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Updated: Apr 13, 2026
Bacterial Detection & Identification Using Electrochemical Sensors
Published on: April 23, 2013
Identification of Microorganism in Infected Wounds by Positively Charged Selective Sensor Array and Deep Learning
Guoyang Zhang1, Yufan Ma1, Zirui Wang2
1State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China.
Abstract:
Microorganism are ubiquitous and intimately connected with human health and disease management. The accurate and fast identification of pathogenic microorganisms is especially important for diagnosing infections. Herein, three tetraphenylethylene derivatives (S-TDs: TBN, TPN, and TPI) featuring different cationic groups, charge numbers, emission wavelengths, and hydrophobicities were successfully synthesized. Benefiting from distinct cell wall binding properties, S-TDs were collectively utilized to create a sensor array capable of imaging various microorganisms through their characteristic fluorescent signatures. Furthermore, the interaction mechanism between S-TDs and different microorganisms was explored by calculating the binding energy between S-TDs and cell membrane/wall constituents, including phospholipid bilayer and peptidoglycan. Using a combination of the fluorescence sensor array and a deep learning model of residual network (ResNet), readily differentiation of Gram-negative bacteria (G-), Gram-positive bacteria (G+), fungi, and their mixtures was achieved. Specifically, by extensive training of two ResNet models with large quantities of images data from 14 kinds of microorganism stained with S-TDs, identification of microorganism was achieved at high-level accuracy: over 92.8% for both Gram species and antibiotic-resistant species, with 90.35% accuracy for the detection of mixed microorganism in infected wound. This novel method provides a rapid and accurate method for microbial classification, potentially aiding in the diagnosis and treatment of infectious diseases.
Insights
This study introduces a novel fluorescence sensor array using tetraphenylethylene derivatives for rapid microorganism identification. The sensor array, combined with deep learning, accurately differentiates bacteria, fungi, and mixed infections, aiding infectious disease diagnosis.
Area of Science:
- Microbiology
- Biochemistry
- Materials Science
Background:
- Microorganisms are crucial to human health and disease.
- Accurate and rapid identification of pathogens is vital for infection diagnosis.
- Existing methods for microorganism identification can be time-consuming or lack specificity.
Purpose of the Study:
- To develop a novel fluorescence sensor array for rapid and accurate microorganism identification.
- To investigate the interaction mechanism between synthesized tetraphenylethylene derivatives and microbial cell components.
- To utilize a deep learning model for classifying various microorganisms and their mixtures.
Main Methods:
- Synthesis of three tetraphenylethylene derivatives (S-TDs) with varying properties.
- Creation of a fluorescence sensor array utilizing S-TDs for microorganism imaging.
- Calculation of binding energies between S-TDs and microbial cell wall constituents.
- Application of a deep learning residual network (ResNet) model for image analysis and classification.
Main Results:
- The S-TDs sensor array successfully imaged diverse microorganisms via distinct fluorescent signatures.
- Binding energy calculations elucidated the interaction mechanisms between S-TDs and microbial cell components.
- The fluorescence sensor array combined with ResNet achieved high accuracy in differentiating Gram-negative bacteria, Gram-positive bacteria, and fungi.
- Accuracies exceeded 92.8% for Gram species and antibiotic-resistant species, and 90.35% for mixed microorganism detection in infected wounds.
Conclusions:
- A novel, rapid, and accurate method for microbial classification was developed using a fluorescence sensor array and deep learning.
- This approach holds significant potential for improving the diagnosis and treatment of infectious diseases.
- The study demonstrates the utility of tailored fluorescent probes and AI in microbial diagnostics.
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