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.

How much energy will your NGN consume? A model for energy consumption in next generation access networks: The case of Spain

The contribution to global energy consumption of the information and communications technology (ICT) sector has increased considerably in the last decade, along with its growing relevance to the overall economy. This trend will continue due to the seemingly ever greater use of these technologies, with broadband data traffic generated by the usage of telecommunication networks as a primary component. In fact, in response to user demand, the telecommunications industry is initiating the deployment of next generation networks (NGNs). However, energy consumption is mostly absent from the debate on these deployments, in spite of the potential impact on both expenses and sustainability. In addition, consumers are unaware of the energy impact of their choices in ultra-broadband services. This paper focuses on forecasting energy consumption in the access part of NGNs by modelling the combined effect of the deployment of two different ultra-broadband technologies (FTTH-GPON and LTE), the evolution of traffic per user, and the energy consumption in each of the networks and user devices. Conclusions are presented on the levels of energy consumption, their cost and the impact of different network design parameters. The effect of technological developments, techno-economic and policy decisions on energy consumption is highlighted. On the consumer side, practical figures and comparisons across technologies are provided. Although the paper focuses on Spain, the analysis can be extended to similar countries.

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.

Extracción de conocimientos de la diabetes tipo 1 utilizando la metodología de "Data Mining"

La diabetes mellitus es un trastorno en la metabolización de los carbohidratos, caracterizado por la nula o insuficiente segregación de insulina (hormona producida por el páncreas), como resultado del mal funcionamiento de la parte endocrina del páncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos químicos más pequeños mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polipéptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentración de glucosa en sangre, ya que las células no pueden metabolizarla; consumo de ácidos grasos mediante el hígado, liberando cuerpos cetónicos para aportar la energía a las células. Esta situación expone al enfermo crónico, a una concentración de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo múltiples problemas médicos: oftalmológicos, renales, cardiovasculares, cerebrovasculares, neurológicos… La diabetes representa un gran problema de salud pública y es la enfermedad más común en los países desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparición de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentación clínica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las células beta del páncreas (productoras de insulina) resultando necesaria la administración de insulina exógena. Dicho esto, el paciente con diabetes tipo 1 deberá seguir un tratamiento con insulina administrada por la vía subcutánea, adaptado a sus necesidades metabólicas y a sus hábitos de vida. Para abordar esta situación de regulación del control metabólico del enfermo, mediante una terapia de insulina, no serviremos del proyecto “Páncreas Endocrino Artificial” (PEA), el cual consta de una bomba de infusión de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisión, eficacia y seguridad en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. El PEA se instala mediante vía subcutánea, por lo que, el retardo introducido por la acción de la insulina, el retardo de la medida de glucosa, así como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducción de variables de entrada. De este modo lo que conseguimos, es una predicción de los estados futuros en los que se puede encontrar la glucosa del paciente, sirviéndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando parámetros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se están empleando actualmente en el PEA no están funcionando correctamente por la cantidad de información y variables que debe de manejar. Data Mining, también referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracción no trivial de información implícita, previamente desconocida y potencialmente útil. Todo ello, sirviéndonos las siguientes fases del proceso de extracción del conocimiento: selección de datos, pre-procesado, transformación, minería de datos, interpretación de los resultados, evaluación y obtención del conocimiento. Con todo este proceso buscamos generar un único modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con cálculos en tiempo real, a través de unos parámetros introducidos. Este trabajo busca extraer la información contenida en una base de datos de pacientes diabéticos tipo 1 obtenidos a partir de la experimentación clínica. Para ello emplearemos técnicas de Data Mining. Para la consecución del objetivo implícito a este proyecto hemos procedido a implementar una interfaz gráfica que nos guía a través del proceso del KDD (con información gráfica y estadística) de cada punto del proceso. En lo que respecta a la parte de la minería de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a través de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el código. Mediante estos dispositivos y lo expuesto en el proyecto se podrían implementar o incluso crear nuevas aplicaciones novedosas y muy útiles para este campo. Como conclusión del proyecto, y tras un exhaustivo análisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological … The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the “Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin–glucose model for each patient.

Conformational changes couple Na+ and glucose transport

The mechanism by which cotransport proteins couple their substrates across cell membranes is not known. A commonly proposed model is that cotransport results from ligand-induced conformational transitions that change the accessibility of ligand-binding sites from one side of the membrane to the other. To test this model, we have measured the accessibility of covalent probes to a cysteine residue (Q457C) placed in the putative sugar-translocation domain of the Na+/glucose cotransporter (SGLT1). The mutant protein Q457C was able to transport sugar, but transport was abolished after alkylation by methanethiosulfonate reagents. Alkylation blocked sugar translocation but not sugar binding. Accessibility of Q457C to alkylating reagents required external Na+ and was blocked by external sugar and phlorizin. The voltage dependence of accessibility was directly correlated with the presteady–state charge movement of SGLT1. Voltage-jump experiments with rhodamine-6-maleimide-labeled Q457C showed that the time course and level of changes in fluorescence closely followed the presteady–state charge movement. We conclude that conformational changes are responsible for the coupling of Na+ and sugar transport and that Q457 plays a critical role in sugar translocation by SGLT1.

