Novel Real-time Digital Pressure Sensor Reveals Wide Variations in Current Nerve Crush Injury Models.

introductionPeripheral 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.

methodsTwo 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.

resultsData 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%.

conclusionsThis 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.