Artificial pancreas: past, present, future

Recent advances in the precision control strategy of artificial pancreas

The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future.

Orexin neurons track temporal features of blood glucose in behaving mice

Does the brain track how fast our blood glucose is changing? Knowing such a rate of change would enable the prediction of an upcoming state and a timelier response to this new state. Hypothalamic arousal-orchestrating hypocretin/orexin neurons (HONs) have been proposed to be glucose sensors, yet whether they track glucose concentration (proportional tracking) or rate of change (derivative tracking) is unknown. Using simultaneous recordings of HONs and blood glucose in behaving male mice, we found that maximal HON responses occur in considerable temporal anticipation (minutes) of glucose peaks due to derivative tracking. Analysis of >900 individual HONs revealed glucose tracking in most HONs (98%), with derivative and proportional trackers working in parallel, and many (65%) HONs multiplexed glucose and locomotion information. Finally, we found that HON activity is important for glucose-evoked locomotor suppression. These findings reveal a temporal dimension of brain glucose sensing and link neurobiological and algorithmic views of blood glucose perception in the brain's arousal orchestrators.

Observer-based control for plasma glucose regulation in type 1 diabetes mellitus patients with unknown input delay

This article introduces an observer-based control strategy tailored for regulating plasma glucose in type 1 diabetes mellitus patients, addressing challenges like unknown time-varying delays and meal disturbances. This control strategy is based on an extended Bergman minimal model, a nonlinear glucose-insulin model to encompass unknown inputs, such as unplanned meals, exercise disturbances, or delays. The primary contribution lies in the design of an observer-based state feedback control in the presence of unknown long delays, which seeks to support and enhance the performance of the traditional artificial pancreas by considering realistic scenarios. The observer and control gains for the observer-based control are computed through linear matrix inequalities formulated from Lyapunov conditions that guarantee closed-loop stability. This design deploys a soft and gentle dynamic response, similar to a natural pancreas, despite meal disturbances and input delays. Numerical tests demonstrate the scheme's effectiveness in glycemic level regulation and hypoglycemic episode avoidance.

Predicting Blood Glucose Levels with Organic Neuromorphic Micro-Networks

Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results for reliable glucose predictions, offering timely warnings for glucose fluctuations. The translation of these software-based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality of life for diabetic patients. ANNs are transforming this field, potentially leading to implantable smart prediction devices and ultimately to a fully artificial pancreas. However, this transition presents several challenges, including the need for specialized, compact, lightweight, and low-power hardware. Organic polymer-based electronics are a promising solution as they have the ability to implement the behavior of neural networks, operate at low voltage, and possess key attributes like flexibility, stretchability, and biocompatibility. Here, the study focuses on implementing software-based neural networks for glucose prediction into hardware systems. How to minimize network requirements, downscale the architecture, and integrate the neural network with electrochemical neuromorphic organic devices, meeting the strict demands of smart implants for in-body computation of glucose prediction is investigated.

Exploring the Impact of Glycemic Control on Diabetic Retinopathy: Emerging Models and Prognostic Implications

This review addresses the complexities of type 1 diabetes (T1D) and its associated complications, with a particular focus on diabetic retinopathy (DR). This review outlines the progression from non-proliferative to proliferative diabetic retinopathy and diabetic macular edema, highlighting the role of dysglycemia in the pathogenesis of these conditions. A significant portion of this review is devoted to technological advances in diabetes management, particularly the use of hybrid closed-loop systems (HCLSs) and to the potential of open-source HCLSs, which could be easily adapted to different patients' needs using big data analytics and machine learning. Personalized HCLS algorithms that integrate factors such as patient lifestyle, dietary habits, and hormonal variations are highlighted as critical to reducing the incidence of diabetes-related complications and improving patient outcomes.

Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review

Background

Accurate dietary assessment remains a challenge, particularly in free-living settings. Continuous glucose monitoring (CGM) shows promise in optimizing the assessment and monitoring of ingestive activity (IA, i.e., consumption of calorie-containing foods/beverages), and it might enable administering dietary Just-In-Time Adaptive Interventions (JITAIs).

Objective

In a scoping review, we aimed to answer the following questions: (1) Which CGM approaches to automatically detect IA in (near-)real-time have been investigated? (2) How accurate are these approaches? (3) Can they be used in the context of JITAIs?

Methods

We systematically searched four databases until October 2023 and included publications in English or German that used CGM-based approaches for human (all ages) IA detection. Eligible publications included a ground-truth method as a comparator. We synthesized the evidence qualitatively and critically appraised publication quality.

Results

Of 1,561 potentially relevant publications identified, 19 publications (17 studies, total N = 311; for 2 studies, 2 publications each were relevant) were included. Most publications included individuals with diabetes, often using meal announcements and/or insulin boluses accompanying meals. Inpatient and free-living settings were used. CGM-only approaches and CGM combined with additional inputs were deployed. A broad range of algorithms was tested. Performance varied among the reviewed methods, ranging from unsatisfactory to excellent (e.g., 21% vs. 100% sensitivity). Detection times ranged from 9.0 to 45.0 min.

Conclusion

Several CGM-based approaches are promising for automatically detecting IA. However, response times need to be faster to enable JITAIs aimed at impacting acute IA. Methodological issues and overall heterogeneity among articles prevent recommending one single approach; specific cases will dictate the most suitable approach.

In-human testing of a non-invasive continuous low-energy microwave glucose sensor with advanced machine learning capabilities

Continuous glucose monitoring schemes that avoid finger pricking are of utmost importance to enhance the comfort and lifestyle of diabetic patients. To this aim, we propose a microwave planar sensing platform as a potent sensing technology that extends its applications to biomedical analytes. In this paper, a compact planar resonator-based sensor is introduced for noncontact sensing of glucose. Furthermore, in vivo and in-vitro tests using a microfluidic channel system and in clinical trial settings demonstrate its reliable operation. The proposed sensor offers real-time response and a high linear correlation (R2 ∼ 0.913) between the measured sensor response and the blood glucose level (GL). The sensor is also enhanced with machine learning to predict the variation of body glucose levels for non-diabetic and diabetic patients. This addition is instrumental in triggering preemptive measures in cases of unusual glucose level trends. In addition, it allows for the detection of common artifacts of the sensor as anomalies so that they can be removed from the measured data. The proposed system is designed to noninvasively monitor interstitial glucose levels in humans, introducing the opportunity to create a customized wearable apparatus with the ability to learn.

Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities

BACKGROUND

Hybrid closed-loop control of glucose levels in people with type 1 diabetes mellitus (T1D) is limited by the requirements on users to manually announce physical activity (PA) and meals to the artificial pancreas system. Multivariable automated insulin delivery (mvAID) systems that can handle unannounced PAs and meals without any manual announcements by the user can improve glycemic control by modulating insulin dosing in response to the occurrence and intensity of spontaneous physical activities.

METHODS

An mvAID system is developed to supplement the glucose measurements with additional physiological signals from a wristband device, with the signals analyzed using artificial intelligence algorithms to automatically detect the occurrence of PA and estimate its intensity. This additional information gained from the physiological signals enables more proactive insulin dosing adjustments in response to both planned exercise and spontaneous unanticipated physical activities.

RESULTS

In silico studies of the mvAID illustrate the safety and efficacy of the system. The mvAID is translated to pilot clinical studies to assess its performance, and the clinical experiments demonstrate an increased time in range and reduced risk of hypoglycemia following unannounced PA and meals.

CONCLUSIONS

The mvAID systems can increase the safety and efficacy of insulin delivery in the presence of unannounced physical activities and meals, leading to improved lives and less burden on people with T1D.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

A 2-dimensional model framework for blood glucose prediction based on iterative learning control architecture

The accurate, timely, and personalized prediction for future blood glucose (BG) levels is undoubtedly needed for further advancement of diabetes management technologies. Human inherent circadian rhythm and regular lifestyle resulting in similarity of daily glycemic dynamics play a positive role in the prediction of blood glucose. Inspired by the iterative learning control (ILC) method in the field of automatic control, a 2-dimensional (2-D) model framework is constructed to predict the future blood glucose levels by taking both the short-range information within a day (intra-day) and long-range information between days (inter-day) into account. In this framework, the radial basis function neural network was applied to capture nonlinear relationships in glycemic metabolism, that is, short-range temporal dependence and long-range contemporaneous dependence on previous days. We build models for each patient, and the models were tested on the in silico datasets at various prediction horizons (PHs). The learning model developed in the 2-D framework successfully increases the accuracy and reduces the delay of predictions. This modeling framework provides a new point of view for BG level prediction and contributes to the development of personalized glucose management, such as hypoglycemia warning and glycemic control.

Prediction of Blood Risk Score in Diabetes Using Deep Neural Networks

Improving the prediction of blood glucose concentration may improve the quality of life of people living with type 1 diabetes by enabling them to better manage their care. Given the anticipated benefits of such a prediction, numerous methods have been proposed. Rather than attempting to predict glucose concentration, a deep learning framework for prediction is proposed in which prediction is performed using a scale for hypo- and hyper-glycemia risk. Using the blood glucose risk score formula proposed by Kovatchev et al., models with different architectures were trained, including, a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short-term memory (LSTM) network, and an encoder-like convolutional neural network (CNN). The models were trained using the OpenAPS Data Commons data set, comprising 139 individuals, each with tens of thousands of continuous glucose monitor (CGM) data points. The training set was composed of 7% of the data set, while the remaining was used for testing. Performance comparisons between the different architectures are presented and discussed. To evaluate these predictions, performance results are compared with the last measurement (LM) prediction, through a sample-and-hold approach continuing the last known measurement forward. The results obtained are competitive when compared to other deep learning methods. A root mean squared error (RMSE) of 16 mg/dL, 24 mg/dL, and 37 mg/dL were obtained for CNN prediction horizons of 15, 30, and 60 min, respectively. However, no significant improvements were found for the deep learning models compared to LM prediction. Performance was found to be highly dependent on architecture and the prediction horizon. Lastly, a metric to assess model performance by weighing each prediction point error with the corresponding blood glucose risk score is proposed. Two main conclusions are drawn. Firstly, going forward, there is a need to benchmark model performance using LM prediction to enable the comparison between results obtained from different data sets. Secondly, model-agnostic data-driven deep learning models may only be meaningful when combined with mechanistic physiological models; here, it is argued that neural ordinary differential equations may combine the best of both approaches. These findings are based on the OpenAPS Data Commons data set and are to be validated in other independent data sets.

Automated insulin delivery systems: from early research to routine care of type 1 diabetes

Automated insulin delivery (AID) systems, so-called closed-loop systems or artificial pancreas, are based upon the concept of insulin supply driven by blood glucose levels and their variations according to body glucose needs, glucose intakes and insulin action. They include a continuous glucose monitoring device which provides a signal to a control algorithm tuning insulin delivery from an infusion pump. The control algorithm is the key of the system since it commands insulin administration in order to maintain blood glucose in a predefined target range and close to a near-normal glucose level. The last two decades have shown dramatic advances toward the use in free life of AID systems for routine care of type 1 diabetes through step-by-step demonstrations of feasibility, safety and efficacy in successive hospital, transitional and outpatient trials. Because of the constraints of pharmacokinetics and dynamics of subcutaneous insulin delivery, the currently available AID systems are all 'hybrid' or 'semi-automated' insulin delivery systems with a need of meal and exercise announcements in order to anticipate rapid glucose variations through pre-meal bolus or pre-exercise reduction of infusion rate. Nevertheless, these AID systems significantly improve time spent in a near-normal range with a reduction of the risk of hypoglycemia and the mental load of managing diabetes in everyday life, representing a milestone in insulin therapy. Expected progression toward fully automated, further miniaturized and integrated, possibly implantable on long-term and more physiological closed-loop systems paves the way for a functional cure of type 1 diabetes.

Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice

Background and objective

Hypoglycemia is a key barrier to achieving optimal glycemic control in people with diabetes, which has been proven to cause a set of deleterious outcomes, such as impaired cognition, increased cardiovascular disease, and mortality. Hypoglycemia prediction has come to play a role in diabetes management as big data analysis and machine learning (ML) approaches have become increasingly prevalent in recent years. As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention.

Materials and methods

PubMed, EMBASE, and the Cochrane Library were searched for relevant studies published between 1 January 2015 and 8 December 2022. Five hypoglycemia prediction aspects were covered: real-time hypoglycemia, mild and severe hypoglycemia, nocturnal hypoglycemia, inpatient hypoglycemia, and other hypoglycemia (postprandial, exercise-related).

Results

From the 5,042 records retrieved, we included 79 studies in our analysis. Two major categories of prediction models are identified by an overview of the chosen studies: simple or logistic regression models based on clinical data and data-based ML models (continuous glucose monitoring data is most commonly used). Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction.

Conclusion

In this study, we looked deep into the currently established hypoglycemia prediction models and identified hypoglycemia risk factors from various perspectives, which may provide readers with a better understanding of future trends in this topic.

