Characterization of cytochrome P450 4F subfamily: Response in traumatic brain injury and gene regulation

CYP4F enzymes metabolize endogenous molecules including arachidonic acid, leukotrienes and prostaglandins. The involvement of these eisosanoids in inflammation has led to the hypothesis that CYP4Fs may modulate inflammatory conditions after traumatic brain injury (TBI). In rat, TBI elicited changes in mRNA expression of CYP4Fs as a function of time in the cerebrum region. These changes in CYP4F mRNA levels inversely correlated with the cerebral leukotriene B4 (LTB4) level following injury at the same time points. TBI also resulted in changes in CYP4F protein expression and localization around the injury site, where CYP4F1 and CYP4F6 immunoreactivity increased in surrounding astrocytes and CYP4F4 immunoreactivity shifted from endothelia of cerebral vessels to astrocytes. The study with rat primary astrocytes indicated that pro-inflammatory cytokines TNFα and IL-1β could affect the transcription of CYP4Fs to a certain degree, whereas the changing pattern in the primary astrocytes appeared to be different from that in the in vivo TBI model.^ In addition, the regulation of CYP4F genes has been an unsolved issue although factors including cytokines and fatty acids appear to affect CYP4Fs expression in multiple models. In this project, HaCaT cells were used as an in vitro cellular model to define signaling pathways involved in the regulation of human CYP4F genes. Retinoic acids inhibited CYP4F11 expression, whereas cytokines TNFα and IL-1β induced transcription of CYP4F11 in HaCaT cells. The induction of CYP4F11 by both cytokines could be blocked by a JNK specific inhibitor, indicating the involvement of the JNK pathway in the up-regulation of CYP4F11. Retinoic acids are known to function in gene regulation through nuclear receptors RARs and RXRs. The RXR agonist LG268 greatly induced transcription of CYP4F11, whereas RAR agonist TTNPB obviously inhibited CYP4F11 transcription, indicating that the down-regulation of CYP4F11 by retinoic acid was mediated by RARs, and that inhibition of CYP4F11 by retinoic acid may also be related to the competition for RXR receptors. Thus, the CYP4F11 gene is regulated by signaling pathways including the RXR pathway and the JNK pathway. In contrast, the regulation mechanism of other CYP4Fs by retinoic acids appears to be different from that of CYP4F11.^

Role of TRP Channels in Mediating the Calcium Signaling Response of Brain Endothelial Cells to Mechanical Stretch

Traumatic brain injury (TBI) often results in disruption of the blood brain barrier (BBB), which is an integral component to maintaining the central nervous system homeostasis. Recently cytosolic calcium levels ([Ca2+]i), observed to elevate following TBI, have been shown to influence endothelial barrier integrity. However, the mechanism by which TBI-induced calcium signaling alters the endothelial barrier remains unknown. In the present study, an in vitro BBB model was utilized to address this issue. Exposure of cells to biaxial mechanical stretch, in the range expected for TBI, resulted in a rapid cytosolic calcium increase. Modulation of intracellular and extracellular Ca2+ reservoirs indicated that Ca2+ influx is the major contributor for the [Ca2+]i elevation. Application of pharmacological inhibitors was used to identify the calcium-permeable channels involved in the stretch-induced Ca2+ influx. Antagonist of transient receptor potential (TRP) channel subfamilies, TRPC and TRPP, demonstrated a reduction of the stretch-induced Ca2+ influx. RNA silencing directed at individual TRP channel subtypes revealed that TRPC1 and TRPP2 largely mediate the stretch-induced Ca2+ response. In addition, we found that nitric oxide (NO) levels increased as a result of mechanical stretch, and that inhibition of TRPC1 and TRPP2 abolished the elevated NO synthesis. Further, as myosin light chain (MLC) phosphorylation and actin cytoskeleton rearrangement are correlated with endothelial barrier disruption, we investigated the effect mechanical stretch had on the myosin-actin cytoskeleton. We found that phosphorylated MLC was increased significantly by 10 minutes post-stretch, and that inhibition of TRP channel activity or NO synthesis both abolished this effect. In addition, actin stress fibers formation significantly increased 2 minutes post-stretch, and was abolished by treatment with TRP channel inhibitors. These results suggest that, in brain endothelial cells, TRPC1 and TRPP2 are activated by TBI-mechanical stress and initiate actin-myosin contraction, which may lead to disruption of the BBB.

