IEEE Transactions on NanoBioscience最新文献

Skin health monitoring focuses on identifying diseases through the assessment of the mechanical properties of the skin. These properties may degrade with time, which can alter the skin's natural frequencies and the form of the modes associated with those frequencies. Exploring the skin's mechanical properties can enhance our understanding of its dynamics, improving clinical trials and diagnostics. In this work, the dynamics of the skin were measured using a laser-based non-invasive optical sensor experiment. We measured the skin's mechanical properties over time by analyzing its resonant frequencies and mode shapes. A nanocarrier gel and ketoconazole cream were topically applied to keep the skin hydrated and facilitate deeper penetration of the additives in the skin. Time-based research was used to assess the effect of different formulations on skin elasticity. Experimental results for the modulus of elasticity were compared with those obtained using Finite Element Analysis (FEA) simulations. We observed a reduction in frequencies of cream and gel-treated skin by 29.98% and 44.029% respectively compared to normal skin (frequency: 263.3 ± 1.18 Hz and Modulus of elasticity: 7.56 ± 2.60 MPa). A decrease in stiffness (function of frequency) attributed to increased water content, was observed in cream- and nanocarrier gel-treated skin compared to normal skin. Experimental and numerical results are found to be consistent with one another. This optical sensor-based approach has the potential for studying diseased skin mechanics and its response to gel and cream treatments, aiming to reduce skin disorder morbidity and severity.

The purpose of this study was to investigate the effects of two different types of gold nanoparticles (AuNPs) delivered by intraperitoneal (IP) injection on blood and kidney tissue changes in a mouse model. Three groups of fifteen adult male BALB/c healthy mice, weighing approximately 25-30 g, were used for the experiment and designated G1, G2, and G3, respectively. G1 mice received vehicle, whereas G2 and G3 received an IP injection of 10 mg/kg body weight of methoxy poly ethylene glycol gold nanoparticles (PEG-AuNPs) and fluorescently dye labeled gold nanoparticles (Dye-AuNPs), respectively. Hematological parameters were measured based on the standard complete blood cell count (CBC) technique. The two nanoparticles, i.e., PEG-AuNPs and Dye-AuNPs, significantly reduced most red blood cell (RBC) parameters in the groups with the exception of a nonsignificant effect on hemoglobin (HBG) levels. Both gold nanoparticles, i.e., PEG-AuNPs and Dye-AuNPs, led to a reduced RBC count, mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH) level when compared with the control. Notably, Dye-AuNPs and PEG-AuNPs resulted in a considerably higher RBC distribution RDW-(CV % and SD fL). Glomerular injury was suggested based on the development of hydropic degeneration and the presence of a protein-rich fluid inside the tubules. Renal tissue and blood indices changed significantly in response to the two nanoparticles, suggesting possible organ injury.

Through this paper, a three-dimensional molecular communication (MC) inside a cuboid container is considered. Instead of normal diffusion phenomenon, the anomalous diffusion phenomenon is incorporated which enhances the practicability of the model. The Fick's law is re-defined for the considering rectangular coordinate system in which information carrying molecules (ICMs) diffuse anomalously in the environment. The impact of flow of the fluid along the +x direction in the environment is also considered. Moreover, considering free propagator phenomenon, the expressions of spatio-temporal probability density function (PDF) of the ICMs is derived for the considered model. Further, the novel closed-form expressions for first arrival time density (FATD) of the ICM, survival probability (SP) at any time, and its corresponding first arrival probability (FAP) are also derived. Furthermore, the considered MC model is also analyzed in terms of minimum bit-error-rate (BER) using log-likelihood ratio test (LLRT) optimal detector. The derived expressions are verified using MATLAB based particle-based and Monte-Carlo simulations.

This paper presents a terahertz metasurface based sensor design incorporating graphene and other plasmonic materials for highly sensitive detection of different chemicals. The proposed sensor employs the combination of multiple resonator designs - including circular and square ring resonators - to attain enhanced sensitivity among other performance parameters. Machine learning techniques like Random Forest regression, are employed to enhance the sensor design and predict its performance. The optimized sensor demonstrates excellent sensitivity of 417 GHzRIU^-1 and a low detection limit of 0.264 RIU for ethanol and benzene detection. Furthermore, the integration of machine learning cuts down the simulation time and computational requirements by approximately 90% without compromising accuracy. The sensor's unique design and performance characteristics, including its high-quality factor of 14.476, position it as a promising candidate for environmental monitoring and chemical sensing applications. Moreover, it also demonstrates potential for 2-bit encoding applications through strategic modulation of graphene chemical potential values. On the other hand, it also shows prospects of 2-bit encoding applications via the modulation of graphene chemical. This work provides a major advancement to the terahertz sensing application by proposing new materials, structures, and methods in computation in order to develop a high-performance chemical sensor.