EPIC (Entorno de Páncreas Artificial con Interfaz Clínica)

La Diabetes Mellitus se define como el trastorno del metabolismo de los carbohidratos, resultante de una producción insuficiente o nula de insulina en las células beta del páncreas, o la manifestación de una sensibilidad reducida a la insulina por parte del sistema metabólico. La diabetes tipo 1 se caracteriza por la nula producción de insulina por la destrucción de las células beta del páncreas. Si no hay insulina en el torrente sanguíneo, la glucosa no puede ser absorbida por las células, produciéndose un estado de hiperglucemia en el paciente, que a medio y largo plazo si no es tratado puede ocasionar severas enfermedades, conocidos como síndromes de la diabetes. La diabetes tipo 1 es una enfermedad incurable pero controlable. La terapia para esta enfermedad consiste en la aplicación exógena de insulina con el objetivo de mantener el nivel de glucosa en sangre dentro de los límites normales. Dentro de las múltiples formas de aplicación de la insulina, en este proyecto se usará una bomba de infusión, que unida a un sensor subcutáneo de glucosa permitirá crear un lazo de control autónomo que regule la cantidad optima de insulina aplicada en cada momento. Cuando el algoritmo de control se utiliza en un sistema digital, junto con el sensor subcutáneo y bomba de infusión subcutánea, se conoce como páncreas artificial endocrino (PAE) de uso ambulatorio, hoy día todavía en fase de investigación. Estos algoritmos de control metabólico deben de ser evaluados en simulación para asegurar la integridad física de los pacientes, por lo que es necesario diseñar un sistema de simulación mediante el cual asegure la fiabilidad del PAE. Este sistema de simulación conecta los algoritmos con modelos metabólicos matemáticos para obtener una visión previa de su funcionamiento. En este escenario se diseñó DIABSIM, una herramienta desarrollada en LabViewTM, que posteriormente se trasladó a MATLABTM, y basada en el modelo matemático compartimental propuesto por Hovorka, con la que poder simular y evaluar distintos tipos de terapias y reguladores en lazo cerrado. Para comprobar que estas terapias y reguladores funcionan, una vez simulados y evaluados, se tiene que pasar a la experimentación real a través de un protocolo de ensayo clínico real, como paso previo al PEA ambulatorio. Para poder gestionar este protocolo de ensayo clínico real para la verificación de los algoritmos de control, se creó una interfaz de usuario a través de una serie de funciones de simulación y evaluación de terapias con insulina realizadas con MATLABTM (GUI: Graphics User Interface), conocido como Entorno de Páncreas artificial con Interfaz Clínica (EPIC). EPIC ha sido ya utilizada en 10 ensayos clínicos de los que se han ido proponiendo posibles mejoras, ampliaciones y/o cambios. Este proyecto propone una versión mejorada de la interfaz de usuario EPIC propuesta en un proyecto anterior para gestionar un protocolo de ensayo clínico real para la verificación de algoritmos de control en un ambiente hospitalario muy controlado, además de estudiar la viabilidad de conectar el GUI con SimulinkTM (entorno gráfico de Matlab de simulación de sistemas) para su conexión con un nuevo simulador de pacientes aprobado por la JDRF (Juvenil Diabetes Research Foundation). SUMMARY The diabetes mellitus is a metabolic disorder of carbohydrates, as result of an insufficient or null production of insulin in the beta cellules of pancreas, or the manifestation of a reduced sensibility to the insulin from the metabolic system. The type 1 diabetes is characterized for a null production of insulin due to destruction of the beta cellules. Without insulin in the bloodstream, glucose can’t be absorbed by the cellules, producing a hyperglycemia state in the patient and if pass a medium or long time and is not treated can cause severe disease like diabetes syndrome. The type 1 diabetes is an incurable disease but controllable one. The therapy for this disease consists on the exogenous insulin administration with the objective to maintain the glucose level in blood within the normal limits. For the insulin administration, in this project is used an infusion pump, that permit with a subcutaneous glucose sensor, create an autonomous control loop that regulate the optimal insulin amount apply in each moment. When the control algorithm is used in a digital system, with the subcutaneous senor and infusion subcutaneous pump, is named as “Artificial Endocrine Pancreas” for ambulatory use, currently under investigate. These metabolic control algorithms should be evaluates in simulation for assure patients’ physical integrity, for this reason is necessary to design a simulation system that assure the reliability of PAE. This simulation system connects algorithms with metabolic mathematics models for get a previous vision of its performance. In this scenario was created DIABSIMTM, a tool developed in LabView, that later was converted to MATLABTM, and based in the compartmental mathematic model proposed by Hovorka that could simulate and evaluate several different types of therapy and regulators in closed loop. To check the performance of these therapies and regulators, when have been simulated and evaluated, will be necessary to pass to real experimentation through a protocol of real clinical test like previous step to ambulatory PEA. To manage this protocol was created an user interface through the simulation and evaluation functions od therapies with insulin realized with MATLABTM (GUI: Graphics User Interface), known as “Entorno de Páncreas artificial con Interfaz Clínica” (EPIC).EPIC have been used in 10 clinical tests which have been proposed improvements, adds and changes. This project proposes a best version of user interface EPIC proposed in another project for manage a real test clinical protocol for checking control algorithms in a controlled hospital environment and besides studying viability to connect the GUI with SimulinkTM (Matlab graphical environment in systems simulation) for its connection with a new patients simulator approved for the JDRF (Juvenil Diabetes Research Foundation).

