Sensorineural hearing loss (SNHL) is typically a permanent and often progressive condition that is commonly attributed to sensory cell loss. All vertebrates except mammals can regenerate lost sensory cells. Thus, SNHL is currently only treated with hearing aids or cochlear implants. There has been extensive research to understand how regeneration occurs in nonmammals, how hair cells form during development, and what limits regeneration in maturing mammals. These studies motivated efforts to identify therapeutic interventions to regenerate hair cells as a treatment for hearing loss, with a focus on targeting supporting cells to form new sensory hair cells. The approaches include gene therapy and small molecule delivery to the inner ear. At the time of this publication, early-stage clinical trials have been conducted to test targets that have shown evidence of regenerating sensory hair cells in preclinical models. As these potential treatments move closer to a clinical reality, it will be important to understand which therapeutic option is most appropriate for a given population. It is also important to consider which audiological tests should be administered to identify hearing improvement while considering the pharmacokinetics and mechanism of a given approach. Some impacts on audiological practice could include implementing less common audiological measures as standard procedure. As devices are not capable of repairing the damaged underlying biology, hair-cell regeneration treatments could allow patients to benefit more from their devices, move from a cochlear implant candidate to a hearing aid candidate, or move a subject to not needing an assistive device. Here, we describe the background, current state, and future implications of hair-cell regeneration research.Damage to auditory hair cells is a key feature of sensorineural hearing loss due to aging, noise exposure, or ototoxic drugs. Though hair-cell loss is permanent in humans, research in bird species led to the discovery that analogous hair cells of the avian basilar papilla are able to regenerate after being damaged by ototoxic agents. Regeneration appears to occur through a combination of the mitotic expansion of a precursor population of supporting cells and direct transdifferentiation of supporting cells into functioning hair cells. This review will synthesize the relevant anatomy and pathophysiology of sensorineural hearing loss, the historical observations that led to the genesis of the hair-cell regeneration field, and perspectives on initial human hair-cell regeneration trials.Millions of people worldwide have disabling hearing loss because one of their genes generates an incorrect version of some specific protein the ear requires for hearing. In many of these cases, delivering the correct version of the gene to a specific target cell within the inner ear has the potential to restore cochlear function to enable high-acuity physiologic hearing. Purpose In this review, we outline our strategy for the development of genetic medicines with the potential to treat hearing loss. We will use the example of otoferlin gene (OTOF)-mediated hearing loss, a sensorineural hearing loss due to autosomal recessive mutations of the OTOF gene.

MicroRNA (miRNA) expression profiles from human perilymph correlate to post cochlear implantation (CI) hearing outcomes.

The high inter-individual variability in speech perception among cochlear implant recipients is still poorly understood. MiRNA expression in perilymph can be used to characterize the molecular processes underlying inner ear disease and to predict performance with a cochlear implant.

Perilymph collected during CI from 17 patients was analyzed using microarrays. MiRNAs were identified and multivariable analysis using consonant-nucleus-consonant testing at 6 and 18 months post implant activation was performed. Variables analyzed included age, gender, preoperative pure tone average (PTA), and preoperative speech discrimination (word recognition [WR]). Gene ontology analysis was performed to identify potential functional implications of changes in the identified miRNAs.

Distinct miRNA profiles correlated to preoperative PTA and WR. Patients classified as poor performers showed downregulation of six miRNAs that potentially regulate pathways related to neuronal function and cell survival.

Individual miRNA profiles can be identified in microvolumes of perilymph. Distinct non-coding RNA expression profiles correlate to preoperative hearing and postoperative cochlear implant outcomes.