Exploring the Impact of Decreased AMPK Pathway Activity on Brain Injury Outcome

Each year, 150 million people sustain a Traumatic Brain Injury (TBI). TBI results in life-long cognitive impairments for many survivors. One observed pathological alteration following TBI are changes in glucose metabolism. Altered glucose uptake occurs in the periphery as well as in the nervous system, with an acute increase in glucose uptake, followed by a prolonged metabolic suppression. Chronic, persistent suppression of brain glucose uptake occurs in TBI patients experiencing memory loss. Abberant post-injury activation of energy-sensing signaling cascades could result in perturbed cellular metabolism. AMP-activated kinase (AMPK) is a kinase that senses low ATP levels, and promotes efficient cell energy usage. AMPK promotes energy production through increasing glucose uptake via glucose transporter 4 (GLUT4). When AMPK is activated, it phosphorylates Akt Substrate of 160 kDa (AS160), a Rab GTPase activating protein that controls Glut4 translocation. Additionally, AMPK negatively regulates energy-consumption by inhibiting protein synthesis via the mechanistic Target of Rapamycin (mTOR) pathway. Given that metabolic suppression has been observed post-injury, we hypothesized that activity of the AMPK pathway is transiently decreased. As AMPK activation increases energy efficiency of the cell, we proposed that increasing AMPK activity to combat the post-injury energy crisis would improve cognitive outcome. Additionally, we expected that inhibiting AMPK targets would be detrimental. We first investigated the role of an existing state of hyperglycemia on TBI outcome, as hyperglycemia correlates with increased mortality and decreased cognitive outcome in clinical studies. Inducing hyperglycemia had no effect on outcome; however, we discovered that AMPK and AS160 phosphorylation were altered post-injury. We conducted vii work to characterize this period of AMPK suppression and found that AMPK phosphorylation was significantly decreased in the hippocampus and cortex between 24 hours and 3 days post-injury, and phosphorylation of its downstream targets was consistently altered. Based on this period of observed decreased AMPK activity, we administered an AMPK activator post-injury, and this improved cognitive outcome. Finally, to examine whether AMPK-regulated target Glut4 is involved in post-injury glucose metabolism, we applied an inhibitor and found this treatment impaired post-injury cognitive function. This work is significant, as AMPK activation may represent a new TBI therapeutic target.

The five language skills in the EFL classroom

Este artículo analiza la importancia de integrar la cultura de la lengua meta con las cuatro destrezas lingüísticas tradicionales en la clase de inglés como lengua extranjera en el marco de dos posturas pedagógicas que integran las destrezas lingüísticas: una perspectiva que hace hincapié en el contenido del material auténtico a incorporar en la clase de inglés como lengua extranjera por sobre el sistema lingüístico, y la otra perspectiva que parte de la asignación de tareas que integran contenido de la materia a estudiar con las destrezas lingüísticas propiamente dichas. El artículo también destaca la importancia de las denominadas WebQuests como herramientas pedagógicas que ofrecen invalorables oportunidades para desarrollar las destrezas lingüísticas en un ambiente propicio que promociona el aprendizaje cooperativo y autónomo en la clase de inglés como lengua extranjera.

Brain structures identification based on feature descriptor

Traumatic Brain Injury -TBI- -1- is defined as an acute event that causes certain damage to areas of the brain. TBI may result in a significant impairment of an individuals physical, cognitive and psychosocial functioning. The main consequence of TBI is a dramatic change in the individuals daily life involving a profound disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges of TBI Neuroimaging is to develop robust automated image analysis methods to detect signatures of TBI, such as: hyper-intensity areas, changes in image contrast and in brain shape. The final goal of this research is to develop a method to identify the altered brain structures by automatically detecting landmarks on the image where signal changes and to provide comprehensive information to the clinician about them. These landmarks identify injured structures by co-registering the patient?s image with an atlas where landmarks have been previously detected. The research work has been initiated by identifying brain structures on healthy subjects to validate the proposed method. Later, this method will be used to identify modified structures on TBI imaging studies.

