Sistema de control borroso en lazo cerrado para terapias con insulina en diabetes tipo I

La diabetes mellitus es un trastorno del metabolismo de los carbohidratos producido por la insuficiente o nula producción de insulina o la reducida sensibilidad a esta hormona. Es una enfermedad crónica con una mayor prevalencia en los países desarrollados debido principalmente a la obesidad, la vida sedentaria y disfunciones en el sistema endocrino relacionado con el páncreas. La diabetes Tipo 1 es una enfermedad autoinmune en la que son destruidas las células beta del páncreas, que producen la insulina, y es necesaria la administración de insulina exógena. Un enfermo de diabetes Tipo 1 debe seguir una terapia con insulina administrada por la vía subcutánea que debe estar adaptada a sus necesidades metabólicas y a sus hábitos de vida, esta terapia intenta imitar el perfil insulínico de un páncreas no patológico. La tecnología actual permite abordar el desarrollo del denominado “páncreas endocrino artificial”, que aportaría precisión, eficacia y seguridad para los pacientes, en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. Permitiría que el paciente no estuviera tan pendiente de su enfermedad. El páncreas artificial consta de un sensor continuo de glucosa, una bomba de infusión de insulina y un algoritmo de control, que calcula la insulina a infusionar usando la glucosa como información principal. Este trabajo presenta un método de control en lazo semi-cerrado mediante un sistema borroso experto basado en reglas. La regulación borrosa se fundamenta en la ambigüedad del lenguaje del ser humano. Esta incertidumbre sirve para la formación de una serie de reglas que representan el pensamiento humano, pero a la vez es el sistema que controla un proceso, en este caso el sistema glucorregulatorio. Este proyecto está enfocado en el diseño de un controlador borroso que haciendo uso de variables como la glucosa, insulina y dieta, sea capaz de restaurar la función endocrina del páncreas de forma tecnológica. La validación del algoritmo se ha realizado principalmente mediante experimentos en simulación utilizando una población de pacientes sintéticos, evaluando los resultados con estadísticos de primer orden y algunos más específicos como el índice de riesgo de Kovatchev, para después comparar estos resultados con los obtenidos por otros métodos de control anteriores. Los resultados demuestran que el control borroso (FBPC) mejora el control glucémico con respecto a un sistema predictivo experto basado en reglas booleanas (pBRES). El FBPC consigue reducir siempre la glucosa máxima y aumentar la mínima respecto del pBRES pero es en terapias desajustadas, donde el FBPC es especialmente robusto, hace descender la glucosa máxima 8,64 mg/dl, el uso de insulina es 3,92 UI menor, aumenta la glucosa mínima 3,32 mg/dl y lleva al rango de glucosa 80 – 110 mg/dl 15,33 muestras más. Por lo tanto se puede concluir que el FBPC realiza un mejor control glucémico que el controlador pBRES haciéndole especialmente efectivo, robusto y seguro en condiciones de desajustes de terapia basal y con gran capacidad de mejora futura. SUMMARY The diabetes mellitus is a metabolic disorder caused by a poor or null insulin secretion or a reduced sensibility to insulin. Diabetes is a chronic disease with a higher prevalence in the industrialized countries, mainly due to obesity, the sedentary life and endocrine disfunctions connected with the pancreas. Type 1 diabetes is a self-immune disease where the beta cells of the pancreas, which are the responsible of secreting insulin, are damaged. Hence, it is necessary an exogenous delivery of insulin. The Type 1 diabetic patient has to follow a therapy with subcutaneous insulin administration which should be adjusted to his/her metabolic needs and life style. This therapy tries to mimic the insulin profile of a non-pathological pancreas. Current technology lets the development of the so-called endocrine artificial pancreas that would provide accuracy, efficiency and safety to patients, in regards to the glycemic control normalization and reduction of the risk of hypoglycemic. In addition, it would help the patient not to be so concerned about his disease. The artificial pancreas has a continuous glucose sensor, an insulin infusion pump and a control algorithm, that calculates the insulin infusion using the glucose as main information. This project presents a method of control in semi-closed-loop, through an expert fuzzy system based on rules. The fuzzy regulation is based on the human language ambiguity. This uncertainty serves for construction of some rules that represent the human language besides it is the system that controls a process, in this case the glucoregulatory system. This project is focus on the design of a fuzzy controller that, using variables like glucose insulin and diet, will be able to restore the pancreas endocrine function with technology. The algorithm assessment has mainly been done through experiments in simulation using a population of synthetic patients, evaluating the results with first order statistical parameters and some other more specific such as the Kovatchev risk index, to compare later these results with the ones obtained in others previous methods of control. The results demonstrate that the fuzzy control (FBPC) improves the glycemic control connected with a predictive expert system based on Booleans rules (pBRES). The FBPC is always able to reduce the maximum level of glucose and increase the minimum level as compared with pBRES but it is in unadjusted therapies where FBPC is especially strong, it manages to decrease the maximum level of glucose and insulin used by 8,64 mg/dl and 3,92 UI respectively, also increases the value of minimum glucose by 3,32 mg/dl, getting 15,33 samples more inside the 80-110 mg/dl glucose rank. Therefore we can conclude that FBPC achieves a better glycemic control than the controller pBRES doing it especially effective, robust and safe in conditions of mismatch basal therapy and with a great capacity for future improvements.

