Mah Sue Ann, Avci Recep, Vanderwinden Jean-Marie, Du Peng
Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
Laboratoire de Neurophysiologie, Faculté de Médecine, Université Libre de Bruxelles, Brussels, Belgium.
Cell Mol Bioeng. 2023 Nov 27;17(1):67-81. doi: 10.1007/s12195-023-00789-5. eCollection 2024 Feb.
Several functional gastrointestinal disorders (FGIDs) have been associated with the degradation or remodeling of the network of interstitial cells of Cajal (ICC). Introducing fractal analysis to the field of gastroenterology as a promising data analytics approach to extract key structural characteristics that may provide insightful features for machine learning applications in disease diagnostics. Fractal geometry has advantages over several physically based parameters (or classical metrics) for analysis of intricate and complex microstructures that could be applied to ICC networks.
In this study, three fractal structural parameters: Fractal Dimension, Lacunarity, and Succolarity were employed to characterize scale-invariant complexity, heterogeneity, and anisotropy; respectively of three types of gastric ICC network structures from a flat-mount transgenic mouse stomach.
The Fractal Dimension of ICC in the longitudinal muscle layer was found to be significantly lower than ICC in the myenteric plexus and circumferential muscle in the proximal, and distal antrum, respectively (both p < 0.0001). Conversely, the Lacunarity parameters for ICC-LM and ICC-CM were found to be significantly higher than ICC-MP in the proximal and in the distal antrum, respectively (both p < 0.0001). The Succolarity measures of ICC-LM network in the aboral direction were found to be consistently higher in the proximal than in the distal antrum (p < 0.05).
The fractal parameters presented here could go beyond the limitation of classical metrics to provide better understanding of the structural-functional relationship between ICC networks and the conduction of gastric bioelectrical slow waves.
几种功能性胃肠疾病（FGIDs）与 Cajal 间质细胞（ICC）网络的退化或重塑有关。将分形分析引入胃肠病学领域，作为一种有前景的数据分析方法，以提取关键结构特征，这些特征可为疾病诊断中的机器学习应用提供有洞察力的特征。分形几何在分析复杂的微观结构方面比一些基于物理的参数（或经典度量）具有优势，这些微观结构可应用于 ICC 网络。
在本研究中，采用了三个分形结构参数：分形维数、孔隙率和起伏度，分别表征来自平铺转基因小鼠胃的三种类型胃 ICC 网络结构的尺度不变复杂性、异质性和各向异性。
发现纵肌层 ICC 的分形维数分别显著低于近端和远端胃窦肌间神经丛及环肌层中的 ICC（均 p < 0.0001）。相反，发现近端和远端胃窦中 ICC-LM 和 ICC-CM 的孔隙率参数分别显著高于 ICC-MP（均 p < 0.0001）。发现 ICC-LM 网络在远侧方向的起伏度测量值在近端始终高于远端胃窦（p < 0.05）。
本文提出的分形参数可以超越经典度量的局限性，以更好地理解 ICC 网络与胃生物电慢波传导之间的结构 - 功能关系。
