Péter Szalay

Pathologic Alterations of the Outer Retina in Streptozotocin-Induced Diabetes

PURPOSE Neurodegeneration as an early event of diabetic retinopathy preceding clinically detectable vascular alterations is a widely proven issue today. While there is evidence for the impairment of color vision and contrast sensitivity in early diabetes, suggesting deteriorated photoreceptor function, the underlying neuropathology of these functional alterations is still unknown. The aim of the present study was to investigate the effects of early diabetes on the outer retinal cells. METHODS The retinal pigment epithelium, photopigment expression, and density and morphology of photoreceptors were studied using immunocytochemistry in streptozotocin-induced diabetes in two rat strains. The fine structure of photoreceptors and pigment epithelium was also investigated with transmission electron microscopy. RESULTS Here we found that retinal thickness was unchanged in diabetic animals and that no significant increase in the number of apoptotic cells was present. Although the density of cones expressing middle (M)- and shortwave (S)-sensitive opsins was similar in diabetic and control retinas, we detected remarkable morphologic signs of degeneration in the outer segments of diabetic rods, most M-cones, and some S-cones. A decrease in thickness and RPE65 protein immunoreactivity of the pigment epithelium were evident. Furthermore, an increased number of dual cones, coexpressing both M- and S-opsins, was detected at the peripheral retina of diabetic rats. CONCLUSIONS Degenerative changes of photoreceptors and pigment epithelium shown here prior to apoptotic loss of photoreceptors may contribute to functional alterations reported in diabetic human patients and different animal models, thus may serve as a potential model for testing the efficacy of neuroprotective agents in diabetes.

Linear matrix inequality-based robust controller design for type-1 diabetes model

Abstract This paper investigates the capabilities of a sophisticated robust nonlinear controller designed directly for a widely known and used high-order nonlinear type 1 diabetes (T1DM) model to lessen the dependency from patient compliance and to answer practical requirements such as avoiding hypoglycaemia. The resulting controller can perform adequately in nominal conditions, but expected to keep this performance even in extreme situations, e.g. high carbohydrate intake, rejecting hypoglycaemic episodes.

Modeling and optimal control strategies of diseases with high public health impact

This paper summarizes the results of research tasks in the field of physiological modeling and control of diseases with high public health impact carried out by the Biomedical Engineering Laboratory of the Budapest University of Technology and Economics. The developed and presented optimal algorithms and strategies focus on three diseases with high public health impact diabetes (the question of artificial pancreas), obesity (predicting obesity-related risks) and cancer (antiangiogenic chemotherapy). The studies are done together with different Hungarian hospitals, from where measurement data were obtained.

The significance of LPV modeling of a widely used T1DM model

The paper investigates the specificity of Linear Parameter Varying (LPV) modeling and robust controller design on a widely used Type 1 Diabetes Mellitus model. LPV systems can be seen as an extension of linear time invariant systems, which allows us to extend some powerful control methodologies to the highly nonlinear and uncertain models of the human metabolism. Different LPV models are proposed with their own advantages and disadvantages. The possible choices are separately analyzed for both controller and observer design perspective.

Quasi in-silico validations of a nonlinear LPV model-based robust glucose control algorithm for type i diabetes

Abstract Generating optimal control algorithms for artificial pancreases is an intensively researched problem. In this paper the feedback control of type 1 diabetic patients using subcutaneous insulin delivery and subcutaneous glucose monitoring is considered on the high-complexity modified nonlinear diabetic patient Sorensen-model. An acceptable compromise between the models complexity and the developed control algorithm is certainly the choice of the parametrically varying system (LPV) description. A recently developed nonlinear model-based LPV robust controller is used and its efficiency is tested in quasi in silico mode. Min. 1 weeks real data of 30 type 1 diabetic patient (aged between 6-52 years) equipped with Medtronic insulin pump were compared with simulation results of the control algorithm using static and dynamic glucose absorption profiles. The developed framework kept blood glucose level more than 90% of the time inside the 4-8 mmol/l interval (without any recalibration of the algorithm) proving its robustness.

Model-based optimal therapy for high-impact diseases

The current paper represents a continuation of the conference papers presented at the INES 2011 and 2012 conferences. A brief summary of the newest results in the field of physiological modeling and control are presented focusing on high public health impact diseases: diabetes, tumor and obesity. In the case of diabetes control, preliminary model-free results of the developed robust control framework is presented, while in the case of tumor control we have started to create a new tumor growth model based on clinical experiments. In the case of biostatistical investigation of obesity, new aspects of relationship between overweight/obesity and laboratory results was examined.

Model-based control algorithms for optimal therapy of high-impact public health diseases

The current paper represents a continuation of the conference paper presented at the INES 2011 conference. It summarizes the updated results of research tasks in the field of physiological modeling and control of diseases with high public health impact carried out by the Biomedical Engineering Laboratory of the Budapest University of Technology and Economics. The developed and presented optimal algorithms and strategies focus on three cases: blood glucose control in case of the artificial pancreas problem, tight glycaemic control under intensive care and optimal antiangiogenic therapy in the treatment of cancerous tumor.

Asymptotic output tracking in blood glucose control. A case study

Glucose is the primary source of energy for the human body. Keeping the blood glucose level between certain thresholds is essential for the proper energy transport. Insulin plays a key role in maintaining the glucose homeostasis. Because of its great importance, many models were published on either to describe the glucose-insulin interaction in case of patients under Intensive Care Unit (ICU), or to model Type 1 Diabetes Mellitus (T1DM). Currently for most of the models linear control concepts are used in order to design an appropriate controller. The aim of the current paper is to investigate applicability of nonlinear control theory providing exact mathematical background in the control problem of glucose-insulin interaction. Both ICU and T1DM cases are analyzed on well-known models with different complexity. Our aim is to hide the nonlinearity of the models by transforming the input signal so that the response of the model would mimic the behavior of a linear system; hence extending the validity of linear controllers. The asymptotic tracking problem needs the value of the state variables; therefore extended Kalman-filter is applied. The capabilities of this approach are examined through classical control algorithms and input data recorded in clinical environment.

Comparison of sigma-point filters for state estimation of diabetes models

In physiological control there is a need to estimate signals that cannot be measured directly. Burdened by measurement noise and unknown disturbances this proves to be challenging, since the models are usually highly nonlinear. Sigma-point filters could represent an adequate choice to overcome this problem. The paper investigates the applicability of several different versions of sigma-point filters for the Artificial Pancreas problem on the widely used Cambridge (Hovorka)-model.

Optimal Tight Glycaemic Control Supported by Differential Geometric Methods

Optimal control of an Intensive Care Unit (ICU) metabolic model from nonlinear control point of view is presented in the current paper. The transformation of a clinically validated nonlinear model into a series of integrators via exact linearization and asymptotic output tracking is performed. Both methods need the value of the state variables; therefore Kalman-filter extended for nonlinear systems is applied. Finally, linear optimal LQ method is applied on the ICU model handled with differential geometric approach. Results are demonstrated on input data recorded in actual clinical environment.