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zenodo48/100

Histological Dataset for Microvascular Segmentation of Tissue-Engineered Vascular Grafts

Objectives: The pursuit of understanding vascular tissue regeneration within tissue-engineered vascular grafts (TEVGs) is of paramount importance due to the critical role these grafts play in replacing damaged or diseased blood vessels. TEVGs offer a promising alternative to traditional grafts, with the potential to integrate into the host's tissue and support the natural regenerative processes. However, challenges such as thrombosis, inflammation, and the need for grafts that can adapt to the dynamic biological environment remain. By studying the regenerative processes in TEVGs, researchers can gain insights into the mechanisms that underpin successful graft integration and function, which is essential for improving patient outcomes in vascular surgeries. This dataset, with its detailed annotations of histological features, provides a valuable resource for developing and refining machine-learning models that can analyze and predict patterns of vascular tissue regeneration. The ability to accurately segment and quantify microvessels and immune cells in regenerated arteries is a significant step forward in distinguishing between physiological and pathological regeneration, ultimately contributing to the design of more effective and reliable TEVGs for clinical use. Ethical Approval: Experimental strategy of the study is described in detail in <a href="https://www.mdpi.com/2073-4360/14/23/5149" target="_blank" rel="noopener">[1]</a> and <a href="https://www.mdpi.com/1422-0067/24/10/8540" target="_blank" rel="noopener">[2]</a>. The study was conducted according to the guidelines of the Declaration of Helsinki, and was approved by the Local Ethical Committee of the Research Institute for Complex Issues of Cardiovascular Diseases (Kemerovo, Russia, protocol code 2020/06, date of approval: 19 February 2020). Animal experiments were performed in accordance with the European Convention for the Protection of Vertebrate Animals (Strasbourg, 1986) and Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes. For the implantation, we used female Edilbay sheep of 42–45 kg body weight which were received from the Animal Core Facility of the Research Institute for Complex Issues of Cardiovascular Diseases (Kemerovo, Russia) and selected for the surgery by Doppler ultrasonography to identify those having carotid artery diameter of 4.0 ± 0.2 mm. Description: The dataset comprises a collection of Whole Slide Images (WSIs) obtained from biodegradable TEVGs implanted into the carotid arteries of 20 sheep. A total of 104 WSIs were acquired, each measuring an average size of 135,000 x 123,000 pixels. These WSIs were stained using Hematoxylin and Eosin (H&E), a common practice for highlighting the structure of tissue sections, which facilitates the detailed examination of histological features. These WSIs were automatically sliced into 99,831 patches of 3,000 x 3,000 pixels and subsequently filtered, resulting in 1,401 selected patches for manual annotation. Annotation Method: Two pathologists independently selected and meticulously annotated the 1401 patches, identifying nine distinct histological features associated with vascular tissue regeneration. These features include arteriole lumen (AL), arteriole media (AM), arteriole adventitia (AA), venule lumen (VL), venule wall (VW), capillary lumen (CL), capillary wall (CW), immune cells (IC), and nerve trunks (NT). The annotations were performed using binary masks, delineating each feature within the patches. Subsequently, a senior pathologist conducted a triple verification process, reviewing and refining the annotations to ensure accuracy and consistency. The annotations are provided in the form of binary masks, meticulously defined for each feature within the patches. Dataset Split: Given the limited number of subjects studied, comprising 20 sheep, we employed a 5-fold cross-validation technique to split our dataset. This method was chosen because it allows for the efficient use of limited data, ensuring that each observation has the opportunity to be used in both the training and testing sets, thus reducing bias and providing a more accurate estimate of the model's performance. In this approach, each fold involved 16 sheep for training and the remaining 4 for testing (see Table 1 and Figure 3). This partitioning scheme was consistently applied to maintain the integrity of subject groups within each subset and to prevent data leakage. The 5-fold cross-validation is particularly beneficial for our study's objectives as it maximizes the training data available for developing robust machine learning models while also ensuring that the models are tested on unseen data, thereby enhancing the generalizability of our findings. Access to the Study: Further information about this study, including curated source code, dataset details, and trained models, can be accessed through the following repositories: <ul> <li>Source code: <a href="https://github.com/ViacheslavDanilov/histology_segmentation" target="_blank" rel="noopener">https://github.com/ViacheslavDanilov/histology_segmentation</a></li> <li>Dataset: <a href="https://doi.org/10.5281/zenodo.10838384" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10838384</a></li> <li>Models: <a href="https://doi.org/10.5281/zenodo.10838431" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10838431</a></li> </ul> <div> </div> <div>Table 1. Patch and feature distributions across folds and subsets <table> <tbody> <tr> <td> Fold </td> <td> Subset </td> <td> Patches </td> <td> AL </td> <td> AM </td> <td> AA </td> <td> VL </td> <td> VW </td> <td> CL </td> <td> CW </td> <td> IC </td> <td> NT </td> <td> Total </td> </tr> <tr> <td> 1 </td> <td> Train </td> <td> 1168 </td> <td> 510 </td> <td> 512 </td> <td> 220 </td> <td> 675 </td> <td> 648 </td> <td> 770 </td> <td> 765 </td> <td> 409 </td> <td> 448 </td> <td> 4957 </td> </tr> <tr> <td>1</td> <td> Test </td> <td> 233 </td> <td> 81 </td> <td> 84 </td> <td> 36 </td> <td> 186 </td> <td> 169 </td> <td> 178 </td> <td> 182 </td> <td> 91 </td> <td> 25 </td> <td> 1032 </td> </tr> <tr> <td> 2 </td> <td> Train </td> <td> 1053 </td> <td> 406 </td> <td> 411 </td> <td> 179 </td> <td> 678 </td> <td> 638 </td> <td> 743 </td> <td> 746 </td> <td> 423 </td> <td> 315 </td> <td> 4539 </td> </tr> <tr> <td>2</td> <td> Test </td> <td> 348 </td> <td> 185 </td> <td> 185 </td> <td> 77 </td> <td> 183 </td> <td> 179 </td> <td> 205 </td> <td> 201 </td> <td> 77 </td> <td> 158 </td> <td> 1450 </td> </tr> <tr> <td> 3 </td> <td> Train </td> <td> 1127 </td> <td> 507 </td> <td> 511 </td> <td> 222 </td> <td> 743 </td> <td> 702 </td> <td> 759 </td> <td> 760 </td> <td> 299 </td> <td> 423 </td> <td> 4926 </td> </tr> <tr> <td>3</td> <td> Test </td> <td> 274 </td> <td> 84 </td> <td> 85 </td> <td> 34 </td> <td> 118 </td> <td> 115 </td> <td> 189 </td> <td> 187 </td> <td> 201 </td> <td> 50 </td> <td> 1063 </td> </tr> <tr> <td> 4 </td> <td> Train </td> <td> 1064 </td> <td> 466 </td> <td> 472 </td> <td> 199 </td> <td> 611 </td> <td> 566 </td> <td> 759 </td> <td> 758 </td> <td> 423 </td> <td> 291 </td> <td> 4545 </td> </tr> <tr> <td>4</td> <td> Test </td> <td> 337 </td> <td> 125 </td> <td> 124 </td> <td> 57 </td> <td> 250 </td> <td> 251 </td> <td> 189 </td> <td> 189 </td> <td> 77 </td> <td> 182 </td> <td> 1444 </td> </tr> <tr> <td> 5 </td> <td> Train </td> <td> 1192 </td> <td> 475 </td> <td> 478 </td> <td> 204 </td> <td> 737 </td> <td> 714 </td> <td> 761 </td> <td> 759 </td> <td> 446 </td> <td> 415 </td> <td> 4989 </td> </tr> <tr> <td>5</td> <td> Test </td> <td> 209 </td> <td> 116 </td> <td> 118 </td> <td> 52 </td> <td> 124 </td> <td> 103 </td> <td> 187 </td> <td> 188 </td> <td> 54 </td> <td> 58 </td> <td> 1000 </td> </tr> </tbody> </table> </div>

