The artificial pancreas aims at the automatic delivery of insulin for glycemic control in patients with type 1 diabetes, i.e., closed-loop glucose control. One of the challenges of the artificial pancreas is to avoid controller overreaction leading to hypoglycemia, especially in the late postprandial period. In this study, an original proposal based on sliding mode reference conditioning ideas is presented as a way to reduce hypoglycemia events induced by a closed-loop glucose controller. The method is inspired in the intuitive advantages of two-step constrained control algorithms. It acts on the glucose reference sent to the main controller shaping it so as to avoid violating given constraints on the insulin-on-board. Some distinctive features of the proposed strategy are that 1) it provides a safety layer which can be adjusted according to medical criteria; 2) it can be added to closed-loop controllers of any nature; 3) it is robust against sensor failures and overestimated prandial insulin doses; and 4) it can handle nonlinear models. The method is evaluated in silico with the ten adult patients available in the FDA-accepted UVA simulator.

Objective: Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy. Subjects and Methods: An individual patient's model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t 0 ) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG 0-5h ) and variability as the individual coefficient of variation (CV) of AUC-PG 0-5h . The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t 0 (PG t0 ), and insulin dose to AUC-PG 0-5h and its CV was also investigated. Results: AUC-PG 0-5h was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5 -127.5 vs. 689.2 -180.7 mg/dL$h) or 100-g (752.1 -237.7 vs. 760.0 -263.2 mg/dL$h) CHO meals. A multiple regression analysis revealed a significant model only for the tBolus, with PG t0 being the best predictor of AUC-PG 0-5h explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7 -15.3% vs. 10.1 -12.5%) but independent of the factors studied. Conclusions: A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identified and analyzed for further optimization of postprandial glycemic control.

Brewer's spent grain (BSG) is a by‐product of the brewing industry that contributes to a large volume of wastes. The lignocellulosic nature of this waste, together with presence of functional components such as antioxidants, represents an attractive for the composite's industry. In this work, BSG has been used as functional filler for polypropylene matrix to give an additional use to this industrial waste. Addition of BSG filler improves the overall environmental efficiency of the polypropylene matrix thus leading to high environmentally friendly materials. BSG can be loaded in the 10–40 wt% range with easy manufacturing, balanced mechanical properties, and additionally, excellent antioxidant properties are achieved with increasing BSG loading due to natural antioxidants that have not been removed during the brewing process. In particular, the onset of the thermo‐oxidative degradation of polypropylene is improved by 15–20°C for different compositions. Due to the lignocellulosic nature of BSG, water uptake is a clear drawback of PP–BSG composites but formulations containing 10‐30 wt% BSG hold the water uptake at very low values. POLYM. COMPOS., 38:40–47, 2017. © 2015 Society of Plastics Engineers

The algorithm presented here is a robust nonheuristic alternative to deal with postprandial glycemic control. It is shown as a powerful tool that could be integrated in future smart insulin pumps.

Intensive insulin therapy in type 1 diabetes is based on the well-established practice of adjusting basal and bolus insulin independently. Basal insulin delivery is designed to optimize glucose concentrations between meals and overnight, while bolus insulin delivery is designed to optimize postprandial glucose concentrations. However, this strategy shows some limitations in the postprandial glucose control, especially for meals with high carbohydrate content. Strategies based on coordinating basal and bolus insulin in the postprandial period help in overcoming these limitations. An algorithm, based on mathematically guaranteed techniques (interval analysis), is presented in this paper. It determines, given the current glycemic state of the patient and the meal to be ingested, a basal-bolus combination that will yield a tight postprandial glycemic control according to the International Diabetes Federation guidelines. For a given meal, the algorithm reveals which bolus administration mode will enable a good postprandial performance: standard, square-wave, dual-wave, or temporal basal decrement. The algorithm is validated through an in silico study using the 30 subjects in the educational version of the Food and Drug Administration accepted University of Virginia simulator.