An interferon-inducible protein, p202, in cancer gene therapy

Interferons (IFNs) have been shown to exert antiviral, cell growth regulatory, and immunomodulatory effects on target cells. Both type I (α and β) and type II (γ) IFNs regulate cellular activities by specifically inducing the expression or activation of endogenous proteins that perform distinct biological functions. p202 is a 52 kDa nuclear phosphoprotein known to be induced by IFNs. p202 interacts with a variety of cellular transcription and growth regulatory factors and affects their functions. ^ In this report, we showed that the expression of p202 was associated with an anti-proliferative effect on human prostate cancer cells. Cells that expressed p202 showed reduced ability to grow in soft-agar, indicating a loss of transformation phenotype. More importantly, p202 expression reduced the tumorigenicity of human prostate cancer cells. p202-expressing cells exhibit an elevated level of hypophosphorylated form of pRb, and reduced level of cyclin B1 and p55CDC. ^ Our data suggest that p202 is a growth inhibitor gene in prostate cancer cells and its expression may also suppress transformation phenotype and tumorigenicity of prostate cancer cells. ^ In addition to inhibiting in vitro cell growth, suppressing the tumorigenicity of breast cancer cells in vivo, p202 expression could sensitize breast cancer cells to apoptosis induced by TNF-α treatment. One possible mechanism contributing to this sensitization is the inactivation of NF-κB by its interaction with p202. These results provide a scientific basis for a novel therapeutic strategy that combines p202 and TNF-α treatment against breast cancer. ^ It has been reported that NF-κB is constitutively active in human pancreatic cancer cells. Since p202 interacts with NF-κB and inhibits its activity, we examined a potential p202-mediated anti-tumor activity in pancreatic cancer. We used both ectopic and orthotopic xenograft models and demonstrated that p202 expression is associated with multiple anti-tumor activities that include inhibition of tumor growth, reduced tumorigenicity, prolonged survival, and remarkably, suppression of metastasis and angiogenesis. In vitro invasion assay also showed that p202-expressing pancreatic cancer cells are less invasive than those without p202 expression. That observation was supported by the findings that p202-expressing tumors showed reduced expression of angiogenic factors such as IL-8, and VEGF by inhibiting their transcription, and p202-expressing pancreatic cancer cells have reduced level of MAP-2 activity, a secreted protease activity important for metastasis. Together, our results strongly suggest that p202 expression mediates multiple anti-tumor activities against pancreatic cancer, and that may provide a scientific basis for developing a p202-based gene therapy in pancreatic cancer treatment. ^ Importantly, we demonstrated a treatment efficacy by using p202/SN2 liposome complex in a nude mice orthotopic breast cancer, and an ectopic pancreatic cancer xenograft model, through systemic and intra-tumor injection respectively. These results suggest a feasibility of using p202/SN2 liposome in future pre-clinical gene therapy experiments. ^

The utility of N5 for gene therapy of human cancer

Cancer is a result of defects in the coordination of cell proliferation and programmed cell death. The extent of cell death is physiologically controlled by the activation of a programmed suicide pathway that results in a morphologically recognizable form of death termed apoptosis. Inducing apoptosis in tumor cells by gene therapy provides a potentially effective means to treat human cancers. The p84N5 is a novel nuclear death domain containing protein that has been shown to bind an amino terminal domain of retinoblastoma tumor suppressor gene product (pRb). Expression of N5 can induce apoptosis that is dependent upon its intact death domain and is inhibited by pRb. In many human cancer cells the functions of pRb are either lost through gene mutation or inactivated by different mechanisms. N5 based gene therapy may induce cell death preferentially in tumor cells relative to normal cells. We have demonstrated that N5 gene therapy is less toxic to normal cells than to tumor cells. To test the possibility that N5 could be used in gene therapy of cancer, we have generated a recombinant adenovirus engineered to express N5 and test the effects of viral infection on growth and tumorigenicity of human cancer cells. Adenovirus N5 infection significantly reduced the proliferation and tumorigenicity of breast, ovarian, and osteosarcoma tumor cell lines. Reduced proliferation and tumorigenicity were mediated by an induction of apoptosis as indicated by DNA fragmentation in infected cells. We also test the potential utility of N5 for gene therapy of pancreatic carcinoma that typically respond poorly to conventional treatment. Adenoviral mediated N5 gene transfer inhibits the growth of pancreatic cancer cell lines in vitro. N5 gene transfer also reduces the growth and metastasis of human pancreatic adenocarcinoma in subcutaneous and orthotopic mouse model. Interestingly, the pancreatic adenocarcinoma cells are more sensitive to N5 than they are to p53, suggesting that N5 gene therapy may be effective in tumors resistant to p53. We also test the possibilities of the use of N5 and p53 together on the inhibition of pancreatic cancer cell growth in vitro and vivo. Simultaneous use of N5 and RbΔCDK has been found to exert a greater extent on the inhibition of pancreatic cancer cell growth in vitro and in vivo. ^