Dysfunctional 3D model based on structural and neuropsychological information

Acquired brain injury (ABI) 1-2 refers to any brain damage occurring after birth. It usually causes certain damage to portions of the brain. ABI may result in a significant impairment of an individuals physical, cognitive and/or psychosocial functioning. The main causes are traumatic brain injury (TBI), cerebrovascular accident (CVA) and brain tumors. The main consequence of ABI is a dramatic change in the individuals daily life. This change involves a disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges in neurorehabilitation is to obtain a dysfunctional profile of each patient in order to personalize the treatment. This paper proposes a system to generate a patient s dysfunctional profile by integrating theoretical, structural and neuropsychological information on a 3D brain imaging-based model. The main goal of this dysfunctional profile is to help therapists design the most suitable treatment for each patient. At the same time, the results obtained are a source of clinical evidence to improve the accuracy and quality of our rehabilitation system. Figure 1 shows the diagram of the system. This system is composed of four main modules: image-based extraction of parameters, theoretical modeling, classification and co-registration and visualization module.

A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of quality

Tradicionalmente, el uso de técnicas de análisis de datos ha sido una de las principales vías para el descubrimiento de conocimiento oculto en grandes cantidades de datos, recopilados por expertos en diferentes dominios. Por otra parte, las técnicas de visualización también se han usado para mejorar y facilitar este proceso. Sin embargo, existen limitaciones serias en la obtención de conocimiento, ya que suele ser un proceso lento, tedioso y en muchas ocasiones infructífero, debido a la dificultad de las personas para comprender conjuntos de datos de grandes dimensiones. Otro gran inconveniente, pocas veces tenido en cuenta por los expertos que analizan grandes conjuntos de datos, es la degradación involuntaria a la que someten a los datos durante las tareas de análisis, previas a la obtención final de conclusiones. Por degradación quiere decirse que los datos pueden perder sus propiedades originales, y suele producirse por una reducción inapropiada de los datos, alterando así su naturaleza original y llevando en muchos casos a interpretaciones y conclusiones erróneas que podrían tener serias implicaciones. Además, este hecho adquiere una importancia trascendental cuando los datos pertenecen al dominio médico o biológico, y la vida de diferentes personas depende de esta toma final de decisiones, en algunas ocasiones llevada a cabo de forma inapropiada. Ésta es la motivación de la presente tesis, la cual propone un nuevo framework visual, llamado MedVir, que combina la potencia de técnicas avanzadas de visualización y minería de datos para tratar de dar solución a estos grandes inconvenientes existentes en el proceso de descubrimiento de información válida. El objetivo principal es hacer más fácil, comprensible, intuitivo y rápido el proceso de adquisición de conocimiento al que se enfrentan los expertos cuando trabajan con grandes conjuntos de datos en diferentes dominios. Para ello, en primer lugar, se lleva a cabo una fuerte disminución en el tamaño de los datos con el objetivo de facilitar al experto su manejo, y a la vez preservando intactas, en la medida de lo posible, sus propiedades originales. Después, se hace uso de efectivas técnicas de visualización para representar los datos obtenidos, permitiendo al experto interactuar de forma sencilla e intuitiva con los datos, llevar a cabo diferentes tareas de análisis de datos y así estimular visualmente su capacidad de comprensión. De este modo, el objetivo subyacente se basa en abstraer al experto, en la medida de lo posible, de la complejidad de sus datos originales para presentarle una versión más comprensible, que facilite y acelere la tarea final de descubrimiento de conocimiento. MedVir se ha aplicado satisfactoriamente, entre otros, al campo de la magnetoencefalografía (MEG), que consiste en la predicción en la rehabilitación de lesiones cerebrales traumáticas (Traumatic Brain Injury (TBI) rehabilitation prediction). Los resultados obtenidos demuestran la efectividad del framework a la hora de acelerar y facilitar el proceso de descubrimiento de conocimiento sobre conjuntos de datos reales. ABSTRACT Traditionally, the use of data analysis techniques has been one of the main ways of discovering knowledge hidden in large amounts of data, collected by experts in different domains. Moreover, visualization techniques have also been used to enhance and facilitate this process. However, there are serious limitations in the process of knowledge acquisition, as it is often a slow, tedious and many times fruitless process, due to the difficulty for human beings to understand large datasets. Another major drawback, rarely considered by experts that analyze large datasets, is the involuntary degradation to which they subject the data during analysis tasks, prior to obtaining the final conclusions. Degradation means that data can lose part of their original properties, and it is usually caused by improper data reduction, thereby altering their original nature and often leading to erroneous interpretations and conclusions that could have serious implications. Furthermore, this fact gains a trascendental importance when the data belong to medical or biological domain, and the lives of people depends on the final decision-making, which is sometimes conducted improperly. This is the motivation of this thesis, which proposes a new visual framework, called MedVir, which combines the power of advanced visualization techniques and data mining to try to solve these major problems existing in the process of discovery of valid information. Thus, the main objective is to facilitate and to make more understandable, intuitive and fast the process of knowledge acquisition that experts face when working with large datasets in different domains. To achieve this, first, a strong reduction in the size of the data is carried out in order to make the management of the data easier to the expert, while preserving intact, as far as possible, the original properties of the data. Then, effective visualization techniques are used to represent the obtained data, allowing the expert to interact easily and intuitively with the data, to carry out different data analysis tasks, and so visually stimulating their comprehension capacity. Therefore, the underlying objective is based on abstracting the expert, as far as possible, from the complexity of the original data to present him a more understandable version, thus facilitating and accelerating the task of knowledge discovery. MedVir has been succesfully applied to, among others, the field of magnetoencephalography (MEG), which consists in predicting the rehabilitation of Traumatic Brain Injury (TBI). The results obtained successfully demonstrate the effectiveness of the framework to accelerate and facilitate the process of knowledge discovery on real world datasets.

