A Probabilistic Approach to Blood Glucose Prediction in Type 1 Diabetes Under Meal Uncertainties.

Currently, most reliable and commercialized artificial pancreas systems for type 1 diabetes are hybrid closed-loop systems, which require the user to announce every meal and its size. However, estimating the amount of carbohydrates in a meal and announcing each and every meal is an error-prone process that introduces important uncertainties to the problem, which when not considered, lead to sub-optimal outcomes of the controller. To address this problem, we propose a novel deep-learning-based model for probabilistic glucose prediction, called the Input and State Recurrent Kalman Network (ISRKN), which consists in the incorporation of an input and state Kalman filter in the latent space of a deep neural network so that the posterior distributions can be computed in closed form and the uncertainty can be propagated using the Kalman equations.

Examining the Acute Glycemic Effects of Different Types of Structured Exercise Sessions in Type 1 Diabetes in a Real-World Setting: The Type 1 Diabetes and Exercise Initiative (T1DEXI).

Objective: Maintenance of glycemic control during and after exercise remains a major challenge for individuals with type 1 diabetes. Glycemic responses to exercise may differ by exercise type (aerobic, interval, or resistance), and the effect of activity type on glycemic control after exercise remains unclear.

Research Design And Methods: The Type 1 Diabetes Exercise Initiative (T1DEXI) was a real-world study of at-home exercise.

Safe and Personalized Meal Bolus Calculator for Type-1 Diabetes Using Bayesian Optimization.

Despite several developments in artificial pancreas technology, postprandial glycemic regulation remains to be a major challenge for type 1 diabetes management. Typically, the large spike in blood glucose concentration induced by meals require an appropriate dose of bolus insulin. Although matching bolus insulin to carbohydrate intake has been shown to improve glycemic regulation, current state-of-the-art meal bolus calculators depend on patient-specific parameters and/or historical clinical data, which may not be easily available.

Outpatient Randomized Crossover Automated Insulin Delivery Versus Conventional Therapy with Induced Stress Challenges.

Automated insulin delivery (AID) systems have not been evaluated in the context of psychological and pharmacological stress in type 1 diabetes. Our objective was to determine glycemic control and insulin use with Zone Model Predictive Control (zone-MPC) AID system enhanced for states of persistent hyperglycemia versus sensor-augmented pump (SAP) during outpatient use, including in-clinic induced stress. Randomized, crossover, 2-week trial of zone-MPC AID versus SAP in 14 adults with type 1 diabetes.

Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions.

This paper introduces methods to estimate aspects of physical activity and sedentary behavior from three-axis accelerometer data collected with a wrist-worn device at a sampling rate of 32 [Hz] on adults with type 1 diabetes (T1D) in free-living conditions. In particular, we present two methods able to detect and grade activity based on its intensity and individual fitness as sedentary, mild, moderate or vigorous, and a method that performs activity classification in a supervised learning framework to predict specific user behaviors. Population results for activity level grading show multi-class average accuracy of 99.

More Time in Glucose Range During Exercise Days than Sedentary Days in Adults Living with Type 1 Diabetes.

This study analysis was designed to examine the 24-h effects of exercise on glycemic control as measured by continuous glucose monitoring (CGM). Individuals with type 1 diabetes (ages: 15-68 years; hemoglobin A1c: 7.5% ± 1.

Using Iterative Learning for Insulin Dosage Optimization in Multiple-Daily-Injections Therapy for People With Type 1 Diabetes.

Objective: In this work, we design iterative algorithms for the delivery of long-acting (basal) and rapid-acting (bolus) insulin, respectively, for people with type 1 diabetes (T1D) on multiple-daily-injections (MDIs) therapy using feedback from self-monitoring of blood glucose (SMBG) measurements.

Methods: Iterative learning control (ILC) updates basal therapy consisting of one long-acting insulin injection per day, while run-to-run (R2R) adapts meal bolus therapy via the update of the mealtime-specific insulin-to-carbohydrate ratio (CR). Updates are due weekly and are based upon sparse SMBG measurements.

Randomized Crossover Comparison of Automated Insulin Delivery Versus Conventional Therapy Using an Unlocked Smartphone with Scheduled Pasta and Rice Meal Challenges in the Outpatient Setting.

Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of prescribed meals. We evaluated performance of our interoperable artificial pancreas system (iAPS) in the at-home setting, running on an unlocked smartphone, with scheduled meal challenges in a randomized crossover trial. Ten adults with type 1 diabetes completed 2 weeks of AID-based control and 2 weeks of conventional therapy in random order where they consumed regular pasta or extra-long grain white rice as part of a complete dinner meal on six different occasions in both arms (each meal thrice in random order).

The Effect of Two Types of Pasta Versus White Rice on Postprandial Blood Glucose Levels in Adults with Type 1 Diabetes: A Randomized Crossover Trial.

Food choices are essential to successful glycemic control for people with diabetes. We compared the impact of three carbohydrate-rich meals on the postprandial glycemic response in adults with type 1 diabetes (T1D). We performed a randomized crossover study in 12 adults with T1D (age 58.

Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study.

Background: Wrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise.

Reducing risks in type 1 diabetes using H∞ control.

A control scheme was designed in order to reduce the risks of hyperglycemia and hypoglycemia in type 1 diabetes mellitus (T1DM). This structure is composed of three main components: an H∞ robust controller, an insulin feedback loop (IFL), and a safety mechanism (SM). A control-relevant model that is employed to design the robust controller is identified.

Estimating optimal profiles of genetic alterations using constraint-based models.

Metabolic engineering involves application of recombinant DNA methods to manipulate metabolic networks to improve cellular properties. It is critical that the genetic alterations be performed in an optimal manner to maximize profit. In addition to the product yield, productivity consideration is also critical, especially for the production of bulk chemicals such as 1,3-propanediol.

On-line optimization of recombinant product in a fed-batch bioreactor.

In this paper, an efficient scheme for on-line optimization of a recombinant product in a fed-batch bioreactor is presented. This scheme is based on the parametrization of the system states and the elimination of a subset of the dynamic equations in the mathematical model of the fed-batch bioreactor. The fed-batch bioreactor considered here involves the production of chloramphenicol acetyltransferase (CAT) in a genetically modified E.