Seafood is an emerging health food, and interest in improving the quality of seafood is increasing. Saxitoxin (STX) is a neurotoxin produced by marine dinoflagellates that is accumulated in seafood. It can block the neuronal transmission between nerves and muscle cell membranes, resulting in the disturbance of neuromuscular transmission and subsequent voluntary muscle paralysis. Here, we developed a new aptamer for the detection of STX using graphene oxide-systematic evolution of ligands by exponential enrichment (GO-SELEX). Furthermore, we confirmed sensitivity and selectivity of the developed aptamer specific to STX using a localized surface plasmon resonance (LSPR) sensor. The sensing chip was fabricated by fixing the new STX aptamer immobilized on the gold nanorod (GNR) substrate. The STX LSPR aptasensor showed a broad, linear detection range from 5 to 10,000 μg/L, with a limit of detection (LOD) of 2.46 μg/L (3σ). Moreover, it was suitable for the detection of STX (10, 100, and 2000 μg/L) in spiked mussel samples and showed a good recovery rate (96.13-116.05%). The results demonstrated that the new STX aptamer-modified GNR chip was sufficiently sensitive and selective to detect STX and can be applied to real samples as well. This LSPR aptasensor is a simple, label-free, cost-effective sensing system with a wide detectable range.
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http://dx.doi.org/10.3390/toxins11050274 | DOI Listing |
J Hand Surg Am
January 2025
Division of Plastic and Reconstructive Surgery, Department of Surgery, University of Florida, Gainesville, FL.
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January 2025
Universidad Austral de Chile, Instituto de Ciencias Químicas, CHILE.
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View Article and Find Full Text PDFJ Sci Food Agric
January 2025
College of Food Science and Technology, Bohai University, Jinzhou, China.
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Center for Experimental Chemistry Education of Shandong University, School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China.
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View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Electrical and Computer Engineering, Iowa State University of Science and Technology, Ames, IA 50011, USA.
Reconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in RIS-assisted networks. Integrating LiDAR sensors into the network will be instrumental, offering high-speed and precise 3D mapping capabilities, even in low light or adverse weather conditions.
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