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Cobelli's glucose-insulin model is the only computer simulator of glucose-insulin interactions accepted by Food Drug Administration as a substitute to animal trials. However, it consists of multiple differential equations that make it hard to be implemented on a hardware platform. In this investigation, the Cobelli's model is simplified by Padé approximant method and implemented on a field-programmable gate array-based platform as a hardware model for predicting glucose changes in subjects with type 1 diabetes mellitus. Compared with the original Cobelli's model, the implemented hardware model provides a nearly perfect approximation in predicting glucose changes with rather small root-mean-square errors and maximum errors. The RMSE results for 30 subjects show that the method for simplifying and implementing Cobelli's model has good robustness and applicability. The successful hardware implementation of Cobelli's model will promote a wider adoption of this model that can substitute animal trials, provide fast and reliable glucose and insulin estimation, and ultimately assist the further development of an artificial pancreas system. [ABSTRACT FROM AUTHOR]

Despite continuous research effort, patients with type 1 diabetes mellitus (T1D) experience difficulties in daily adjustments of their blood glucose concentrations. New technological developments in the form of implanted intravenous infusion pumps and continuous blood glucose sensors might alleviate obstacles for the automatic adjustment of blood glucose concentration. These obstacles consist, for example, of large time-delays and insulin storage effects for the subcutaneous/interstitial route. Towards the goal of an artificial pancreas, we present a novel feedback controller approach that combines classical loop-shaping techniques with gain-scheduling and modern H∞ -robust control approaches. A disturbance rejection design is proposed in discrete frequency domain based on the detailed model of the diabetic Göttingen minipig. The model is trimmed and linearised over a large operating range of blood glucose concentrations and insulin sensitivity values. Controller parameters are determined for each of these operating points. A discrete H∞ loop-shaping compensator is designed to increase robustness of the artificial pancreas against general coprime factor uncertainty. The gain scheduled controller uses subcutaneous insulin injection as a control input and determines the controller input error from intravenous blood glucose concentration measurements, where parameter scheduling is achieved by an estimator of the insulin sensitivity parameter. Thus, only one controller stabilises a family of animal models. The controller is validated in silico with a total number of five Göttingen Minipig models, which were previously obtained by experimental identification procedures. Its performance is compared with an experimentally tested switching PI controller. [ABSTRACT FROM AUTHOR]

The artificial pancreas (closed-loop system) addresses the unmet clinical need for improved glucose control whilst reducing the burden of diabetes self-care in type 1 diabetes. Glucose-responsive insulin delivery above and below a preset insulin amount informed by sensor glucose readings differentiates closed-loop systems from conventional, threshold-suspend and predictive-suspend insulin pump therapy. Insulin requirements in type 1 diabetes can vary between one-third-threefold on a daily basis. Closed-loop systems accommodate these variations and mitigate the risk of hypoglycaemia associated with tight glucose control. In this review we focus on the progress being made in the development and evaluation of closed-loop systems in outpatient settings. Randomised transitional studies have shown feasibility and efficacy of closed-loop systems under supervision or remote monitoring. Closed-loop application during free-living, unsupervised conditions by children, adolescents and adults compared with sensor-augmented pumps have shown improved glucose outcomes, reduced hypoglycaemia and positive user acceptance. Innovative approaches to enhance closed-loop performance are discussed and we also present the outlook and strategies used to ease clinical adoption of closed-loop systems. [ABSTRACT FROM AUTHOR]

We have expanded a former compartmental model of blood glucose regulation for healthy and type 2 diabetic subjects. The former model was a detailed physiological model which considered the interactions of three substances, glucose, insulin and glucagon on regulating the blood sugar. The main drawback of the former model was its restriction on the route of glucose entrance to the body which was limited to the intravenous glucose injection. To handle the oral glucose intake, we have added a model of glucose absorption in the gastrointestinal tract to the former model to address the resultant variations of blood glucose concentrations following an oral glucose intake. Another model representing the incretins production in the gastrointestinal tract along with their hormonal effects on boosting pancreatic insulin production is also added to the former model. We have used two sets of clinical data obtained during oral glucose tolerance test and isoglycemic intravenous glucose infusion test from both type 2 diabetic and healthy subjects to estimate the model parameters and to validate the model results. The estimation of model parameters is accomplished through solving a nonlinear optimization problem. The results show acceptable precision of the estimated model parameters and demonstrate the capability of the model in accurate prediction of the body response during the clinical studies. [ABSTRACT FROM AUTHOR]

