Epithelial-specific isoforms of protein 4.1R promote adherens junction assembly in maturing epithelia.

Epithelial adherens junctions (AJs) and tight junctions (TJs) undergo disassembly and reassembly during morphogenesis and pathological states. The membrane-cytoskeleton interface plays a crucial role in junctional reorganization. Protein 4.1R (4.1R), expressed as a diverse array of spliceoforms, has been implicated in linking the AJ and TJ complex to the cytoskeleton. However, which specific 4.1 isoform(s) participate and the mechanisms involved in junctional stability or remodeling remain unclear. We now describe a role for epithelial-specific isoforms containing exon 17b and excluding exon 16 4.1R (4.1R+17b) in AJs. 4.1R+17b is exclusively co-localized with the AJs. 4.1R+17b binds to the armadillo repeats 1-2 of ß-catenin via its membrane-binding domain. This complex is linked to the actin cytoskeleton via a bispecific interaction with an exon 17b-encoded peptide. Exon 17b peptides also promote fodrin-actin complex formation. Expression of 4.1R+17b forms does not disrupt the junctional cytoskeleton and AJs during the steady-state or calcium-dependent AJ reassembly. Overexpression of 4.1R-17b forms, which displace the endogenous 4.1R+17b forms at the AJs, as well as depletion of the 4.1R+17b forms both decrease junctional actin and attenuate the recruitment of spectrin to the AJs and also reduce E-cadherin during the initial junctional formation of the AJ reassembly process. Expressing 4.1R+17b forms in depleted cells rescues junctional localization of actin, spectrin, and E-cadherin assembly at the AJs. Together, our results identify a critical role for 4.1R+17b forms in AJ assembly and offer additional insights into the spectrin-actin-4.1R-based membrane skeleton as an emerging regulator of epithelial integrity and remodeling.

Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff.

OBJECTIVES: Mask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital. DESIGN: Single-site, observational cohort study. SETTING: An urban, academic hospital in Boston, Massachusetts, USA. PARTICIPANTS: We enrolled adult hospital staff entering the hospital at a key ingress point. INTERVENTIONS: Consenting participants entering the hospital were invited to experience the computer vision mask detection system. Key aspects of the detection algorithm and feedback were described to participants, who then completed a quantitative assessment to understand their perceptions and acceptance of interacting with the system to detect their mask adherence. OUTCOME MEASURES: Primary outcomes were willingness to interact with the mask system, and the degree of comfort participants felt in interacting with a public facing computer vision mask algorithm. RESULTS: One hundred and eleven participants with mean age 40 (SD15.5) were enrolled in the study. Males (47.7%) and females (52.3%) were equally represented, and the majority identified as white (N=54, 49%). Most participants (N=97, 87.3%) reported acceptance of the system and most participants (N=84, 75.7%) were accepting of deployment of the system to reinforce mask adherence in public places. One third of participants (N=36) felt that a public facing computer vision system would be an intrusion into personal privacy.Public-facing computer vision software to detect and provide feedback around mask adherence may be acceptable in the hospital setting. Similar systems may be considered for deployment in locations where mask adherence is important.

An automated all-in-one system for carbohydrate tracking, glucose monitoring, and insulin delivery.

Glycemic control through titration of insulin dosing remains the mainstay of diabetes mellitus treatment. Insulin therapy is generally divided into dosing with long- and short-acting insulin, where long-acting insulin provides basal coverage and short-acting insulin supports glycemic excursions associated with eating. The dosing of short-acting insulin often involves several steps for the user including blood glucose measurement and integration of potential carbohydrate loads to inform safe and appropriate dosing. The significant burden placed on the user for blood glucose measurement and effective carbohydrate counting can manifest in substantial effects on adherence. Through the application of computer vision, we have developed a smartphone-based system that is able to detect the carbohydrate load of food by simply taking a single image of the food and converting that information into a required insulin dose by incorporating a blood glucose measurement. Moreover, we report the development of comprehensive all-in-one insulin delivery systems that streamline all operations that peripheral devices require for safe insulin administration, which in turn significantly reduces the complexity and time required for titration of insulin. The development of an autonomous system that supports maximum ease and accuracy of insulin dosing will transform our ability to more effectively support patients with diabetes.