Artificial pancreas system (APS) is a viable option to treat diabetic patients. Researchers, however, have not conclusively determined the best control method for APS. Due to intra-/inter-variability of insulin absorption and action, an individualized algorithm is required to control blood glucose level (BGL) for each patient. To this end, we developed model predictive control (MPC) based on artificial neural networks (ANNs), which combines ANN for BGL prediction based on inputs and MPC for BGL control based on the ANN (NN-MPC). First, we developed a mathematical model for diabetic rats, which was used to identify individual virtual subjects by fitting to empirical data collected through an APS, including BGL data, insulin injection, and food intake. Then, the virtual subjects were used to generate datasets for training ANNs. The NN-MPC determines control actions (insulin injection) based on BGL predicted by the ANN. To evaluate the NN-MPC, we conducted experiments using four virtual subjects under three different scenarios. Overall, the NN-MPC maintained BGL within the normal range about 90% of the time with a mean absolute deviation of 4.7 mg/dl from a desired BGL. Our findings suggest that the NN-MPC can provide subject-specific BGL control in conjunction with a closed-loop APS. Graphical abstract ᅟ.
Destruction of the insulin-producing β-cells is the key determinant of diabetes mellitus regardless of their types. Due to their anatomical location within the islets of Langerhans scattered throughout the pancreas, it is difficult to monitor β-cell function and mass clinically. To this end, we propose to use a mathematical model of glucose-insulin homeostasis to estimate insulin secretion, glucose uptake by tissues, and hepatic handling of glucose. We applied the mathematical model by Lombarte et al. (2013) to compare various rate constants representing glucose-insulin homeostasis between lean (11% fat)- and high fat diet (HFD; 45% fat)-fed mice. Mice fed HFD (n = 12) for 3 months showed significantly higher body weights (49.97 ± 0.52 g vs. 29.86 ± 0.46 g), fasting blood glucose levels (213.08 ± 10.35 mg/dl vs. 121.91 ± 2.26 mg/dl), and glucose intolerance compared to mice fed lean diet (n = 12). Mice were injected with 1 g/kg glucose intraperitoneally and blood glucose levels were measured at various intervals for 120 min. We performed simulation using Arena™ software based on the mathematical model and estimated the rate constants (9 parameters) for various terms in the differential equations using OptQuest™. The simulated data fit accurately to the observed data for both lean and obese mice, validating the use of the mathematical model in mice at different stages of diabetes progression. Among 9 parameters, 5 parameters including basal insulin, k2 (rate constant for insulin-dependent glucose uptake to tissues), k3 (rate constant for insulin-independent glucose uptake to tissues), k4 (rate constant for liver glucose transfer), and Ipi (rate constant for insulin concentration where liver switches from glucose release to uptake) were significantly different between lean- and HFD-fed mice. Basal blood insulin levels, k3, and Ipi were significantly elevated but k2 and k4 were reduced in mice fed a HFD compared to those fed a lean diet. Non-invasive assessment of the key components of glucose-insulin homeostasis including insulin secretion, glucose uptake by tissues, and hepatic handling of glucose may be helpful for individualized drug therapy and designing a customized control algorithm for the artificial pancreas.
Obesity is one of the primary causes of type 2 diabetes mellitus (T2DM). To better understand how obesity impairs glucose-insulin homeostasis, we tracked fasting blood glucose and insulin levels and the key components of glucose-insulin homeostasis for 7 months in high fat diet (HFD; 45% fat) fed mice (n ¼ 8). Every 2 weeks we measured body weight, fasting blood glucose and insulin levels, and estimated 5 key rate constants of glucoseinsulin homeostasis using the methods established previously (Heliyon 3: e00310, 2017). Mice gained weight steadily, more than doubling their weights after 7 months (23.6 AE 0.5 to 52.3 AE 1.4 g). Fasting (basal) insulin levels were elevated (221.3 AE 16.7 to 1043.1 AE 90.5 pmol l -1 ) but fasting blood glucose levels unexpectedly returned to the baseline levels (152.8 AE 7.0 to 152.0 AE 7.2 mg/dl) despite significantly elevated levels (216.8 AE 44.9 mg/dl, average of 3 highest values for 8 mice) during the experimental period. After 7 months of HFD feeding, the rate constants for insulin secretion (k 1 ), insulin-independent glucose uptake (k 3 ), and insulin concentration where liver switches from glucose uptake to release (I pi ) were significantly elevated. Insulin-dependent glucose uptake (k 2 ) and rate constant of liver glucose transfer (k 4 ) were lowered but no statistical significance was reached. The novel and key finding of this study is the wide range of fluctuations of the rate constants during the course of obesity, reflecting the body's compensatory responses against metabolic alterations caused by obesity.
The prediction of risk to the patient in ascending thoracic aortic aneurysm (ATAA) is a significant challenge and the subject of much active research. In the present work, a combination of mouse model experiments and computer simulations was used to explore potential biomarkers that correlate with mouse lifespan, used as a surrogate for risk of a catastrophic event. Image-based, mouse-specific fluid-structure-interaction models were developed for Fbln4 SMKO mice (n = 10) at ages two and six months. The results of the simulations were used to quantify potential biofluidic biomarkers, complementing the geometrical biomarkers obtained directly from the images. Comparing the different geometrical and biofluidic biomarkers to the mouse lifespan, it was found that mean oscillatory shear index (OSI mean ) and minimum time-averaged wall shear stress (TAWSS min ) at six months showed the largest correlation with lifespan (r 2 = 0.70, 0.56), with both correlations being positive (i.e., mice with high OSI mean and high TAWSS min tended to live longer). When change between two and six months was considered, the change in TAWSS min showed a much stronger correlation than OSI mean (r 2 = 0.75 vs. 0.24), and the correlation was negative (i.e., mice with increasing TAWSS min over this period tended to live less long). The results highlight potential biomarkers of ATAA outcomes that can be obtained through noninvasive imaging and computational simulations.
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