Highly efficient potentiometric glucose biosensor based on functionalized InN quantum dots

We present a fast, highly sensitive, and efficient potentiometric glucose biosensor based on functionalized InN quantum-dots (QDs). The InN QDs are grown by molecular beam epitaxy. The InN QDs are bio-chemically functionalized through physical adsorption of glucose oxidase (GOD). GOD enzyme-coated InN QDs based biosensor exhibits excellent linear glucose concentration dependent electrochemical response against an Ag/AgCl reference electrode over a wide logarithmic glucose concentration range (1 × 10−5 M to 1 × 10−2 M) with a high sensitivity of 80 mV/decade. It exhibits a fast response time of less than 2 s with good stability and reusability and shows negligible response to common interferents such as ascorbic acid and uric acid. The fabricated biosensor has full potential to be an attractive candidate for blood sugar concentration detection in clinical diagnoses.

Exercise during pregnancy improves maternal glucose screen at 24-28 weeks: a randomised controlled trial

Objective. The influence of an exercise programme performed by healthy pregnant women on maternal glucose tolerance was studied. Study design. A physical activity (PA, land/aquatic activities) programme during the entire pregnancy (three sessions per week) was conducted by a qualified instructor. 83 healthy pregnant women were randomly assigned to either an exercise group (EG, n=40) or a control (CG, n=43) group. 50 g maternal glucose screen (MGS), maternal weight gain and several pregnancy outcomes were recorded. Results. Significant differences were found between study groups on the 50 g MGS. Values corresponding to the EG (103.8±20.4 mg/dl) were better than those of the CG (126.9±29.5 mg/dl), p=0.000. In addition, no differences in maternal weight gain and no cases of gestational diabetes in EG versus 3 in CG (7%) (p>0.05) were found. Conclusion. A moderate PA programme performed during pregnancy improves levels of maternal glucose tolerance.

Using a causal smoothing to improve the performance of an on-line neural network glucose prediction algorithm

This work evaluates a spline-based smoothing method applied to the output of a glucose predictor. Methods:Our on-line prediction algorithm is based on a neural network model (NNM). We trained/validated the NNM with a prediction horizon of 30 minutes using 39/54 profiles of patients monitored with the Guardian® Real-Time continuous glucose monitoring system The NNM output is smoothed by fitting a causal cubic spline. The assessment parameters are the error (RMSE), mean delay (MD) and the high-frequency noise (HFCrms). The HFCrms is the root-mean-square values of the high-frequency components isolated with a zero-delay non-causal filter. HFCrms is 2.90±1.37 (mg/dl) for the original profiles.

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%.

Carotid body chemoreceptor regulation of blood glucose during aerobic exercise

Regulación carótida en ejercicio aeróbico

Glucose-Insulin regulator for type 1 diabetes using high order neural networks

In this paper a Glucose-Insulin regulator for Type 1 Diabetes using artificial neural networks (ANN) is proposed. This is done using a discrete recurrent high order neural network in order to identify and control a nonlinear dynamical system which represents the pancreas? beta-cells behavior of a virtual patient. The ANN which reproduces and identifies the dynamical behavior system, is configured as series parallel and trained on line using the extended Kalman filter algorithm to achieve a quickly convergence identification in silico. The control objective is to regulate the glucose-insulin level under different glucose inputs and is based on a nonlinear neural block control law. A safety block is included between the control output signal and the virtual patient with type 1 diabetes mellitus. Simulations include a period of three days. Simulation results are compared during the overnight fasting period in Open-Loop (OL) versus Closed- Loop (CL). Tests in Semi-Closed-Loop (SCL) are made feedforward in order to give information to the control algorithm. We conclude the controller is able to drive the glucose to target in overnight periods and the feedforward is necessary to control the postprandial period.