Large scale multiphysics modeling of neurites

Situado en el límite entre Ingeniería, Informática y Biología, la mecánica computacional de las neuronas aparece como un nuevo campo interdisciplinar que potencialmente puede ser capaz de abordar problemas clínicos desde una perspectiva diferente. Este campo es multiescala por naturaleza, yendo desde la nanoescala (como, por ejemplo, los dímeros de tubulina) a la macroescala (como, por ejemplo, el tejido cerebral), y tiene como objetivo abordar problemas que son complejos, y algunas veces imposibles, de estudiar con medios experimentales. La modelización computacional ha sido ampliamente empleada en aplicaciones Neurocientíficas tan diversas como el crecimiento neuronal o la propagación de los potenciales de acción compuestos. Sin embargo, en la mayoría de los enfoques de modelización hechos hasta ahora, la interacción entre la célula y el medio/estímulo que la rodea ha sido muy poco explorada. A pesar de la tremenda importancia de esa relación en algunos desafíos médicos—como, por ejemplo, lesiones traumáticas en el cerebro, cáncer, la enfermedad del Alzheimer—un puente que relacione las propiedades electrofisiológicas-químicas y mecánicas desde la escala molecular al nivel celular todavía no existe. Con ese objetivo, esta investigación propone un marco computacional multiescala particularizado para dos escenarios respresentativos: el crecimiento del axón y el acomplamiento electrofisiológicomecánico de las neuritas. En el primer caso, se explora la relación entre los constituyentes moleculares del axón durante su crecimiento y sus propiedades mecánicas resultantes, mientras que en el último, un estímulo mecánico provoca deficiencias funcionales a nivel celular como consecuencia de sus alteraciones electrofisiológicas-químicas. La modelización computacional empleada en este trabajo es el método de las diferencias finitas, y es implementada en un nuevo programa llamado Neurite. Aunque el método de los elementos finitos es también explorado en parte de esta investigación, el método de las diferencias finitas tiene la flexibilidad y versatilidad necesaria para implementar mode los biológicos, así como la simplicidad matemática para extenderlos a simulaciones a gran escala con un coste computacional bajo. Centrándose primero en el efecto de las propiedades electrofisiológicas-químicas sobre las propiedades mecánicas, una versión adaptada de Neurite es desarrollada para simular la polimerización de los microtúbulos en el crecimiento del axón y proporcionar las propiedades mecánicas como función de la ocupación de los microtúbulos. Después de calibrar el modelo de crecimiento del axón frente a resultados experimentales disponibles en la literatura, las características mecánicas pueden ser evaluadas durante la simulación. Las propiedades mecánicas del axón muestran variaciones dramáticas en la punta de éste, donde el cono de crecimiento soporta las señales químicas y mecánicas. Bansándose en el conocimiento ganado con el modelo de diferencias finitas, y con el objetivo de ir de 1D a 3D, este esquema preliminar pero de una naturaleza innovadora allana el camino a futuros estudios con el método de los elementos finitos. Centrándose finalmente en el efecto de las propiedades mecánicas sobre las propiedades electrofisiológicas- químicas, Neurite es empleado para relacionar las cargas mecánicas macroscópicas con las deformaciones y velocidades de deformación a escala microscópica, y simular la propagación de la señal eléctrica en las neuritas bajo carga mecánica. Las simulaciones fueron calibradas con resultados experimentales publicados en la literatura, proporcionando, por tanto, un modelo capaz de predecir las alteraciones de las funciones electrofisiológicas neuronales bajo cargas externas dañinas, y uniendo lesiones mecánicas con las correspondientes deficiencias funcionales. Para abordar simulaciones a gran escala, aunque otras arquitecturas avanzadas basadas en muchos núcleos integrados (MICs) fueron consideradas, los solvers explícito e implícito se implementaron en unidades de procesamiento central (CPU) y unidades de procesamiento gráfico (GPUs). Estudios de escalabilidad fueron llevados acabo para ambas implementaciones mostrando resultados prometedores para casos de simulaciones extremadamente grandes con GPUs. Esta tesis abre la vía para futuros modelos mecánicos con el objetivo de unir las propiedades electrofisiológicas-químicas con las propiedades mecánicas. El objetivo general es mejorar el conocimiento de las comunidades médicas y de bioingeniería sobre la mecánica de las