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Related Experiment Video

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High-throughput point-of-care serum iron testing utilizing machine learning-assisted deep eutectic solvent

Hui Li1, Hengmao Yue2, Haixiang Li1

  • 1School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China; Tianjin Key Laboratory of Therapeutic Substance of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China; National Key Laboratory of Chinese Medicine Modernization, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China.

Journal of Colloid and Interface Science
|November 22, 2024
PubMed
Summary

A new high-throughput, point-of-care testing system uses machine learning and carbon quantum dots to rapidly detect trace iron ions in serum. This portable platform offers sensitive and reliable iron detection, crucial for health monitoring.

Keywords:
Carbon quantum dotsHigh throughput point-of-care testingHydrophobic deep eutectic solventIron ion detectionMachine learning

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Biomedical Engineering

Background:

  • Accurate serum iron detection is vital for diagnosing and monitoring various health conditions.
  • Existing methods for iron detection can be time-consuming, complex, and not suitable for point-of-care applications.
  • There is a need for rapid, sensitive, and portable systems for iron ion quantification.

Purpose of the Study:

  • To develop a high-throughput point-of-care testing (HT-POCT) system for serum iron detection.
  • To utilize a hydrophobic deep eutectic solvent (HDES) fluorescence detection platform integrated with machine learning.
  • To enable intelligent and rapid detection of trace iron ions.

Main Methods:

  • Synthesis of blue fluorescent hydrophobic carbon quantum dots (CQDs) with a 36.6% quantum yield via solvothermal method.
  • Integration of CQDs with specially filtered HDESs for enhanced iron ion concentration and fluorescence detection.
  • Development of a machine learning-assisted portable platform using the YOLOv8 algorithm for image analysis and iron concentration determination.
  • Density functional theory (DFT) modeling for understanding HDES synthesis and Fe3+ extraction principles.

Main Results:

  • The developed CQD-HDES system achieved a low limit of detection for Fe3+ ions as low as 33 nM.
  • The system demonstrated significant enhancement of fluorescence signals and reduced interference from hydrophilic substances.
  • The machine learning platform accurately analyzed multiple samples from fluorescence images, with Relative Standard Deviations (RSDs) below 10% for both single and multi-sample tests.
  • Observed fluorescence quenching and visible color changes with increasing Fe3+ concentration.

Conclusions:

  • The developed HT-POCT system offers a reliable, sensitive, and rapid method for detecting trace iron ions in serum.
  • The combination of CQDs, HDES, and machine learning provides an intelligent and portable solution for point-of-care diagnostics.
  • This platform has the potential to improve the diagnosis and management of iron-related health issues.