Biomimetic Analysis of Neurotransmitters for Disease Diagnosis through Light-Driven Nanozyme Sensor Array and Machine Learning.

Abstract

Neurological diseases, including Alzheimer's disease, Parkinson's disease, and multiple sclerosis, pose a significant global health challenge due to their complex pathogenesis and widespread prevalence. These disorders are often associated with disruptions in neurotransmitter regulation, leading to progressive cognitive and motor impairments. Conventional diagnostic methods are time-consuming and lack the sensitivity required for early-stage detection. Herein, for the first time a novel photoresponsive nanozyme sensor array is presented that integrates metal-organic frameworks (MOFs) and machine learning algorithms for the rapid, sensitive, and multiplexed detection of neurotransmitters. Wherein, Zn(II) meso-Tetra(4-carboxyphenyl)porphine (ZnTCPP) -based MOFs, with their large specific surface area, enhance the interaction between reactant substrates and catalytic active sites within the material, significantly improving response sensitivity. Additionally, light-driven catalysis greatly accelerates the response speed of the nanozyme. Mimicking the mammalian olfactory system, the array responds to various neurotransmitters in a patterned manner, enabling accurate differentiation and quantification within minutes. It maintains high precision even in complex biological samples such as serum and cerebrospinal fluid. The biomimetic sensor can detect neurotransmitter signatures linked to neurological disorders, such as Alzheimer's disease. This platform offers significant potential for early diagnosis and continuous monitoring of neurological conditions.