Understanding the roles of Non-homologous end joining and p53 after DNA damage

The inability to maintain genomic stability and control proliferation are hallmarks of many cancers, which become exacerbated in the presence of unrepaired DNA damage. Such genotoxic stresses trigger the p53 tumor suppressor network to activate transient cell cycle arrest allowing for DNA repair; if the damage is excessive or irreparable, apoptosis or cellular senescence is triggered. One of the major DNA repair pathway that mends DNA double strand breaks is non-homologous end joining (NHEJ). Abrogating the NHEJ pathway leads to an accumulation of DNA damage in the lymphoid system that triggers p53-mediated apoptosis; complete deletion of p53 in this system leads to aggressive lymphomagenesis. Therefore, to study the effect of p53-dependent cell cycle arrest, we utilized a hypomorphic, separation-of-function mutant, p53p/p, which completely abrogates apoptosis yet retains partial cell cycle arrest ability. We crossed DNA ligase IV deficiency, a downstream ligase crucial in mending breaks during NHEJ, into the p53p/p background (Lig4-/-p53p/p). The accumulation of DNA damage activated the p53/p21 axis to trigger cellular senescence in developing lymphoid cells, which absolutely suppressed tumorigenesis. Interestingly, these mice progressively succumb to severe diabetes. Mechanistic analysis revealed that spontaneous DNA damage accumulated in the pancreatic b-cells, a unique subset of endocrine cells solely responsible for insulin production to regulate glucose homeostasis. The genesis of adult b-cells predominantly occurs through self-replication, therefore modulating cellular proliferation is an essential component for renewal. The progressive accumulation of DNA damage, caused by Lig4-/-, activated p53/p21-dependent cellular senescence in mutant pancreatic b-cells that lead to islet involution. Insulin levels subsequently decreased, deregulating glucose homeostasis driving overt diabetes. Our Lig4-/-p53p/p model aptly depicts the dichotomous role of cellular senescence—in the lymphoid system prevents tumorigenesis yet in the endocrine system leads to the decrease of insulin-producing cells causing diabetes. To further delineate the function of NHEJ in pancreatic b-cells, we analyzed mice deficient in another component of the NHEJ pathway, Ku70. Although most notable for its role in DNA damage recognition and repair within the NHEJ pathway, Ku70 has NHEJ-independent functions in telomere maintenance, apoptosis, and transcriptional regulation/repression. To our surprise, Ku70-/-p53p/p mutant mice displayed a stark increase in b-cell proliferation, resulting in islet expansion, heightened insulin levels and hypoglycemia. Augmented b-cell proliferation was accompanied with the stabilization of the canonical Wnt pathway, responsible for this phenotype. Interestingly, the progressive onset of cellular senescence prevented islet tumorigenesis. This study highlights Ku70 as an important modulator in not only maintaining genomic stability through NHEJ-dependent functions, but also reveals a novel NHEJ-independent function through regulation of pancreatic b-cell proliferation. Taken in aggregate, these studies underscore the importance for NHEJ to maintain genomic stability in b-cells as well as introduces a novel regulator for pancreatic b-cell proliferation.

