Type 1 diabetes mellitus is a common and highly morbid disease for which there is no cure. Treatment primarily involves exogenous insulin administration, and, under specific circumstances, islet or pancreas transplantation. However, insulin replacement alone fails to replicate the endocrine function of the pancreas and does not provide durable euglycemia. In addition, transplantation requires lifelong use of immunosuppressive medications, which has deleterious side effects, is expensive, and is inappropriate for use in adolescents. A bioartificial pancreas that provides total endocrine pancreatic function without immunosuppression is a potential therapy for treatment of type 1 diabetes. Numerous models are in development and take different approaches to cell source, encapsulation method, and device implantation location. We review current therapies for type 1 diabetes mellitus, the requirements for a bioartificial pancreas, and quantitatively compare device function.

Objective: Medical podcasts are becoming increasingly available; however, it is unclear how these new resources are being used by trainees or whether they influence clinical practice. This study explores the preferences and experiences of otolaryngology residents with otolaryngology-specific podcasts, and the impact of these podcasts on resident education and clinical practice. Methods: An 18-question survey was distributed anonymously to a representative junior (up to post-graduate year 3) and senior (post-graduate year 4 or greater) otolaryngology residents at most programs across the US. Along with demographic information, the survey was designed to explore the preferences of educational materials, podcast listening habits and motivations, and influence of podcasts on medical practice. Descriptive statistics and student t-tests were used to analyze the results. Results: The survey was distributed to 198 current otolaryngology residents representing 94% of eligible residency programs and was completed by 73 residents (37% response rate). Nearly 3-quarters of respondents reported previous use of otolaryngology podcasts, among which 83% listen at least monthly. Over half of residents changed their overall clinical (53%) and consult (51%) practice based on podcast use. Residents rank-ordered listening to podcasts last among traditional options for asynchronous learning, including reading textbooks and watching online videos. Conclusions: While other asynchronous learning tools remain popular, most residents responding to this survey use podcasts and report that podcasts influence their clinical practice. This study reveals how podcasts are currently used as a supplement to formal otolaryngology education. Results from the survey may inform how medical podcasts could be implemented into resident education in the future.

Solid organ transplantation (SOT) is the standard of care for end-stage organ disease. The most frequent complication of SOT involves allograft rejection, which may occur via T cell– and/or antibody-mediated mechanisms. Diagnosis of rejection in the clinical setting requires an invasive biopsy as there are currently no reliable biomarkers to detect rejection episodes. Likewise, it is virtually impossible to identify patients who exhibit operational tolerance and may be candidates for reduced or complete withdrawal of immunosuppression. Emerging single-cell technologies, including cytometry by time-of-flight (CyTOF), imaging mass cytometry, and single-cell RNA sequencing, represent a new opportunity for deep characterization of pathogenic immune populations involved in both allograft rejection and tolerance in clinical samples. These techniques enable examination of both individual cellular phenotypes and cell-to-cell interactions, ultimately providing new insights into the complex pathophysiology of allograft rejection. However, working with these large, highly dimensional datasets requires expertise in advanced data processing and analysis using computational biology techniques. Machine learning algorithms represent an optimal strategy to analyze and create predictive models using these complex datasets and will likely be essential for future clinical application of patient level results based on single-cell data. Herein, we review the existing literature on single-cell techniques in the context of SOT. Supplementary Information The online version contains supplementary material available at 10.1007/s00281-022-00958-0.

No abstract