Patients affected by type 1 diabetes (T1D) suffer from lack of insulin secretion by the pancreas, due to the autoimmune destruction of the insulinproducing beta cells. This translates into elevated plasma glucose concentration with deleterious effects in the cardiovascular system, leading to serious long-term complications. With the improvement of continuous glucose monitoring (CGM) technology, a significant amount of research has been focused in the last decade on the development of an artificial pancreas (AP), that is, a closed-loop glucose control system that automatically dispenses insulin. The AP is now closer to reality [1]. An AP system was first approved by the U.S. Food and Drug Administration (FDA) in 2016 [2]. The system still requires user intervention at mealtime, as currently done in openloop insulin pump therapy, to administer an insulin bolus based on carbohydrate counting (a hybrid closed-loop system).

This work describes Internet functions that include video file generation and real-time control as MWS (MATLAB Web server) features, which are useful for undergraduate courses. With these functions, using virtual processes, which in turn allow video animations of simulated processes, can enhance virtual laboratories. Furthermore, MWS allows the implementation of remote laboratories operating in batch mode. WinCom or any other suitable software can be used to implement online laboratories. These methodologies provide a straightforward approach for the developer of the teaching material, the control engineering lecturer, and a low-cost option for the student user.

Continuous Glucose Monitoring (CGM) has been a springboard of new diabetes management technologies such as integrated sensor-pump systems, the artificial pancreas, and more recently, smart pens. It also allows patients to make better informed decisions compared to a few measurements per day from a glucometer. However, CGM accuracy is reportedly affected during exercise periods, which can impact the effectiveness of CGM-based treatments. In this review, several studies that used CGM during exercise periods are scrutinized. An extensive literature review of clinical trials including exercise and CGM in type 1 diabetes was conducted. The gathered data were critically analysed, especially the Mean Absolute Relative Difference (MARD), as the main metric of glucose accuracy. Most papers did not provide accuracy metrics that differentiated between exercise and rest (non-exercise) periods, which hindered comparative data analysis. Nevertheless, the statistic results confirmed that CGM during exercise periods is less accurate.

This brief is focused on the closed-loop control of postprandial glucose levels of patients with type 1 diabetes mellitus after unannounced meals, which is still a major challenge toward a fully autonomous artificial pancreas. The main limitations are the delays introduced by the subcutaneous insulin pharmacokinetics and the glucose sensor, which typically leads to insulin overdelivery. Current solutions reported in the literature typically resort to meal announcement, which requires patient intervention. In this brief, a disturbance observer (DOB) is used to estimate the effect of unannounced meals, and the insulin pharmacokinetics is taken into account by means of a feedforward compensator. The proposed strategy is validated in silico with the UVa/Padova metabolic simulator. It is demonstrated how the DOB successfully estimates and counteracts not only the effect of meals but also the sudden drops in the glucose levels that may lead to hypoglycemia. For unannounced meals, results show a median time-in-range of 80% in a 30-day scenario with high carbohydrate content and large intrasubject variability. Optionally, users may decide to announce meals. In this case, considering severe bolus mismatch due to carbohydrate counting errors, the median time-in-range is increased up to 88%. In every case, hypoglycemia is avoided.