Deep Learning-Enhanced Hand-Driven Microfluidic Chip for Multiplexed Nucleic Acid Detection Based on RPA/CRISPR.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Published Date: Mar 31, 2025

Abstract

The early detection of high-risk human papillomavirus (HR-HPV) is crucial for the assessment and improvement of prognosis in cervical cancer. However, existing PCR-based screening methods suffer from inadequate accessibility, which dampens the enthusiasm for screening among grassroots populations, especially in resource-limited areas, and contributes to the persistently high mortality rate of cervical cancer. Here, a portable system is proposed for multiplexed nucleic acid detection, termed R-CHIP, that integrates Recombinase polymerase amplification (RPA), CRISPR detection, Hand-driven microfluidics, and an artificial Intelligence Platform. The system can go from sample pre-processing to results readout in less than an hour with simple manual operation. Optimized for sensitivity of 10 M for HPV-16 and 10 M for HPV-18, R-CHIP has an accuracy of over 95% in 300 tests on clinical samples. In addition, a smartphone microimaging system combined with the ResNet-18 deep learning model is used to improve the readout efficiency and convenience of the detection system, with initial prediction accuracies of 96.0% and 98.0% for HPV-16 and HPV-18, respectively. R-CHIP, as a user-friendly and intelligent detection platform, has great potential for community-level HR-HPV screening in resource-constrained settings, and contributes to the prevention and early diagnosis of other diseases.

Authors

  • Tao Xu
    Department of Urology, Peking University People's Hospital, Beijing, China.
  • Ying Zhang
    Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
  • Shunji Li
    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.
  • Chenxi Dai
    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.
  • Hongguo Wei
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
  • Dongjuan Chen
    Department of Laboratory Medicine, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Yunjun Zhao
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
  • He Liu
    Division of Endodontics, Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC, Canada.
  • Deliang Li
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, 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.
  • Ye Tian
    State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, 361102, China.

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