A Comprehensive Hardware Platform Leveraging Impedimetric nanoMIP Sensors for Fast Evaluation of Trypsin in Artificial Saliva

Pancreatic cancer is a leading cause of death worldwide, primarily due to late-stage diagnoses and limited treatment options. With the aim of introducing new screening strategies, research has intensified around trypsin, a promising biomarker exhibiting altered expression in pancreatic cancer patients. Conventional trypsin detection methods in biofluids show problems related to the need for trained personnel, bulky instrumentation, and high measurement time. The latter should be a key feature to accelerate the training of robust regression models with large datasets, gain prompt predictions in clinical environments, and reduce measurement errors due to intrinsic variations and drift of the sensors. Moreover, despite advancements in sensing technology, a gap remains in deploying high-performance sensors to point-of-care devices. This article addresses these issues by presenting a comprehensive hardware platform for fast, noninvasive trypsin evaluation in saliva aimed to pancreatic cancer screening. The key finding of the work regards the adoption of a fast electrochemical impedance spectroscopy (EIS) time-based read-out approach, applied to nanoMIP technology, to drastically reduce the detection time of trypsin in artificial saliva samples. The detection is possible thanks to the adoption of a neural network (NN). The root-mean-squared error (RMSE), obtained by testing the system with trypsin concentrations from 0 to 100 ng·mL-1 in artificial saliva, is equal to 5.9 and 5.4 for training and test, respectively. The results prove the efficacy of the system as a portable, user-friendly device for rapid trypsin evaluation.