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2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic

Zhijun Li1, Yizhou Jiang1, Shihuan Tang2

  • 1Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China.

Mikrochimica Acta
|July 6, 2022
PubMed
Summary

A novel sensing array with machine learning rapidly identifies pathogenic microorganisms, including drug-resistant strains, with 97.9% accuracy. This method bypasses the need for large datasets common in traditional microbial identification techniques.

Keywords:
Accurate recognitionMachine learning approachMolecular response differential profilingPathogenic microbial taxonomic

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Area of Science:

  • Biotechnology
  • Nanotechnology
  • Microbiology

Background:

  • Accurate and rapid identification of pathogenic microorganisms is crucial for effective treatment and public health.
  • Conventional methods often require extensive culturing or large datasets for pattern recognition, leading to delays and resource intensiveness.

Purpose of the Study:

  • To develop an integrated sensing array combined with machine learning for rapid and accurate taxonomic identification of pathogenic microorganisms.
  • To establish a molecular response differential profiling (MRDP) method that overcomes the limitations of big data dependence in microbial identification.

Main Methods:

  • Development of a custom cross-response sensing array utilizing two-dimensional nanomaterials (2D-n) and fluorescently labeled single-stranded DNA (ssDNA) as sensing elements.
  • Extraction of differential response profiles for each microorganism based on the competitive interaction between ssDNA, 2D-n, and the target microbe.
  • Application of a visible machine learning approach, specifically Linear Discriminant Analysis (LDA), to classify eight common pathogenic microorganisms based on generated fluorescent fingerprints.

Main Results:

  • The six-sensing array achieved a high overall accuracy of 97.9% in identifying pathogenic microorganisms, including drug-resistant strains.
  • The method demonstrated precise identification at low detection concentrations, ranging from 10^2 to 10^8 CFU/mL for various bacteria and fungi.
  • The developed MRDP approach successfully classified eight distinct microorganisms, showcasing its efficacy in microbial phenotyping.

Conclusions:

  • The integrated sensing array and machine learning approach provide a powerful strategy for precision pathogenic microbial taxonomic identification.
  • This novel method offers a rapid, accurate, and data-efficient alternative to conventional microbial identification techniques.
  • The MRDP strategy holds significant potential for clinical diagnostics and microbial surveillance applications.