A low-profile 3-D printable metastructure for performance improvement of aperture antennas.

In order to increase the radiation performance of aperture-type antennas, this paper demonstrates a low-profile, planar, single-layer, three-dimensional (3-D) printable metastructure. The proposed hybridized metastructure is highly transparent as it is made out of novel hybrid meta-atoms having transmission coefficient magnitudes greater than - 0.72 dB and fully complies with the near-field phase transformation principle. The hybridized design approach makes the metastructure planar, low-profile, light in weight, and compatible with additive printing technology. For the proof-of-concept, such metastructure is developed and numerically verified to enhance the radiation performance of a resonant cavity antenna (RCA). With the proposed metastructure, the peak directivity of the RCA is improved by 8.6 dBi (from 11.4 dBi to 20 dBi) at the operating frequency of 12.4 GHz. The aperture efficiency and 3-dB directivity bandwidth of the RCA with the metastructure are 41.46% and 16.5%, respectively. Using readily accessible thermoplastics or polymers and copper with cost-effective fused deposition modeling (FDM) 3-D printing technology, the proposed planar hybridized metastructure can be prototyped commercially.

The Current State of Proteomics and Metabolomics for Inner Ear Health and Disease.

Characterising inner ear disorders represents a significant challenge due to a lack of reliable experimental procedures and identified biomarkers. It is also difficult to access the complex microenvironments of the inner ear and investigate specific pathological indicators through conventional techniques. Omics technologies have the potential to play a vital role in revolutionising the diagnosis of ear disorders by providing a comprehensive understanding of biological systems at various molecular levels. These approaches reveal valuable information about biomolecular signatures within the cochlear tissue or fluids such as the perilymphatic and endolymphatic fluid. Proteomics identifies changes in protein abundance, while metabolomics explores metabolic products and pathways, aiding the characterisation and early diagnosis of diseases. Although there are different methods for identifying and quantifying biomolecules, mass spectrometry, as part of proteomics and metabolomics analysis, could be utilised as an effective instrument for understanding different inner ear disorders. This study aims to review the literature on the application of proteomic and metabolomic approaches by specifically focusing on Meniere's disease, ototoxicity, noise-induced hearing loss, and vestibular schwannoma. Determining potential protein and metabolite biomarkers may be helpful for the diagnosis and treatment of inner ear problems.

Surface Modification of Polystyrene with Boronic Acid for Immunoaffinity-Based Cell Enrichment.

Obtaining an enriched and phenotypically pure cell population from heterogeneous cell mixtures is important for diagnostics and biosensing. Existing techniques such as fluorescent-activated cell sorting (FACS) and magnetic-activated cell sorting (MACS) require preincubation with antibodies (Ab) and specialized equipment. Cell immunopanning removes the need for preincubation and can be done with no specialized equipment. The majority of the available antibody-mediated analyte capture techniques require a modification to the Abs for binding. In this work, no antibody modification is used because we take advantage of the carbohydrate chain in the Fc region of Ab. We use boronic acid as a cross-linker to bind the Ab to a modified surface. The process allows for functional orientation and cleavable binding of the Ab. In this study, we created an immunoaffinity matrix on polystyrene (PS), an inexpensive and ubiquitous plastic. We observed a 37% increase in Ab binding compared with that of a passive adsorption approach. The method also displayed a more consistent antibody binding with 17 times less variation in Ab loading among replicates than did the passive adsorption approach. Surface topography analysis revealed that a dextran coating reduced nonspecific antibody binding. Elemental analysis (XPS) was used to characterize the surface at different stages and showed that APBA molecules can bind upside-down on the surface. While upside-down antibodies likely remain functional, their elution behavior might differ from those bound in the desired way. Cell capture experiments show that the new surface has 43% better selectivity and 2.4-fold higher capture efficiency compared to a control surface of passively adsorbed Abs. This specific surface chemistry modification will allow the targeted capture of cells or analytes with the option of chemical detachment for further research and characterization.

Performance of personalised prosthesis under static pressure: Numerical analysis and experimental validation.

This study investigates the performance of personalised middle ear prostheses under static pressure through a combined approach of numerical analysis and experimental validation. The sound transmission performances of both normal and reconstructed middle ears undergo changes under high positive or negative pressure within the middle ear cavity. This pressure fluctuation has the potential to result in prosthesis displacement/extrusion in patients. To optimise the design of middle ear prostheses, it is crucial to consider various factors, including the condition of the middle ear cavity in which the prosthesis is placed. The integration of computational modelling techniques with non-invasive imaging modalities has demonstrated significant promise and distinct prospects in middle ear surgery. In this study, we assessed the efficacy of Finite Element (FE) analysis in modelling the responses of both normal and reconstructed middle ears to elevated static pressure within the ear canal. The FE model underwent validation using experimental data derived from human cadaveric temporal bones before progressing to subsequent investigations. Afterwards, we assessed stapes and umbo displacements in the reconstructed middle ear under static pressure, with either a columella-type prosthesis or a prosthetic incus, closely resembling a healthy incus. Results indicated the superior performance of the prosthetic incus in terms of both sound transmission to the inner ear and stress distribution patterns on the TM, potentially lowering the risk of prosthesis displacement/extrusion. This study underscores the potential of computational analysis in middle ear surgery, encompassing aspects such as prosthesis design, predicting outcomes in ossicular chain reconstruction (OCR), and mitigating experimental costs.

Artificial Hair Cell Sensor Based on Nanofiber-Reinforced Thin Metal Films.

Engineering artificial mechanosensory hair cells offers a promising avenue for developing diverse biosensors spanning applications from biomedicine to underwater sensing. Unfortunately, current artificial sensory hair cells do not have the ability to simultaneously achieve ultrahigh sensitivity with low-frequency threshold detection (e.g., 0.1 Hz). This work aimed to solve this gap by developing an artificial sensory hair cell inspired by the vestibular sensory apparatus, which has such functional capabilities. For device characterization and response testing, the sensory unit was inserted in a 3D printed lateral semicircular canal (LSCC) mimicking the environment of the labyrinth. The sensor was fabricated based on platinum (Pt) thin film which was reinforced by carbon nanofibers (CNFs). A Pi-shaped hair cell sensor was created as the sensing element which was tested under various conditions of simulated head motion. Results reveal the hair cell sensor displayed markedly higher sensitivity compared to other reported artificial hair cell sensors (e.g., 21.47 mV Hz-1 at 60°) and low frequency detection capability, 0.1 Hz < f < 1.5 Hz. Moreover, like the LSCC hair cells in biology, the fabricated sensor was most sensitive in a given plane of rotational motion, demonstrating features of directional sensitivity.

Enhancing Water Safety: Exploring Recent Technological Approaches for Drowning Detection.

Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection systems. However, the image-processing approach requires substantial resources and sophisticated MLAs, making it costly and complex to implement. Conversely, sensor-based approaches offer practical, cost-effective, and widely applicable solutions for drowning detection. These approaches involve data transmission from the swimmer's condition to the processing unit through sensing technology, utilising both wired and wireless communication channels. This paper explores the recent developments in drowning detection systems while considering costs, complexity, and practicality in selecting and implementing such systems. The assessment of various technological approaches contributes to ongoing efforts aimed at improving water safety and reducing the risks associated with drowning incidents.