Recent progress and perspectives of continuous in vivo testing device

Devices for continuous in-vivo testing (CIVT) can detect target substances in real time, thus providing a valuable window into a patient's condition, their response to therapeutics, metabolic activities, and neurotransmitter transmission in the brain. Therefore, CIVT devices have received increased attention because they are expected to greatly assist disease diagnosis and treatment and research on human pathogenesis. However, CIVT has been achieved for only a few markers, and it remains challenging to detect many key markers. Therefore, it is important to summarize the key technologies and methodologies of CIVT, and to examine the direction of future development of CIVT. We review recent progress in the development of CIVT devices, with consideration of the structure of these devices, principles governing continuous detection, and nanomaterials used for electrode modification. This detailed and comprehensive review of CIVT devices serves three purposes: (1) to summarize the advantages and disadvantages of existing devices, (2) to provide a reference for development of CIVT equipment to detect additional important markers, and (3) to discuss future prospects with emphasis on problems that must be overcome for further development of CIVT equipment. This review aims to promote progress in research on CIVT devices and contribute to future innovation in personalized medical treatments.

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A detailed and comprehensive review of continuous in vivo testing device.

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The nanomaterials, delicate structures and detection principles of the works are discussed.

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The achievements and shortcomings of the existing devices are summarized.

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The problems that should be solved in the further development of the devices and the future prospects are put forward.

Continuous Glucose Monitor, Insulin Pump, and Automated Insulin Delivery Therapies for Type 1 Diabetes: An Update on Potential for Cardiovascular Benefits

PURPOSE OF REVIEW

The incidence of type 1 diabetes (T1D) is rising in all age groups. T1D is associated with chronic microvascular and macrovascular complications but improving glycemic trends can delay the onset and slow the progression of these complications. Utilization of technological devices for diabetes management, such as continuous glucose monitors (CGM) and insulin pumps, is increasing, and these devices are associated with improvements in glycemic trends. Thus, device use may be associated with long-term prevention of T1D complications, yet few studies have investigated the direct impacts of devices on chronic complications in T1D. This review will describe common diabetes devices and combination systems, as well as review relationships between device use and cardiovascular outcomes in T1D.

RECENT FINDINGS

Findings from existing cohort and national registry studies suggest that pump use may aid in improving cardiovascular risk factors such as hypertension and dyslipidemia. Furthermore, pump users have been shown to have lower arterial stiffness and better measures of myocardial function. In registry and case-control longitudinal data, pump use has been associated with fewer cardiovascular events and reduction of cardiovascular disease (CVD) and all-cause mortality. CVD is the leading cause of morbidity and mortality in T1D. Consistent use of diabetes devices may protect against the development and progression of macrovascular complications such as CVD through improvement in glycemic trends. Existing literature is limited, but findings suggest that pump use may reduce acute cardiovascular risk factors as well as chronic cardiovascular complications and overall mortality in T1D.

The past, present, and future of chemotherapy with a focus on individualization of drug dosing

While there have been rapid advances in developing new and more targeted drugs to treat cancer, much less progress has been made in individualizing dosing. Even though the introduction of immunotherapies such as CAR T-cells and checkpoint inhibitors, as well as personalized therapies that target specific mutations, have transformed clinical treatment of cancers, chemotherapy remains a mainstay in oncology. Chemotherapies are typically dosed on either a body surface area (BSA) or weight basis, which fails to account for pharmacokinetic differences between patients. Drug absorption, distribution, metabolism, and excretion rates can vary between patients, resulting in considerable differences in exposure to the active drugs. These differences result in suboptimal dosing, which can reduce efficacy and increase side-effects. Therapeutic drug monitoring (TDM), genotype guided dosing, and chronomodulation have been developed to address this challenge; however, despite improving clinical outcomes, they are rarely implemented in clinical practice for chemotherapies. Thus, there is a need to develop interventions that allow for individualized drug dosing of chemotherapies, which can help maximize the number of patients that reach the most efficacious level of drug in the blood while mitigating the risks of underdosing or overdosing. In this review, we discuss the history of the development of chemotherapies, their mechanisms of action and how they are dosed. We discuss substantial intraindividual and interindividual variability in chemotherapy pharmacokinetics. We then propose potential engineering solutions that could enable individualized dosing of chemotherapies, such as closed-loop drug delivery systems and bioresponsive biomaterials.

Control of blood glucose induced by meals for type-1 diabetics using an adaptive backstepping algorithm

In this study, an adaptive backstepping method is proposed to regulate the blood glucose induced by meals for type-1 diabetic patients. The backstepping controller is used to control the blood glucose level and an adaptive algorithm is utilized to compensate for the blood glucose induced by meals. Moreover, the effectiveness of the proposed method is evaluated by comparing results in two different case studies: in the presence of actuator faults and the loss of control input for a short while during treatment. Effects of unannounced meals three times a day are investigated for a nominal patient in every case. It is argued that adaptive backstepping is the preferred control method in either case. The Lyapunov theory is used to prove the stability of the proposed method. Obtained results, indicated that the adaptive backstepping controller is stable, and the desired level of glucose concentration is being tracked efficiently.

Data-Driven Supervised Compression Artifacts Detection on Continuous Glucose Sensors

Continuous Glucose Monitoring (CGM) sensors micro-invasively provide frequent glucose readings, improving the management of Type 1 diabetic patients' life and making available reach data-sets for retrospective analysis. Unlikely, CGM sensors are subject to failures, such as compression artifacts, that might impact on both real-time and respective CGM use. In this work is focused on retrospective detection of compression artifacts. An in-silico dataset is generated using the T1D UVa/Padova simulator and compression artifacts are subsequently added in known position, thus creating a dataset with perfectly accurate faulty/not-faulty labels. The problem of compression artifact detection is then faced with supervised data-driven techniques, in particular using Random Forest algorithm. The detection performance guaranteed by the method on in-silico data is satisfactory, opening the way for further analysis on real-data.

A Nonlinear State Observer for the Bi-Hormonal Intraperitoneal Artificial Pancreas

Currently, continuous glucose monitoring sensors are used in the artificial pancreas to monitor blood glucose levels. However, insulin and glucagon concentrations in different parts of the body cannot be measured in real-time, and determining body glucagon sensitivity is not feasible. Estimating these states provides more information about the current system status, facilitating improved decision-making by the model-based controller. In this regard, the aim of this paper is to design a nonlinear high-gain observer for a bi-hormonal artificial pancreas in the presence of measurement noises, model uncertainties, and disturbances. The model used in the observer is based on an existing intraperitoneal nonlinear animal model in the literature. This model is modified by assuming that insulin can directly transfer from the peritoneal cavity to the bloodstream. Based on a set of realistic assumptions, one model is considered after each hormone infusion, and two observers are separately designed. The model is divided into the insulin-phase and glucagon-phase models based on a set of realistic assumptions. Thereafter, two high-gain observers are designed separately for these phases contributing to estimating the non-measurable states. The observer error is proven to be locally uniformly ultimately bounded, and it is verified that any asymptotically stable control laws remain stable in the presence of the observer. The performance of the observers with different gains is evaluated for a scenario with multiple insulin and glucagon infusions. The proposed observer converges to a finite error, according to the results. Clinical relevance- In Type 1 diabetic patients, the developed observer can be employed in a closed-loop artificial pan-creas to improve the performance of model-based controllers. It estimates the key states, which are necessary for forecasting the body's response to insulin and glucagon boluses.