Background: The oral minimal model is a simple, useful tool for the assessment of β-cell function and insulin sensitivity across the spectrum of glucose tolerance, including normal glucose tolerance (NGT), prediabetes, and type 2 diabetes mellitus (T2DM) in humans. Methods: Plasma glucose, insulin, and C-peptide levels were measured during a 180-minute, 75-g oral glucose tolerance test in 24 Korean subjects with NGT (n= 10) and T2DM (n= 14). The parameters in the computational model were estimated, and the indexes for insulin sensitivity and β-cell function were compared between the NGT and T2DM groups. Results: The insulin sensitivity index was lower in the T2DM group than the NGT group. The basal index of β-cell responsivity, basal hepatic insulin extraction ratio, and post-glucose challenge hepatic insulin extraction ratio were not different between the NGT and T2DM groups. The dynamic, static, and total β-cell responsivity indexes were significantly lower in the T2DM group than the NGT group. The dynamic, static, and total disposition indexes were also significantly lower in the T2DM group than the NGT group. Conclusion: The oral minimal model can be reproducibly applied to evaluate β-cell function and insulin sensitivity in Koreans. [ABSTRACT FROM AUTHOR]

Objective: To evaluate subjects with type 1 diabetes for hepatic glycogen depletion after repeated doses of glucagon, simulating delivery in a bihormonal closed-loop system.Research Design and Methods: Eleven adult subjects with type 1 diabetes participated. Subjects underwent estimation of hepatic glycogen using (13)C MRS. MRS was performed at the following four time points: fasting and after a meal at baseline, and fasting and after a meal after eight doses of subcutaneously administered glucagon at a dose of 2 µg/kg, for a total mean dose of 1,126 µg over 16 h. The primary and secondary end points were, respectively, estimated hepatic glycogen by MRS and incremental area under the glucose curve for a 90-min interval after glucagon administration.Results: In the eight subjects with complete data sets, estimated glycogen stores were similar at baseline and after repeated glucagon doses. In the fasting state, glycogen averaged 21 ± 3 g/L before glucagon administration and 25 ± 4 g/L after glucagon administration (mean ± SEM) (P = NS). In the fed state, glycogen averaged 40 ± 2 g/L before glucagon administration and 34 ± 4 g/L after glucagon administration (P = NS). With the use of an insulin action model, the rise in glucose after the last dose of glucagon was comparable to the rise after the first dose, as measured by the 90-min incremental area under the glucose curve.Conclusions: In adult subjects with well-controlled type 1 diabetes (mean A1C 7.2%), glycogen stores and the hyperglycemic response to glucagon administration are maintained even after receiving multiple doses of glucagon. This finding supports the safety of repeated glucagon delivery in the setting of a bihormonal closed-loop system. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the optimal use of XR-RV3 GafChromic® films to assess patient skin dose in interventional radiology while addressing the means to reduce uncertainties in dose assessment. Methods: XR-Type R GafChromic films have been shown to represent the most efficient and suitable solution to determine patient skin dose in interventional procedures. As film dosimetry can be associated with high uncertainty, this paper presents the EURADOS WG 12 initiative to carry out a comprehensive study of film characteristics with a multisite approach. The considered sources of uncertainties include scanner, film, and fitting-related errors. The work focused on studying film behavior with clinical high-dose-rate pulsed beams (previously unavailable in the literature) together with reference standard laboratory beams. Results: First, the performance analysis of six different scanner models has shown that scan uniformity perpendicular to the lamp motion axis and that long term stability are the main sources of scanner-related uncertainties. These could induce errors of up to 7% on the film readings unless regularly checked and corrected. Typically, scan uniformity correction matrices and reading normalization to the scanner-specific and daily background reading should be done. In addition, the analysis on multiple film batches has shown that XR-RV3 films have generally good uniformity within one batch (<1.5%), require 24 h to stabilize after the irradiation and their response is roughly independent of dose rate (<5%). However, XR-RV3 films showed large variations (up to 15%) with radiation quality both in standard laboratory and in clinical conditions. As such, and prior to conducting patient skin dose measurements, it is mandatory to choose the appropriate calibration beam quality depending on the characteristics of the x-ray systems that will be used clinically. In addition, yellow side film irradiations should be preferentially used since they showed a lower dependence on beam parameters compared to white side film irradiations. Finally, among the six different fit equations tested in this work, typically used third order polynomials and more rational and simplistic equations, of the form dose inversely proportional to pixel value, were both found to provide satisfactory results. Fitting-related uncertainty was clearly identified as a major contributor to the overall film dosimetry uncertainty with up to 40% error on the dose estimate. Conclusions: The overall uncertainty associated with the use of XR-RV3 films to determine skin dose in the interventional environment can realistically be estimated to be around 20% (k = 1). This uncertainty can be reduced to within 5% if carefully monitoring scanner, film, and fitting-related errors or it can easily increase to over 40% if minimal care is not taken. This work demonstrates the importance of appropriate calibration, reading, fitting, and other film-related and scan-related processes, which will help improve the accuracy of skin dose measurements in interventional procedures. [ABSTRACT FROM AUTHOR]

