Quantum machine learning-based electrokinetic mining for the identification of nanoparticles and exosomes with minimal training data.

Journal: Bioactive materials
Published Date: May 21, 2025

Abstract

Synthetic and naturally occurring particles, such as nanoparticles (NPs) and exosomes; a type of extracellular vesicles (EVs), have garnered widespread attention across various fields, including biomaterials, oncology, and delivery systems for drugs and vaccines. Traditional methods for identifying NPs and EVs, such as transmission electron microscopy, are often prohibitively expensive and labor-intensive. As an alternative, the assessment of electrokinetic attributes such as zeta potential or electrophoretic mobility, conductance, and mean count rate, offers a more cost-effective, rapid, and reliable means of characterizing these particles. In this context, we introduce the first application of a quantum machine learning (QML)-based electrokinetic mining for the identification of green-synthesized iron- and cobalt-based NPs, as well as exosomes derived from human embryonic stem cells (hESC), human lung cancer (A549) cells, and colorectal cancer (CRC) cells, based solely on their electrokinetic attributes. Comparative analyses involving cross-validation, train-test splits, confusion matrices, and Receiver Operating Characteristic (ROC) curves revealed that classical ML techniques could accurately identify the types of NPs and EVs. Notably, QML demonstrated proficiency in differentiating between various NPs and EVs, including the distinction of EVs in the plasma of CRC patients versus those of healthy individuals. Furthermore, QML's application has been extended to the identification of NPs along with EVs in the plasma of CRC patients and experimental mice, achieving higher prediction performance even with a minimal training dataset, demonstrating that QML based electrokinetic mining could identify NPs or EVs with minimal training data, thereby facilitating novel clinical development in the realm of liquid biopsies.

Authors

  • Abhimanyu Thakur
    Department of Biomedical Sciences, City University of Hong Kong, Kowloon Tong, Hong, Hong Kong.
  • Pedro Correia Santos Bezerra
    The University of Chicago Booth School of Business, Chicago, IL, USA.
  • Abhishek
    Department of Analytics, Northeastern University, Boston, MA, USA.
  • Shihao Zeng
  • Kui Zhang
    Department of Neurology, Mudanjiang Second People's Hospital, Mudanjiang 157013, Heilongjiang, China.
  • Werner Treptow
    Laboratório de Biologia Teórica e Computacional (LBTC), Universidade de Brasília DF, Brasília, Brazil. treptow@unb.br.
  • Alexander Luna
    Section of Gastroenterology, Hepatology and Nutrition, Department of Medicine, The University of Chicago, Chicago, IL, USA.
  • Urszula Dougherty
    Section of Gastroenterology, Hepatology and Nutrition, Department of Medicine, The University of Chicago, Chicago, IL, USA.
  • Akushika Kwesi
    Section of Gastroenterology, Hepatology and Nutrition, Department of Medicine, The University of Chicago, Chicago, IL, USA.
  • Isabella R Huang
    The Laboratory Schools, The University of Chicago, Chicago, IL, USA.
  • Christine Bestvina
    Department of Medicine, Section of Hematology/Oncology, The University of Chicago, Chicago, IL, USA.
  • Marina Chiara Garassino
    Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA.
  • Fuyu Duan
    Pritzker School of Molecular Engineering, University of Chicago, Illinois, USA.
  • Yash Gokhale
    Pritzker School of Molecular Engineering, University of Chicago, Illinois, USA.
  • Bin Duan
    Mary & Dick Holland Regenerative Medicine Program and Division of Cardiology, Department of Internal Medicine, University of Nebraska Medical Center, Omaha, NE, USA.
  • Yin Chen
    The Byoryn Technology Co., Ltd, Shenzhen, 518122, China.
  • Qizhou Lian
    Faculty of Synthetic Biology, Shenzhen University of Advanced Technology, Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Marc Bissonnette
    Section of Gastroenterology, Hepatology and Nutrition, Department of Medicine, The University of Chicago, Chicago, IL, USA.
  • Jianpan Huang
    Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China.
  • Huanhuan Joyce Chen
    Pritzker School of Molecular Engineering, University of Chicago, Illinois, USA.

Keywords

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