Practical constraint definition in safety schemes for artificial pancreas systems

Introduction:

Artificial pancreas systems usually define an insulin-on-board constraint ([Formula: see text]) for safety schemes to limit the insulin infusion and avoid hypoglycemia during the closed-loop performance. Several methods have been proposed with impractical considerations requiring information from the prandial events or complex procedures for ambulatory use.

Methods:

This paper presents a simple method that consists of two novel rules that allow finding an [Formula: see text] based only on common clinical parameters that do not require patient intervention. The method robustness was evaluated using a control system coupled to a safety layer under demanding scenarios implemented on the FDA-approved simulator for preclinical studies.

Results:

The method maintains a safe performance, even in the face of interpatient variability, hybrid and fully automatic implementations of an artificial pancreas system, and uncertain settings. Both proposed rules work as effectively or even better and without the patient intervention than other methods that have already been clinically validated.

Conclusion:

This method can be used to define a constant [Formula: see text] that ensures performance and safety of the control system, even under scenarios with incorrect clinical data. Unlike other methods, this method only requires reliable information that is easily obtained from the patient, such as their total daily dose of insulin or body mass.

Two Decades of Research in Artificial Pancreas: Insights from a Bibliometric Analysis

BACKGROUND

Artificial pancreas is a well-known research topic devoted to achieving better glycemic outcomes that has been attracting increasing attention over the years. However, there is a lack of systematic, chronological, and synthesizing studies that show the background of the knowledge generation in this field. This study implements a bibliometric analysis to recognize the main documents, type of publications, research categories, countries, keywords, organizations, and authors related to this topic.

METHODS

Web of Science core collection database was accessed from 2000 to 2020 in order to select high-quality scientific documents based on a specific search query. Bibexcel, MS Excel, Power BI, R-Studio, VOSviewer, and CorText software were used for a descriptive and network analysis based on the local database obtained. Bibliometric parameters as the h-index, frequencies, co-authorship and co-ocurrences were computed.

RESULTS

A total of 756 documents were included that show a growing scientific production on this topic with an increasing contribution from engineering. Outstanding authors, organizations, and countries were identified. An analysis of trends in research was conducted according to the scientific categories of the Web of Science database to identify the main research interests of the last 2 decades and the emerging areas with greater prominence in the coming years. A keyword network analysis allowed to identify the main stages in the development of the AP research over time.

CONCLUSIONS

Results reveal a comprehensive background of the knowledge generation for the AP topic during the last 2 decades, which has been strengthened with international collaborations and a remarkable interdisciplinarity between endocrinology and engineering, giving rise to a growing number of research areas over time, where computer science and medical informatics stand out as the main emerging research areas.

Switching from predictive low glucose suspend to advanced hybrid closed loop control: Effects on glucose control and patient reported outcomes

AIMS

Automated insulin delivery improves glucose control. Aim of this study was to compare in real life the effects on glucose control and patient reported outcomes of an advanced hybrid closed loop system (Control-IQ), versus a simpler system with predictive low glucose suspend function (Basal-IQ).

METHODS

Thirty-one type 1 diabetic subjects were studied during Basal-IQ and after switching to Control-IQ. Variables analyzed were time spent in range (70-180 mg/dL), in tight range (70-140 mg/dL), above range (>180 mg/dL), below range (<70 mg/dL), mean glucose, coefficient of variation and glycated hemoglobin. Questionnaires were administered regarding therapy satisfaction (Diabetes Treatment Satisfaction Questionnaire in status/change form), fear of hypoglycemia (Hypoglycemia Fear Survey), quality of sleep (Pittsburgh Sleep Quality Index).

RESULTS

After 12 weeks of Control-IQ, time in range increased from 62.7 to 74.0%, p < 0.0001, time in tight range increased from 37.1 to 44.6 %, p < 0.001, time above range decreased from 35.6 to 24.4% p < 0.0001. Improvements were observed in mean glucose and glucose variability. Glycated hemoglobin decreased from 7.0% (53 mmol/mol) to 6.6% (49 mmol/mol), p < 0.0001. Subjects using Control-IQ manifested greater satisfaction and less fear of hypoglycemia.

CONCLUSION

Compared to Basal-IQ, Control-IQ improves glucose control and therapy satisfaction.

Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review

Background

Type 1 diabetes (T1D) is a chronic autoimmune disease in which a deficiency in insulin production impairs the glucose homeostasis of the body. Continuous subcutaneous infusion of insulin is a commonly used treatment method. Artificial pancreas systems (APS) use continuous glucose level monitoring and continuous subcutaneous infusion of insulin in a closed-loop mode incorporating a controller (or control algorithm). However, the operation of APS is challenging because of complexities arising during meals, exercise, stress, sleep, illnesses, glucose sensing and insulin action delays, and the cognitive burden. To overcome these challenges, options to augment APS through integration of additional inputs, creating multi-input APS (MAPS), are being investigated.

Objective

The aim of this survey is to identify and analyze input data, control architectures, and validation methods of MAPS to better understand the complexities and current state of such systems. This is expected to be valuable in developing improved systems to enhance the quality of life of people with T1D.

Methods

A literature survey was conducted using the Scopus, PubMed, and IEEE Xplore databases for the period January 1, 2005, to February 10, 2020. On the basis of the search criteria, 1092 articles were initially shortlisted, of which 11 (1.01%) were selected for an in-depth narrative analysis. In addition, 6 clinical studies associated with the selected studies were also analyzed.

Results

Signals such as heart rate, accelerometer readings, energy expenditure, and galvanic skin response captured by wearable devices were the most frequently used additional inputs. The use of invasive (blood or other body fluid analytes) inputs such as lactate and adrenaline were also simulated. These inputs were incorporated to switch the mode of the controller through activity detection, directly incorporated for decision-making and for the development of intermediate modules for the controller. The validation of the MAPS was carried out through the use of simulators based on different physiological models and clinical trials.

