Automated Electrical Detection of Proteins for Oral Squamous Cell Carcinoma in an Integrated Microfluidic Chip Using Multi-Frequency Impedance Cytometry and Machine Learning.
Journal:
Sensors (Basel, Switzerland)
PMID:
40096419
Abstract
Proteins can act as suitable biomarkers for the prognosis and diagnosis of certain conditions and can help us gain an understanding of the fundamental processes that occur inside an organism. In this work, we present a fully automated machine learning-assisted label-free method for the electrical detection of proteins in an integrated microfluidic chip using multi-frequency impedance cytometry and off-the-shelf components for realizing an automated and programmable fluid control system. We verify the robustness of our mixing method on our custom microfluidic mixer composed of polydimethylsiloxane (PDMS) serpentine channels optically using a fluorescent sandwich immunoassay and comparing the results with a commercial benchtop mixer. Salivary IL-6 is a biomarker for oral squamous cell carcinoma (OSCC), and we have demonstrated that our system can be used for the detection of quantification of Interleukin-6 (IL-6) levels in a solution using the impedance response of beads conjugated with the protein of interest, which passes through the microfluidic chip with reasonable accuracy (96%). Although we have demonstrated the detection and quantification of IL-6, our system can be adapted to any protein of interest with slight modification in the reagents and bead-binding protocols.