opencc-by-4.0Mar 2024View details →

zenodo40/100

Averaged results of blood flow simulations with discrete RBC tracking for microvascular networks

The dataset contains the results for blood flow simulations in 3 cerebral micorvascular networks.The microvascular networks are from the mouse parietal cortex (Blinder et al., 2013) and embedded in a tissue volume of approximately 1 cubic mm. For the blood flow simulations we used a numercial model with discrete tracking of RBCs which is described in Schmid et al., 2017. For each network the following data are provided: - Microvascular network with averaged flow and pressure field, as well as averaged values for the distribution and motion of red blood cells (RBCs). - RBC trajectories describing the motion of individual RBCs through the microvascular networks. - The data is stored as a graph, i.e. vertices connected by edges. - Details regarding the simulation parameters can be found in Schmid et al., 2017. - Data format (pickle - files containing python dictonairies). Microvascular networks: edgesDict.pkl: dictionary with edge related data (dictionary keys: flow [um^3/ms], diameter [um], tuple [-], httBC [-], nkind [-], length [um], htt [-], nRBC [-], diameters [um], points [um]) verticesDict.pkl: dictionary with vertex related data (dictionary keys: pressure [mmHg], coordinates [um], pBC [mmHg]) Additional comments on dictionary keys: - pBC: pressure boundary conditions. 'None' for internal nodes. Assigned based on the hierarchical boundary conditions approach (see Schmid et al. 2017 for details) - tuple: connectivity of graph, tuple of vertices - httBC: tube hematocrit boundary conditions. 'None' for internal nodes. Constant value assigned. - nkind: integere to describe the vessel type. 0: pial artery, 1: pial venule, 2: descending arteriole, 3: ascending venule, 4: capillaries, 5: unknown - htt: tube hematocrit - nRBC: number of red blood cells - points: list of tortuous vessel coordinates per edge - diameters: local diameter measurements associated to the 'points' key. RBC trajectories: RBC_trajectories.pkl: dictonary for each RBC with relevant tracking data (dictionary key: RBC index). The relavant tracking data per RBC is stored in another dictionary with the following keys: edges, lengths, times, pressure, nkindsMod, RBCleft Additional comments on dictionary keys per RBC: - RBCleft: bool to indicate that RBC left the computational domain - edges: edge indices through which the RBC moves on its way through the vasculature - pressure: pressure [mmHg] values at the nodes along the RBC trajectory - times: time [ms] the RBC spends in the respective edge segment - nkindsMod: nkind at the nodes along the RBC trajectory  - lengths: cummulative length travelled [um]

opencc-by-4.0Feb 2017View details →

zenodo40/100

Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.

