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Mathematical analysis for spatial distribution of vessels, mast cells and adipocytes in superficial fascia

Authors :
Yingyue Dong
Dandan Zhang
Yingri Cao
Yanfei Zhang
Xiaozhe Sun
Tongsheng Chen
Yuanyuan Zhang
Guoheng Xu
Source :
Frontiers in Physiology, Vol 13 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

As a novel origin of adipocytes, the superficial fascia, a typical soft connective tissue, has abundant adipocytes and preadipocytes, accompanied by numerous mast cells. Blood vessels pass through the fascia to form a network structure. The more reasonable statistical analysis methods can provide a new method for in-depth study of soft connective tissue by clarifying the spatial distribution relation between cells (point structure) and blood vessels (linear structure). This study adopted the Guidolin et al. statistical analysis methods used by epidemiology and ecology to quantitatively analyze the distribution pattern and correlations among blood vessels, adipocytes, and mast cells. Image-processing software and self-written computer programs were used to analyze images of whole-mounted fascia, and the relevant data were measured automatically. Voronoi’s analysis revealed that the vascular network was non-uniformly distributed. In fascia with average area of 3.75 cm2, quantitative histological analysis revealed 81.16% of mast cells and 74.74% of adipocytes distributed within 60 μm of blood vessels. A Spearman’s correlation coefficient (rs) of >0.7 showed the co-distribution of the two types of cells under different areas. Ridge regression analysis further revealed the spatial correlation among blood vessels, adipocytes and mast cells. The combination of classical epidemiological analysis and extended computer program analysis can better analyze the spatial distribution relation between cells and vessels and should provide an effective analysis method for study of the histology and morphology of fascia and related connective tissues.

Details

Language :
English
ISSN :
1664042X
Volume :
13
Database :
Directory of Open Access Journals
Journal :
Frontiers in Physiology
Publication Type :
Academic Journal
Accession number :
edsdoj.1d60075d71248ba95194470e9fca60a
Document Type :
article
Full Text :
https://doi.org/10.3389/fphys.2022.1026019