Artificial intelligence enhanced microfluidic system for multiplexed point-of-care-testing of biological thiols.

Journal: Talanta
PMID: 39884122

Abstract

Cysteamine (CA) serves as a cystine-depleting agent employed in the management of cystinosis and a range of other medical conditions. Monitoring blood CA levels at the point of care is imperative due to the risk of toxicity associated with elevated CA dosages. An additional significant challenge is presented by the intricate composition of human plasma and the presence of various interfering biological thiols, which possess similar structures or properties. Here, this work proposes an AI-enhanced Lab-on-a-disc system, also termed AI-LOAD, for multiplexed point-of-care testing of cysteamine. The AI-LOAD system incorporates an online whole blood separation mechanism alongside a naked-eye colorimetric detection module, facilitating the rapid and precise visual identification of cysteamine. Remarkably, the system necessitates only 40 μL of whole blood to analyze eight samples within 3-min, achieving a limit of detection as low as 10 μM, which is lower than the physiological toxic concentration of 0.1 mM. By leveraging diverse colorimetric responses generated through interactions between gold nanoparticles of varying sizes and different biological thiols, combined with artificial intelligence methodologies, the system successfully accomplished specific recognition of various biological thiols with 100 % accuracy. The proposed AI-LOAD will drive advancements in centrifugal microfluidics for point-of-care testing, thereby holding potential for broader applications in future biomedical research and in vitro diagnosis.

Authors

  • Siyu Gao
    The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Jingjing Wang
    Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, China; School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, China.
  • Zeyu Miao
    The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Xudong Zhao
  • Ying Zhang
    Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
  • Wei Du
    Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Xiaojun Feng
    The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics and Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Yiwei Li
    New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
  • Jinzhi Liu
    Department of Gerontology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, China.
  • Peng Chen
  • Bi-Feng Liu
    The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics and Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.

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