Diabetes is considered as a global affecting disease with an increasing contribution to both mortality rate and cost damage in the society. Therefore, tight control of blood glucose levels has gained significant attention over the decades. This paper proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on combining the fuzzy logic theory and single order sliding mode control (SOSMC) to improve the properties of sliding mode control method and to alleviate its drawbacks. The aim of the proposed controller that is called SOSMC combined with fuzzy on-line tunable gain is to tune the gain of the controller adaptively. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. As a result, this method can decline the risk of hypoglycemia, a lethal phenomenon in regulating blood glucose level in diabetics caused by a low blood glucose level. Moreover, it attenuates the chattering observed in SOSMC significantly. It is worth noting that in this approach, a mathematical model called minimal model is applied instead of the intravenously infused insulin-blood glucose dynamics. The simulation results demonstrate a good performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. In addition, this method is compared to fuzzy high-order sliding mode control (FHOSMC) and the superiority of the new method compared to FHOSMC is shown in the results. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: The aim of this study was to compare the pharmacokinetics of two different concentrations of insulin aspart (B28Asp human insulin) in children aged 3-6 years with type 1 diabetes. Methods: Young children with type 1 diabetes underwent an open-label, randomised, two-period crossover study in a clinical research facility, 2-6 weeks apart. In random order, diluted (1:5 dilution with saline [154 mmol/l NaCl]; 20 U/ml) or standard strength (100 U/ml) insulin aspart was administered via an insulin pump as a meal bolus and then overnight by closed-loop insulin delivery as determined by a model predictive algorithm. Plasma insulin was measured every 30-60 min from 17:00 hours on day 1 to 8:00 hours on day 2. We measured the time-to-peak insulin concentration ( t), insulin metabolic clearance rate ( MCR) and background insulin concentration ( ins) using compartmental modelling. Results: Eleven children (six male; age range 3.75-6.96 years, HbA 7.6% ± 1.3% [60 ± 14 mmol/mol], BMI standard deviation score 1.0 ± 0.8, duration of diabetes 2.2 ± 1.0 years, total daily dose 12.9 [10.6-16.5] U, fasting C-peptide concentration 5 [5-17.1] pmol/l; mean ± SD or median [interquartile range]) participated in the study. No differences between standard and diluted insulin were observed in terms of t (59.2 ± 14.4 vs 61.6 ± 8.7) min for standard vs diluted, p = 0.59; MCR (1.98 × 10 ± 0.99 × 10 vs 1.89 × 10 ± 0.82 × 10 1/kg/min, p = 0.47), and ins (34 [1-72] vs 23 [3-65] pmol/l, p = 0.66). However, t showed less intersubject variability following administration of diluted aspart (SD 14.4 vs 8.7 min, p = 0.047). Conclusions/interpretation: Diluting insulin aspart does not change its pharmacokinetics. However, it may result in less variable absorption and could be used in young children with type 1 diabetes undergoing closed-loop insulin delivery. Trial registration: Clinicaltrials.gov NCT01557634 Funding: Funding was provided by the JDRF, 7th Framework Programme of the European Union, Wellcome Trust Strategic Award and the National Institute for Health Research Cambridge Biomedical Research Centre. [ABSTRACT FROM AUTHOR]

In this study, several advanced data-driven non-linear identification techniques are compared on a specific problem: a simplified glucoregulatory system modelling example. This problem represents a challenge in the development of an artificial pancreas for Type 1 diabetes mellitus treatment, since for this application good non-linear models are needed to design accurate closed-loop controllers to regulate the glucose level in the blood. Block-oriented as well as state-space models are used to describe both the dynamics and the non-linear behaviour of the insulin-glucose system, and the advantages and drawbacks of each method are pointed out. The obtained non-linear models are accurate in simulating the patient's behaviour, and some of them are also sufficiently simple to be considered in the implementation of a model-based controller to develop the artificial pancreas. [ABSTRACT FROM AUTHOR]

