approaches</p> <p>for clinical applications.</p>
Exercise Capacity According to Coronary Microvascular Dysfunction and Body Composition
ClinicalTrials.gov study NCT04822649. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluation of Systemic Microvascular Reactivity in Patients With Resistant Hypertension
ClinicalTrials.gov study NCT05464849. IPD Sharing: NO. Countries: 1. Publications: 1.
Role of Microvascular Insulin Resistance and Cardiorespiratory Fitness Diabetes
ClinicalTrials.gov study NCT04791371. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.