DOCUMENTATION - Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios. Correspondence: fschmid@ethz.ch (Franca Schmid, ORCID: <a href="https://orcid.org/0000-0002-0689-9366">0000-0002-0689-9366</a>) 1. Related references: The data set is published in context with the manuscript:  [1] The severity of microstrokes depends on local vascular topology and baseline perfusion.  F Schmid, G Conti, P Jenny and B Weber. eLife. 2021. Doi: 10.7554/eLife.60208 The bi-phasic blood flow simulations have been performed in realistic microvascular networks (MVNs) from the mouse somatosensory cortex first published in: [2] The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow. P Blinder, PS Tsai, JP Kaufhold, PM Knutsen, H Suhl and D Kleinfeld. Nature Neuroscience. 2013. Doi: 10.1038/nn.3426 The bi-phasic blood flow model for realistic MVNs has first been published in: [3] Depth-dependent flow and pressure characteristics in cortical microvascular networks. F Schmid, PS Tsai, D Kleinfeld, P Jenny and B Weber. PLoS Computational Biology. 2017. Doi: 10.1371/journal.pcbi.1005392 For further information on how to perform bi-phasic blood flow simulation, please contact the corresponding authors of [1] or [3]. 2. Requirements (software): All simulations and analyses have been performed in Python 2.7. To execute the analysis script the following python libraries need to be installed: cPickle, python-igraph, pandas, seaborn, scipy. The individual analyses script can then be executed by in Python (e.g. “python plot_Figure3.py”). 3. Content: All folders are stored as compressed archives (*.tar.bz2). On unix-based system the folders can be unpacked by: "tar –jxf  ARCHIVE_NAME" 3a. Time-averaged simulation results (python dictionaries stored as python 2.7 pickle files): SimulationResults_Baseline.tar.bz2: Folders: MVN1, MVN2 Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl (generated from prepare_Figure4.py), data_spatial_distribution_AVfactor.pkl (generated from plot_Figure4.py) SimulationResults_SingleCapillaryOcclusions.tar.bz2: Folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out, 2-in-2-out_high, 2-in-2-out_AL1, 2-in-2-out_AL2, 2-in-2-out_AL3, 2-in-2-out_AL4, 2-in-2-out_AL5, 2-in-2-out_closeToDA, 2-in-2-out_farFromDA Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl (only folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out) SimulationResults_MultiCapillaryOcclusions.tar.bz2: Folders: vesselsOccluded_1, vesselsOccluded_3, vesselsOccluded_5, vesselsOccluded_7, vesselsOccluded_9 Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl 3b. Analysis scripts (python 2.7 scripts in folder Analyses_Scripts): For details on the figure content see [1]. The verticesDict* and the edgesDict* are converted into graph structure (python-igraph) for all analyses. The functionality of python-igraph is used heavily throughout the various analyses. helperFunctions.py: various functions used by the other analysis scripts plot_Figure1_and_Figure1-supplement_1_a-d.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl Output: Figures/Figure_1/*, Supplementary_Figures/Figure_1-supplement_1_a-d/* plot_Figure2_and_Figure2_supplement_1_a-d.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl Output: Figures/Figure_2/*, Supplementary_Figures/Figure_2-supplement_1_a-d/* plot_Figure3.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_MultiCapillaryOcclusion/*/edgesDict.pkl, SimulationResults_MultiCapillaryOcclusion/*/verticesDict.pkl  Output: Figures/Figure_3/* prepare_Figure4.py (long execution time!): Input: SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN*/verticesDict_baseline.pkl, Output: SimulationResults_Baseline/MVN*/pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl plot_Figure4.py (long execution time!): Input: SimulationResults_Baseline/MVN*/* Output: SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl (attribute Lfactor_median added), SimulationResults_Baseline/MVN*/data_spatial_distribution_AVfactor.pkl, Figures/Figure_4/* plot_Figure5.py: Input: SimulationResults_Baseline/MVN*/*  Output: Figures/Figure_5/* plot_Figure6.py: Input: SimulationResults_Baseline/MVN1/*, SimulationResults_SingleCapillaryOcclusion/2-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl Output: Figures/Figure_6/* 4. Attributes stored in python dictionaries: 4a. Baseline: verticesDict: contains all relevant information and data stored at vertices. <ul> <li>index: index of vertex</li> <li>pressure: time averaged pressure at vertex [mmHg]</li> <li>inflowE: list of edges delivering blood to the vertex (inflows of the vertex)</li> <li>outflowE: list of edges removing blood from the vertex (outflows of the vertex)</li> <li>coords: coordinates of the vertex [µm]</li> <li>pBC: pressure boundary conditions [mmHg], None for internal vertices</li> <li>corticalDepth: depth from cortical surface [µm]</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> </ul> edgesDict: contains all relevant information and data stored at edges. <ul> <li>diameter: effective vessel diameter [µm]. See [3] for details.