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.

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.