Ultrasensitive detection of vitamin E by signal conversion combined with core-satellite structure-based plasmon coupling effect.

The rapid and sensitive surface-enhanced Raman scattering (SERS) detection of molecular biomarkers from real samples is still a challenge because the intrinsically trace analytes may have a low molecular affinity for metal surfaces. Herein, we develop a smart signal conversion and amplification strategy based on silver-gold-silica core-satellite structure nanoparticles (Ag@Au@SiO2 NPs) to sensitively detect low adsorptive vitamin E using SERS, which has been considered a biomarker of neuromuscular disorders when its abnormal content is measured in the serum of patients. Through the reducibility of vitamin E, Ag+ ions are rapidly reduced to Ag atoms, resulting in the epitaxial growth of Ag nanocrystals on gold nanoparticles forming satellite particle-particle gap-narrowed Ag@Au@SiO2 NPs. The generated strong plasmonic field dramatically enhances the Raman signal of the Raman reporter molecule 4-aminothiophenol (4-ATP) and the detected vitamin E molecules at an estimated level of 58.19 nmol L-1. The sensitivity of this operational SERS strategy provides tremendous prospects for the screening of neuromuscular disorders.

The forward problem of electroarthrography: modeling load-induced electrical potentials at the surface of the knee.

Electroarthrography (EAG) is a novel technology recently proposed to detect cartilage degradation. EAG consists of recording electrical potentials on the knee surface while the joint is undergoing compressive loading. Previous results show that these signals originating from streaming potentials in the cartilage reflect joint cartilage health. The aim of this study is to contribute to the understanding of the generation of the EAG signals and to the development of interpretation criteria using computer models of the human knee. The knee is modeled as a volume conductor composed of different regions characterized by specific electrical conductivities. The source of the EAG signal is the load-induced interstitial fluid flow that transports ions within the compressed cartilage. It is modeled as an impressed current density in different sections of the articular cartilage. The finite-element method is used to compute the potential distribution in two knee models with a realistic geometry. The simulated potential distributions correlate very well with previously measured potential values, which further supports the hypothesis that the EAG signals originate from compressed cartilage. Also, different localized cartilage defects simulated as a reduced impressed current density produce specific potential distributions that may be used to detect and localize cartilage degradation. In conclusion, given the structural and electrophysiological complexity of the knee, computer modeling constitutes an important tool to improve our understanding of the generation of EAG signals and of the various factors that affect the EAG signals so as to help develop the EAG technology as a useful clinical tool.