Rodriguez-Torres Erika E, Viveros-Rogel Jorge, López-García Kenia, Vázquez-Mendoza Enrique, Chávez-Fragoso Gonzalo, Quiroz-González Salvador, Jiménez-Estrada Ismael
Center for Research in Mathematics, Hidalgo State Autonomous University (UAEH), Pachuca, Mexico.
Faculty of Health Sciences, Autonomous University of Tlaxcala, Tlaxcala, Mexico.
Front Physiol. 2020 Jul 23;11:777. doi: 10.3389/fphys.2020.00777. eCollection 2020.
Fiber type composition, organization, and distribution are key elements in muscle functioning. These properties can be modified by intrinsic and/or extrinsic factors, such as undernutrition and injuries. Currently, there is no methodology to quantitatively analyze such modifications. On one hand, we propose a fractal approach to determine fiber type organization, using the fractal correlation method in software Fractalyse. On the other hand, we applied the kernel methodology from machine learning to build radial-basis functions for the spatial distribution of fibers (distribution functions), by dividing into square cells a two-dimensional binary image for the spatial distribution of fibers from a muscle fascicle and mounting on each cell a radial-basis function in such a way that the sum of all cell functions creates a smooth version of the fiber histogram on the cell grid. The distribution functions thus created belong in a reproducing kernel Hilbert space which permits us to regard them as vectors and measure distances and angles between them. In the present study, we analyze fiber type organization and distribution in fascicles (F2, F3, F4, and F5) of the muscle (EDLm) from control and undernourished male rats. Fibers were classified according to the ATPase activity in slow, intermediate, and fast. Then, () coordinates of fibers were used to build binary images and distribution functions for each fiber type and both conditions. The fractal organization analysis showed that fast and intermediate fibers, from both groups, had a fractal organization within the four fascicles, i.e., the fiber assembly is distributed in clusters. We also show that chronic undernutrition altered the organization of fast fibers in the F3, although it still is considered a fractal organization. Distribution function analysis showed that each fiber type (slow, intermediate, and fast) has a unique distribution within the fascicles, in both conditions. However, chronic undernutrition modified the intra-fascicular fiber type distributions, except in the F2. Altogether, these results showed that the methodology herein proposed allows for analyzing fiber type organization and distribution modifications. On the other side, we show that chronic undernutrition alters not only the fiber type composition but also the organization and distribution, which could affect the muscle functioning, and ultimately, its behavior (e.g., locomotion).
纤维类型组成、组织结构及分布是肌肉功能的关键要素。这些特性可因内在和/或外在因素而改变,如营养不足和损伤。目前,尚无定量分析此类改变的方法。一方面,我们提出一种分形方法来确定纤维类型组织,利用软件Fractalyse中的分形相关方法。另一方面,我们应用机器学习中的核方法来构建纤维空间分布的径向基函数(分布函数),即将来自肌肉束的纤维空间分布的二维二值图像划分为方形细胞,并在每个细胞上安装一个径向基函数,使得所有细胞函数的总和在细胞网格上创建纤维直方图的平滑版本。由此创建的分布函数属于再生核希尔伯特空间,这使我们能够将它们视为向量并测量它们之间的距离和角度。在本研究中,我们分析了对照和营养不良雄性大鼠肌肉(EDLm)的肌束(F2、F3、F4和F5)中的纤维类型组织和分布。根据慢肌、中间肌和快肌中的ATP酶活性对纤维进行分类。然后,利用纤维的()坐标为每种纤维类型和两种情况构建二值图像和分布函数。分形组织分析表明,两组的快肌和中间肌在四个肌束内均具有分形组织,即纤维集合呈簇状分布。我们还表明,慢性营养不良改变了F3中快肌的组织结构,尽管它仍被认为是一种分形组织。分布函数分析表明,在两种情况下,每种纤维类型(慢肌、中间肌和快肌)在肌束内均具有独特的分布。然而,慢性营养不良改变了肌束内纤维类型的分布,F2除外。总之,这些结果表明本文提出的方法能够分析纤维类型组织和分布的改变。另一方面,我们表明慢性营养不良不仅会改变纤维类型组成,还会改变组织结构和分布,这可能会影响肌肉功能,并最终影响其行为(如运动)。