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Related Experiment Video

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

Artem Goncharov1, Hyou-Arm Joung1, Rajesh Ghosh2

  • 1Electrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.

Small (Weinheim an Der Bergstrasse, Germany)
|April 27, 2023
PubMed
Summary

A new point-of-care sensor quantifies three cardiac injury biomarkers using a paper-based assay and mobile reader. This rapid, low-cost diagnostic tool shows high accuracy, improving accessibility in resource-limited settings.

Keywords:
cardiac biomarkersconjugated polymer nanoparticlesdeep learningmultiplexed sensingneural networkspaper-based assayspoint-of-care sensingvertical flow assays

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Area of Science:

  • Biomedical Engineering
  • Point-of-Care Diagnostics
  • Biosensing Technologies

Background:

  • Acute cardiac injury diagnosis relies on timely biomarker quantification.
  • Current diagnostic methods can be time-consuming and require specialized equipment.
  • Multiplexed biomarker detection offers a more comprehensive assessment of cardiac status.

Purpose of the Study:

  • To develop and validate a multiplexed computational sensing platform for simultaneous quantification of three cardiac injury biomarkers.
  • To assess the performance of a paper-based fluorescence vertical flow assay (fxVFA) coupled with neural network-based inference.
  • To evaluate the potential of this point-of-care (POC) assay for resource-limited settings.

Main Methods:

  • Development of a paper-based fluorescence vertical flow assay (fxVFA) for multiplexed biomarker detection.
  • Utilized a low-cost mobile reader for data acquisition and analysis.
  • Employed trained neural networks for biomarker quantification from serum samples.
  • Validated the assay using human serum samples for myoglobin, creatine kinase-MB, and heart-type fatty acid binding protein.

Main Results:

  • Achieved a limit-of-detection below 0.52 ng mL⁻¹ for all three cardiac biomarkers with minimal cross-reactivity.
  • Demonstrated high correlation (>0.9 linearity) and low coefficient of variation (<15%) compared to ground truth concentrations.
  • Quantification was performed in under 15 minutes using only 50 µL of serum.
  • Blind testing on 46 cartridges confirmed reliable biomarker concentration measurements.

Conclusions:

  • The multiplexed computational fxVFA is a promising point-of-care diagnostic tool for acute cardiac injury.
  • Its paper-based design, low cost, and rapid results enhance accessibility to diagnostics.
  • This technology has significant potential for deployment in resource-limited healthcare settings.