ArticleBiology2021
3D Imaging and Quantitative Characterization of Mouse Capillary Coronary Network Architecture.
Article in Biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Article
- Microvascular Rarefaction in the Sinoatrial Node: A Potential Mechanism for Pacemaker Dysfunction in Early HFpEF.JACC. Clinical electrophysiology · 2025Article
- Role of channels in the ObioRxiv : the preprint server for biology · 2025Article
- Factor H's Control of Complement Activation Emerges as a Significant and Promising Therapeutic Target for Alzheimer's Disease Treatment.International journal of molecular sciences · 2024Article
- MDM2 Regulation of HIF Signaling Causes Microvascular Dysfunction in Hypertrophic Cardiomyopathy.Circulation · 2023Article
- 3D imaging and morphometric descriptors of vascular networks on optically cleared organs.iScience · 2023Review
- A three-dimensional method for morphological analysis and flow velocity estimation in microvasculature on-a-chip.Bioengineering & translational medicine · 2023Article
- Computational Identification and 3D Morphological Characterization of Renal Glomeruli in Optically Cleared Murine Kidneys.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterization of the cardiac capillary network structure is of critical importance to understand the normal coronary functional properties and coronary microvascular diseases. The aim of our study was to establish an accessible methodology for 3D imaging and 3D processing to quantitatively characterize the capillary coronary network architecture in mice. Experiments were done on C57BL/6J mice. 3D imaging was performed by light sheet microscopy and confocal microscopy on iDISCO+ optical cleared hearts after labelling of the capillary endothelium by lectin injection. 3D images were processed with the open source software ImageJ. Non-visual image segmentation was based of the frequency distribution of the voxel greyscale values, followed by skeletonization and distance mapping. Capillary networks in left and right ventricles and septum were characterized by the volume network density, the fractal dimension, the number of segments and nodes and their ratio, the total network length, and the average length, diameter, and tortuosity of the segments. Scale-dependent parameter values can be impacted by the resolution limit of the 3D imaging technique. The proposed standardized methodology for 3D image processing is easily accessible for a biologist in terms of investment and difficulty level, and allows the quantification of the 3D capillary architecture and its statistical comparison in different conditions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.