THE MOLECULAR INTERACTION BETWEEN TYPE II DIABETES AND ALZHEIMER’S DISEASE THROUGH CROSS-SEEDING OF PROTEIN MISFOLDING

With the population of the world aging, the prominence of diseases such as Type II Diabetes (T2D) and Alzheimer’s disease (AD) are on the rise. In addition, patients with T2D have an increased risk of developing AD compared to age-matched individuals, and the number of AD patients with T2D is higher than among aged-matched non-AD patients. AD is a chronic and progressive dementia characterized by amyloid-beta (Aβ) plaques, neurofibrillary tangles (NFTs), neuronal loss, brain inflammation, and cognitive impairment. T2D involves the dysfunctional use of pancreatic insulin by the body resulting in insulin resistance, hyperglycemia, hyperinsulinemia, pancreatic beta cell (β-cell) death, and other complications. T2D and AD are considered protein misfolding disorders (PMDs). PMDs are characterized by the presence of misfolded protein aggregates, such as in T2D pancreas (islet amyloid polypeptide - IAPP) and in AD brain (amyloid– Aβ) of affected individuals. The misfolding and accumulation of these proteins follows a seeding-nucleation model where misfolded soluble oligomers act as nuclei to propagate misfolding by recruiting other native proteins. Cross-seeding occurs when oligomers composed by one protein seed the aggregation of a different protein. Our hypothesis is that the pathological interactions between T2D and AD may in part occur through cross-seeding of protein misfolding. To test this hypothesis, we examined how each respective aggregate (Aβ or IAPP) affects the disparate disease pathology through in vitro and in vivo studies. Assaying Aβ aggregates influence on T2D pathology, IAPP+/+/APPSwe+/- double transgenic (DTg) mice exhibited exacerbated T2D-like pathology as seen in elevated hyperglycemia compared to controls; in addition, IAPP levels in the pancreas are highest compared to controls. Moreover, IAPP+/+/APPSwe+/- animals demonstrate abundant plaque formation and greater plaque density in cortical and hippocampal areas in comparison to controls. Indeed, IAPP+/+/APPSwe+/- exhibit a colocalization of both misfolded proteins in cerebral plaques suggesting IAPP may directly interact with Aβ and aggravate AD pathology. In conclusion, these studies suggest that cross-seeding between IAPP and Aβ may occur, and that these protein aggregates exacerbate and accelerate disease pathology, respectively. Further mechanistic studies are necessary to determine how these two proteins interact and aggravate both pancreatic and brain pathologies.

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.

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.

