Electrical Impedance Myography (EIM) is a noninvasive neurophysiologic technique to diagnose muscle health. Besides muscle properties, the EIM measurements vary significantly with the change of some other anatomic and nonanatomic factors such as skin fat thickness, shape and thickness of muscle, and electrode size and spacing due to its noninvasive nature of measurement. In this study, genetic algorithm was applied along with finite element model of EIM as an optimization tool in order to figure out an optimized EIM electrode setup, which is less affected by these factors, specifically muscle thickness variation, but does not compromise EIM's ability to detect muscle diseases. The results obtained suggest that a particular arrangement of electrodes and minimization of electrode surface area to its practical limit can overcome the effect of undesired factors on EIM parameters to a larger extent.
Electrical Impedance Myography (ElM) is a neurophysiologic technique in which high-frequency, low intensity electrical current is applied via surface electrodes over a muscle or muscle group of interest and the resulting electrical parameters (resistance, reactance and phase) are analyzed to isolate diseased muscles from healthy one. Beside muscle properties, some other factors like subcutaneous fat (SF) thickness, inter-electrode distance, muscle thickness etc. also impact the major ElM parameters. The purpose of this study is to explore the effect of SF thickness variation on different ElM parameters and propose a parameter which is least affected and also can detect muscle conditions. We analyzed four different parameters in this study for various SF thicknesses and none of them possesses constant profile with alteration in SF thickness.For example, resistance in normal condition varies 24.48% with per millimeter SF thickness variation while phase varies 4.01%.Further investigation shows that among the observed parameters percentage changes in reactance is minimum with fat thickness variation while effectively identifying different muscle conditions.
Traditional molecular techniques for SARS-CoV-2 viral detection are time-consuming and can exhibit a high probability of false negatives. In this work, we present a computational study of SARS-CoV-2 detection using plasmonic gold nanoparticles. The resonance wavelength of a SARS-CoV-2 virus was recently estimated to be in the near-infrared region. By engineering gold nanospheres to specifically bind with the outer surface of the SARS-CoV-2 virus, the resonance frequency can be shifted to the visible range (380 nm -700 nm). Moreover, we show that broadband absorption will emerge in the visible spectrum when the virus is partially covered with gold nanoparticles at a specific coverage percentage. This broadband absorption can be used to guide the development of an efficient and accurate colorimetric plasmon sensor for COVID-19 detection. Our observation also suggests that this technique is unaffected by the number of protein spikes present on the virus outer surface, hence can pave a potential path for a label-free COVID-19 diagnostic tool independent of the number of protein spikes.
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