Impedimetric Sensor Aided by Polydopamine Nanoparticles for Label-Free Detection of Breast Cancer Cells - A Review

Abstract

Integrating an impedimetric sensor with polydopamine nanoparticles presents a novel approach for detecting breast cancer cells without labels. This innovative technique is significant for painless and sensitive early identification of cancerous cells. However, challenges, such as precise characterization and functionalization of polydopamine nanoparticles, may impact the sensor's sensitivity. This review paper suggests Real-Time Enhanced Surface Functionalization (R-TESF), utilizing polydopamine nanoparticles to enhance the sensor's specificity. Impedance data is processed with machine learning and analytical models to accurately distinguish cancer cell signatures from background noise. The method leverages polydopamine's specificity to proteins associated with breast cancer, making it a powerful tool for early detection. Simulation analysis validates and refines the proposed method, providing insights into its performance across various variables and scenarios. Beyond breast cancer diagnostics, the integration of experimental and computational methods showcased in this study has the potential to transform cancer research.