Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes.

In diabetes mellitus (DM) treatment, Continuous Glucose Monitoring (CGM) linked with insulin delivery becomes the main strategy to improve therapeutic outcomes and quality of patients' lives. However, Blood Glucose (BG) regulation with CGM is still hampered by limitations of algorithms and glucose sensors. Regarding sensor technology, current electrochemical glucose sensors do not capture the full spectrum of other physiological signals, i.e., lipids, amino acids or hormones, relaying the general body status. Regarding algorithms, variability between and within patients remains the main challenge for optimal BG regulation in closed-loop therapies. This work highlights the simulation benefits to test new sensing and control paradigms which address the previous shortcomings for Type 1 Diabetes (T1D) closed-loop therapies. The UVA/Padova T1DM Simulator is the core element here, which is a computer model of the human metabolic system based on glucose-insulin dynamics in T1D patients. That simulator is approved by the US Food and Drug Administration (FDA) as an alternative for pre-clinical testing of new devices and closed-loop algorithms. To overcome the limitation of standard glucose sensors, the concept of an islet-based biosensor, which could integrate multiple physiological signals through electrical activity measurement, is assessed here in a closed-loop insulin therapy. This investigation has been addressed by an interdisciplinary consortium, from endocrinology to biology, electrophysiology, bio-electronics and control theory. In parallel to the development of an islet-based closed-loop, it also investigates the benefits of robust control theory against the natural variability within a patient population. Using 4 meal scenarios, numerous simulation campaigns were conducted. The analysis of their results then introduces a discussion on the potential benefits of an Artificial Pancreas (AP) system associating the islet-based biosensor with robust algorithms.

Integrating an Islet-Based Biosensor in the Artificial Pancreas: In Silico Proof-of-Concept.

OBJECTIVE: Current treatment of type 1 diabetes by closed-loop therapy depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore nutrient homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurements. This work aims at investigating the performance of the biosensor in a blood glucose control loop as potential in silico proof-of-concept. METHODS: Two islet algorithm models were identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inborn regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Subsequently, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients. RESULTS: With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as efficient as standard treatments, and outperformed them under challenging conditions. CONCLUSION: This work validates the concept of using inborn pancreatic islets algorithms in an artificial pancreas in silico. SIGNIFICANCE: Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.

On the synthesis of a sliding-mode-observer-based adaptive fault-tolerant flight control scheme.

This paper presents an active Fault-Tolerant Control (FTC) framework to accommodate the actuator faults by using a Fault Estimation (FE) module fitted with an adaptive control law. It focuses on the problem of fault estimation based fault tolerant control for linear uncertain system with mitigation of the reconfiguration transients. In this work, a second-order sliding mode observer is designed to reconstruct the Loss of Effectiveness (LoE) of the actuators. The use of a bumpless strategy to cleverly manage the transient behavior where fault estimation is different from the real one due to an abrupt fault occurrence. Signals provided by the FE module are next used by an augmented fault-tolerant control allocation scheme to accommodate the fault. More precisely, the flight state tracking problem has been addressed with a robust adaptive model-reference Integral Sliding Mode (ISM) control law in a state feedback setting. Next, the problem of stability and performance of the overall FTC scheme with taking into account both FE performance and control law is rigorously considered. At the end, simulation examples on an aircraft from a European program are used to illustrate the effectiveness of the proposed approach.

Tuning of an Artificial Pancreas Controller: an in silico methodology based on clinically-relevant criteria.

This paper presents a methodology to tune an artificial pancreas controller by minimizing the time spent in endangering glycaemic ranges (hypo- and hyperglycaemia). The risk associated to the patient's glycaemia is evaluated with an objective metric (the blood glucose risk index), which has an established clinical relevance. The tuned controller is validated in the UVA/Padova environment where the resulting artificial pancreas achieves minimal glucose risk index in realistic 24-hour long scenarios with unannounced glucose intake.

Analysis and design of second-order sliding-mode algorithms for quadrotor roll and pitch estimation.

The problem addressed in this paper is that of quadrotor roll and pitch estimation without any assumption about the knowledge of perturbation bounds when Inertial Measurement Units (IMU) data or position measurements are available. A Smooth Sliding Mode (SSM) algorithm is first designed to provide reliable estimation under a smooth disturbance assumption. This assumption is next relaxed with the second proposed Adaptive Sliding Mode (ASM) algorithm that deals with disturbances of unknown bounds. In addition, the analysis of the observers are extended to the case where measurements are corrupted by bias and noise. The gains of the proposed algorithms were deduced from the Lyapunov function. Furthermore, some useful guidelines are provided for the selection of the observer turning parameters. The performance of these two approaches is evaluated using a nonlinear simulation model and considering either accelerometer or position measurements. The simulation results demonstrate the benefits of the proposed solutions.