Review—Electrochemical Immunosensors for Depression Markers Detection: Development in Recent Years

Abstract

Depression is a severe mental disorder which faces a challengeable lack of objective diagnosis as well as early screening and predicting treatment responses. Consequently, developing sensitive, efficient, convenient, accurate, and real-time detection technology for depression markers deserves more attention. Electrochemical immunosensors, as a promising method for analyzing disease markers, selectively bind the target antigen with antibodies, converting the biological signal of the antigen-antibody recognition reaction into an electrical signal related to the concentration of the target analyte. This method has thrived in recent years in areas such as point-of-care diagnosis. Here we systematically review the construction of electrochemical immunosensors for the detection of biomarkers of depression over a recent six year period (2016-2022), including the classification of these sensors, commonly used electrode modification materials, and signal optimization strategies. Finally, the challenges and future development of electrochemical immunosensors in clinical applications are prospected.

Enhancing Depression Predictive Models: a Comparative Study of Hybrid Ai, Machine Learning and Deep Learning Techniques

“…Their research advances our knowledge of the physiological indicators of depression and presents a fresh method for tracking treatment results and mental wellness.Using deep learning approaches, Mandava, Vinta, Ghosh, and [28] concentrated on the identi cation and classi cation of yellow rust infection in wheat. This study demonstrates how AI may be used to ght plant diseases and advance sustainable agriculture by using disease management techniques and precision diagnostics.A thorough assessment of the developments in electrochemical immunosensors for the detection of depression indicators was given by Guo, Chen, Mo, Wei, Li, Jia, Hu, and Du (2024) [29], addressing the crucial requirement for objective diagnosis and early treatment evaluation in mental health care. In addition to covering sensor classi cation, electrode modi cation materials, and signal optimisation from 2016 to 2022, the evaluation highlights the sensor's capacity to translate antigen-antibody responses into quanti able electrical signals.…”