Mejora postpradial de un controlador basado en reglas

La Diabetes mellitus es una enfermedad caracterizada por la insuficiente o nula producción de insulina por parte del páncreas o la reducida sensibilidad del organismo a esta hormona, que ayuda a que la glucosa llegue a los tejidos y al sistema nervioso para suministrar energía. La Diabetes tiene una mayor prevalencia en los países desarrollados debido a múltiples factores, entre ellos la obesidad, la vida sedentaria, y disfunciones en el sistema endocrino relacionadas con el páncreas. La Diabetes Tipo 1 es una enfermedad crónica e incurable, en la que son destruidas las células beta del páncreas, que producen la insulina, haciéndose necesaria la administración de insulina de forma exógena para controlar los niveles de glucosa en sangre. El paciente debe seguir una terapia con insulina administrada por 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 sano. La tecnología actual permite abordar el desarrollo del denominado “páncreas endocrino artificial” (PEA), que aportaría precisión, eficacia y seguridad en la aplicación de las terapias con insulina y permitiría una mayor independencia de los pacientes frente a su enfermedad, que en la actualidad están sujetos a una constante toma de decisiones. El PEA consta de un sensor continuo de glucosa, una bomba de infusión de insulina y un algoritmo de control, que calcula la insulina a infusionar utilizando los niveles de glucosa del paciente como información principal. Este trabajo presenta una modificación en el método de control en lazo cerrado propuesto en un proyecto previo. El controlador del que se parte está compuesto por un controlador basal booleano y un controlador borroso postprandial basado en reglas borrosas heredadas del controlador basal. El controlador postprandial administra el 50% del bolo manual (calculado a partir de la cantidad de carbohidratos que el paciente va a consumir) en el instante del aviso de la ingesta y reparte el resto en instantes posteriores. El objetivo es conseguir una regulación óptima del nivel de glucosa en el periodo postprandial. Con el objetivo de reducir las hiperglucemias que se producen en el periodo postprandial se realiza un transporte de insulina, que es un adelanto de la insulina basal del periodo postprandial que se suministrará junto con un porcentaje variable del bolo manual. Este porcentaje estará relacionado con el estado metabólico del paciente previo a la ingesta. Además se modificará la base de conocimiento para adecuar el comportamiento del controlador al periodo postprandial. Este proyecto está enfocado en la mejora del controlador borroso postprandial previo, modificando dos aspectos: la inferencia del controlador postprandial y añadiendo una toma de decisiones automática sobre el % del bolo manual y el transporte. Se ha propuesto un controlador borroso con una nueva inferencia, que no hereda las características del controlado basal, y ha sido adaptado al periodo postprandial. Se ha añadido una inferencia borrosa que modifica la cantidad de insulina a administrar en el momento del aviso de ingesta y la cantidad de insulina basal a transportar del periodo postprandial al bolo manual. La validación del algoritmo se ha realizado mediante experimentos en simulación utilizando una población de diez pacientes sintéticos pertenecientes al Simulador de Padua/Virginia, evaluando los resultados con estadísticos para después compararlos con los obtenidos con el método de control anterior. Tras la evaluación de los resultados se puede concluir que el nuevo controlador postprandial, acompañado de la toma de decisiones automática, realiza un mejor control glucémico en el periodo postprandial, disminuyendo los niveles de las hiperglucemias. ABSTRACT. Diabetes mellitus is a disease characterized by the insufficient or null production of insulin from the pancreas or by a reduced sensitivity to this hormone, which helps glucose get to the tissues and the nervous system to provide energy. Diabetes has more prevalence in developed countries due to multiple factors, including obesity, sedentary lifestyle and endocrine dysfunctions related to the pancreas. Type 1 Diabetes is a chronic, incurable disease in which beta cells in the pancreas that produce insulin are destroyed, and exogenous insulin delivery is required to control blood glucose levels. The patient must follow a therapy with insulin administered by the subcutaneous route that should be adjusted to the metabolic needs and lifestyle of the patient. This therapy tries to imitate the insulin profile of a non-pathological pancreas. Current technology can adress the development of the so-called “endocrine artificial pancreas” (EAP) that would provide accuracy, efficacy and safety in the application of insulin therapies and will allow patients a higher level of independence from their disease. Patients are currently tied to constant decision making. The EAP consists of a continuous glucose sensor, an insulin infusion pump and a control algorithm that computes the insulin amount that has to be infused using the glucose as the main source of information. This work shows modifications to the control method in closed loop proposed in a previous project. The reference controller is composed by a boolean basal controller and a postprandial rule-based fuzzy controller which inherits the rules from the basal controller. The postprandial controller administrates 50% of the bolus (calculated from the amount of carbohydrates that the patient is going to ingest) in the moment of the intake warning, and distributes the remaining in later instants. The goal is to achieve an optimum regulation of the glucose level in the postprandial period. In order to reduce hyperglycemia in the postprandial period an insulin transport is carried out. It consists on a feedforward of the basal insulin from the postprandial period, which will be administered with a variable percentage of the manual bolus. This percentage would be linked with the metabolic state of the patient in moments previous to the intake. Furthermore, the knowledge base is going to be modified in order to fit the controller performance to the postprandial period. This project is focused on the improvement of the previous controller, modifying two aspects: the postprandial controller inference, and the automatic decision making on the percentage of the manual bolus and the transport. A fuzzy controller with a new inference has been proposed and has been adapted to the postprandial period. A fuzzy inference has been added, which modifies both the amount of manual bolus to administrate at the intake warning and the amount of basal insulin to transport to the prandial bolus. The algorithm assessment has been done through simulation experiments using a synthetic population of 10 patients in the UVA/PADOVA simulator, evaluating the results with statistical parameters for further comparison with those obtained with the previous control method. After comparing results it can be concluded that the new postprandial controller, combined with the automatic decision making, carries out a better glycemic control in the postprandial period, decreasing levels of hyperglycemia.

Ontogeny of T cell tolerance to peripherally expressed antigens

Transgenic expression of the influenza virus hemagglutinin (HA) in the pancreatic islet β cells of InsHA mice leads to peripheral tolerance of HA-specific T cells. To examine the onset of tolerance, InsHA mice were immunized with influenza virus A/PR/8 at different ages, and the presence of nontolerant T cells was determined by the induction of autoimmune diabetes. The data revealed a neonatal period wherein T cells were not tolerant and influenza virus infection led to HA-specific β cell destruction and autoimmune diabetes. The ability to induce autoimmunity gradually waned, such that adult mice were profoundly tolerant to viral HA and were protected from diabetes. Because cross-presentation of islet antigens by professional antigen-presenting cells had been reported to induce peripheral tolerance, the temporal relationship between tolerance induction and activation of HA-specific T cells in the lymph nodes draining the pancreas was examined. In tolerant adult mice, but not in 1-week-old neonates, activation and proliferation of HA-specific CD8+ T cells occurred in the pancreatic lymph nodes. Thus, lack of tolerance in the perinatal period correlated with lack of activation of antigen-specific CD8+ T cells. This work provides evidence for the developmental regulation of peripheral tolerance induction.