</li> <li>htd: time averaged discharge hematocrit [-]. </li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>mainAV: identifier for ascending venule (AV) main brain. 1: is AV main brain, 0: no AV main branch</li> <li>mainDA: identifier for descending arteriole (DA) main brain. 1: is DA main brain, 0: no DA main branch</li> <li>flow: time averaged flow rate [µm3 ms-1]</li> <li>length: tortuous vessel length [µm] See [1] and [3] for details.</li> <li>tissueVolume: topological tissue volume supplied by vessel [µm3]. See [1] for details.</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> <li>edgesFulfillingSelection: identifier if vessels fulfils selection criteria to qualify for analysis. 1: vessel included for analysis, 0: vessel not included for analysis. Details on the selection criteria are provided in [1].</li> <li>htt: time averaged tube hematocrit [-]</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate.</li> <li>sign: sign describing the flow direction in the vessel. +: flow direction from source (vertex with lower index) to target (vertex with higher index), -: flow direction from target to source vertex. Based on time averaged pressure values.</li> <li>points: list of tortuous vessel coordinates of the edge [µm]. Starting at the source vertex. Ending at the target vertex.</li> <li>Lfactor_median: AV-factor of the vessel. None if no AV-factor can be assigned. See [1] for details. Attribute added by plot_Figure4.py</li> </ul> pathsDict_allPaths_from_DA_to_AV_mainBranch: contains all flow path from DA main brain to AV main branch. For details see [1]. <ul> <li>startPoint: list of vertex indices of the end point of the DA</li> <li>endPoint: list of vertex indices of the end point of the AV</li> <li>allPaths: list of lists of vertex indices describing all paths between a the associated startPoint and endPoint.</li> </ul> data_spatial_distribution_AVfactor: contains information on the spatial distribution of venule-sided capillaries (AV-factor  > 0.5). For details see [1]. <ul> <li>edges_L_mean_50um: list of all edges for which the average AV-factor in an analysis sphere of 50 µm has been computed.</li> <li>resulting_L_mean_50um: average AV-factor for an analysis sphere for 50 µm (see Figure4/AV_factor_delta_analysisSphere50_MVN*.pkl)</li> <li>shortest_distance_to_closest_vessel: list of shortest distances to any vessel for all discretization points along all venule sided capillaries.</li> <li>shortest_distance_to_Lfactor_lt_05: list of shortest distances to an arteriole-sided capillary (AV-factor < 0.5) for all discretization points along all venule sided capillaries.</li> </ul> 4b. Occlusion scenarios (both single- and multi-capillary occlusions): verticesDict: <ul> <li>index: index of vertex</li> <li>coords: coordinates of the vertex [µm]</li> <li>pressure_strokeIndex_n: time averaged pressure at vertex [mmHg] for the simulation where edge n has been occluded. For details see [1].</li> </ul> edgesDict: <ul> <li>htd_strokeIndex_n: time averaged discharge hematocrit [-] for the simulation where edge n has been occluded. For details see [1]. </li> <li>flow: time averaged flow rate [µm3 ms-1] for the simulation where edge n has been occluded. For details see [1].</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate for the simulation where edge n has been occluded. For details see [1].</li> <li>htt: time averaged tube hematocrit [-] for the simulation where edge n has been occluded. For details see [1].</li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>diameter_strokeIndex_n: effective vessel diameter [µm] for the simulation where edge n has been occluded (only given for multi-capillary occlusions).</li> </ul> pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased: contains all flow path from DA main brain to AV main branch (unique vertex sequences). For details see helperFunctions.py --> function convert_pathsDict_to_unique_vertexSequence. <ul> <li>startPoint_strokeIndex_n: list of vertex indices of the end point of the DA for the simulation where edge n has been occluded.</li> <li>endpoint_strokeIndex_n: list of vertex indices of the end point of the AV for the simulation where edge n has been occluded.</li> <li>allPaths_strokeIndex_n: list of lists of vertex indices describing all paths between a the associated startPoint and endpoint for the simulation where edge n has been occluded.</li> </ul>