Páncreas artificial basado en un controlador Smith paramétrico

La diabetes mellitus es una enfermedad que se caracteriza por la nula o insuficiente producción de insulina, o la resistencia del organismo a la misma. La insulina es una hormona que ayuda a que la glucosa llegue a los tejidos periféricos y al sistema nervioso para suministrar energía. Actualmente existen dos tipos de terapias aplicada en tejido subcutáneo: mediante inyección múltiple realizada con plumas, y la otra es mediante infusión continua de insulina por bomba (CSII). El mayor problema de esta terapia son los retardos por la absorción, tanto de los carbohidratos como de la insulina, y los retardos introducidos por el sensor subcutáneo de glucosa que mide la glucosa del líquido intersticial, lo deseable es controlar la glucosa en sangre. Para intentar independizar al paciente de su enfermedad se está trabajando en el desarrollo del páncreas endocrino artificial (PEA) que dotaría al paciente de una bomba de insulina, un sensor de glucosa y un controlador, el cual se encargaría de la toma de decisiones de las infusiones de insulina. Este proyecto persigue el diseño de un regulador en modo de funcionamiento en CL, con el objetivo de conseguir una regulación óptima del nivel de glucosa en sangre. El diseño de dicho regulador va a ser acometido utilizando la teoría del control por modelo interno (IMC). Esta teoría se basa en la idea de que es necesario realimentar la respuesta de un modelo aproximado del proceso que se quiere controlar. La salida del modelo, comparada con la del proceso real nos da la incertidumbre del modelo de la planta, frente a la planta real. Dado que según la teoría del modelo interno, estas diferencias se dan en las altas frecuencias, la teoría IMC propone un filtro paso bajo como regulador en serie con la inversa del modelo de la planta para conseguir el comportamiento deseado. Además se pretende implementar un Predictor Smith para minimizar los efectos del retardo de la medida del sensor. En el proyecto para conseguir la viabilidad del PEA se ha adaptado el controlador IMC clásico utilizando las ganancias estáticas de un modelo de glucosa, a partir de la ruta subcutánea de infusión y la vía subcutánea de medida. El modo de funcionamiento del controlador en SCL mejora el rango de normoglucemia, necesitando la intervención del paciente indicando anticipadamente el momento de las ingestas al controlador. El uso de un control SCL con el Predictor de Smith mejora los resultados pues se añade al controlador una variable sobre las ingestas con la participación del paciente. ABSTRACT. Diabetes mellitus is a group of metabolic diseases in which a person has high blood sugar, due to the body does not produce enough insulin, or because cells do not respond to the insulin produced. The insulin is a hormone that helps the glucose to reach to outlying tissues and the nervous system to supply energy. There are currently two types of therapies applied in subcutaneous tissue: the first one consists in using the intensive therapy with an insulin pen, and the other one is by continuous subcutaneous insulin infusion (CSII). The biggest problems of this therapy are the delays caused by the absorption of carbohydrates and insulin, and the delays introduced by the subcutaneous glucose sensor that measures glucose from interstitial fluid, it is suitable to control glucose blood. To try to improve these patients quality of life, work is being done on the development of an artificial endocrine pancreas (PEA) consisting of a subcutaneous insulin pump, a subcutaneous glucose sensor and an algorithm of glucose control, which would calculate the bolus that the pump would infuse to patient. This project aims to design a controller for closed-loop therapy, with the objective of obtain an optimal regulation of blood glucose level. The design of this controller will be formed using the theory of internal model control (IMC). This theory is based on the uncertainties given by a model to feedback the system control. Output model, in comparison with the actual process gives the uncertainty of the plant model, compared to the real plant. Since the theory of the internal model, these differences occur at high frequencies, the theory proposes IMC as a low pass filter regulator in series with the inverse model of the plant to get the required behavior. In addition, it will implement a Smith Predictor to minimize the effects of the delay measurement sensor. The project for the viability of PEA has adapted the classic IMC controller using the gains static of glucose model from the subcutaneous infusion and subcutaneous measuring. In simulation the SemiClosed-Loop controller get on the normoglycemia range, requiring patient intervention announce the bolus priming connected to intakes. Using an SCL control with the Smith Predictor improves the outcome because a variable about intakes is added to the controller through patient intervention.