Potent inhibition of tumor survival in vivo by β-lapachone plus taxol: Combining drugs imposes different artificial checkpoints

Ablation of tumor colonies was seen in a wide spectrum of human carcinoma cells in culture after treatment with the combination of β-lapachone and taxol, two low molecular mass compounds. They synergistically induced death of cultured ovarian, breast, prostate, melanoma, lung, colon, and pancreatic cancer cells. This synergism is schedule dependent; namely, taxol must be added either simultaneously or after β-lapachone. This combination therapy has unusually potent antitumor activity against human ovarian and prostate tumor prexenografted in mice. There is little host toxicity. Cells can commit to apoptosis at cell-cycle checkpoints, a mechanism that eliminates defective cells to ensure the integrity of the genome. We hypothesize that when cells are treated simultaneously with drugs activating more than one different cell-cycle checkpoint, the production of conflicting regulatory signaling molecules induces apoptosis in cancer cells. β-Lapachone causes cell-cycle delays in late G1 and S phase, and taxol arrests cells at G2/M. Cells treated with both drugs were delayed at multiple checkpoints before committing to apoptosis. Our findings suggest an avenue for developing anticancer therapy by exploiting apoptosis-prone “collisions” at cell-cycle checkpoints.

Phenotypic alterations in insulin-deficient mutant mice

Two mouse insulin genes, Ins1 and Ins2, were disrupted and lacZ was inserted at the Ins2 locus by gene targeting. Double nullizygous insulin-deficient pups were growth-retarded. They did not show any glycosuria at birth but soon after suckling developed diabetes mellitus with ketoacidosis and liver steatosis and died within 48 h. Interestingly, insulin deficiency did not preclude pancreas organogenesis and the appearance of the various cell types of the endocrine pancreas. The presence of lacZ expressing β cells and glucagon-positive α cells was demonstrated by cytochemistry and immunocytochemistry. Reverse transcription-coupled PCR analysis showed that somatostatin and pancreatic polypeptide mRNAs were present, although at reduced levels, accounting for the presence also of δ and pancreatic polypeptide cells, respectively. Morphometric analysis revealed enlarged islets of Langherans in the pancreas from insulin-deficient pups, suggesting that insulin might function as a negative regulator of islet cell growth. Whether insulin controls the growth of specific islet cell types and the molecular basis for this action remain to be elucidated.

Anchoring of protein kinase A facilitates hormone-mediated insulin secretion

Impaired insulin secretion is a characteristic of non-insulin-dependent diabetes mellitus (NIDDM). One possible therapeutic agent for NIDDM is the insulinotropic hormone glucagon-like peptide 1 (GLP-1). GLP-1 stimulates insulin secretion through several mechanisms including activation of protein kinase A (PKA). We now demonstrate that the subcellular targeting of PKA through association with A-kinase-anchoring proteins (AKAPs) facilitates GLP-1-mediated insulin secretion. Disruption of PKA anchoring by the introduction of anchoring inhibitor peptides or expression of soluble AKAP fragments blocks GLP-1 action in primary islets and cAMP-responsive insulin secretion in clonal beta cells (RINm5F). Displacement of PKA also prevented cAMP-mediated elevation of intracellular calcium suggesting that localized PKA phosphorylation events augment calcium flux.

neurogenin3 is required for the development of the four endocrine cell lineages of the pancreas

