Speakers-Used as sensors for detecting acoustic loads with artificial intelligence.

Previous research demonstrated the potential of using speakers as sensors to detect ear canal conditions. This study continues that effort by using a single speaker to measure electrical impedance across various acoustic loads. Electrical impedance data were collected and preprocessed for machine learning model training. Different image forms were tested, including magnitude only and combined magnitude phase. Using 2100 data samples with convolutional neural network-based models (AlexNet, ResNet, and DenseNet), binary and multiclass classifications achieved average accuracies of 0.9716 and 0.907, respectively. This innovative approach is set to revolutionize acoustic sensing through artificial intelligence.

Digital Therapeutics in Hearing Healthcare: Evidence-Based Review.

Digital therapeutics (DTx) in hearing research have emerged as a new category of therapies providing evidence-based intervention via digital means, such as software, smartphone apps, or websites. However, although relatively new, they are not well-established. In this article, we review DTx technologies in hearing research fields, focusing on three categories: prevention and diagnosis, aid (assistance), and cure (digital medicine). We observe that most DTx systems require interactions with users (or patients) without the direct support of clinical professionals to obtain or collect medical evidence; this makes training (or education) features crucial to the therapy's success. In this view, this article discusses the education or training functions of the current DTx and their contribution and purposes. The impact of emerging artificial intelligence (AI) on DTx in hearing research is being explored, and the future of DTx concerning AI integration is being discussed. We believe that this work will contribute to a better understanding of the current and future DTx technological advancements and, in particular, shed light on the field of hearing research.

Establishment of a conditionally reprogrammed primary eccrine sweat gland culture for evaluation of tissue-specific CFTR function.

BACKGROUND: Sweat chloride concentration is used both for CF diagnosis and for tracking CFTR modulator efficacy over time, but the relationship between sweat chloride and lung health is heterogeneous and informed by CFTR genotype. Here, we endeavored to characterize ion transport in eccrine sweat glands (ESGs). METHODS: First, ESGs were microdissected from a non-CF skin donor to analyze individual glands. We established primary cultures of ESG cells via conditional reprogramming for functional testing of ion transport by short circuit current measurement and examined cell composition by single-cell RNA-sequencing (scRNA-seq) comparing with whole dissociated ESGs. Secondly, we cultured nasal epithelial (NE) cells and ESGs from two people with CF (pwCF) to assess modulator efficacy. Finally, NEs and ESGs were grown from one person with the CFTR genotype F312del/F508del to explore genotype-phenotype heterogeneity. RESULTS: ESG primary cells from individuals without CF demonstrated robust ENaC and CFTR function. scRNA-seq demonstrated both secretory and ductal ESG markers in cultured ESG cells. In both NEs and ESGs from pwCF homozygous for F508del, minimal baseline CFTR function was observed, and treatment with CFTR modulators significantly enhanced function. Notably, NEs from an individual bearing F312del/F508del exhibited significant baseline CFTR function, whereas ESGs from the same person displayed minimal CFTR function, consistent with observed phenotype. CONCLUSIONS: This study has established a novel primary culture technique for ESGs that allows for functional ion transport measurement to assess modulator efficacy and evaluate genotype-phenoytpe heterogeneity. To our knowledge, this is the first reported application of conditional reprogramming and scRNA-seq of microdissected ESGs.

Efficient Analysis of Small Molecules via Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (LDI-TOF MS) Using Gold Nanoshells with Nanogaps.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a commonly used technique for analyzing large biomolecules. However, the utilization of organic matrices limits the small-molecule analysis because of the interferences in the low-mass region and the reproducibility issues. To overcome these limitations, a surface-assisted laser desorption/ionization (SALDI), which utilizes nanostructured metallic surfaces, has been developed. Herein, a novel approach for SALDI-MS was proposed using silica@gold core-shell hybrid materials with a nanogap-rich shell (SiO2@Au NGS), which is an emerging material due to its excellent heat-generating capabilities. The gold shell thickness was controlled by adjusting the concentration of gold precursor for the growth of gold nanoparticles. SALDI-MS measurements were performed on a layer formed by drop-casting a mixture of SiO2@Au NGS and analytes. At the optimized process, the gold shell thickness was observed to be 17.2 nm, which showed the highest absorbance. Based on the enhanced SALDI capability, SiO2@Au NGS was utilized to detect various small molecules, including amino acids, sugars, and flavonoids, and the ionization softness was confirmed with a survival yield upon fragmentation. The limits of detection, reproducibility, and salt tolerance of SiO2@Au NGS demonstrate its potential as an effective and reliable SALDI material for small-molecule analyses.