Conclusions

The integration of additional physiological signals with continuous glucose level monitoring has the potential to optimize glucose control in people with T1D through addressing the identified limitations of APS. Most of the identified additional inputs are related to wearable devices. The rapid growth in wearable technologies can be seen as a key motivator regarding MAPS. However, it is important to further evaluate the practical complexities and psychosocial aspects associated with such systems in real life.

Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach

Optimal and sustainable control of blood glucose levels (BGLs) is the aim of type-1 diabetes management. The automated prediction of BGL using machine learning (ML) algorithms is considered as a promising tool that can support this aim. In this context, this paper proposes new advanced ML architectures to predict BGL leveraging deep learning and ensemble learning. The deep-ensemble models are developed with novel meta-learning approaches, where the feasibility of changing the dimension of a univariate time series forecasting task is investigated. The models are evaluated regression-wise and clinical-wise. The performance of the proposed ensemble models are compared with benchmark non-ensemble models. The results show the superior performance of the developed ensemble models over developed non-ensemble benchmark models and also show the efficacy of the proposed meta-learning approaches.

Artificial intelligence perspective in the future of endocrine diseases

In recent years, artificial intelligence (AI) shows promising results in the diagnosis, prediction, and management of diseases. The move from handwritten medical notes to electronic health records and a huge number of digital data commenced in the era of big data in medicine. AI can improve physician performance and help better clinical decision making which is called augmented intelligence. The methods applied in the research of AI and endocrinology include machine learning, artificial neural networks, and natural language processing. Current research in AI technology is making major efforts to improve decision support systems for patient use. One of the best-known applications of AI in endocrinology was seen in diabetes management, which includes prediction, diagnosis of diabetes complications (measuring microalbuminuria, retinopathy), and glycemic control. AI-related technologies are being found to assist in the diagnosis of other endocrine diseases such as thyroid cancer and osteoporosis. This review attempts to provide insight for the development of prospective for AI with a focus on endocrinology.

From insulin injections to islet transplantation: An overview of the journey

When, in 1869, Paul Langerhans detected the "islands of tissue" in the pancreas, he took the first step on a journey towards islet transplantation as a treatment for type 1 diabetes. The route has embraced developments across biosciences, surgery, gene therapy and clinical research. This review highlights major milestones along that journey involving whole pancreas transplantation, islet transplantation, the creation of surrogate insulin-secreting cells and novel islet-like structures using genetic and bio-engineering technologies. To obviate the paucity of human tissue, pluripotent stem cells and non-β-cells within the pancreas have been modified to create physiologically responsive insulin-secreting cells. Before implantation, these can be co-cultured with endothelial cells to promote vascularisation and with immune defence cells such as placental amnion cells to reduce immune rejection. Scaffolds to contain grafts and facilitate surgical placement provide further opportunities to achieve physiological insulin delivery. Alternatively, xenotransplants such as porcine islets might be reconsidered as opportunities exist to circumvent safety concerns and immune rejection. Thus, despite a long and arduous journey, the prospects for increased use of tissue transplantation to provide physiological insulin replacement are drawing ever closer.

Designing biomaterials for the modulation of allogeneic and autoimmune responses to cellular implants in Type 1 Diabetes

The effective suppression of adaptive immune responses is essential for the success of allogeneic cell therapies. In islet transplantation for Type 1 Diabetes, pre-existing autoimmunity provides an additional hurdle, as memory autoimmune T cells mediate both an autoantigen-specific attack on the donor beta cells and an alloantigen-specific attack on the donor graft cells. Immunosuppressive agents used for islet transplantation are generally successful in suppressing alloimmune responses, but dramatically hinder the widespread adoption of this therapeutic approach and fail to control memory T cell populations, which leaves the graft vulnerable to destruction. In this review, we highlight the capacity of biomaterials to provide local and nuanced instruction to suppress or alter immune pathways activated in response to an allogeneic islet transplant. Biomaterial immunoisolation is a common approach employed to block direct antigen recognition and downstream cell-mediated graft destruction; however, immunoisolation alone still permits shed donor antigens to escape into the host environment, resulting in indirect antigen recognition, immune cell activation, and the creation of a toxic graft site. Designing materials to decrease antigen escape, improve cell viability, and increase material compatibility are all approaches that can decrease the local release of antigen and danger signals into the implant microenvironment. Implant materials can be further enhanced through the local delivery of anti-inflammatory, suppressive, chemotactic, and/or tolerogenic agents, which serve to control both the innate and adaptive immune responses to the implant with a benefit of reduced systemic effects. Lessons learned from understanding how to manipulate allogeneic and autogenic immune responses to pancreatic islets can also be applied to other cell therapies to improve their efficacy and duration. STATEMENT OF SIGNIFICANCE: This review explores key immunologic concepts and critical pathways mediating graft rejection in Type 1 Diabetes, which can instruct the future purposeful design of immunomodulatory biomaterials for cell therapy. A summary of immunological pathways initiated following cellular implantation, as well as current systemic immunomodulatory agents used, is provided. We then outline the potential of biomaterials to modulate these responses. The capacity of polymeric encapsulation to block some powerful rejection pathways is covered. We also highlight the role of cellular health and biocompatibility in mitigating immune responses. Finally, we review the use of bioactive materials to proactively modulate local immune responses, focusing on key concepts of anti-inflammatory, suppressive, and tolerogenic agents.

A comparison among three maximal mathematical models of the glucose-insulin system

The most well-known and widely used mathematical representations of the physiology of a diabetic individual are the Sorensen and Hovorka models as well as the UVAPadova Simulator. While the Hovorka model and the UVAPadova Simulator only describe the glucose metabolism of a subject with type 1 diabetes, the Sorensen model was formulated to simulate the behaviour of both normal and diabetic individuals. The UVAPadova model is the most known model, accepted by the FDA, with a high level of complexity. The Hovorka model is the simplest of the three models, well documented and used primarily for the development of control algorithms. The Sorensen model is the most complete, even though some modifications were required both to the model equations (adding useful compartments for modelling subcutaneous insulin delivery) and to the parameter values. In the present work several simulated experiments, such as IVGTTs and OGTTs, were used as tools to compare the three formulations in order to establish to what extent increasing complexity translates into richer and more correct physiological behaviour. All the equations and parameters used for carrying out the simulations are provided.

