ArticleACS omega2025
Fabrication of MIP-based sensors for femtomolar detection of kynurenic acid for early diagnosis of neurodegenerative diseases.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Neurodegenerative diseases (NDDs), among which Alzheimer's disease (AD) is one of the most significant medical and societal challenges of this century due to its increasing prevalence, require early diagnosis through reliable biomarkers to improve disease management. Among several biomarkers, kynurenic acid (KYNA) has emerged as a newly found metabolite, identified as a sensitive and selective blood-based biomarker, with its levels increasing in the early stages of AD. In this study, we aim to develop a low detection limit, stable, low-cost, and reliable molecularly imprinted polymer-based electrochemical biosensor for the rapid, selective, and sensitive detection of KYNA, a promising biomarker for AD. For this purpose, the glassy carbon electrode was first modified with copper-silver bimetallic structures (Cu-Ag BS). Then, a 3,4-ethylene-dioxythiophene (EDOT) monomer was electropolymerized on the Cu-Ag BS/GCE in the presence of the KYNA analyte. The introduced sensor was characterized through field emission scanning electron microscopy (FESEM), atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), differential pulse voltammetry (DPV), electrochemical impedance spectroscopy (EIS), and cyclic voltammetry (CV). The effect of electrodeposition parameters was optimized. This is the first time the proposed MIP sensor allowed KYNA detection in a wide linear range of 1.0 fM-500 nM with a limit of detection (LOD) of 0.278 fM. Furthermore, the MIP layer provides highly selective binding by forming KYNA-specific recognition cavities. Notably, the sensor has been successfully validated in complex biological media, including fetal bovine serum and human serum, achieving high recovery values. The proposed sensor could potentially be utilized in the future design of a diagnostic kit for the early diagnosis of neurodegenerative diseases.
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