OBJECTIVE Glucagon delivery in closed-loop control of type 1 diabetes is effective in minimizing hypoglycemia. However, high insulin concentration lowers the hyperglycemic effect of glucagon, and small doses of glucagon in this setting are ineffective. There are no studies clearly defining the relationship between insulin levels, subcutaneous glucagon, and blood glucose. RESEARCH DESIGN AND METHODS Using a euglycemic clamp technique in 11 subjects with type 1 diabetes, we examined endogenous glucose production (EGP) of glucagon (25, 75, 125, and 175 µg) at three insulin infusion rates (0.016, 0.032, and 0.05 units/kg/h) in a randomized, crossover study. Infused 6,6-dideuterated glucose was measured every 10 min, and EGP was determined using a validated glucoregulatory model. Area under the curve (AUC) for glucose production was the primary outcome, estimated over 60 min. RESULTS At low insulin levels, EGP rose proportionately with glucagon dose, from 5 ± 68 to 112 ± 152 mg/kg (P = 0.038 linear trend), whereas at high levels, there was no increase in glucose output (19 ± 53 to 26 ± 38 mg/kg, P = NS). Peak glucagon serum levels and AUC correlated well with dose (r² = 0.63, P < 0.001), as did insulin levels with insulin infusion rates (r² = 0.59, P < 0.001). CONCLUSIONS EGP increases steeply with glucagon doses between 25 and 175 µg at lower insulin infusion rates. However, high insulin infusion rates prevent these doses of glucagon from significantly increasing glucose output and may reduce glucagon effectiveness in preventing hypoglycemia when used in the artificial pancreas. [ABSTRACT FROM AUTHOR]

OBJECTIVE Closed-loop insulin delivery offers a promising treatment option, but to date, it has only been evaluated in type 1 diabetes. Our aim was to evaluate the feasibility of fully closed-loop subcutaneous insulin delivery in insulin-naïve patients with type 2 diabetes. RESEARCH DESIGN AND METHODS Twelve subjects (seven males, age 57.2 years, BMI 30.5 kg/m²) with noninsulin-treated type 2 diabetes (HbA1c 8.4% [68 mmol/mol], diabetes duration 7.6 years) underwent two 24-h visits (closed-loop and control) in a randomized crossover design. During closed-loop visits, the subjects' routine diabetes therapy was replaced with model predictive control algorithm-driven subcutaneous insulin pump delivery based on real-time continuous glucose monitoring. Meals were unannounced, and no additional insulin was administered for carbohydrates consumed. During control visits, the usual diabetes regimen was continued (metformin 92%, sulfonylureas 58%, dipeptidyl peptidase-4 inhibitors 33%). On both visits, subjects consumed matched 50- to 80-g carbohydrate meals and optional 15-g carbohydrate snacks and remained largely sedentary. Plasma glucose measurements evaluated closed-loop performance. RESULTS Compared with conventional therapy, 24 h of closed-loop insulin delivery increased overall the median time in target plasma glucose (3.9-8.0 mmol/L) from 24 to 40% (P = 0.016), despite sensor under-reading by a median of 1.2 mmol/L. The benefit of the closed-loop system was more prominent overnight, with greater time in target glucose (median 78 vs. 35%; P = 0.041) and less time in hyperglycemia (22 vs. 65%; P = 0.041). There was no hypoglycemia during either intervention. CONCLUSIONS A closed-loop system without meal announcement and using subcutaneous insulin delivery in insulin-naïve patients with type 2 diabetes appears feasible and safe. Improvement in postprandial glucose control may require further optimization of system performance. [ABSTRACT FROM AUTHOR]

This paper deals with subcutaneous blood glucose level control. Inverse optimal trajectory tracking for discrete time non-linear positive systems is applied. The scheme is developed for MIMO (multi-input, multi-output) affine systems. The control law calculates the subcutaneous insulin delivery rate in order to prevent hyperglycemia and hypoglycemia events. A neural model is obtained from an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF); this neural model has an affine form, which permits the applicability of inverse optimal control scheme. The proposed algorithm is tuned to follow a desired trajectory; this trajectory reproduces the glucose absorption of a healthy person. Then this model is used to synthesize an inverse optimal controller in order to regulate the subcutaneous blood glucose level for a Type 1 Diabetes Mellitus patient the applicability of the proposed scheme is illustrated via simulation using a recurrent neural network in order to model the insulin-glucose dynamics. [ABSTRACT FROM AUTHOR]

SUMMARY Patients with type 1 diabetes mellitus require exogenous insulin infusion to avoid chronic complications related to elevated glucose levels. With diabetes reaching epidemic proportions, recent times have witnessed an increased attention in the field of optimal glucose management by closed-loop insulin delivery system. A proper glucose management scheme, in addition of maintaining the glucose level within the normal range of 80-120 mg/dL, should avoid excessive insulin delivery leading to hypoglycemia. By considering the glucose regulation as a linear quadratic problem, a constrained novel sub-optimal controller is proposed in the present work, for the maintenance of normal glucose level in type 1 diabetic subjects. The observer free state feedback controller is based on the feedback of only physiological variables (plasma glucose and plasma insulin). Constraining the feedback elements corresponding to the non-physiological variables avoids the use of an observer while maintaining the advantages of state feedback control. The implementation of the proposed scheme requires simple measurement protocol with no online computation. The closed-loop performance of the controller is evaluated on a physiologically relevant model for a meal disturbance and continuous glucose infusion. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