HKT2;2/1, a K+-permeable transporter identified in a salt tolerant rice cultivar through surveys of natural genetic polymorphism

We have investigated OsHKT2;1 natural variation in a collection of 49 cultivars with different levels of salt tolerance and geographical origins. The effect of identified polymorphism on OsHKT2;1 activity was analysed through heterologous expression of variants in Xenopus oocytes. OsHKT2;1 appeared to be a highly conserved protein with only five possible amino acid substitutions that have no substantial effect on functional properties. Our study, however, also identified a new HKT isoform, No-OsHKT2;2/1 in Nona Bokra, a highly salt-tolerant cultivar. No-OsHKT2;2/1 probably originated from a deletion in chromosome 6, producing a chimeric gene. Its 5¢ region corresponds to that of OsHKT2;2, whose full-length sequence is not present in Nipponbare but has been identified in Pokkali, a salt-tolerant rice cultivar. Its 3¢ region corresponds to that of OsHKT2;1. No-OsHKT2;2/1 is essentially expressed in roots and displays a significant level of expression at high Na+ concentrations, in contrast to OsHKT2;1. Expressed in Xenopus oocytes or in Saccharomyces cerevisiae, No-OsHKT2;2/1 exhibited a strong permeability to Na+ and K+, even at high external Na+ concentrations, like OsHKT2;2, and in contrast to OsHKT2;1. Our results suggest that No-OsHKT2;2/1 can contribute to Nona Bokra salt tolerance by enabling root K+ uptake under saline conditions.

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.

Distribution of indole-3-acetic acid in Petunia hybrida shoot tip cuttings and relationship between auxin transport, carbohydrate metabolism and adventitious root formation.

To determine the contribution of polar auxin transport (PAT) to auxin accumulation and to adventitious root (AR) formation in the stem base of Petunia hybrida shoot tip cuttings, the level of indole-3-acetic acid (IAA) was monitored in non-treated cuttings and cuttings treated with the auxin transport blocker naphthylphthalamic acid (NPA) and was complemented with precise anatomical studies. The temporal course of carbohydrates, amino acids and activities of controlling enzymes was also investigated. Analysis of initial spatial IAA distribution in the cuttings revealed that approximately 40 and 10% of the total IAA pool was present in the leaves and the stem base as rooting zone, respectively. A negative correlation existed between leaf size and IAA concentration. After excision of cuttings, IAA showed an early increase in the stem base with two peaks at 2 and 24h post excision and, thereafter, a decline to low levels. This was mirrored by the expression pattern of the auxin-responsive GH3 gene. NPA treatment completely suppressed the 24-h peak of IAA and severely inhibited root formation. It also reduced activities of cell wall and vacuolar invertases in the early phase of AR formation and inhibited the rise of activities of glucose-6-phosphate dehydrogenase and phosphofructokinase during later stages. We propose a model in which spontaneous AR formation in Petunia cuttings is dependent on PAT and on the resulting 24-h peak of IAA in the rooting zone, where it induces early cellular events and also stimulates sink establishment. Subsequent root development stimulates glycolysis and the pentosephosphate pathway

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%.

Functional, structural and phylogenetic analysis of domains underlying the Al sensitivity of the aluminum-activated malate/anion transporter, TaALMT1.

Triticum aestivum aluminum-activated malate transporter (TaALMT1) is the founding member of a unique gene family of anion transporters (ALMTs) that mediate the efflux of organic acids. A small sub-group of root-localized ALMTs, including TaALMT1, is physiologically associated with in planta aluminum (Al) resistance. TaALMT1 exhibits significant enhancement of transport activity in response to extracellular Al. In this study, we integrated structure–function analyses of structurally altered TaALMT1 proteins expressed in Xenopus oocytes with phylogenic analyses of the ALMT family. Our aim is to re-examine the role of protein domains in terms of their potential involvement in the Al-dependent enhancement (i.e. Al-responsiveness) of TaALMT1 transport activity, as well as the roles of all its 43 negatively charged amino acid residues. Our results indicate that the N-domain, which is predicted to form the conductive pathway, mediates ion transport even in the absence of the C-domain. However, segments in both domains are involved in Al3+ sensing. We identified two regions, one at the N-terminus and a hydrophobic region at the C-terminus, that jointly contribute to the Al-response phenotype. Interestingly, the characteristic motif at the N-terminus appears to be specific for Al-responsive ALMTs. Our study highlights the need to include a comprehensive phylogenetic analysis when drawing inferences from structure–function analyses, as a significant proportion of the functional changes observed for TaALMT1 are most likely the result of alterations in the overall structural integrity of ALMT family proteins rather than modifications of specific sites involved in Al3+ sensing.

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.