Neural Network-Enhanced Electrochemical/SERS Dual-Mode Microfluidic Platform for Accurate Detection of Interleukin-6 in Diabetic Wound Exudates.

Journal: Analytical chemistry
PMID: 39985433

Abstract

Interleukin-6 (IL-6) plays a pivotal role in the inflammatory response of diabetic wounds, providing critical insights for clinicians in the development of personalized treatment strategies. However, the low concentration of IL-6 in biological samples, coupled with the presence of numerous interfering substances, poses a significant challenge for its rapid and accurate detection. Herein, we present a dual-mode microfluidic platform integrating electrochemical (EC) and surface-enhanced Raman spectroscopy (SERS) to achieve the timely and highly reliable quantification of IL-6. Efficient binding between IL-6 and antibody-conjugated SERS nanoprobes is obtained through a square-wave micromixer with nonleaky obstacles, forming sandwich immunocomplexes with IL-6 capture antibodies on the working electrode in the detection area, enabling acquisition of both EC and SERS signals. This microfluidic platform demonstrates excellent selectivity and sensitivity, with detection limits of 0.085 and 0.047 pg/mL for EC and SERS modes, respectively. Importantly, by incorporating a neural network (NN) with a self-attention (SA) mechanism to evaluate the relative weights of data from both modes, the platform achieves a quantitative accuracy of up to 99.8% across a range of 0.05-1000 pg/mL, demonstrating significant performance at low concentrations. Moreover, the NN-enhanced dual-mode microfluidic platform effectively detects IL-6 in diabetic wound exudates with results that align closely with clinical data. This integrated dual-mode microfluidic platform offers promising potential for the rapid and accurate detection of cytokines.

Authors

  • Mingrui Chen
    Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China.
  • Guan Liu
    Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China.
  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Amin Zhang
    Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China. zhangamin@sjtu.edu.cn.
  • Ziyang Yang
    Institute of Nano Biomedicine and Engineering, School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240, PR China.
  • Xia Li
    Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
  • Zhong Zhang
    School of Mechanical and Aerospace Engineering, Jilin University, Changchun, China.
  • Song Gu
    Department of Radiation Oncology, Hangzhou Yikang Chinese Medicine Oncology Hospital, Hangzhou, China.
  • Daxiang Cui
    Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Shanghai 200240, China. Electronic address: dxcui@sjtu.edu.cn.
  • Hossam Haick
    Department of Chemical Engineering and the Russell Berrie Nanotechnology Institute, Technion─Israel Institute of Technology, Haifa 3200003, Israel.
  • Ning Tang
    Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China.

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