Novel Computational Memristive Bio-detection for CRP Inflammatory Biomarker and PSA Detection

During the last years, memristive computational modelling has been gaining more fame for outlining memristive dynamics and behaviour. Moreover, their capability to link biological processes to their electrical properties explains the emergent need for memristive biosensing simulation models. In the last decades, several research studies have been reported, aiming at a better understanding of the memristive bio- sensing mechanisms through equivalent circuits, as well as multiphysics dynamics allowing for advanced pre-fabrication structural and performance optimization. In our research work, we develop a new methodology for modelling memristive biosensors within COMSOL Multiphysics and we demonstrate our model for C-reactive Protein (CRP), an inflammatory biomarker, and Prostate Specific Antigen (PSA), one of the main biomarkers for Prostate Cancer, as cases of study. A series of simulations are conducted to explore the model's multiphysic dynamics. The variation of different inputs such the electro-geometric material properties, the inlet concentration, and the association-dissociation coefficients, is employed to examine their impact on the memristive bio-sensors output resistive state (RS) and antibody-antigen complex binding. Equally important, this study frameworks the feasibility of simulated data's extraction and mapping for artificial intelligence algorithm deployment. Specifically, the binding concentration of the complex CRP-anti CRP is proved in this work to effectively train a support vector machine classifier (SVMC) for data categorization, achieving a notable accuracy rate of 97%. Concluding, empowering computational memristive biosensing models with artificial intelligence (AI) algorithms, improves various sides of the detection method including, data quality, noise-to-signal ratio, output data reading and labelling, which guarantees an advanced, cost-effective bio-signal recognition with reduced noise, improved specificity and clear read-out. Concisely, this approach introduces a proof-of-concept for machine learning integration with simulated memristive bio-sensing paving the way for validating our model with existing experimental paradigms for label-free prostate cancer and inflammatory biomarkers continuous diagnosis.