In the mammalian pancreas, the endocrine cell types of the islets of Langerhans, including the α-, β-, δ-, and pancreatic polypeptide cells as well as the exocrine cells, derive from foregut endodermal progenitors. Recent genetic studies have identified a network of transcription factors, including Pdx1, Isl1, Pax4, Pax6, NeuroD, Nkx2.2, and Hlxb9, regulating the development of islet cells at different stages, but the molecular mechanisms controlling the specification of pancreatic endocrine precursors remain unknown. neurogenin3 (ngn3) is a member of a family of basic helix–loop–helix transcription factors that is involved in the determination of neural precursor cells in the neuroectoderm. ngn3 is expressed in discrete regions of the nervous system and in scattered cells in the embryonic pancreas. We show herein that ngn3-positive cells coexpress neither insulin nor glucagon, suggesting that ngn3 marks early precursors of pancreatic endocrine cells. Mice lacking ngn3 function fail to generate any pancreatic endocrine cells and die postnatally from diabetes. Expression of Isl1, Pax4, Pax6, and NeuroD is lost, and endocrine precursors are lacking in the mutant pancreatic epithelium. Thus, ngn3 is required for the specification of a common precursor for the four pancreatic endocrine cell types.

Spontaneous diabetes mellitus in transgenic mice expressing human islet amyloid polypeptide.

The islet in non-insulin-dependent diabetes mellitus (NIDDM) is characterized by loss of beta cells and large local deposits of amyloid derived from the 37-amino acid protein, islet amyloid polypeptide (IAPP). We have hypothesized that IAPP amyloid forms intracellularly causing beta-cell destruction under conditions of high rates of expression. To test this we developed a homozygous transgenic mouse model with high rates of expression of human IAPP. Male transgenic mice spontaneously developed diabetes mellitus by 8 weeks of age, which was associated with selective beta-cell death and impaired insulin secretion. Small intra- and extracellular amorphous IAPP aggregates were present in islets of transgenic mice during the development of diabetes mellitus. However, IAPP derived amyloid deposits were found in only a minority of islets at approximately 20 weeks of age, notably after development of diabetes mellitus in male transgenic mice. Approximately 20% of female transgenic mice spontaneously developed diabetes mellitus at 30+ weeks of age, when beta-cell degeneration and both amorphous and amyloid deposits of IAPP were present. We conclude that overexpression of human IAPP causes beta-cell death, impaired insulin secretion, and diabetes mellitus. Large deposits of IAPP derived amyloid do not appear to be important in this cytotoxicity, but early, small amorphous intra- and extracellular aggregates of human IAPP were consistently present at the time of beta-cell death and therefore may be the most cytotoxic form of IAPP.

IA-2, a transmembrane protein of the protein tyrosine phosphatase family, is a major autoantigen in insulin-dependent diabetes mellitus.

IA-2 is a 105,847 Da transmembrane protein that belongs to the protein tyrosine phosphatase family. Immunoperoxidase staining with antibody raised against IA-2 showed that this protein is expressed in human pancreatic islet cells. In this study, we expressed the full-length cDNA clone of IA-2 in a rabbit reticulocyte transcription/translation system and used the recombinant radiolabeled IA-2 protein to detect autoantibodies by immunoprecipitation. Coded sera (100) were tested: 50 from patients with newly diagnosed insulin-dependent diabetes mellitus (IDDM) and 50 from age-matched normal controls. Sixty-six percent of the sera from patients, but none of the sera from controls, reacted with IA-2. The same diabetic sera tested for autoantibodies to islet cells (ICA) by indirect immunofluorescence and glutamic acid decarboxylase (GAD65Ab) by depletion ELISA showed 68% and 52% positivity, respectively. Up to 86% of the IDDM patients had autoantibodies to IA-2 and/or GAD65. Moreover, greater than 90% (14 of 15) of the ICA-positive but GAD65Ab-negative sera had autoantibodies to IA-2. Absorption experiments showed that the immunofluorescence reactivity of ICA-positive sera was greatly reduced by prior incubation with recombinant IA-2 or GAD65 when the respective antibody was present. A little over one-half (9 of 16) of the IDDM sera that were negative for ICA were found to be positive for autoantibodies to IA-2 and/or GAD65, arguing that the immunofluorescence test for ICA is less sensitive than the recombinant tests for autoantibodies to IA-2 and GAD65. It is concluded that IA-2 is a major islet cell autoantigen in IDDM, and, together with GAD65, is responsible for much of the reactivity of ICA with pancreatic islets. Tests for the detection of autoantibodies to recombinant IA-2 and GAD65 may eventually replace ICA immunofluorescence for IDDM population screening.