opencc-by-4.0Jul 2021View details →

dryad40/100

Data from: Endometrial decidualization status modulates endometrial microvascular complexity and trophoblast outgrowth in gelatin methacryloyl hydrogels

Open the record for dataset details and reuse information.

publicMay 2024View details →

zenodo36/100

Supplementary material to: Angioarchitecture and hemodynamics of microvascular arterio-venous malformations

Supplementary material (code and raw data) to the manuscript "Angioarchitecture and hemodynamics of microvascular arterio-venous malformations" by Frey et al., under consideration for publication in PLOS One

opencc-by-4.0Apr 2018View details →

zenodo36/100

Microvascular network remodeling in the ischemic brain defined by light sheet microscopy

This dataset contains the raw image data set used in the study entitled "Microvascular network remodeling in the ischemic brain defined by light sheet microscopy" by Hagemann et al.. Images were analysed using VesselExpress software (see also <a href="https://pubmed.ncbi.nlm.nih.gov/37056368/">https://pubmed.ncbi.nlm.nih.gov/37056368/</a>) using the provided config file with gamma values ranging from 50 to 400.

opencc-by-4.0Apr 2023View details →

zenodo36/100

Topological characterization of the retinal microvascular network visualized by portable fundus camera- effects of chronic disease (TREND2) database