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.

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.

Conditional inactivation of the Men1 gene leads to pancreatic and pituitary tumorigenesis but does not affect normal development of these tissues

Mutations of the MEN1 gene, encoding the tumor suppressor menin, predispose individuals to the cancer syndrome multiple endocrine neoplasia type 1, characterized by the development of tumors of the endocrine pancreas and anterior pituitary and parathyroid glands. We have targeted the murine Men1 gene by using Cre recombinase-loxP technology to develop both total and tissue-specific knockouts of the gene. Conditional homozygous inactivation of the Men1 gene in the pituitary gland and endocrine pancreas bypasses the embryonic lethality associated with a constitutional Men1(-/-) genotype and leads to beta-cell hyperplasia in less than 4 months and insulinomas and prolactinomas starting at 9 months. The pituitary gland and pancreas develop normally in the conditional absence of menin, but loss of this transcriptional cofactor is sufficient to cause beta-cell hyperplasia in some islets; however, such loss is not sufficient to initiate pituitary gland tumorigenesis, suggesting that additional genetic events are necessary for the latter.

Time-related, cross-sectional and prospective follow-up of pancreatic endocrine function after pancreas allograft transplantation in type 1 (insulin-dependent) diabetic patients

It has been established that successful pancreas transplantation in Type 1 (insulin-dependent) diabetic patients results in normal but exaggerated phasic glucose-induced insulin secretion, normal intravenous glucose disappearance rates, improved glucose recovery from insulin-induced hypoglycaemia, improved glucagon secretion during insulin-induced hypoglycaemia, but no alterations in pancreatic polypeptide responses to hypoglycaemia. However, previous reports have not segregated the data in terms of the length of time following successful transplantation and very little prospective data collected over time in individual patients has been published. This article reports that in general there are no significant differences in the level of improvement when comparing responses as early as three months post-operatively up to as long as two years post-operatively when examining the data cross-sectionally in patients who have successfully maintained their allografts. Moreover, this remarkable constancy in pancreatic islet function is also seen in a smaller group of patients who have been examined prospectively at various intervals post-operatively. It is concluded that successful pancreas transplantation results in remarkable improvements in Alpha and Beta cell but not PP cell function that are maintained for at least one to two years.

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.