OBJECTIVE--To compare two validated closed-loop (CL) algorithms versus patient self- control with CSII in terms of glycemic control. RESEARCH DESIGN AND METHODS--This study was a multicenter, randomized, d three-way crossover, open-label trial in 48 patients with type 1 diabetes mellitus for at least 6 months, treated with continuous subcutaneous insulin infusion. Blood glucose was controlled for 23 h by the algorithm of the Universities of Pavia and Padova with a Safety Supervision Module developed at the Universities of Virginia and California at Santa Barbara (international artificial pancreas [iAP]), by the algorithm of University of Cambridge (CAM), or by patients themselves in open loop (OL) during three hospital admissions including meals and exercise. The main analysis was on an intention-to-treat basis. Main outcome measures included time spent in target (glucose levels between 3.9 and 8.0 mmol/L or between 3.9 and 10.0 mmol/L after meals). RESULTS--Time spent in the target range was similar in CL and OL: 62.6% for OL, 59.2% for iAP, and 58.3% for CAM.While mean glucose level was significantly lower in OL (7.19, 8.15, and 8.26 mmol/L, respectively) (overall P = 0.001), percentage of time spent in hypoglycemia (<3.9mmol/L) was almost threefold reduced during CL (6.4%, 2.1%, and 2.0%) (overall P = 0.001) with less time ≤2.8 mmol/L (overall P = 0.038). There were no significant differences in outcomes between algorithms. CONCLUSIONS--Both CAM and iAP algorithms provide safe glycemic control. [ABSTRACT FROM AUTHOR]

Blood glucose control, for example, in diabetes mellitus or severe illness, requires strict adherence to a protocol of food, insulin administration and exercise personalized to each patient. An artificial pancreas for automated treatment could boost quality of glucose control and patients' independence. The components required for an artificial pancreas are: i) continuous glucose monitoring (CGM), ii) smart controllers and iii) insulin pumps delivering the optimal amount of insulin. In recent years, medical devices for CGM and insulin administration have undergone rapid progression and are now commercially available. Yet, clinically available devices still require regular patients' or caregivers' attention as they operate in open-loop control with frequent user intervention. Dosage-calculating algorithms are currently being studied in intensive care patients , for short overnight control to supplement conventional insulin delivery , and for short periods where patients rest and follow a prescribed food regime . Fully automated algorithms that can respond to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and which provide the necessary personalized control for individuals is currently beyond the state-of-the-art. Here, we review and discuss reinforcement learning algorithms, controlling insulin in a closed-loop to provide individual insulin dosing regimens that are reactive to the immediate needs of the patient. [ABSTRACT FROM AUTHOR]

Inverse optimal trajectory tracking via a control Lyapunov function (CLF) for discrete time non-linear systems is developed and applied to type 1 diabetes mellitus patients control. The control law calculates the insulin delivery rate in order to prevent hyperglycemia and hypoglycemia levels. To synthesize the inverse optimal control law a quadratic candidate CLF is used. The proposed algorithm is tuned to follow a desired trajectory; this trajectory reproduces the glucose absorption of a healthy person. Simulation results applied for two different patients illustrate the applicability of the control law and a comparison with inverse optimal neural control via passivity is included. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Successful postprandial glycaemia management requires understanding of absorption patterns after meals containing variable complex carbohydrates. We studied eight young participants with type 1 diabetes to investigate a large low-glycaemic-load (LG) meal and another eight participants to investigate a high-glycaemic-load (HG) meal matched for carbohydrates (121 g). Methods: On Visit 1, participants consumed an evening meal. On follow-up Visit 2, a variable-target glucose clamp was performed to reproduce glucose and insulin levels from Visit 1. Adopting stable-label tracer dilution methodology, we measured endogenous glucose production on Visit 2 and subtracted it from total glucose appearance measured on Visit 1 to obtain meal-attributable glucose appearance. Results: After the LG meal, 25%, 50% and 75% of cumulative glucose appearance was at 88 ± 21, 175 ± 39 and 270 ± 54 min (mean ± SD), whereas glucose from the HG meal appeared significantly faster at 56 ± 12, 100 ± 25 and 153 ± 39 min ( p < 0.001 to 0.003), and resulted in a 50% higher peak appearance ( p < 0.001). Higher apparent bioavailability by 15% ( p = 0.037) was observed after the LG meal. We documented a 20 min deceleration of dietary mixed carbohydrates compared with dietary glucose for the HG meal and a twofold deceleration for the LG meal. Conclusions/interpretation: Absorption patterns may be influenced by glycaemic load and/or meal composition, affecting optimum prandial insulin dosing in type 1 diabetes. [ABSTRACT FROM AUTHOR]