Introduction Topological characterization of the Retinal microvascular nEtwork visualized by portable fuNDus camera (TREND 2) is a database of digital color eye fundus images created as an addition to TREND  database (https://zenodo.org/badge/DOI/10.5281/zenodo.4521044.svg). TREND 2 databse was created by medical professionals of the Faculty of Medicine of the University of Montenegro in 2023.   Purpose 1) to provide a standard that defines normal and abnormal retinal anatomy and microvascular geometry as it appears when visualized by the portable fundus camera 2) to help the development of new methods for stratification of the risk for the development of various eye diseases, as well as systemic diseases that affect microvasculature 3) to aid the development of biomarkers of accelerated aging 4) to provide a standard that can be used to develop software for segmentation of retinal microvasculature, grading the quality of retinal digital images, and computer-aided diagnosis of systemic and chronic diseases. All color digital images were acquired with a hand-held portable, non-mydriatic MiiS HORUS Scope DEC 200 with 45º FOV and 2560 X 1920 pixel resolution.   Data The TREND public database contains 28 color fundus images of old subjects (20 images from subjects with one or more chronic diseases such as type 2 diabetes mellitus, hypertension or Alzheimer's dementia- O_CD group, and 8 images from subjects with no chronic diseases- O_NCD group). Each image is associated with a corresponding binarized image of the manually segmented microvascular network. <table> <caption>Inclusion and Exclusion Criteria</caption> <thead> <tr> <th scope="col">O_NCD group</th> <th scope="col">O_CD group</th> </tr> </thead> <tbody> <tr> <td>Inclusion Criteria</td> <td>Inclusion Criteria</td> </tr> <tr> <td>- at least 56 years old</td> <td>- at least 56 years old</td> </tr> <tr> <td> - no current acute disease - no history of alcohol, or drug abuse, or psychiatric disease </td> <td> - no current acute disease - no history of alcohol, or drug abuse, or psychiatric disease </td> </tr> <tr> <td> - no history of alcohol, or drug abuse, or psychiatric disease </td> <td>- no history of alcohol, or drug abuse, or psychiatric disease</td> </tr> <tr> <td>- negative history of any chronic disease</td> <td> - controlled hypertension (blood pressure<140/90 mmHg), and/or - controlled type 2 diabetes mellitus, and/or - Alzheimer's dementia </td> </tr> <tr> <td>Exclusion Criteria</td> <td>Exclusion Criteria</td> </tr> <tr> <td> - presence of opacities of the transparent media in both eyes affecting - myopia ≥5 diopters </td> <td> - presence of opacities of the transparent media in both eyes affecting - myopia ≥5 diopters </td> </tr> </tbody> </table> Files: 1_OLD WITH CHRONIC DISEASE_RAW (20 images in tif format) 2_OLD WITH CHRONIC DISEASE_SEGMENTED (20 images in png format) 3_OLD WITH NO CHRONIC DISEASE_RAW (8 images in tif format) 4_OLD WITH NO CHRONIC DISEASE SEGMENTED (8 images in png format) 5_ASSOCIATED DATA (xslx format) 6_RETINAL PATHOLOGY (docx format)

opencc-by-4.0Feb 2023View details →

ClinicalTrials.gov36/100

A Placebo-Controlled Trial of CLBS16 in Subjects With Coronary Microvascular Dysfunction

ClinicalTrials.gov study NCT04614467. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

The Correlation Between Obstructive Sleep Apnea-Related Nocturnal Hypoxemia Parameters and Coronary Microvascular Dysfunction: A Prospective Cohort Study (SLEEP-CMD)

ClinicalTrials.gov study NCT07315399. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov36/100

Microvascular Dysfunction in Nonischemic Cardiomyopathy: Insights From CMR Assessment of Coronary Flow Reserve

ClinicalTrials.gov study NCT03249272. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

Microvascular Angina Intervention With Compound Danshen Dripping Pill (MAIDS)

ClinicalTrials.gov study NCT06092736. IPD Sharing: YES. Countries: 1. Publications: 6.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov36/100

Cardiovascular and Renal Microvascular Outcome Study With Linagliptin in Patients With Type 2 Diabetes Mellitus (CARMELINA)

ClinicalTrials.gov study NCT01897532. IPD Sharing: Not stated. Countries: 27. Publications: 10.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

Effect of Hyperglycemia on Microvascular Perfusion in Healthy Adults

ClinicalTrials.gov study NCT03520569. IPD Sharing: NO. Countries: 1. Publications: 31.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov36/100

Microvascular and Antiinflammatory Effects of Rivaroxaban Compared to Aspirin in Type-2 Diabetic Patients With Subclinical Inflammation and High Cardiovascular Risk

ClinicalTrials.gov study NCT02164578. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

Cheese Consumption and Human Microvascular Function

ClinicalTrials.gov study NCT03376555. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov36/100

Assessing Microvascular Resistance Via IMR To Predict Cumulative Outcome in STEMI Patients Undergoing Primary PCI

ClinicalTrials.gov study NCT02325973. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

Treatment With Ranolazine in Microvascular Coronary Dysfunction (MCD): Impact on Angina Myocardial Ischemia

ClinicalTrials.gov study NCT01342029. IPD Sharing: YES. Countries: 1. Publications: 3.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov36/100

Molecular Landscape of Microvascular Inflammation in Kidney Allografts

ClinicalTrials.gov study NCT06342128. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

Microvascular Coronary Disease In Women: Impact Of Ranolazine

ClinicalTrials.gov study NCT00570089. IPD Sharing: YES. Countries: 1. Publications: 15.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov36/100

Acute Microvascular Changes With LDL Apheresis

ClinicalTrials.gov study NCT02388633. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record