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Deep Learning Algorithms Enabled Visual Detection of Anthrax Biomarkers by Mn3O4 Nanozyme-Based Colorimetric Sensor

Ziqian Gao1, Mengxuan Liu1, Lei Meng2

  • 1College of Materials Science and Engineering, Jilin University of Chemical Technology, Jilin 132022, China.

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|December 17, 2025
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This study introduces a colorimetric sensor array using nanozymes to detect the anthrax biomarker 2,6-pyridine dicarboxylic acid (2,6-PDA) and its analogs. Advanced algorithms enable rapid, accurate, and automated detection in complex samples.

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Area of Science:

  • * Nanomaterials and Sensor Technology
  • * Biomarker Detection and Chemical Analysis
  • * Artificial Intelligence in Diagnostics

Background:

  • * Accurate detection of the anthrax biomarker 2,6-pyridine dicarboxylic acid (2,6-PDA) is crucial for public health.
  • * Existing detection methods can be time-consuming and labor-intensive.
  • * Development of rapid, sensitive, and selective sensor systems is needed.

Purpose of the Study:

  • * To develop an innovative colorimetric sensor array (CSA) for detecting 2,6-PDA and its structural analogs.
  • * To integrate advanced algorithms, including deep learning, for automated analysis and classification.
  • * To enhance the speed, accuracy, and cost-effectiveness of biomarker detection.

Main Methods:

  • * Fabrication of a CSA using phenylalanine-modified Mn3O4 nanozymes with tunable oxidase-like activity.
  • * Catalysis of 3,3',5,5'-tetramethylbenzidine (TMB) oxidation by nanozymes, producing quantifiable color changes.
  • * Application of multivariate statistical analysis and the YOLOv8 deep learning algorithm for data interpretation and classification.

Main Results:

  • * The CSA successfully discriminated among 2,6-PDA and six structural analogs.
  • * Achieved a low detection limit (LOD) of 0.015 ± 0.002 μM for 2,6-PDA.
  • * The YOLOv8 algorithm demonstrated high performance (mAP 0.98-0.99) for automated detection and classification.

Conclusions:

  • * The developed CSA integrated with advanced algorithms offers a rapid and accurate method for 2,6-PDA detection.
  • * Automated analysis using YOLOv8 significantly reduces detection time and labor costs.
  • * This approach provides a robust platform for real-world applications in complex environments, such as biological fluid analysis.