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Vascular networks are fundamental components of biological systems. Quantitative analysis and observation of the features of these networks can improve our understanding of their roles in health and disease. Recent advancements in imaging technologies have enabled the generation of large-scale vasculature datasets, but barriers to analyzing these datasets remain. Modern analysis options are mainly limited to paid applications or open-source terminal-based software that requires programming knowledge with high learning curves. Here, we describe VesselVio, an open-source application developed to analyze and visualize pre-binarized vasculature datasets and pre-constructed vascular graphs. Vasculature datasets and graphs can be loaded with annotations and processed with custom parameters. Here, the program is tested on ground-truth datasets and is compared with current pipelines. The utility of VesselVio is demonstrated by the analysis of multiple formats of 2D and 3D datasets acquired with several imaging modalities, including annotated mouse whole-brain vasculature volumes.
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http://dx.doi.org/10.1016/j.crmeth.2022.100189 | DOI Listing |
Microsc Microanal
September 2025
Division of Anatomy, Center for Anatomy and Cell Biology, RPMI, Medical University Vienna, Währinger Strasse 13, 1090 Vienna, Austria.
The dermal arteries of the finger are organized in discrete units. We hypothesized that the anatomy of the dermal arterial units and the number and complexity of dermal Sucquet-Hoyer canals (SHCs) differ between the tip and center of the pad of the thumb. To test this, digital HREM volume datasets (voxel dimensions of 1-3 μm³) were created from biopsies harvested from the thumb tip and pad of six body donors.
View Article and Find Full Text PDFSci Rep
August 2025
Division of Anatomy, Center for Anatomy and Cell Biology, Medical University Vienna, 1090, Vienna, Austria.
The left sided segments of the prenatal liver receive blood enriched with oxygen and nutrients, while the right ones receive blood with low levels of oxygen and nutrients. We aimed at testing whether this affects the postnatal transcriptome of cells populating different liver segments and to explore the suitability of body donor material for transcriptomic analysis. 72 biopsies were harvested from 6 liver segments (LS) of 4 human body donors within the first 10 h post-mortem.
View Article and Find Full Text PDFSci Data
August 2025
PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy.
Optical coherence tomography angiography (OCTA) has emerged as a promising tool for non-invasive vascular imaging in dermatology. However, the field lacks standardized methods for processing and analyzing these complex images, as well as sufficient annotated datasets for developing automated analysis tools. We present DERMA-OCTA, the first open-access dermatological OCTA dataset, comprising 330 volumetric scans from 74 subjects with various skin conditions.
View Article and Find Full Text PDFFuture Med Chem
August 2025
Department of Medical Biotechnology, School of advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Aims: This study aims to develop a receptor-dependent 4D-QSAR model to overcome key limitations of traditional QSAR, including its dependency on molecular alignment and poor performance with small datasets, by integrating ligand - target interaction information.
Materials & Methods: Angiogenesis-related receptors, including VEGFR2, FGFR1-4, EGFR, PDGFR, RET, and HGFR (MET) were chosen based on the biological relevance in cancer. Ligand datasets with known IC₅₀ values were extracted from PubChem.
J Biophotonics
August 2025
State Key Laboratory of Extreme Photonics and Instrumentation, Centre for Optical and Electromagnetic Research, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, China.
Three-photon fluorescence microscopy (3PFM) enables high-resolution volumetric imaging in deep tissues but is often hindered by motion artifacts in dynamic physiological environments. Existing solutions, including surgical fixation and conventional image registration algorithms, frequently fail under intense and nonuniform motions, particularly in low-texture or highly deformed regions. To overcome these problems, we propose StabiFormer, a transformer-based optical flow learning network designed for robust motion correction.
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