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Smell cancer by machine learning-assisted peptide/MXene bioelectronic array.

Jiawang Hu1, Nanlin Hu2, Donglei Pan1

  • 1Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China; Key Laboratory of Industrial Biocatalysis, Ministry of Education, Tsinghua University, Beijing, 100084, China.

Biosensors & Bioelectronics
|July 17, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a novel biosensor array for non-invasive tumor detection using exhaled gas analysis. Machine learning enhances the system

Keywords:
Machine learningMimetic biosensor arrayNon-invasive diagnosis of cancerPeptide/MXene biocompositeReal-time testing platform

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Area of Science:

  • Biomedical Engineering
  • Materials Science
  • Analytical Chemistry

Background:

  • Non-invasive tumor detection is crucial for early diagnosis and improved patient outcomes.
  • Analyzing exhaled breath for volatile organic compounds (VOCs) presents a promising, non-invasive diagnostic approach.
  • Challenges remain in developing sensitive and specific sensors for complex breath gas mixtures.

Purpose of the Study:

  • To develop advanced biosensors for enhanced gas-sensing characteristics.
  • To create a mimetic biosensor array (MBA) integrated with a real-time testing platform (RTP).
  • To utilize machine learning (ML) algorithms for accurate detection and identification of exhaled gas signals for tumor diagnosis.

Main Methods:

  • Fabrication of peptide-MXene self-assembled biosensors with specific gas-binding capabilities.
  • Construction of a mimetic biosensor array (MBA) and integration into a real-time testing platform (RTP).
  • Application of pattern recognition and machine learning algorithms for signal analysis and disease classification.

Main Results:

  • Peptide-MXene biosensors exhibited significantly enhanced gas-sensing performance (up to 150% greater response) compared to pristine MXene.
  • The MBA successfully identified 15 odor molecules across five categories (alcohols, ketones, aldehydes, esters, acids) using pattern recognition.
  • The ML-assisted RTP achieved high accuracies in detecting breath samples from healthy individuals (100%) and patients with lung (94.1%), upper digestive tract (90%), and lower digestive tract (95.2%) cancers.

Conclusions:

  • A cost-effective and precise model for non-invasive tumor diagnosis has been developed using peptide-MXene biosensors and ML.
  • The developed platform demonstrates versatility for diagnosing other conditions, including nephropathy and diabetes, through breath analysis.
  • This research offers a significant advancement in non-invasive disease diagnostics via exhaled gas analysis.