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Related Concept Videos

Microbial Biosensors01:17

Microbial Biosensors

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Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...
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Rapid Homogeneous Detection of Biological Assays Using Magnetic Modulation Biosensing System
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Detection of carcinoembryonic antigen specificity using microwave biosensor with machine learning.

Yajuan Lei1, Dongjie Zhang2, Qingzhou Wang1

  • 1College of Electronics and Information, Qingdao University, Qingdao, 266071, China.

Biosensors & Bioelectronics
|November 16, 2024
PubMed
Summary

A novel microwave biosensor utilizing a split-ring resonator detects Carcinoembryonic Antigen (CEA) at low concentrations. This technology, combined with machine learning, offers a promising tool for early cancer diagnosis and monitoring tumor recurrence.

Keywords:
CEA detectionMachine learningMicrowave biosensorTumor biomarker

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Area of Science:

  • Biomedical Engineering
  • Biosensing Technology
  • Cancer Diagnostics

Background:

  • Early cancer diagnosis and tumor marker screening are crucial for effective treatment and improved patient prognosis.
  • Accurate detection of tumor markers like Carcinoembryonic Antigen (CEA) aids in monitoring recurrence and metastasis.

Purpose of the Study:

  • To develop and validate a novel microwave biosensor for sensitive and rapid detection of Carcinoembryonic Antigen (CEA).
  • To integrate machine learning for enhanced prediction of CEA concentration in biological samples.
  • To establish the biosensor's reliability at the cellular level for auxiliary cancer diagnosis.

Main Methods:

  • Fabrication of a split-ring resonator (SRR) based interdigital electrode microwave biosensor.
  • Utilizing the biosensor to detect varying concentrations of CEA by analyzing generated microwave frequencies.
  • Applying machine learning algorithms to predict CEA levels in blood samples and validating results with Western Blot (WB).

Main Results:

  • The biosensor demonstrated excellent resonance linearity (R² = 0.999) and high sensitivity (27.5 MHz/(ng/mL)) for CEA detection.
  • Achieved a very low limit of detection for CEA (39 pg/mL).
  • Machine learning predictions closely matched sensor-detected CEA concentrations, and biosensor results agreed with WB validation at the cellular level.

Conclusions:

  • The developed microwave biosensor coupled with machine learning provides a highly sensitive and reliable method for detecting low concentrations of CEA.
  • This technology represents a significant advancement in auxiliary cancer diagnostic tools, enabling convenient and rapid tumor marker detection.
  • The study is the first to validate biosensor reliability at the cellular level, highlighting its potential clinical implications.