A lifelong-implanted and completely automated artificial or bioartificial pancreas (BAP) is the holy grail for type 1 diabetes treatment, and could be a definitive solution even for other severe pathologies, such as pancreatitis and pancreas cancer. Technology has made several important steps forward in the last years, providing new hope for the realization of such devices, whose feasibility is strictly connected to advances in glucose sensor technology, subcutaneous and intraperitoneal insulin pump development, the design of closed-loop control algorithms for mechatronic pancreases, as well as cell and tissue engineering and cell encapsulation for biohybrid pancreases. Furthermore, smart integration of the mentioned components and biocompatibility issues must be addressed, bearing in mind that, for mechatronic pancreases, it is most important to consider how to recharge implanted batteries and refill implanted insulin reservoirs without requiring periodic surgical interventions. This review describes recent advancements in technologies and concepts related to artificial and bioartificial pancreases, and assesses how far we are from a lifelong-implanted and self-working pancreas substitute that can fully restore the quality of life of a diabetic (or other type of) patient. [ABSTRACT FROM AUTHOR]

The author comments on an article by Breton et al. on artificial pancreas in patients with type 1 diabetes. According to the author, ideal control by an artificial pancreas would therefore encompass autonomous control with normalization both of glycemia and of the glucose fluxes that contribute to it under all physiological circumstances. The author says that it would reduce the complications that arise form their dysregulation.

SUMMARY An optimal state feedback controller is designed with the objective of minimizing the elevated glucose levels caused by meal intake in Type 1 diabetic subjects, by the minimal infusion of insulin. The states for the controller based on linear quadratic regulator theory are estimated from noisy data using Kalman filter. The controller designed for a physiological relevant mathematical model is coupled with another model for simulating meal dynamics, which converts meal intake into glucose appearance rate in the plasma. The tuning parameters (weighting matrices) of the controller and the design parameters (noise covariance matrices) of the Kalman filter are optimized using genetic algorithm. The controller based on the combined framework of evolutionary computing and state estimated linear quadratic regulator is found to maintain normoglycemia for meal intakes of varying carbohydrate content. The proposed approach addresses noisy output measurement, modeling error and delay in sensor measurement. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, a discrete-time inverse optimal trajectory tracking for a class of nonlinear positive systems is proposed. The scheme is developed for MIMO affine discrete-time positive nonlinear systems. This optimal controller is based on discrete time passivity and positive systems theory. The advantage of this scheme is that it avoids solving the associated Hamilton-Jacobi-Bellman equation and minimizes a meaningful cost function. The affine discrete-time positive nonlinear system is obtained from an online neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter. The applicability of the proposed approach is illustrated via simulation by trajectory tracking control of type 1 diabetes mellitus patients. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents study that proposed a novel blend of model predictive control (MPC) and iterative learning control (ILC), also called learning-type MPC (L-MPC), for closed-loop control in an artificial pancreatic beta-cell. The proposed approach is initially tested on the Adult Average subject. The study showed that the blood glucose concentrations, after 20 days, can be maintained within 68-145 milligrams per decilitre (mg/dl) when the means are repetitive. L-MPC can generate superior control performance compared with that realized under MPC.

OBJECTIVE -- Current state-of-the-art artificial pancreas systems are either based on traditional linear control theory or rely on mathematical models of glucose-insulin dynamics. Blood glucose control using these methods is limited due to the complexity of the biological system. The aim of this study was to describe the principles and clinical performance of the novel MD-Logic Artificial Pancreas (MDLAP) System. RESEARCH DESIGN AND METHODS -- The MDLAP applies fuzzy logic theory to imitate lines of reasoning of diabetes caregivers. It uses a combination of control-to-range and control-to-target strategies to automatically regulate individual glucose levels. Feasibility clinical studies were conducted in seven adults with type 1 diabetes (aged 19-30 years, mean diabetes duration 10 ± 4 years, mean A1C 6.6 ± 0.7%). All underwent 14 full, closed-loop control sessions of 8 h (fasting and meal challenge conditions) and 24 h. RESULTS -- The mean peak postprandial (overall sessions) glucose level was 224 ± 22 mg/dl. Postprandial glucose levels returned to <180 mg/dl within 2.6 ± 0.6 h and remained stable in the normal range for at least 1 h. During 24-h closed-loop control, 73% of the sensor values ranged between 70 and 180 mg/dl, 27% were >180 mg/dl, and none were <70 mg/dl. There were no events of symptomatic hypoglycemia during any of the trials. CONCLUSIONS -- The MDLAP system is a promising tool for individualized glucose control in patients with type 1 diabetes. It is designed to minimize high glucose peaks while preventing hypoglycemia. Further studies are planned in the broad population under daily-life conditions. [ABSTRACT FROM AUTHOR]

