RESUMEN
Cadaveric islet and stem cell-derived transplantations hold promise as treatments for type 1 diabetes. To tackle the issue of immunocompatibility, numerous cellular macroencapsulation techniques have been developed that utilize diffusion to transport insulin across an immunoisolating barrier. However, despite several devices progressing to human clinical trials, none have successfully managed to attain physiologic glucose control or insulin independence. Based on empirical evidence, macroencapsulation methods with multilayered, high islet surface density are incompatible with homeostatic, on-demand insulin delivery and physiologic glucose regulation, when reliant solely on diffusion. An additional driving force is essential to overcome the distance limit of diffusion. In this study, we present both theoretical proof and experimental validation that applying pressure at levels comparable to physiological diastolic blood pressure significantly enhances insulin flux across immunoisolation membranes-increasing it by nearly three orders of magnitude. This significant enhancement in transport rate allows for precise, sub-minute regulation of both bolus and basal insulin delivery. By incorporating this technique with a pump-based extravascular system, we demonstrate the ability to rapidly reduce glucose levels in diabetic rodent models, effectively replicating the timescale and therapeutic effect of subcutaneous insulin injection or infusion. This advance provides a potential path towards achieving insulin independence with islet macroencapsulation. One Sentence Summary: Towards improved glucose control, applying sub-minute pressure at physiological levels enhances therapeutic insulin transport from macroencapsulated islets.
RESUMEN
Progress in understanding brain-viscera interoceptive signaling is hindered by a dearth of implantable devices suitable for probing both brain and peripheral organ neurophysiology during behavior. Here we describe multifunctional neural interfaces that combine the scalability and mechanical versatility of thermally drawn polymer-based fibers with the sophistication of microelectronic chips for organs as diverse as the brain and the gut. Our approach uses meters-long continuous fibers that can integrate light sources, electrodes, thermal sensors and microfluidic channels in a miniature footprint. Paired with custom-fabricated control modules, the fibers wirelessly deliver light for optogenetics and transfer data for physiological recording. We validate this technology by modulating the mesolimbic reward pathway in the mouse brain. We then apply the fibers in the anatomically challenging intestinal lumen and demonstrate wireless control of sensory epithelial cells that guide feeding behaviors. Finally, we show that optogenetic stimulation of vagal afferents from the intestinal lumen is sufficient to evoke a reward phenotype in untethered mice.
RESUMEN
N95 filtering facepiece respirators (FFR) and surgical masks are essential in reducing airborne disease transmission, particularly during the COVID-19 pandemic. However, currently available FFR's and masks have major limitations, including masking facial features, waste, and integrity after decontamination. In a multi-institutional trial, we evaluated a transparent, elastomeric, adaptable, long-lasting (TEAL) respirator to evaluate success of qualitative fit test with user experience and biometric evaluation of temperature, respiratory rate, and fit of respirator using a novel sensor. There was a 100% successful fit test among participants, with feedback demonstrating excellent or good fit (90% of participants), breathability (77.5%), and filter exchange (95%). Biometric testing demonstrated significant differences between exhalation and inhalation pressures among a poorly fitting respirator, well-fitting respirator, and the occlusion of one filter of the respirator. We have designed and evaluated a transparent elastomeric respirator and a novel biometric feedback system that could be implemented in the hospital setting.
RESUMEN
Recent studies have uncovered a strong effect of host genetic variation on the composition of host-associated microbiota. Here, we present HOMINID, a computational approach based on Lasso linear regression, that given host genetic variation and microbiome taxonomic composition data, identifies host single nucleotide polymorphisms (SNPs) that are correlated with microbial taxa abundances. Using simulated data, we show that HOMINID has accuracy in identifying associated SNPs and performs better compared with existing methods. We also show that HOMINID can accurately identify the microbial taxa that are correlated with associated SNPs. Lastly, by using HOMINID on real data of human genetic variation and microbiome composition, we identified 13 human SNPs in which genetic variation is correlated with microbiome taxonomic composition across body sites. In conclusion, HOMINID is a powerful method to detect host genetic variants linked to microbiome composition and can facilitate discovery of mechanisms controlling host-microbiome interactions.