Abstract
The gut-brain axis has recently emerged as a crucial link in the development and progression of Parkinson's disease (PD). Dysregulation of the gut microbiota has been implicated in the pathogenesis of this disease, sparking growing interest in the quest for non-invasive biomarkers derived from the gut for early PD diagnosis. Herein, an artificial intelligence-guided gut-microenvironment-triggered imaging sensor (Eu-MOF@Au-Aptmer) to achieve non-invasive, accurate screening for various stages of PD is presented. The sensor works by analyzing α-Syn in the gut using deep learning algorithms. By monitoring changes in α-Syn, the sensor can predict the onset of PD with high accuracy. This work has the potential to revolutionize the diagnosis and treatment of PD by allowing for early intervention and personalized treatment plans. Moreover, it exemplifies the promising prospects of integrating artificial intelligence (AI) and advanced sensors in the monitoring and prediction of a broad spectrum of diseases and health conditions.