Normal hepatic glucose production in the absence of GLUT2 reveals an alternative pathway for glucose release from hepatocytes

Glucose production by liver is a major physiological function, which is required to prevent development of hypoglycemia in the postprandial and fasted states. The mechanism of glucose release from hepatocytes has not been studied in detail but was assumed instead to depend on facilitated diffusion through the glucose transporter GLUT2. Here, we demonstrate that in the absence of GLUT2 no other transporter isoforms were overexpressed in liver and only marginally significant facilitated diffusion across the hepatocyte plasma membrane was detectable. However, the rate of hepatic glucose output was normal. This was evidenced by (i) the hyperglycemic response to i.p. glucagon injection; (ii) the in vivo measurement of glucose turnover rate; and (iii) the rate of release of neosynthesized glucose from isolated hepatocytes. These observations therefore indicated the existence of an alternative pathway for hepatic glucose output. Using a [14C]-pyruvate pulse-labeling protocol to quantitate neosynthesis and release of [14C]glucose, we demonstrated that this pathway was sensitive to low temperature (12°C). It was not inhibited by cytochalasin B nor by the intracellular traffic inhibitors brefeldin A and monensin but was blocked by progesterone, an inhibitor of cholesterol and caveolae traffic from the endoplasmic reticulum to the plasma membrane. Our observations thus demonstrate that hepatic glucose release does not require the presence of GLUT2 nor of any plasma membrane glucose facilitative diffusion mechanism. This implies the existence of an as yet unsuspected pathway for glucose release that may be based on a membrane traffic mechanism.

Non-pharmacological interventions to reduce the risk of diabetes in people with impaired glucose regulation : a systematic review and economic evaluation

Peer reviewed

Gilbert syndrome and glucose-6-phosphate dehydrogenase deficiency: A dose-dependent genetic interaction crucial to neonatal hyperbilirubinemia

Severe jaundice leading to kernicterus or death in the newborn is the most devastating consequence of glucose-6-phosphate dehydrogenase (EC 1.1.1.49; G-6-PD) deficiency. We asked whether the TA repeat promoter polymorphism in the gene for uridinediphosphoglucuronate glucuronosyltransferase 1 (EC 2.4.1.17; UDPGT1), associated with benign jaundice in adults (Gilbert syndrome), increases the incidence of neonatal hyperbilirubinemia in G-6-PD deficiency. DNA from term neonates was analyzed for UDPGT1 polymorphism (normal homozygotes, heterozygotes, variant homozygotes), and for G-6-PD Mediterranean deficiency. The variant UDPGT1 promoter allele frequency was similar in G-6-PD-deficient and normal neonates. Thirty (22.9%) G-6-PD deficient neonates developed serum total bilirubin ≥ 257 μmol/liter, vs. 22 (9.2%) normals (P = 0.0005). Of those with the normal homozygous UDPGT1 genotype, the incidence of hyperbilirubinemia was similar in G-6-PD-deficients and controls (9.7% and 9.9%). In contrast, in the G-6-PD-deficient neonates, those with the heterozygous or homozygous variant UDPGT1 genotype had a higher incidence of hyperbilirubinemia than corresponding controls (heterozygotes: 31.6% vs. 6.7%, P < 0.0001; variant homozygotes: 50% vs. 14.7%, P = 0.02). Among G-6-PD-deficient infants the incidence of hyperbilirubinemia was greater in those with the heterozygous (31.6%, P = 0.006) or variant homozygous (50%, P = 0.003) UDPGT1 genotype than in normal homozygotes. In contrast, among those normal for G-6-PD, the UDPGT1 polymorphism had no significant effect (heterozygotes: 6.7%; variant homozygotes: 14.7%). Thus, neither G-6-PD deficiency nor the variant UDPGT1 promoter, alone, increased the incidence of hyperbilirubinemia, but both in combination did. This gene interaction may serve as a paradigm of the interaction of benign genetic polymorphisms in the causation of disease.

Enhanced stability of maize endosperm ADP-glucose pyrophosphorylase is gained through mutants that alter subunit interactions

Temperature lability of ADP-glucose pyrophosphorylase (AGP; glucose-1-phosphate adenylyltransferase; ADP: α-d-glucose-1-phosphate adenylyltransferase, EC 2.7.7.27), a key starch biosynthetic enzyme, may play a significant role in the heat-induced loss in maize seed weight and yield. Here we report the isolation and characterization of heat-stable variants of maize endosperm AGP. Escherichia coli cells expressing wild type (WT) Shrunken2 (Sh2), and Brittle2 (Bt2) exhibit a reduced capacity to produce glycogen when grown at 42°C. Mutagenesis of Sh2 and coexpression with WT Bt2 led to the isolation of multiple mutants capable of synthesizing copious amounts of glycogen at this temperature. An increase in AGP stability was found in each of four mutants examined. Initial characterization revealed that the BT2 protein was elevated in two of these mutants. Yeast two-hybrid studies were conducted to determine whether the mutant SH2 proteins more efficiently recruit the BT2 subunit into tetramer assembly. These experiments showed that replacement of WT SH2 with the heat-stable SH2HS33 enhanced interaction between the SH2 and BT2 subunits. In agreement, density gradient centrifugation of heated and nonheated extracts from WT and one of the mutants, Sh2hs33, identified a greater propensity for heterotetramer dissociation in WT AGP. Sequencing of Sh2hs33 and several other mutants identified a His-to-Tyr mutation at amino acid position 333. Hence, a single point mutation in Sh2 can increase the stability of maize endosperm AGP through enhanced subunit interactions.