Engineered insulin-polycation complexes for glucose-responsive delivery with high insulin loading

Glucose-responsive insulin delivery systems have the potential to improve quality of life for individuals with diabetes by improving blood sugar control and limiting the risk of hypoglycemia. However, systems with desirable insulin release kinetics and high loading capacities have proven difficult to achieve. Here, we report the development of electrostatic complexes (ECs) comprised of insulin, a polycation, and glucose oxidase (GOx). Under normoglycemic physiological conditions, insulin carries a slight negative charge and forms a stable EC with the polycation. In hyperglycemia, the encapsulated glucose-sensing enzyme GOx converts glucose to gluconic acid and lowers the pH of the microenvironment, causing insulin to adopt a positive charge. Thus, the electrostatic interactions are disrupted, and insulin is released. Using a model polycation, we conducted molecular dynamics simulations to model these interactions, synthesized ECs with > 50% insulin loading capacity, and determined in vitro release kinetics. We further showed that a single dose of ECs can provide a glycemic profile in streptozotocin-induced diabetic mice that mimics healthy mice over a 9 h period with 2 glucose tolerance tests.

A review of biosensor technology and algorithms for glucose monitoring

Diabetes mellitus (DM) has become a serious illness in the whole world. Until now, there is no effective cure for patients with DM. It is well known that the glucose level is one key factor to determine the progress of DM. It is also an important reference to carry out the accurate and timely treatment for patients with DM. In this article, the related biosensors technology that can be utilized to identify and predict glucose level are reviewed in detail, including the algorithms that can help to achieve numerical value of glucose level. Firstly, the biosensor technology based on the physiological fluids are illustrated, including blood, sweat, interstitial fluid, ocular fluid, and other available fluids. Secondly, the algorithms for achieving numerical value of glucose level are investigated, including the physiological model-based method and the machine learning-based method. Finally, the future development trend and challenges of glucose level monitoring are given and the conclusions are drawn.

Multifunctional Biosensors Made with Self-Healable Silk Fibroin Imitating Skin

We report on robust silk fibroin (SF) gels fabricated by incorporating cellulose nanocrystals (SF/CNC) as a "tough" unit and photopolymerization of acrylamide as an "elastic" segment. The addition of CNC affects the refolding process of SF molecules controlled by nucleation via templating, resulting in a stable mesoscopic structure. The gel shows robust mechanical stability (88.8% of initial stress after 1000 compression cycles) and excellent adhesion to various materials. The connected gel can recover its ionic conductivity within 20 s and be stretched to a maximum strain of 498% after healing for 10 h with an efficiency of 95.2%. This multifunctional gel sensor can sensitively detect different toxic gases and small-scale and large-scale human motions in real-time. Its sensitivity is calculated as GF = 3.84 at 0-200% strain. Especially, the gel with 5 wt % thermochromic pigments as a visual temperature indicator can quickly reflect abnormal human body temperature according to the color change. Therefore, the strategy shows potential applications in flexible electrodes, biomimetic sensors, and visual biosensors.

Modulating the foreign body response of implants for diabetes treatment

Diabetes Mellitus is a group of diseases characterized by high blood glucose levels due to patients' inability to produce sufficient insulin. Current interventions often require implants that can detect and correct high blood glucose levels with minimal patient intervention. However, these implantable technologies have not reached their full potential in vivo due to the foreign body response and subsequent development of fibrosis. Therefore, for long-term function of implants, modulating the initial immune response is crucial in preventing the activation and progression of the immune cascade. This review discusses the different molecular mechanisms and cellular interactions involved in the activation and progression of foreign body response (FBR) and fibrosis, specifically for implants used in diabetes. We also highlight the various strategies and techniques that have been used for immunomodulation and prevention of fibrosis. We investigate how these general strategies have been applied to implants used for the treatment of diabetes, offering insights on how these devices can be further modified to circumvent FBR and fibrosis.

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study

BACKGROUND

Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.

METHODS

We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6).

RESULTS

Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%).

CONCLUSIONS

Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

Current treatment options and challenges in patients with Type 1 diabetes: Pharmacological, technical advances and future perspectives

Type 1 diabetes mellitus imposes a significant burden of complications and mortality, despite important advances in treatment: subjects affected by this disease have also a worse quality of life-related to disease management. To overcome these challenges, different new approaches have been proposed, such as new insulin formulations or innovative devices. The introduction of insulin pumps allows a more physiological insulin administration with a reduction of HbA1c level and hypoglycemic risk. New continuous glucose monitoring systems with better accuracy have allowed, not only better glucose control, but also the improvement of the quality of life. Integration of these devices with control algorithms brought to the creation of the first artificial pancreas, able to independently gain metabolic control without the risk of hypo- and hyperglycemic crisis. This approach has revolutionized the management of diabetes both in terms of quality of life and glucose control. However, complete independence from exogenous insulin will be obtained only by biological approaches that foresee the replacement of functional beta cells obtained from stem cells: this will be a major challenge but the biggest hope for the subjects with type 1 diabetes. In this review, we will outline the current scenario of innovative diabetes management both from a technological and biological point of view, and we will also forecast some cutting-edge approaches to reduce the challenges that hamper the definitive cure of diabetes.

Wearable patch delivery system for artificial pancreas health diagnostic-therapeutic application: A review

The advanced stimuli-responsive approaches for on-demand drug delivery systems have received tremendous attention as they have great potential to be integrated with sensing and multi-functional electronics on a flexible and stretchable single platform (all-in-one concept) in order to develop skin-integration with close-loop sensation for personalized diagnostic and therapeutic application. The wearable patch pumps have evolved from reservoir-based to matrix patch and drug-in-adhesive (single-layer or multi-layer) type. In this review, we presented the basic requirements of an artificial pancreas, surveyed the design and technologies used in commercial patch pumps available on the market and provided general information about the latest wearable patch pump. We summarized the various advanced delivery strategies with their mechanisms that have been developed to date and representative examples. Mechanical, electrical, light, thermal, acoustic and glucose-responsive approaches on patch form have been successfully utilized in the controllable transdermal drug delivery manner. We highlighted key challenges associated with wearable transdermal delivery systems, their research direction and future development trends.

Challenge of diabetes mellitus and researchers’ contributions to its control

The aim of this review study was to assess the past significant events on diabetes mellitus, transformations that took place over the years in the medical records of treatment, countries involved, and the researchers who brought about the revolutions. This study used the content analysis to report the existence of diabetes mellitus and the treatments provided by researchers to control it. The focus was mainly on three main types of diabetes (type 1, type 2, and type 3 diabetes). Ethical consideration has also helped to boost diabetic studies globally. The research has a history path from pharmaceuticals of organic-based drugs to metal-based drugs with their nanoparticles in addition to the impacts of nanomedicine, biosensors, and telemedicine. Ongoing and future studies in alternative medicine such as vanadium nanoparticles (metal nanoparticles) are promising.

