Machine Learning-Assisted Pattern Recognition of Amyloid Beta Aggregates with Fluorescent Conjugated Polymers and Graphite Oxide Electrostatic Complexes

Five fluorescent positively charged poly( para -aryleneethynylene) ( P1 – P5 ) were designed to construct electrostatic complexes C1 – C5 with negatively charged graphene oxide ( GO ). The fluorescence of conjugated polymers was quenched by the quencher GO . Three electrostatic complexes were enough to distinguish between 12 proteins with 100% accuracy. Furthermore, using these sensor arrays, we could identify the levels of A β40 and Aβ42 aggregates (monomers, oligomers, and fibrils) via employing machine learning algorithms, making it an attractive strategy for early diagnosis of Alzheimer’s disease.