Department of Orthopaedics and Rehabilitation, Center for Orthopaedic Research and Translational Science, The Pennsylvania State University College of Medicine, Milton S. Hershey Medical Center, Hershey, PA 17033, USA.
Department of Orthopedic Surgery, Hanyang University College of Medicine, Hayang University Guri Hospital, Guri-si, Gyeonggi-do, 11923, South Korea.
Mil Med. 2021 Jan 25;186(Suppl 1):473-478. doi: 10.1093/milmed/usaa346.
Peripheral nerve crush injury (PNCI) models are commonly used to study nerve damage and the potential beneficial effects of novel therapeutic strategies. Current models of PNCI rely on inter-device and operator precision to limit the variation with applied pressure. Although the inability to accurately quantify the PNCI pressure may result in reduced reproducibility between animals and studies, there is very limited information on the standardization and quantification of applied pressure with PNCI. To address this deficit, we constructed a novel device comprised of an Arduino UNO microcontroller board and Force Sensitive Resistor capable of reporting the real-time pressure applied to a nerve.
Two forceps and two needle drivers were used to perform 30-second PNCIs to the sciatic nerves of mice (n = 5/group). Needle drivers were set to the first notch, and a jig was used to hold the forceps pinch at a reproducible pressure. The Force Sensitive Resistor was interposed in-series between the nerve and instrument during PNCI.
Data collected from these procedures displayed average needle driver pressures an order of multitude greater than forceps pressures. Additionally, needle driver inter- and intra-procedure pressure remained more consistent than forceps pressure, with needle driver coefficient of variation equal to 14.5% vs. a forceps coefficient of variation equal to 45.4%.
This is the first demonstration of real-time pressure measurements in PNCI models and it reveals that the applied pressures are dependent on the types of device used. The large disparity in pressure represents an inability to apply graded accurate and consistent intermediate pressure gradients in PNCI. These findings indicate a need for documentation of pressure severity as a screening for PNCI in animals, and the real-time pressure sensor could be a useful tool in monitoring and applying consistent pressure, reducing the outcome variability within the same experimental model of PNCI.
周围神经挤压伤 (PNCI) 模型常用于研究神经损伤以及新的治疗策略的潜在有益效果。目前的 PNCI 模型依赖于设备之间和操作人员之间的精度，以限制所施加压力的变化。尽管无法准确量化 PNCI 压力可能会导致动物和研究之间的重现性降低，但关于 PNCI 施加压力的标准化和量化的信息非常有限。为了解决这个问题，我们构建了一种由 Arduino UNO 微控制器板和力敏电阻器组成的新型设备，能够报告施加到神经上的实时压力。
使用两把镊子和两把针夹驱动器对小鼠的坐骨神经进行 30 秒的 PNCI（每组 n=5）。将针夹驱动器设置为第一档，并使用夹具以可重复的压力固定镊子夹。在 PNCI 过程中，力敏电阻器被插入神经和仪器之间的串联位置。
从这些程序中收集的数据显示，针夹驱动器的平均压力比镊子的压力高出一个数量级。此外，针夹驱动器的内外程序压力比镊子的压力更稳定，针夹驱动器的变异系数等于 14.5%，而镊子的变异系数等于 45.4%。
这是首次在 PNCI 模型中进行实时压力测量的演示，它表明所施加的压力取决于所使用的设备类型。压力的巨大差异表明无法在 PNCI 中施加分级准确且一致的中等压力梯度。这些发现表明，作为动物 PNCI 的筛选，需要记录压力严重程度，并且实时压力传感器可以成为监测和施加一致压力的有用工具，从而减少同一 PNCI 实验模型中的结果变异性。