Evaluation of the Accuracy of Current Tubeless Pumps for Continuous Subcutaneous Insulin Infusion

Background: Recently two new tubeless pumps for insulin therapy were introduced. They were tested for accuracy and occlusion detection and compared with the established patch pump Omnipod^® (OP). Methods: Using a modified setup for tubeless pumps based on IEC 60601-2-24, the basal rate and bolus delivery of the Accu-Chek^® Solo micropump system (ACS) and the A6 TouchCare^® System (A6) were measured with a microgravimetric method. Bolus sizes of 0.2, 1, and 10 U, and basal rates of 0.1 and 1 U/h were evaluated in nine repetitions. For each parameter, mean deviation and number of individual boluses or 1-h basal rate windows within ±15% from target were calculated. In addition, occlusion detection time at basal rates of 0.1 and 1 U/h was determined. Results: Mean deviation of boluses of different volumes in the pumps ranged from -3.3% to +4.0% and 40%-100% of individual boluses were within ±15% of the target. During basal rate delivery, 48% to 98% of 1-h windows were within ±15% of the target with a mean deviation between -5.3% and +6.5%. In general, considerable differences between pump models were observed and deviations decreased with increasing doses. In most parameters, ACS was more accurate, and A6 less accurate, than OP. Mean occlusion detection time ranged from ∼3 to 7.5 h at 1 U/h and was >24 h or absent at 0.1 U/h. Conclusions: In this evaluation, significant differences between the tested tubeless pump models were observed that became most evident when regarding delivery errors over short time and small volumes.

Review: Glucose-sensitive insulin

BACKGROUND

Hypoglycemia, the condition of low blood sugar, is a common occurance in people with diabetes using insulin therapy. Protecting against hypoglycaemia by engineering an insulin preparation that can auto-adjust its biological activity to fluctuating blood glucose levels has been pursued since the 1970s, but despite numerous publications, no system that works well enough for practical use has reached clinical practise.

SCOPE OF REVIEW

This review will summarise and scrutinise known approaches for producing glucose-sensitive insulin therapies. Notably, systems described in patent applications will be extensively covered, which has not been the case for earlier reviews of this area.

MAJOR CONCLUSIONS

The vast majority of published systems are not suitable for product development, but a few glucose-sensitive insulin concepts have recently reached clinical trials, and there is hope that glucose-sensitive insulin will become available to people with diabetes in the near future.

Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

BACKGROUND

Blood glucose (BG) management is crucial for type-1 diabetes patients resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent years, deep learning techniques have been utilized for a more accurate BG level prediction system. However, continuous glucose monitoring (CGM) readings are susceptible to sensor errors. As a result, inaccurate CGM readings would affect BG prediction and make it unreliable, even if the most optimal machine learning model is used.

METHODS

In this work, we propose a novel approach to predicting blood glucose level with a stacked Long short-term memory (LSTM) based deep recurrent neural network (RNN) model considering sensor fault. We use the Kalman smoothing technique for the correction of the inaccurate CGM readings due to sensor error.

RESULTS

For the OhioT1DM (2018) dataset, containing eight weeks' data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 min and 60 min of prediction horizon (PH), respectively.

CONCLUSIONS

To the best of our knowledge, this is the leading average prediction accuracy for the ohioT1DM dataset. Different physiological information, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus insulin, and cumulative step counts in a fixed time interval, are crafted to represent meaningful features used as input to the model. The goal of our approach is to lower the difference between the predicted CGM values and the fingerstick blood glucose readings-the ground truth. Our results indicate that the proposed approach is feasible for more reliable BG forecasting that might improve the performance of the artificial pancreas and insulin infusion system for T1D diabetes management.

Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes

A virtual metabolic human model is a valuable complement to experimental biology and clinical studies, because in vivo research involves serious ethical and technical problems. I have proposed a multi-organ and multi-scale kinetic model that formulates the reactions of enzymes and transporters with the regulation of hormonal actions at postprandial and postabsorptive states. The computational model consists of 202 ordinary differential equations for metabolites with 217 reaction rates and 1,140 kinetic parameter constants. It is the most comprehensive, largest, and highly predictive model of the whole-body metabolism. Use of the model revealed the mechanisms by which individual disorders, such as steatosis, β cell dysfunction, and insulin resistance, were combined to cause diabetes. The model predicted a glycerol kinase inhibitor to be an effective medicine for type 2 diabetes, which not only decreased hepatic triglyceride but also reduced plasma glucose. The model also enabled us to rationally design combination therapy.

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A standard of virtual metabolic human dynamic models is proposed

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It integrates the three scales of molecules, organs, and whole body

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It gets insight into pathological mechanisms of type 1 and type 2 diabetes

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It enables the computer-aided design of medication treatment for diabetes

Soft implantable drug delivery device integrated wirelessly with wearable devices to treat fatal seizures

Personalized biomedical devices have enormous potential to solve clinical challenges in urgent medical situations. Despite this potential, a device for in situ treatment of fatal seizures using pharmaceutical methods has not been developed yet. Here, we present a novel treatment system for neurological medical emergencies, such as status epilepticus, a fatal epileptic condition that requires immediate treatment, using a soft implantable drug delivery device (SID). The SID is integrated wirelessly with wearable devices for monitoring electroencephalography signals and triggering subcutaneous drug release through wireless voltage induction. Because of the wireless integration, bulky rigid components such as sensors, batteries, and electronic circuits can be moved from the SID to wearables, and thus, the mechanical softness and miniaturization of the SID are achieved. The efficacy of the prompt treatment could be demonstrated with animal experiments in vivo, in which brain damages were reduced and survival rates were increased.

Microgel encapsulated nanoparticles for glucose-responsive insulin delivery

An insulin delivery system that self-regulates blood glucose levels has the potential to limit hypoglycemic events and improve glycemic control. Glucose-responsive insulin delivery systems have been developed by coupling glucose oxidase with a stimuli-responsive biomaterial. However, the challenge of achieving desirable release kinetics (i.e., insulin release within minutes after glucose elevation and duration of release on the order of weeks) still remains. Here, we develop a glucose-responsive delivery system using encapsulated glucose-responsive, acetalated-dextran nanoparticles in porous alginate microgels. The nanoparticles respond rapidly to changes in glucose concentrations while the microgels provide them with protection and stability, allowing for extended glucose-responsive insulin release. This system reduces blood sugar in a diabetic mouse model at a rate similar to naked insulin and responds to a glucose challenge 3 days after administration similarly to a healthy animal. With 2 doses of microgels containing 60 IU/kg insulin each, we are able to achieve extended glycemic control in diabetic mice for 22 days.