Role of Ptf1a in the development of endocrine and exocrine pancreas of Xenopus laevis embryos

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Role of Ptf1a in the development of endocrine and exocrine pancreas of Xenopus laevis embryos

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Targeting histone deacetylases for the treatment of immune, endocrine and metabolic disorders

The 'histone code' is a well-established hypothesis describing the idea that specific patterns of post-translational modifications to histones act like a molecular "code" recognised and used by non-histone proteins to regulate specific chromatin functions. One modification which has received significant attention is that of histone acetylation. The enzymes which regulate this modification are described as histone acetyltransferases or HATs, and histone deacetylases or HDACs. Due to their conserved catalytic domain HDACs have been actively targeted as a therapeutic target. The proinflammatory environment is increasingly being recognised as a critical element for both degenerative diseases and cancer. The present review will discuss the current knowledge surrounding the clinical potential & current development of histone deacetylases for the treatment of diseases for which a proinflammatory environment plays important roles, and the molecular mechanisms by which such inhibitors may play important functions in modulating the proinflammatory environment. © 2009 Bentham Science Publishers Ltd.

A metastatic neuroendocrine anaplastic small cell tumor in a patient with multiple endocrine neoplasia type 1 syndrome : assessment of disease status and response to doxorubicin, cyclophosphamide, etoposide chemotherapy through scintigraphic imaging with 111In-pentetreotide

Extrapulmonary small cell and small cell neuroendocrine tumors of unknown primary site are, in general, aggressive neoplasms with a short median survival. Like small cell lung cancer (SCLC), they often are responsive to chemotherapy and radiotherapy. Small cell lung cancer and well differentiated neuroendocrine carcinomas of the gastrointestinal tract and pancreas tend to express somatostatin receptors. These tumors may be localized in patients by scintigraphic imaging using radiolabeled somatostatin analogues. A patient with an anaplastic neuroendocrine small cell tumor arising on a background of multiple endocrine neoplasia type 1 syndrome is reported. The patient had a known large pancreatic gastrinoma and previously treated parathyroid adenopathy. At presentation, there was small cell cancer throughout the liver and skeleton. Imaging with a radiolabeled somatostatin analogue, 111In- pentetreotide (Mallinckrodt Medical B. V., Petten, Holland), revealed all sites of disease detected by routine biochemical and radiologic methods. After six cycles of chemotherapy with doxorubicin, cyclophosphamide, and etoposide, there was almost complete clearance of the metastatic disease. 111In-pentetreotide scintigraphy revealed uptake consistent with small areas of residual disease in the liver, the abdomen (in mesenteric lymph nodes), and posterior thorax (in a rib). The primary gastrinoma present before the onset of the anaplastic small cell cancer showed no evidence of response to the treatment. The patient remained well for 1 year and then relapsed with brain, lung, liver, and skeletal metastases. Despite an initial response to salvage radiotherapy and chemotherapy with carboplatin and dacarbazine, the patient died 6 months later.

NEUROPEPTIDE-Y AND NEUROPEPTIDE-Y 3-36 - ISOLATION FROM HUMAN PANCREATIC ENDOCRINE TUMORS

Using an antiserum raised to the C-terminal region of neuropeptide Y (NPY) which does not cross-react with pancreatic polypeptide (PP), immunoreactivity has been detected in two different endocrine tumours of the human pancreas in concentrations permitting isolation and structural analysis. In a clinically-typical gastrinoma, resected from the head of pancreas, the concentration of NPY immunoreactivity was 3.4 nmol/g. Reverse phase HPLC analysis of extracts of this tumour resolved a single immunoreactive peptide coeluting with synthetic human NPY. The molecular mass of the isolated peptide, determined by mass spectroscopy, was 4270 Da, which was in close agreement with that derived from the deduced primary structure of human tumour NPY (4271.7 Da), obtained by gas-phase sequencing. A somatostatinoma, resected from the region of the ampulla of Vater, contained 3.8 nmol/g of NPY immunoreactivity and isolation of this immunoreactive peptide followed by structural analyses, indicated a molecular structure consistent with NPY 3-36. These data suggest that NPY immunoreactivity detected in human pancreatic endocrine tumours is molecularly heterogenous, a finding which may be of relevance in the symptomatology of such tumours as attenuation of the N-terminus of this peptide generates receptor selectivity.