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

Journal: Analytical chemistry
PMID: 35084168

Abstract

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

Authors

  • Hao Wang
    Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Mingqi Chen
    State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, College of Engineering, China Pharmaceutical University, Nanjing 211109, China.
  • Yimin Sun
    School of Pharmacy, China Pharmaceutical University, Nanjing 211109, China.
  • Lian Xu
    State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, College of Engineering, China Pharmaceutical University, Nanjing 211109, China.
  • Fei Li
    Institute for Precision Medicine, Tsinghua University, Beijing, China.
  • Jinsong Han
    State Key Laboratory of Natural Medicines and National R&D Center for Chinese Herbal Medicine Processing, College of Engineering, China Pharmaceutical University, Nanjing 211109, China.

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