Atoh1, a basic helix-loop-helix transcription factor, plays a critical role in the differentiation of several epithelial and neural cell types. We found that beta-catenin, the key mediator of the canonical Wnt pathway, increased expression of Atoh1 in mouse neuroblastoma cells and neural progenitor cells, and baseline Atoh1 expression was decreased by siRNA directed at beta-catenin. The up-regulation of Atoh1 was caused by an interaction of beta-catenin with the Atoh1 enhancer that could be demonstrated by chromatin immunoprecipitation. We found that two putative Tcf-Lef sites in the 3' enhancer of the Atoh1 gene displayed an affinity for beta-catenin and were critical for the activation of Atoh1 transcription because mutation of either site decreased expression of a reporter gene downstream of the enhancer. Tcf-Lef co-activators were found in the complex that bound to these sites in the DNA together with beta-catenin. Inhibition of Notch signaling, which has previously been shown to induce bHLH transcription factor expression, increased beta-catenin expression in progenitor cells of the nervous system. Because this could be a mechanism for up-regulation of Atoh1 after inhibition of Notch, we tested whether siRNA to beta-catenin prevented the increase in Atoh1 and found that beta-catenin expression was required for increased expression of Atoh1 after Notch inhibition.

Primary sinonasal mucosal melanoma is a rare disease, occurring far less often than cutaneous lesions. The objective of this study was to review the records of patients diagnosed with primary sinonasal mucosal melanoma. We performed a retrospective review of the medical records of 23 patients with sinonasal mucosal melanoma who were treated at Taipei Veterans General Hospital between 1982 and 2002. Sixteen of the 23 patients were male and 7 were female; their mean age was 68.2 years (range, 39-87 years). At diagnosis, the melanoma was limited to lesions located in the sinonasal area in 20 patients, and had spread in 3 patients. Local recurrence developed in 9 patients, neck metastasis in 5, and distant metastasis in 19. The 5-year disease-specific survival and local control rates were 22.26% and 52.30%, respectively. In our experience, primary sinonasal mucosa melanoma is prone to spread from the site of origin. The major obstacle in improving overall survival is achieving systemic control.

Background: Hearing loss is one of the most common disabilities worldwide and affects both individual and public health. Pure tone audiometry (PTA) is the gold standard for hearing assessment, but it is often not available in many settings, given its high cost and demand for human resources. Smartphone-based audiometry may be equally effective and can improve access to adequate hearing evaluations. Objective: The aim of this systematic review is to synthesize the current evidence of the role of smartphone-based audiometry in hearing assessments and further explore the factors that influence its diagnostic accuracy. Methods: Five databases—PubMed, Embase, Cochrane Library, Web of Science, and Scopus—were queried to identify original studies that examined the diagnostic accuracy of hearing loss measurement using smartphone-based devices with conventional PTA as a reference test. A bivariate random-effects meta-analysis was performed to estimate the pooled sensitivity and specificity. The factors associated with diagnostic accuracy were identified using a bivariate meta-regression model. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results: In all, 25 studies with a total of 4470 patients were included in the meta-analysis. The overall sensitivity, specificity, and area under the receiver operating characteristic curve for smartphone-based audiometry were 89% (95% CI 83%-93%), 93% (95% CI 87%-97%), and 0.96 (95% CI 0.93-0.97), respectively; the corresponding values for the smartphone-based speech recognition test were 91% (95% CI 86%-94%), 88% (95% CI 75%-94%), and 0.93 (95% CI 0.90-0.95), respectively. Meta-regression analysis revealed that patient age, equipment used, and the presence of soundproof booths were significantly related to diagnostic accuracy. Conclusions: We have presented comprehensive evidence regarding the effectiveness of smartphone-based tests in diagnosing hearing loss. Smartphone-based audiometry may serve as an accurate and accessible approach to hearing evaluations, especially in settings where conventional PTA is unavailable.

Middle ear diseases such as otitis media and middle ear effusion, for which diagnoses are often delayed or misdiagnosed, are among the most common issues faced by clinicians providing primary care for children and adolescents. Artificial intelligence (AI) has the potential to assist clinicians in the detection and diagnosis of middle ear diseases through imaging. Otoendoscopic images obtained by otolaryngologists from Taipei Veterans General Hospital in Taiwan between Jany 1, 2011 to Dec 31, 2019 were collected retrospectively and de-identified. The images were entered into convolutional neural network (CNN) training models after data pre-processing, augmentation and splitting. To differentiate sophisticated middle ear diseases, nine CNN-based models were constructed to recognize middle ear diseases. The best-performing models were chosen and ensembled in a small CNN for mobile device use. The pretrained model was converted into the smartphone-based program, and the utility was evaluated in terms of detecting and classifying ten middle ear diseases based on otoendoscopic images. A class activation map (CAM) was also used to identify key features for CNN classification. The performance of each classifier was determined by its accuracy, precision, recall, and F1-score. A total of 2820 clinical eardrum images were collected for model training. The programme achieved a high detection accuracy for binary outcomes (pass/refer) of otoendoscopic images and ten different disease categories, with an accuracy reaching 98.0% after model optimisation. Furthermore, the application presented a smooth recognition process and a user-friendly interface and demonstrated excellent performance, with an accuracy of 97.6%. A fifty-question questionnaire related to middle ear diseases was designed for practitioners with different levels of clinical experience. The AI-empowered mobile algorithm's detection accuracy was generally superior to that of general physicians, resident doctors, and otolaryngology specialists (36.0%, 80.0% and 90.0%, respectively). Our results show that the proposed method provides sufficient treatment recommendations that are comparable to those of specialists. We developed a deep learning model that can detect and classify middle ear diseases. The use of smartphone-based point-of-care diagnostic devices with AI-empowered automated classification can provide real-world smart medical solutions for the diagnosis of middle ear diseases and telemedicine. This study was supported by grants from the Ministry of Science and Technology (MOST110-2622-8-075-001, MOST110-2320-B-075-004-MY3, MOST-110-2634-F-A49 -005, MOST110-2745-B-075A-001A and MOST110-2221-E-075-005), Veterans General Hospitals and University System of Taiwan Joint Research Program (VGHUST111-G6-11-2, VGHUST111c-140), and Taipei Veterans General Hospital (V111E-002-3).

Hereditary hearing impairment (HHI) is a common but heterogeneous clinical entity caused by mutations in a plethora of deafness genes. Research over the past few decades has shown that the genetic epidemiology of HHI varies significantly across populations. In this study, we used different genetic examination strategies to address the genetic causes of HHI in a large Taiwanese cohort composed of >5000 hearing-impaired families. We also analyzed the clinical features associated with specific genetic mutations. Our results demonstrated that next-generation sequencing-based examination strategies could achieve genetic diagnosis in approximately half of the families. Common deafness-associated genes in the Taiwanese patients assessed, in the order of prevalence, included GJB2, SLC26A4, OTOF, MYO15A, and MTRNR1, which were similar to those found in other populations. However, the Taiwanese patients had some unique mutations in these genes. These findings may have important clinical implications for refining molecular diagnostics, facilitating genetic counseling, and enabling precision medicine for the management of HHI.