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Patient with diabetes must regularly monitor blood glucose level. Drawing a blood sample is a painful and discomfort experience. Alternatively, the patient measures interstitial fluid glucose level with a sensor installed in subcutaneous tissue. Then, a model of glucose dynamics calculates blood glucose level from the sensor-measured, i.e., interstitial fluid glucose level of subcutaneous tissue. Interstitial fluid glucose level can significantly differ from blood glucose level. The sensor is either factory-calibrated, or the patient calibrates the sensor periodically by drawing blood samples, when glucose levels of both compartments are steady. In both cases, the sensor lifetime is limited up to 14 days. This is the present state of the art. With a physiological model, we would like to prolong the sensor lifetime with an adaptive approach, while requiring no additional blood sample. Prolonging sensor's lifetime, while reducing the associated discomfort, would considerably improve patient's quality of life. We demonstrate that it is possible to determine personalized model parameters from multiple CGMS-signals only, using an animal experiment with a hyperglycemic clamp. The experimenter injected separate glucose and insulin boluses to trigger rapid changes, on which we evaluated the ability to react to non-steady glucose levels in different compartments. With the proposed model, 70%, 80% and 95% of the calculated blood glucose levels had relative error less than or equal to 21.9%, 32.5% and 43.6% respectively. Without the model, accuracy of the sensor-estimated blood glucose level decreased to 39.4%, 49.9% and 99.0% relative errors. This confirms feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Diabetic patient desires to avoid hypo- and hyperglycemic episodes, which result from insufficient insulin production. As the diabetes disease progresses, it requires an advance control of external insulin administration with an insulin pump. Given the importance of blood-glucose level prediction for the insulin therapy, there is a Blood-Glucose Level Prediction Challenge. This prediction is based on a post-mortem dataset, which include a number of signals related to the daily life of a diabetic patient. We propose replacing these post-mortem signals with an in-silico diabetic patient. For this purpose, we can use the SmartCGMS continuous glucose monitoring and controlling framework together with an FDA-accepted diabetic patient simulation. As a result, a competing researcher have the same conditions as a developer of a real-life insulin pump, connected to a real diabetic patient. When using SmartCGMS, simulated, prototyped and real devices can work together. This approach reduces the difference between laboratory and practical results, thus increasing the level of realism for the entire challenge. As a report on the current SmartCGMS state, we describe the previously unpublished features, which enable an improved glucose level prediction and/or control challenge. [ABSTRACT FROM AUTHOR]

Glucose is an important source of energy for cells. In clinical practice, we measure glucose level in blood and interstitial fluid. Each method has its pros and cons, and both levels correlate with each other. As the body tries to maintain the glucose level within a particular range to avoid adverse effects, it is desirable to predict future glucose levels in order to aid provided health care. We can see this desire in research, e.g., research on glucose transporters of cells. As yet another example, we can see it with diabetic patients, patients in a metabolic intensive care unit, particularly. In this paper, a glucose level prediction method is proposed. [ABSTRACT FROM PUBLISHER]