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Generating artificial pancreatic beta cells by using synthetic materials to mimic glucose-responsive insulin secretion in a robust manner holds promise for improving clinical outcomes in people with diabetes. Here, we describe the construction of artificial beta cells (AβCs) with a multicompartmental 'vesicles-in-vesicle' superstructure equipped with a glucose-metabolism system and membrane-fusion machinery. Through a sequential cascade of glucose uptake, enzymatic oxidation and proton efflux, the AβCs can effectively distinguish between high and normal glucose levels. Under hyperglycemic conditions, high glucose uptake and oxidation generate a low pH (<5.6), which then induces steric deshielding of peptides tethered to the insulin-loaded inner small liposomal vesicles. The peptides on the small vesicles then form coiled coils with the complementary peptides anchored on the inner surfaces of large vesicles, thus bringing the membranes of the inner and outer vesicles together and triggering their fusion and insulin 'exocytosis'.

Reduced β-cell function and insulin deficiency are hallmarks of diabetes mellitus, which is often accompanied by the malfunction of glucagon-secreting α-cells. While insulin therapy has been developed to treat insulin deficiency, the on-demand supplementation of glucagon for acute hypoglycemia treatment remains inadequate. Here, we describe a transdermal patch that mimics the inherent counterregulatory effects of β-cells and α-cells for blood glucose management by dynamically releasing insulin or glucagon. The two modules share a copolymerized matrix but comprise different ratios of the key monomers to be “dually responsive” to both hyper- and hypoglycemic conditions. In a type 1 diabetic mouse model, the hybrid patch effectively controls hyperglycemia while minimizing the occurrence of hypoglycemia in the setting of insulin therapy with simulated delayed meal or insulin overdose.

Glucose-responsive insulin delivery systems that mimic pancreatic endocrine function could enhance health and improve quality of life for people with type 1 and type 2 diabetes with reduced β-cell function. However, insulin delivery systems with rapid in vivo glucose-responsive behaviour typically have limited insulin-loading capacities and cannot be manufactured easily. Here, we show that a single removable transdermal patch, bearing microneedles loaded with insulin and a non-degradable glucose-responsive polymeric matrix, and fabricated via in situ photopolymerization, regulated blood glucose in insulin-deficient diabetic mice and minipigs (for minipigs >25 kg, glucose regulation lasted >20 h with patches of ~5 cm 2 ). Under hyperglycaemic conditions, phenylboronic acid units within the polymeric matrix reversibly form glucose–boronate complexes that—owing to their increased negative charge—induce the swelling of the polymeric matrix and weaken the electrostatic interactions between the negatively charged insulin and polymers, promoting the rapid release of insulin. This proof-of-concept demonstration may aid the development of other translational stimuli-responsive microneedle patches for drug delivery. A single removable transdermal patch bearing microneedles loaded with insulin and a non-degradable glucose-responsive polymeric matrix regulates blood glucose in insulin-deficient diabetic mice and minipigs.

Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.

Insulin therapy in the setting of type 1 and advanced type 2 diabetes is complicated by increased risk of hypoglycemia. This potentially fatal complication could bemitigated by a glucose-responsive insulin analog. We report an insulin-facilitated glucose transporter (Glut) inhibitor conjugate, in which the insulin molecule is rendered glucose-responsive via conjugation to an inhibitor of Glut. The binding affinity of this insulin analog to endogenous Glut is modulated by plasma and tissue glucose levels. In hyperglycemic conditions (e.g., uncontrolled diabetes or the postprandial state), the in situ-generated insulin analog−Glut complex is driven to dissociate, freeing the insulin analog and glucose-accessible Glut to restore normoglycemia. Upon overdose, enhanced binding of insulin analog to Glut suppresses the glucose transport activity of Glut to attenuate further uptake of glucose. We demonstrate the ability of this insulin conjugate to regulate blood glucose levels within a normal range while mitigating the risk of hypoglycemia in a type 1 diabetic mouse model.

Background A growing number of older adults (ages 65+) live with Type 1 diabetes. Simultaneously, technologies such as continuous glucose monitoring (CGM) have become standard of care. There is thus a need to understand better the complex dynamics that promote use of CGM (and other care innovations) over time in this age group. Our aim was to adapt methods from systems thinking, specifically a participatory approach to system dynamics modeling called group model building (GMB), to model the complex experiences that may underlie different trajectories of CGM use among this population. Herein, we report on the feasibility, strengths, and limitations of this methodology. Methods We conducted a series of GMB workshops and validation interviews to collect data in the form of questionnaires, diagrams, and recordings of group discussion. Data were integrated into a conceptual diagram of the “system” of factors associated with uptake and use of CGM over time. We evaluate the feasibility of each aspect of the study, including the teaching of systems thinking to older adult participants. We collected participant feedback on positive aspects of their experiences and areas for improvement. Results We completed nine GMB workshops with older adults and their caregivers ( N  = 33). Each three-hour in-person workshop comprised: (1) questionnaires; (2) the GMB session, including both didactic components and structured activities; and (3) a brief focus group discussion. Within the GMB session, individual drawing activities proved to be the most challenging for participants, while group activities and discussion of relevant dynamics over time for illustrative (i.e., realistic but not real) patients yielded rich engagement and sufficient information for system diagramming. Study participants liked the opportunity to share experiences with peers, learning and enhancing their knowledge, peer support, age-specific discussions, the workshop pace and structure, and the systems thinking framework. Participants gave mixed feedback on the workshop duration. Conclusions The study demonstrates preliminary feasibility, acceptability, and the value of GMB for engaging older adults about key determinants of complex health behaviors over time. To our knowledge, few studies have extended participatory systems science methods to older adult stakeholders. Future studies may utilize this methodology to inform novel approaches for supporting health across the lifespan.

