An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients

In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.

Economic efficiency analysis of different strategies to control post-weaning multi-systemic wasting syndrome and porcine circovirus type 2 subclinical infection in 3-weekly batch system farms.

The study assessed the economic efficiency of different strategies for the control of post-weaning multi-systemic wasting syndrome (PMWS) and porcine circovirus type 2 subclinical infection (PCV2SI), which have a major economic impact on the pig farming industry worldwide. The control strategies investigated consisted on the combination of up to 5 different control measures. The control measures considered were: (1) PCV2 vaccination of piglets (vac); (2) ensuring age adjusted diet for growers (diets); (3) reduction of stocking density (stock); (4) improvement of biosecurity measures (bios); and (5) total depopulation and repopulation of the farm for the elimination of other major pathogens (DPRP). A model was developed to simulate 5 years production of a pig farm with a 3-weekly batch system and with 100 sows. A PMWS/PCV2SI disease and economic model, based on PMWS severity scores, was linked to the production model in order to assess disease losses. This PMWS severity scores depends on the combination post-weaning mortality, PMWS morbidity in younger pigs and proportion of PCV2 infected pigs observed on farms. The economic analysis investigated eleven different farm scenarios, depending on the number of risk factors present before the intervention. For each strategy, an investment appraisal assessed the extra costs and benefits of reducing a given PMWS severity score to the average score of a slightly affected farm. The net present value obtained for each strategy was then multiplied by the corresponding probability of success to obtain an expected value. A stochastic simulation was performed to account for uncertainty and variability. For moderately affected farms PCV2 vaccination alone was the most cost-efficient strategy, but for highly affected farms it was either PCV2 vaccination alone or in combination with biosecurity measures, with the marginal profitability between 'vac' and 'vac+bios' being small. Other strategies such as 'diets', 'vac+diets' and 'bios+diets' were frequently identified as the second or third best strategy. The mean expected values of the best strategy for a moderately and a highly affected farm were £14,739 and £57,648 after 5 years, respectively. This is the first study to compare economic efficiency of control strategies for PMWS and PCV2SI. The results demonstrate the economic value of PCV2 vaccination, and highlight that on highly affected farms biosecurity measures are required to achieve optimal profitability. The model developed has potential as a farm-level decision support tool for the control of this economically important syndrome.

Type I hypersensitivity and its pharmacological modulation in {\it Trichinella\/}-induced intestinal fluid secretion and rapid rejection of the parasite in immunized rats

Studies were performed to test the hypothesis that type I hypersensitivity underlies worm induced intestinal fluid secretion and the rapid rejection of Trichinella spiralis from immunized rats, and the two events may be related in a cause-effect manner.^ Two approaches were taken. One was to determine whether inhibition of anaphylaxis-mediated Cl$\sp{-}$ and fluid secretion accompanying a secondary infection impedes worm rejection from immune hosts. The other was to determine whether induction of intestinal fluid secretion in nonimmune hosts interfered with worm establishment. In both studies, fluid secretion was measured volumetrically 30 min after a challenge infection and worms were counted.^ In immunized rats indomethacin did not affect the worm-induced fluid secretion when used alone, despite inhibiting mucosal prostaglandin synthesis. Fluid secretion was reduced by treatment with diphenhydramine and further reduced by the combination of diphenhydramine and indomethacin. The paradoxical effects of indomethacin when used alone compared with its coadministration with diphenhydramine is explained by the enhancing effect of indomethacin on histamine release. Abolishing net fluid secretion in these studies had no effect on rapid worm rejection in immune hosts.^ Worm establishment was reduced in recipients of immune serum containing IgE antibodies. Net intestinal fluid secretion induced in normal rats by PGE$\sb2$, cholera toxin, or hypertonic mannitol solution had no effect on worm establishment compared with untreated controls.^ In a related experiment, worm-induced intestinal fluid secretion and worm rejection in immune rats were partially blocked by concurrent injection with 5-HT$\sb2$ and 5-HT$\sb3$ blockers (Ketanserin and MDL-72222), suggesting that 5-HT is involved. This possible involvement was supported in that treatment of nonimmune rats with 5-HT significantly inhibited worm establishment in the intestine.^ Results indicate that anaphylaxis is the basis for both worm-induced intestinal fluid secretion and rapid rejection of T. spiralis in immune rats, but these events are independent of one another. 5-HT is a possible mediator of worm rejection, however, its mechanism of action is related to something other than fluid secretion. ^

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

Root system of singular perturbations of the harmonic oscillator type operators

We analyze perturbations of the harmonic oscillator type operators in a Hilbert space H, i.e. of the self-adjoint operator with simple positive eigenvalues μ k satisfying μ k+1 − μ k ≥ Δ > 0. Perturbations are considered in the sense of quadratic forms. Under a local subordination assumption, the eigenvalues of the perturbed operator become eventually simple and the root system contains a Riesz basis.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

Role of organic cation transporters in the renal secretion of nucleoside analogs

The mammalian kidney maintains homeostasis of the extracellular environment and eliminates toxic substances from the body, in part via secretion by the organic cation transporters (OCT). Some nucleosides are also secreted by the kidney. Previous work indicated that the deoxyadenosine analog, 2′ -deoxytubercidin (dTub), is secreted by mouse kidney through the OCTs. This study examines the role of OCTs in the renal secretion of dTub and other nucleoside analogs. ^ Using the Xenopus laevis oocyte expression system, the basolateral type rat organic cation transporter rOCT1 was shown to transport dTub and other nucleosides. The positive charged form of dTub (dTub +) appears to be the substrate for rOCT1. Tetraethylammonium (TEA) and dTub competitively inhibit the other's uptake by rOCT1 in a manner consistent with their interaction at a common site. Although 67% homologous with rOCT1, rOCT2 does not mediate the uptake of these nucleosides. Kinetic studies demonstrated the difference in substrate specificity between rOCT1 and rOCT2 to be largely due to a poor affinity of rOCT2 for dTub+. This difference in affinity is located within transmembrane domains 2–7 as determined by chimeric constructs. ^ OCT1 knockout mice were used to evaluate the role of OCT1 in the renal secretion of dTub. No significant difference in tissue distribution and urinary excretion of dTub was observed between the knockout and wild-type mice, indicating that OCT1 is not necessary for the renal secretion of dTub. Apical transporters are postulated to participate in its active secretion. To characterize a possible apical transporter, we screened several renal cell lines for a nucleoside-sensitive OCT. American opossum kidney proximal tubule cells (OK) express a TEA efflux transporter that is inhibited by dTub and other nucleoside analogs. This carrier is metabolic-dependent and distinct from the cloned OCTs to date, i.e. it is sodium- and proton-independent. In conclusion, dTub is a good substrate for OCT1; however, this OCT is not necessary for its renal secretion in mice. The novel TEA efflux transporter identified in OK cells is likely to participate in the renal secretion of dTub and perhaps other nucleoside analogs. ^

Identification of maltreatment type in children with disabilities using the National Child Abuse and Neglect Data System (NCANDS)

This study was a retrospective design and used secondary data from the National Child Abuse and Neglect Data System (NCANDS), provided by the National Data Archive on Child Abuse and Neglect Family Life Development Center administered by Cornell University. The dataset contained information for the year 2005 on children from birth to 18 years of age. Child abuse and neglect for disabled children, was evaluated in-depth in the present study. Descriptive and statistical analysis was performed using the children with and without disabilities. It was found that children with disabilities have a lower rate of substantiation that likely indicates the interference of reporting due to their handicap. The results of this research demonstrate the important need to teach professionals and laypersons alike on how to recognize and substantiate abuse among disabled children.^