Deep Learning-Enabled Multiplexed Point-of-Care Sensor using a Paper-Based Fluorescence Vertical Flow Assay.

Journal: Small (Weinheim an der Bergstrasse, Germany)
PMID: 37104829

Abstract

Multiplexed computational sensing with a point-of-care serodiagnosis assay to simultaneously quantify three biomarkers of acute cardiac injury is demonstrated. This point-of-care sensor includes a paper-based fluorescence vertical flow assay (fxVFA) processed by a low-cost mobile reader, which quantifies the target biomarkers through trained neural networks, all within <15 min of test time using 50 µL of serum sample per patient. This fxVFA platform is validated using human serum samples to quantify three cardiac biomarkers, i.e., myoglobin, creatine kinase-MB, and heart-type fatty acid binding protein, achieving less than 0.52 ng mL limit-of-detection for all three biomarkers with minimal cross-reactivity. Biomarker concentration quantification using the fxVFA that is coupled to neural network-based inference is blindly tested using 46 individually activated cartridges, which shows a high correlation with the ground truth concentrations for all three biomarkers achieving >0.9 linearity and <15% coefficient of variation. The competitive performance of this multiplexed computational fxVFA along with its inexpensive paper-based design and handheld footprint makes it a promising point-of-care sensor platform that can expand access to diagnostics in resource-limited settings.

Authors

  • Artem Goncharov
    Electrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Hyou-Arm Joung
    Department of Electrical & Computer Engineering , University of California , Los Angeles , California 90025 , United States.
  • Rajesh Ghosh
    Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Gyeo-Re Han
    Electrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Zachary S Ballard
    Electrical Engineering Department, ‡Bioengineering Department, and §California NanoSystems Institute (CNSI), University of California , Los Angeles, California 90095, United States.
  • Quinn Maloney
    Electrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Alexandra Bell
    Chemistry & Biochemistry Department, University of California, Los Angeles, CA, 90095, USA.
  • Chew Tin Zar Aung
    Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, 90095, USA.
  • Omai B Garner
    Department of Pathology and Laboratory Medicine , University of California , Los Angeles , California 90025 , United States.
  • Dino Di Carlo
    Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

Keywords

External Resources

Popular Topics
Recent Journals