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Printed Electrochemical Strip for the Detection of MiRNA-29a: A Possible Biomarker Related to Alzheimer's Disease

Overview
Journal Anal Chem
Specialty Chemistry
Date 2022 Nov 1
PMID 36318963
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Abstract

The development of electrochemical strips, as extremely powerful diagnostic tools, has received much attention in the field of sensor analysis and, in particular, the detection of nucleic acids in complex matrixes is a hot topic in the electroanalytical area, especially when directed toward the development of emerging technologies, for the purpose of facilitating personal healthcare. One of the major diseases for which early diagnosis is crucial is represented by Alzheimer's disease (AD). AD is a progressive neurodegenerative disease, and it is the most common cause of dementia worldwide. In this context microRNAs (miRNAs), which are small noncoding RNAs, have recently been highlighted for their promising role as biomarkers for early diagnosis. In particular, miRNA-29 represents a class of miRNAs known to regulate pathogenesis of AD. In this work we developed an electrochemical printed strip for the detection of miRNA-29a at low levels. The architecture was characterized by the presence of gold nanoparticles (AuNPs) and an anti-miRNA-29a probe labeled with a redox mediator. The novel analytical tool has been characterized with microscale thermophoresis and electrochemical methods, and it has been optimized by selection of the most appropriate probe density to detect low target concentration. The present tool was capable to detect miRNA-29a both in standard solution and in serum, respectively, down to 0.15 and 0.2 nM. The platform highlighted good repeatability (calculated as the relative standard deviation) of ca. 10% and satisfactory selectivity in the presence of interfering species. This work has the objective to open a way for the study and possible early diagnosis of a physically and socially devastating disease such as Alzheimer's. The results demonstrate the suitability of this approach in terms of ease of use, time of production, sensitivity, and applicability.

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