Hair cells in the inner ear have been shown to be susceptible to ototoxicity from some beneficial pharmaceutical drugs, such as aminoglycosides and cisplatin. Thus, there is great interest in discovering new targets or compounds that protect hair cells from these ototoxic drugs. Epigenetic regulation is closely related to inner ear development; however, little is known about epigenetic regulation in the process of ototoxic drugs-induced hearing loss.

The utricle is one of the five sensory organs in the mammalian vestibular system, and while the utricle has a limited ability to repair itself, this is not sufficient for the recovery of vestibular function after hair cell (HC) loss induced by ototoxic drugs. In order to further explore the possible self-recovery mechanism of the adult mouse vestibular system, we established a reliable utricle epithelium injury model for studying the regeneration of HCs and examined the toxic effects of 3,3′-iminodiproprionitrile (IDPN) on the utriclein vivoin C57BL/6J mice, which is one of the most commonly used strains in inner ear research. This work focused on the epithelial cell loss, vestibular dysfunction, and spontaneous cell regeneration after IDPN administration. HC loss and supporting cell (SC) loss after IDPN treatment was dose-dependent and resulted in dysfunction of the vestibular system, as indicated by the swim test and the rotating vestibular ocular reflex (VOR) test. EdU-positive SCs were observed only in severely injured utricles wherein above 47% SCs were dead. No EdU-positive HCs were observed in either control or injured utricles. RT-qPCR showed transient upregulation ofHes5andHey1and fluctuating upregulation ofAxin2andβ-cateninafter IDPN administration. We conclude that a single intraperitoneal injection of IDPN is a practical way to establish an injured utricle model in adult C57BL/6J micein vivo. We observed activation of Notch and Wnt signaling during the limited spontaneous HC regeneration after vestibular sensory epithelium damage, and such signaling might act as the promoting factors for tissue self-repair in the inner ear.

The transplantation of neural stem cells (NSCs) has become an emerging treatment for neural degeneration. A key factor in such treatments is to manipulate NSC behaviors such as proliferation and differentiation, resulting in the eventual regulation of NSC fate. Novel bionanomaterials have shown usefulness in guiding the proliferation and differentiation of NSCs due to the materials’ unique morphological and topological properties. Among the nanomaterials, graphene has drawn increasing attention for neural regeneration applications based on the material’s excellent physicochemical properties, surface modifications, and biocompatibility. In this review, we summarize recent works on the use of graphene-based biomaterials for regulating NSC behaviors and the potential use of these materials in clinical treatment. We also discuss the limitations of graphene-based nanomaterials for use in clinical practice. Finally, we provide some future prospects for graphene-based biomaterial applications in neural regeneration.

Background: The purpose of this study was to explore the common characteristics of fenestral otosclerosis (OS) which are misdiagnosed, and develop a deep learning model for the diagnosis of fenestral OS based on temporal bone high-resolution computed tomography scans. Methods: We conducted a study to explicitly analyze the clinical performance of otolaryngologists in diagnosing fenestral OS and developed an explainable deep learning model using 134,574 temporal bone high-resolution computed tomography (HRCT) slices collected from 1,294 patients for the automatic diagnosis of fenestral OS. We prospectively created an external test set with 31,774 CT slices from 144 patients, which contained 86 fenestral OS ears and 202 normal ears and used it to evaluate the performance of our otosclerosis-Logical Neural Network (LNN) model to assess its potential clinical utility. In addition, we compared the diagnostic acumen of seven otolaryngologists with the otosclerosis-LNN approach in the clinical test set, which was mixed with 78 fenestral OS and 62 normal ears. Finally, to evaluate the assisting value of the model, the seven participants were again invited to classify all cases in the clinical test set after referring to the diagnostic results of the model, to which they were blinded. Results: The diagnostic performance of otologists was not satisfactory, and those CT samples which were misdiagnosed had similar characteristics. Based on this finding, we defined three subtypes of fenestral OS lesions that are suitable for clinical diagnosis guidance: “focal”, “transitional”, and “typical” fenestral OS. The most encouraging result is that the model achieved an area under the curve (AUC) of 99.5% (per-ear-sensitivity of 96.4%, per-ear-specificity of 98.9%) on the prospective unknown external test. Furthermore, we used this model to assist otologists and observed a consistent and significant improvement in diagnostic performance, especially for the newly defined focal and transitional fenestral OS, which led to the initial high misdiagnosis rate. Conclusions: Our findings of the fine-grained classification of fenestral OS could have implications for future diagnosis and prevention programs. In addition, our deep OS localization network is an effective approach providing assistance to otologists to deal with the significant challenge of the misdiagnosis of fenestral OS.