Individual miRNA profiles can be identified in microvolumes of perilymph. Distinct non-coding RNA expression profiles correlate to preoperative hearing and postoperative cochlear implant outcomes.Objective.This work assessed the relationship between image signal-to-noise ratio (SNR) and total-body noise-equivalent count rate (NECR)-for both non-time-of-flight (TOF) NECR and TOF-NECR-in a long uniform water cylinder and 14 healthy human subjects using the uEXPLORER total-body PET/CT scanner.Approach.A TOF-NEC expression was modified for list-mode PET data, and both the non-TOF NECR and TOF-NECR were compared using datasets from a long uniform water cylinder and 14 human subjects scanned up to 12 h after radiotracer injection.Main results.The TOF-NECR for the uniform water cylinder was found to be linearly proportional to the TOF-reconstructed image SNR2in the range of radioactivity concentrations studied, but not for non-TOF NECR as indicated by the reducedR2value. The results suggest that the use of TOF-NECR to estimate the count rate performance of TOF-enabled PET systems may be more appropriate for predicting the SNR of TOF-reconstructed images.Significance.Image quality in PET is commonly characterized by image SNR and, correspondingly, the NECR. While the use of NECR for predicting image quality in conventional PET systems is well-studied, the relationship between SNR and NECR has not been examined in detail in long axial field-of-view total-body PET systems, especially for human subjects. Furthermore, the current NEMA NU 2-2018 standard does not account for count rate performance gains due to TOF in the NECR evaluation. The relationship between image SNR and total-body NECR in long axial FOV PET was assessed for the first time using the uEXPLORER total-body PET/CT scanner.Objective.Machine learning (ML) based radiation treatment planning addresses the iterative and time-consuming nature of conventional inverse planning. Given the rising importance of magnetic resonance (MR) only treatment planning workflows, we sought to determine if an ML based treatment planning model, trained on computed tomography (CT) imaging, could be applied to MR through domain adaptation.Methods.In this study, MR and CT imaging was collected from 55 prostate cancer patients treated on an MR linear accelerator. ML based plans were generated for each patient on both CT and MR imaging using a commercially available model in RayStation 8B. The dose distributions and acceptance rates of MR and CT based plans were compared using institutional dose-volume evaluation criteria. The dosimetric differences between MR and CT plans were further decomposed into setup, cohort, and imaging domain components.Results.MR plans were highly acceptable, meeting 93.1% of all evaluation criteria compared to 96.3% of CT plans, with dose equivalence for all evaluation criteria except for the bladder wall, penile bulb, small and large bowel, and one rectum wall criteria (p less then 0.05). Changing the input imaging modality (domain component) only accounted for about half of the dosimetric differences observed between MR and CT plans. Anatomical differences between the ML training set and the MR linac cohort (cohort component) were also a significant contributor.Significance.We were able to create highly acceptable MR based treatment plans using a CT-trained ML model for treatment planning, although clinically significant dose deviations from the CT based plans were observed. Future work should focus on combining this framework with atlas selection metrics to create an interpretable quality assurance QA framework for ML based treatment planning.Objective.The accuracy of navigation in minimally invasive neurosurgery is often challenged by deep brain deformations (up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach). We propose a deep learning-based deformable registration method to address such deformations between preoperative MR and intraoperative CBCT.Approach.The registration method uses a joint image synthesis and registration network (denoted JSR) to simultaneously synthesize MR and CBCT images to the CT domain and perform CT domain registration using a multi-resolution pyramid. JSR was first trained using a simulated dataset (simulated CBCT and simulated deformations) and then refined on real clinical images via transfer learning. The performance of the multi-resolution JSR was compared to a single-resolution architecture as well as a series of alternative registration methods (symmetric normalization (SyN), VoxelMorph, and image synthesis-based registration methods).Main results.JSR achieved median Dice coefficient (DSC) of 0.69 in deep brain structures and median target registration error (TRE) of 1.94 mm in the simulation dataset, with improvement from single-resolution architecture (median DSC = 0.68 and median TRE = 2.14 mm). Additionally, JSR achieved superior registration compared to alternative methods-e.g. SyN (median DSC = 0.54, median TRE = 2.77 mm), VoxelMorph (median DSC = 0.52, median TRE = 2.66 mm) and provided registration runtime of less than 3 s. Similarly in the clinical dataset, JSR achieved median DSC = 0.72 and median TRE = 2.05 mm.Significance.The multi-resolution JSR network resolved deep brain deformations between MR and CBCT images with performance superior to other state-of-the-art methods. The accuracy and runtime support translation of the method to further clinical studies in high-precision neurosurgery.We revisit the pressure-induced order-disorder transition between phases II and IV in ammonium bromide-d4using neutron diffraction measurements to characterise both the average and local structures. We identify a very sluggish transition that does not proceed to full conversion and local structure correlations indicate a slight preference for ammonium cation ordering along ⟨110⟩ crystallographic directions, as pressure is increased. Simultaneous cooling below ambient temperature appears to facilitate the pressure-induced transition. Variable-temperature, ambient-pressure measurements across the IV → III → II transitions show slower conversion than previously observed, and that phase III exhibits metastability above ambient temperature.Matrigel is a polymeric extracellular matrix material produced by mouse cancer cells. Over the past four decades, Matrigel has been shown to support a wide variety of two- and three-dimensional cell and tissue culture applications including organoids. Despite widespread use, transport of molecules, cells, and colloidal particles through Matrigel can be limited. These limitations restrict cell growth, viability, and function and limit Matrigel applications. A strategy to improve transport through a hydrogel without modifying the chemistry or composition of the gel is to physically restructure the material into microscopic microgels and then pack them together to form a porous material. These 'granular’ hydrogels have been created using a variety of synthetic hydrogels, but granular hydrogels composed of Matrigel have not yet been reported. Here we present a drop-based microfluidics approach for structuring Matrigel into a three-dimensional, mesoporous material composed of packed Matrigel microgels, which we call granular Matrigel.