Diabet. Med. 27, 480–484 (2010) Aims We assessed an extended interruption of subcutaneous insulin delivery during overnight closed-loop glucose control in children and adolescents with Type 1 diabetes (T1D). Methods In seven young subjects with T1D [age 14.2 ± 2.1 years, diabetes duration 6.9 ± 4.0 years, glycated haemoglobin (HbA1c) 8.0 ± 1.5%, body mass index (BMI) 21.4 ± 4.0 kg/m2, total daily insulin dose 0.9 ± 0.2 units/kg/day; mean ± sd) participating in overnight closed-loop glucose control studies, insulin delivery was interrupted for at least 90 min on the basis of predicted hypoglycaemia, low prevailing glucose levels or a too-steep decline in glucose levels. Results Insulin delivery was interrupted for 165 (105, 210) min [median, interquartile range (IQR)]. Plasma glucose was 6.2 ± 3.2 mmol/l at the time of interruption and 5.5 ± 2.0 mmol/l 105 min later ( P = 0.15, paired t-test). Plasma glucose declined during the first hour of the interruption at a rate of 0.02 ± 0.03 mmol/l per min and reached a nadir of 5.2 ± 2.7 mmol/l; 105 min after the interruption, plasma glucose was increasing at a rate of 0.01 ± 0.03 mmol/l per min. When insulin delivery restarted, plasma glucose was 6.4 ± 2.2 mmol/l and peaked at 7.9 ± 2.1 mmol/l in 60 min ( P = 0.01). Physiological levels of plasma insulin were measured throughout with a nadir of 119 ± 78 pmol/l. Conclusions A prolonged interruption of insulin delivery during overnight closed-loop glucose control to prevent hypoglycaemia was not associated with an increased risk of hyperglycaemia in young people with T1D. [ABSTRACT FROM AUTHOR]

Intensive insulin therapy aimed at achieving normal glucose levels significantly reduces the complications that are associated with diabetes but is also associated with an increased risk of low glucose levels (hypoglycemia). The growing use of continuous glucose monitors has stimulated the development of the artificial pancreas, a closed-loop insulin-delivery system aimed at restoring near-normal glucose levels while reducing the risk of hypoglycemia. The artificial pancreas comprises three components: a continuous glucose monitor, an insulin infusion pump and a control algorithm delivering insulin according to real-time glucose readings. In this article, we review closed-loop glucose control, including its components, development, testing and clinical application. INSET: Key issues. [ABSTRACT FROM AUTHOR]

The article presents a study examining the influence of input excitation upon the quality of empirical dynamic models used for the prediction of blood glucose trends in type 1 diabetes patients. The ability for high-quality models to improve glycemic regulation for individuals with type 1 diabetes is noted. The fact that bolused insulin and meal carbohydrates are the most influential inputs influencing the concentration of glucose in the blood is mentioned. Also noted is the possibility that inputs with high levels of excitation may cause extreme hypoglycemia or hyperglycemia for the patient.

For people with type 1 diabetes, automatic controllers aim to maintain the blood glucose concentration within the desired range of 60–120 mg/dL by infusing the appropriate amount of insulin in the presence of meal and exercise disturbances. Blood glucose concentration outside the desired range can be harmful to an individual’s health but concentration below 60 mg/dL, a state known as hypoglycemia, is considered to be more harmful than the concentration above 120 mg/dL, a state known as hyperglycemia. In this paper, two techniques to address this issue within a multi-parametric model based control framework are presented. The first technique introduces asymmetry into the objective function to penalize the deviation towards hypoglycemia more than the deviation towards hyperglycemia. The second technique is based upon placing higher priority on satisfaction of constraints on hypoglycemia than on satisfaction of constraints on hyperglycemia. The performance of both the control techniques is analyzed and compared in the presence of disturbances. [ABSTRACT FROM AUTHOR]

Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied. In addition, we have designed population and personalized models and studied the generalizability of the models and the influence of physical activity (PA) on them. Obtaining 30 g of rescue carbohydrates (CHO) is the optimal value for preventing NH, so it can be asserted that this is the value with which the time under 70 mg/dL decreases the most, with almost a 35% reduction, while increasing the time in the target range by 1.3%. This study supports the feasibility of using ML techniques to address the prediction of NH in patients with T1D under MDI therapy, using continuous glucose monitoring (CGM) and a PA tracker. The results obtained prove that BG predictions can not only be critical in achieving safer diabetes management, but also assist physicians and patients to make better and safer decisions regarding insulin therapy and their day-to-day lives. [ABSTRACT FROM AUTHOR]

