Tick-Borne Encephalitis Electrochemical Detection by Multilayer Perceptron on Liquid-Metal Interface

Area of Science:

• Electrochemistry
• Materials Science
• Biosensing

Background:

• Tick-borne encephalitis (TBE) virus poses a significant public health threat.
• Current diagnostic methods for TBE virus can be complex and time-consuming.
• Development of rapid and accurate detection methods is crucial.

Purpose of the Study:

• To develop a novel electrochemical interface for TBE virus detection.
• To utilize machine learning for accurate identification of TBE virus components.
• To establish a convenient method for pathogen detection.

Main Methods:

• Fabrication of an electrochemical interface using a hydrogel-eutectic gallium indium alloy.
• Recording nonlinear current-voltage responses based on hydrogel composition.
• Training a multilayer perceptron machine learning model with current-voltage data.

Main Results:

• The electrochemical interface demonstrated nonlinear current-voltage responses.
• The machine learning model achieved 93% accuracy in recognizing TBE antibodies, antigens, and complexes.
• The model successfully distinguished TBE components from interfering substances like bovine serum albumin.

Conclusions:

• The developed electrochemical hydrogel-eutectic gallium indium alloy interface is effective for TBE virus detection.
• Machine learning integration enables high-accuracy identification of viral components.
• This approach offers a convenient and accurate method for detecting viruses and pathogens.