ObjectiveTo characterise the reporting practices of sequential multiple assignment randomised trials (SMARTs) in human health research.DesignScoping review of protocol and primary analysis papers describing SMARTs published between January 2009 and February 2024.BackgroundSMARTs are innovative trial designs that allow for multiple stages of randomisation to treatment, with randomization potentially based on a patient’s response(s) to previous treatment(s). They are uniquely designed to develop sequential adaptive interventions (dynamic treatment regimes (DTRs)) to support personalized clinical decision-making over time. Previous reviews have identified inconsistencies in how the design, implementation and results of SMARTs have been reported in published studies. A comprehensive assessment of SMART reporting practices is lacking and necessary for developing standardised SMART-specific reporting guidelines.MethodsWe systematically searched multiple databases for SMART-related protocol and primary analysis papers published between January 2009 and February 2024. Title, abstract and full-text screenings were performed by pairs of reviewers, with disagreements resolved by consensus. Data extraction included study characteristics, design elements and analytical approaches for embedded or tailored DTRs. Results were synthesised qualitatively and presented descriptively.ResultsFrom 5486 screened studies, 103 (59 protocol papers, 16 primary analysis papers, 14 protocol papers with corresponding primary analysis papers) met the inclusion criteria. Most studies targeted adults (62.7% protocols, 62.5% primary analyses, 42.9% protocol+primary analyses) and were primarily conducted in the USA. Behavioural and mental health constituted the most frequent therapeutic domain. While intervention descriptions and re-randomisation criteria were consistently reported, operational characteristics such as blinding (protocols: 64.4%, primary analyses: 62.5%, protocols+primary analyses: 71.4%) and randomisation details (protocols: 55.9%, primary analyses: 37.5%, protocols+primary analyses: 50.0%) were inconsistently documented. Only 46.7% of primary analyses evaluated embedded DTRs, and none explored deeply tailored DTRs.ConclusionsDespite the increased adoption of SMART designs, substantial reporting variability persists. Most primary analyses underuse the capability of SMARTs to generate data for developing DTRs. SMART-specific standardised reporting guidelines can help accelerate the scientific and clinical impact of SMARTs.

In this survey study of 346 older adults with type 1 diabetes, many reported hemoglobin A1c goals of < 7%, consistent with early/middle adulthood targets, but not necessarily older adulthood. Participants reported stricter targets than their providers, reflecting the complexity of modifying glycemic goals in practice.

The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible healthcare for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time and subsequently to deliver interventions if, when, and in the dose that they are needed. Dynamic treatment regimes formalize individualized treatment plans as sequences of decision rules, one per stage of clinical intervention, that map current patient information to a recommended treatment. However, most existing methods for estimating optimal dynamic treatment regimes are designed for a small number of fixed decision points occurring on a coarse time-scale. We propose a new reinforcement learning method for estimating an optimal treatment regime that is applicable to data collected using mobile technologies in an outpatient setting. The proposed method accommodates an indefinite time horizon and minute-by-minute decision making that are common in mobile health applications. We show that the proposed estimators are consistent and asymptotically normal under mild conditions. The proposed methods are applied to estimate an optimal dynamic treatment regime for controlling blood glucose levels in patients with type 1 diabetes.

Background Medical training focuses heavily on clinical skills but lacks in training for navigating challenging clinical scenarios especially with regard to diversity issues. Our objective was to assess third-year medical students’ preparedness to navigate such scenarios. Methods A 24-item survey was administered electronically to third-year medical students describing a range of specific interactions with patients, peers, and “upper-levels” or superiors including residents and attendings, spanning subjects including gender, race/ethnicity, politics, age, sexual orientation/identity, disability, and religion. Students rated their level of comfort via a 5-point Likert scale ranging from 1 (“Very Uncomfortable”) to 5 (“Very Comfortable”). Basic demographics were collected and data were summarized for trends. Results Data were analyzed from 120 students (67% response rate, 54.2% female, 60.8% non-Hispanic white). Students reported lower comfort with peer and superiors compared to patient interactions ( p  < 0.0001). Students reported the highest comfort with sexual orientation/identity- and religion-related interactions (median (IQR): 3.3 (1.3) and 3.4 (10.0), respectively) and the lowest comfort with gender-, race/ethnicity-, and disability- related interactions (median (IQR): 2.3 (1.3), 2.0 (1.0), 2.5 (1.5), respectively). Males reported significantly higher median comfort levels for scenarios with upper-level, gender, and religion related interactions. Males were more likely to be completely comfortable versus females across the 24 scenarios, although multiple male response patterns showed evidence of a bimodal distribution. Conclusions Third-year medical students report generally inadequate comfort with navigating complex clinical scenarios, particularly with peers and supervisors and relating to gender-, race/ethnicity-, and disability-specific conflicts. There are differences across gender with regards to median comfort and distribution of scores suggesting that there is a subgroup of males report high/very high comfort with challenging clinical scenarios. Students may benefit from enhanced training modules and personalized toolkits for navigating these scenarios.