Modeling a Penta-analyte Biochip for Physiological Status Monitoring in the Triage of Hemorrhagic Trauma and for Allograft Stratification

INTRODUCTION

Hemorrhage, a life-threatening condition marked by rapid blood loss and tachycardia, requires real-time monitoring of key physiological markers for optimal management. A similar requirement exists for allografts under bioreactor preservation conditions. In both scenarios, the Hemorrhage Intensive Severity and Survivability (HISS) score integrates metabolic indicators (glucose, lactate, pH, potassium, pO2) that are directly measured using a minimally invasive biochip array - The Physiological Status Monitoring Biochip (PSM Biochip).

METHODS

Computational models of the five microlithographically fabricated sensor elements of the PSM Biochip were designed in both 2D and 3D using COMSOL Multiphysics v6.0 run on a PC. The biosensors were Microdisc Electrode Arrays (MDEA) for mediated enzyme-amperometric measurement of glucose or lactate. Potentiometric measurement of potassium used a Microdisc Electrode (MDE). Acidosis used a pH-responsive hydrogel on an Interdigitated Microsensor Electrode (IME) and an MDE was used for the voltametric measurement of pO₂.

RESULTS AND DISCUSSION

The biosensors, employing glucose oxidase and lactate oxidase, were validated in 0.1 MFcCO₂H and modeled as PPy/PPy⁺•PSS^- mediated enzyme-amperometric reactions of linked Hill and Butler-Volmer equations. The potentiometric response of the MDE K⁺ sensor was modeled using the Nikolsky–Eisenman equation. The impedimetric response of the IME sensor was validated in 0.1M [Fe(CN)6]^3-/4- and the pH responsive AEMA-hydrogel-IME was modeled as a cationic hydrogel by coupling the Langmuir availability of ionic states of the ionogen with the electrical charge given by Poisson’s equation across physiologic pH ranges. The MDE pO₂ sensor was modeled as a voltametric sensor using the microelectrode form of the Randles-Sevcik equation that was linked to a Langmuir adsorption of O₂ to nano-enabled Pt. The effect of overlapping electric fields and minimum feature size were examined to determine the smallest possible biochip footprint. All systems showed excellent agreement (p>0.05) with previously published sensor data.