Ahmadi Bahareh, Duarte Felipe C K, Srbely John, Bartlewski Pawel M
Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada.
School of Health, Medical and Applied Sciences, Central Queensland University, Brisbane, QLD, Australia.
Exp Biol Med (Maywood). 2024 Feb 29;249:10064. doi: 10.3389/ebm.2024.10064. eCollection 2024.
Ultrasonographic characteristics of skeletal muscles are related to their health status and functional capacity, but they still provide limited information on muscle composition during the inflammatory process. It has been demonstrated that an alteration in muscle composition or structure can have disparate effects on different ranges of ultrasonogram pixel intensities. Therefore, monitoring specific clusters or bands of pixel intensity values could help detect echotextural changes in skeletal muscles associated with neurogenic inflammation. Here we compare two methods of ultrasonographic image analysis, namely, the echointensity (EI) segmentation approach (EI banding method) and detection of selective pixel intensity ranges correlated with the expression of inflammatory regulators using an in-house developed computer algorithm (r-Algo). This study utilized an experimental model of neurogenic inflammation in segmentally linked myotomes (i.e., rectus femoris (RF) muscle) of rats subjected to lumbar facet injury. Our results show that there were no significant differences in RF echotextural variables for different EI bands (with 50- or 25-pixel intervals) between surgery and sham-operated rats, and no significant correlations among individual EI band pixel characteristics and protein expression of inflammatory regulators studied. However, mean numerical pixel values for the pixel intensity ranges identified with the proprietary r-Algo computer program correlated with protein expression of ERK1/2 and substance P (both 86-101-pixel ranges) and CaMKII (86-103-pixel range) in RF, and were greater ( < 0.05) in surgery rats compared with their sham-operated counterparts. Our findings indicate that computer-aided identification of specific pixel intensity ranges was critical for ultrasonographic detection of changes in the expression of inflammatory mediators in neurosegmentally-linked skeletal muscles of rats after facet injury.
骨骼肌的超声特征与它们的健康状况和功能能力相关，但在炎症过程中，它们提供的肌肉组成信息仍然有限。已经证明，肌肉组成或结构的改变可能对超声图像像素强度的不同范围产生不同的影响。因此，监测特定的像素强度值簇或带有助于检测与神经源性炎症相关的骨骼肌回声纹理变化。在这里，我们比较了两种超声图像分析方法，即回声强度（EI）分割方法（EI条带法）和使用自行开发的计算机算法（r-Algo）检测与炎症调节因子表达相关的选择性像素强度范围。本研究利用了腰椎小关节损伤大鼠节段性相连的肌节（即股直肌（RF））中的神经源性炎症实验模型。我们的结果表明，手术大鼠和假手术大鼠之间不同EI条带（间隔50或25像素）的RF回声纹理变量没有显著差异，并且所研究的单个EI条带像素特征与炎症调节因子的蛋白质表达之间没有显著相关性。然而，用专利r-Algo计算机程序确定的像素强度范围的平均数值像素值与RF中ERK1/2和P物质（均为86 - 101像素范围）以及CaMKII（86 - 103像素范围）的蛋白质表达相关，并且手术大鼠中的该值比假手术大鼠更大（<0.05）。我们的研究结果表明，计算机辅助识别特定的像素强度范围对于超声检测小关节损伤后大鼠神经节段性相连骨骼肌中炎症介质表达的变化至关重要。