OBJECTIVE -- To assess whether prandial insulin, in addition to basal insulin, has an effect on the rate of glucose appearance from a meal in people with type 1 diabetes. RESEARCH DESIGN AND METHODS -- The rate of glucose appearance from a mixed meal (Ra[sub meal]) was investigated in six adult (aged 24 ± 2 years), lean (BMI 23.6 ± 1.5 kg/m²) subjects with well-controlled type 1 diabetes (duration 7.9 ± 6.9 years, A1C 7.6 ± 0.9%) with/without prandial insulin. Actrapid was infused to maintain euglycemia before meals were consumed. Subjects consumed two identical meals on separate occasions, and Ra[sub meal] was measured using a dual isotope method. [6,6-²H[sub 2]] glucose was incorporated into the meal (0.081 g/kg body wt), and a primed constant/variable rate infusion of [1,2,3,4,5,6,6-²H[sub 2]]glucose was administered. In the tests with prandial insulin, an additional bolus dose of Actrapid was given 20 min before the meal at 0.1 units/kg body wt. RESULTS -- Insulin concentration with prandial insulin was significantly higher than during basal insulin studies (119 ± 16 vs. 66 ± 15 pmol/l, P = 0.03 by paired t test). Despite differences in insulin concentration, there were no differences in total glucose appearance (3,398 ± 197 vs. 3,307 ± 343 µmol/kg) or time taken for 25% (33.1 ± 3.3 vs. 31.7 ± 3.5 min), 50% (54.6 ± 3.5 vs. 54.1 ± 4.7 min), and 75% (82.9 ± 7.1 vs. 82.8 ± 5.8 min) of total glucose appearance. The fraction of the glucose dose appearing in the circulation was the same for basal (73 ± 8%) and prandial (75 ± 4%) study days. CONCLUSIONS -- These results suggest that meal glucose appearance is independent of prandial insulin concentration in people with type 1 diabetes. [ABSTRACT FROM AUTHOR]

Tight glycaemic control (TGC) in critically ill patients improves clinical outcome, but is difficult to establish The primary objective of the present study was to compare glucose control in medical ICU patients applying a computer-based enhanced model predictive control algorithm (eMPC) extended to include time-variant sampling against an implemented glucose management protocol. Open randomised controlled trial. Nine-bed medical intensive care unit (ICU) in a tertiary teaching hospital. Fifty mechanically ventilated medical ICU patients. Patients were included for a study period of up to 72 h. Patients were randomised to the control group ( n = 25), treated by an implemented insulin algorithm, or to the eMPC group ( n = 25), using the laptop-based algorithm. Target range for blood glucose (BG) was 4.4–6.1 mM. Efficacy was assessed by mean BG, hyperglycaemic index (HGI) and BG sampling interval. Safety was assessed by the number of hypoglycaemic-episodes < 2.2 mM. Each participating nurse filled-in a questionnaire regarding the usability of the algorithm. BG and HGI were significantly lower in the eMPC group [BG 5.9 mM (5.5–6.3), median (IQR); HGI 0.4 mM (0.2–0.9)] than in control patients [BG 7.4 mM (6.9–8.6), p < 0.001; HGI 1.6 mM (1.1–2.4), p < 0.001]. One hypoglycaemic episode was detected in the eMPC group; no such episodes in the control group. Sampling interval was significantly shorter in the eMPC group [eMPC 117 min (± 34), mean (± SD), vs 174 min (± 27); p < 0.001]. Thirty-four nurses filled-in the questionnaire. Thirty answered the question of whether the algorithm could be applied in daily routine in the affirmative. The eMPC algorithm was effective in maintaining tight glycaemic control in severely ill medical ICU patients. [ABSTRACT FROM AUTHOR]

Background: Analyzing the dynamics of insulin concentration in the blood is necessary for a comprehensive understanding of the effects of insulin in vivo. Insulin removal from the blood has been addressed in many studies. The results are highly variable with respect to insulin clearance and the relative contributions of hepatic and renal insulin degradation. Results: We present a dynamic mathematical model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. The model describes renal and hepatic insulin degradation, pancreatic insulin secretion and nonspecific insulin binding in the liver. Hepatic insulin receptor activation by insulin binding, receptor internalization and autophosphorylation is explicitly included in the model. We present a detailed mathematical analysis of insulin degradation and insulin clearance. Stationary model analysis shows that degradation rates, relative contributions of the different tissues to total insulin degradation and insulin clearance highly depend on the insulin concentration. Conclusion: This study provides a detailed dynamic model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. Experimental data sets from literature are used for the model validation. We show that essential dynamic and stationary characteristics of insulin degradation are nonlinear and depend on the actual insulin concentration. [ABSTRACT FROM AUTHOR]