Brain-derived neurotrophic factor electrochemical biosensors: A review
Year: 2026
Type: review
Abstract: • Emerging trends and mechanisms of electrochemical BDNF sensors are discussed. • Microelectrode-based sensors may reduce non-specific adsorption. • Sensors with strong redox probes perform exceptionally well with DPV. • Sensors face challenges on cost, selectivity, reusability, and anti-fouling. • Multiplexed detection and AI-based integration are promising future directions. Brain-derived neurotrophic factor (BDNF) is a critical neurotrophin governing neuronal growth, survival, and plasticity and its dysregulation is strongly associated with neurological, psychiatric, and cardiovascular disorders. Despite growing clinical interest in BDNF as a biomarker, its reliable quantification remains challenging due to low physiological concentrations, structural similarity to its precursor proBDNF, and interference from complex biological matrices. In response to these challenges, electrochemical biosensing has emerged as a promising strategy offering high sensitivity, scalability, and compatibility with point-of-care diagnostics. This review critically analyzes recent advances in electrochemical BDNF sensors, with a specific focus on how bio-recognition elements—including antibodies, aptamers, and molecularly imprinted polymers—are integrated with electrochemical transduction techniques such as electrochemical impedance spectroscopy and voltammetry-based sensing. We provide a comparative evaluation of functional nanomaterials, including gold nanoparticles, nitrogen-doped graphene, and carbon nanotube architectures, highlighting their roles in enhancing signal amplification, selectivity, and detection limits. Importantly, this review synthesizes emerging evidence on unresolved limitations, particularly cross-reactivity with proBDNF, matrix effects in real biological samples, and long-term sensor stability. Finally, we outline future design directions toward reusable, multiplexed, and clinically relevant and deployable platforms, and discuss how AI-assisted signal processing and data-driven calibration strategies may further improve analytical robustness. By synthesizing performance trends, material strategies, and translational challenges, this review offers a unified framework to guide the development of next-generation electrochemical biosensors for BDNF detection.
Source: Next research.
Authors: Arvan Kayal, Matiar M.R. Howlader
Institution: McMaster University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,500
OpenAlex ID: W7155704511