NUMERICAL SIMULATION OF AN ELECTRODYNAMIC TRANSDUCER CONTROL OF INSULIN PROVISION IN THE BERGMAN’S AND THE CHENG’S MODELS FOR THE DYNAMICS OF THE COUPLE GLUCOSE-INSULIN IN DIABETICS

This paper deals with the numerical simulation of a model of blood glucose level control of a diabetic person using an electrodynamic transducer. Two mathematical models describing the dynamics of the couple glucose–insulin are used: the Bergman’s and the Cheng’s models. First, the adaptive control is applied on the dynamics of a reservoir opener by an electrodynamic transducer. Then it is applied on the two models of the glucose–insulin dynamics. It is found that the control of the reservoir opener and that of the glycemia of a diabetic patient are efficient for some values of the control parameters.

The Diabits App for Smartphone-Assisted Predictive Monitoring of Glycemia in Patients With Diabetes: Retrospective Observational Study

BACKGROUND

Diabetes mellitus, which causes dysregulation of blood glucose in humans, is a major public health challenge. Patients with diabetes must monitor their glycemic levels to keep them in a healthy range. This task is made easier by using continuous glucose monitoring (CGM) devices and relaying their output to smartphone apps, thus providing users with real-time information on their glycemic fluctuations and possibly predicting future trends.

OBJECTIVE

This study aims to discuss various challenges of predictive monitoring of glycemia and examines the accuracy and blood glucose control effects of Diabits, a smartphone app that helps patients with diabetes monitor and manage their blood glucose levels in real time.

METHODS

Using data from CGM devices and user input, Diabits applies machine learning techniques to create personalized patient models and predict blood glucose fluctuations up to 60 min in advance. These predictions give patients an opportunity to take pre-emptive action to maintain their blood glucose values within the reference range. In this retrospective observational cohort study, the predictive accuracy of Diabits and the correlation between daily use of the app and blood glucose control metrics were examined based on real app users' data. Moreover, the accuracy of predictions on the 2018 Ohio T1DM (type 1 diabetes mellitus) data set was calculated and compared against other published results.

RESULTS

On the basis of more than 6.8 million data points, 30-min Diabits predictions evaluated using Parkes Error Grid were found to be 86.89% (5,963,930/6,864,130) clinically accurate (zone A) and 99.56% (6,833,625/6,864,130) clinically acceptable (zones A and B), whereas 60-min predictions were 70.56% (4,843,605/6,864,130) clinically accurate and 97.49% (6,692,165/6,864,130) clinically acceptable. By analyzing daily use statistics and CGM data for the 280 most long-standing users of Diabits, it was established that under free-living conditions, many common blood glucose control metrics improved with increased frequency of app use. For instance, the average blood glucose for the days these users did not interact with the app was 154.0 (SD 47.2) mg/dL, with 67.52% of the time spent in the healthy 70 to 180 mg/dL range. For days with 10 or more Diabits sessions, the average blood glucose decreased to 141.6 (SD 42.0) mg/dL (P<.001), whereas the time in euglycemic range increased to 74.28% (P<.001). On the Ohio T1DM data set of 6 patients with type 1 diabetes, 30-min predictions of the base Diabits model had an average root mean square error of 18.68 (SD 2.19) mg/dL, which is an improvement over the published state-of-the-art results for this data set.

CONCLUSIONS

Diabits accurately predicts future glycemic fluctuations, potentially making it easier for patients with diabetes to maintain their blood glucose in the reference range. Furthermore, an improvement in glucose control was observed on days with more frequent Diabits use.

A revised Sorensen model: Simulating glycemic and insulinemic response to oral and intra-venous glucose load

In 1978, Thomas J. Sorensen defended a thesis in chemical engineering at the University of California, Berkeley, where he proposed an extensive model of glucose-insulin control, model which was thereafter widely employed for virtual patient simulation. The original model, and even more so its subsequent implementations by other Authors, presented however a few imprecisions in reporting the correct model equations and parameter values. The goal of the present work is to revise the original Sorensen's model, to clearly summarize its defining equations, to supplement it with a missing gastrio-intestinal glucose absorption and to make an implementation of the revised model available on-line to the scientific community.

A micro-bioimpedance meter for monitoring insulin bioavailability in personalized diabetes therapy

An on-chip transducer, for monitoring noninvasively the insulin bio-availability in real time after administration in clinical diabetology, is proposed. The bioavailability is assessed as insulin decrease in situ after administration by means of local impedance measurement. Inter-and-intra individual reproducibility is enhanced by a personalized model, specific for the subject, identified and validated during each insulin administration. Such a real-time noninvasive bioavailability measurement allows to increase the accuracy of insulin bolus administration, by attenuating drawbacks of glycemic swings significantly. In the first part of this paper, the concept, the architecture, and the operation of the transducer, as well as details about its prototype, are illustrated. Then, the metrological characterization and validation are reported in laboratory, in vitro on eggplants, ex vivo on pig abdominal non-perfused muscle, and in vivo on a human subject, using injection as a reference subcutaneous delivery of insulin. Results of significant intra-individual reproducibility in vitro and ex vivo point out noteworthy scenarios for assessing insulin bioavailability in clinical diabetology.

Clinically Accurate Prediction of Glucose Levels in Patients with Type 1 Diabetes

Background and Aims: Accurate prediction of glucose levels in patients with type 1 diabetes mellitus (T1DM) is critical both for their glycemic control and for the development of closed-loop systems. Methods: In this study, we utilized real-life, retrospective, continuous glucose monitoring data from 141 T1DM patients (9,083 connection days, 1,592,506 glucose measurements) and in silico data generated by the UVA/Padova T1DM simulator to evaluate different computational methods for glucose prediction. We evaluated the performance of the models using both measures of numerical accuracy, measured by the root mean square error, and clinical accuracy, measured by the percentage of time in each of the Clarke error grid (CEG) zones, and compared the predictions done by autoregressive (AR) models, tree-based methods, artificial neural networks, and a novel model that we devised and optimized for this task. Results: Our novel model, constructed on real-life data, achieved clinical accuracy of 99.3% and 95.8% in predicting the glucose level 30 and 60 min ahead, respectively, and reduced the percentage of glucose predictions in zones C-E of the CEG by 60.6% and 38.4% in these prediction horizons, compared with a standard AR model. The model was superior to all other models across all age groups and achieved higher clinical accuracy in subgroups of patients with high glucose variability and greater time spent in hypoglycemia. Compared with real-life data, when evaluated on in silico data, the model had a higher clinical and numerical accuracy. Conclusions: A model that optimizes for CEG zones may significantly improve clinical accuracy and clinical outcomes of treatment decisions in T1DM patients. Results obtained from simulated data may overestimate the performance of models on real-life data.