neuronas y las deficiencias funcionales que aparecen de los daños producidos por traumatismos mecánicos, como lesiones traumáticas en el cerebro, o enfermedades neurodegenerativas como la enfermedad del Alzheimer. ABSTRACT Sitting at the interface between Engineering, Computer Science and Biology, Computational Neuron Mechanics appears as a new interdisciplinary field potentially able to tackle clinical problems from a new perspective. This field is multiscale by nature, ranging from the nanoscale (e.g., tubulin dimers) to the macroscale (e.g., brain tissue), and aims at tackling problems that are complex, and sometime impossible, to study through experimental means. Computational modeling has been widely used in different Neuroscience applications as diverse as neuronal growth or compound action potential propagation. However, in the majority of the modeling approaches done in this field to date, the interactions between the cell and its surrounding media/stimulus have been rarely explored. Despite of the tremendous importance of such relationship in several medical challenges—e.g., traumatic brain injury (TBI), cancer, Alzheimer’s disease (AD)—a bridge between electrophysiological-chemical and mechanical properties of neurons from the molecular scale to the cell level is still lacking. To this end, this research proposes a multiscale computational framework particularized for two representative scenarios: axon growth and electrophysiological-mechanical coupling of neurites. In the former case, the relation between the molecular constituents of the axon during its growth and its resulting mechanical properties is explored, whereas in the latter, a mechanical stimulus provokes functional deficits at cell level as a consequence of its electrophysiological-chemical alterations. The computational modeling approach chosen in this work is the finite difference method (FDM), and was implemented in a new program called Neurite. Although the finite element method (FEM) is also explored as part of this research, the FDM provides the necessary flexibility and versatility to implement biological models, as well as the mathematical simplicity to extend them to large scale simulations with a low computational cost. Focusing first on the effect of electrophysiological-chemical properties on the mechanical proper ties, an adaptation of Neurite was developed to simulate microtubule polymerization in axonal growth and provide the axon mechanical properties as a function of microtubule occupancy. After calibrating the axon growth model against experimental results available in the literature, the mechanical characteristics can be tracked during the simulation. The axon mechanical properties show dramatic variations at the tip of the axon, where the growth cone supports the chemical and mechanical signaling. Based on the knowledge gained from the FDM scheme, and in order to go from 1D to 3D, this preliminary yet novel scheme paves the road for future studies with FEM. Focusing then on the effect of mechanical properties on the electrophysiological-chemical properties, Neurite was used to relate macroscopic mechanical loading to microscopic strains and strain rates, and simulate the electrical signal propagation along neurites under mechanical loading. The simulations were calibrated against experimental results published in the literature, thus providing a model able to predict the alteration of neuronal electrophysiological function under external damaging load, and linking mechanical injuries to subsequent acute functional deficits. To undertake large scale simulations, although other state-of-the-art architectures based on many integrated cores (MICs) were considered, the explicit and implicit solvers were implemented for central processing units (CPUs) and graphics processing units (GPUs). Scalability studies were done for both implementations showing promising results for extremely large scale simulations with GPUs. This thesis opens the avenue for future mechanical modeling approaches aimed at linking electrophysiological- chemical properties to mechanical properties. Its overarching goal is to enhance the bioengineering and medical communities knowledge on neuronal mechanics and functional deficits arising from damages produced by direct mechanical insults, such as TBI, or neurodegenerative evolving illness, such as AD.

Tratados bilaterales de inversión :Las regulaciones del comercio y su influencia en el derecho humano al agua y al saneamiento

El derecho humano al agua y al saneamiento está seriamente amenazado. Además del ámbito del Derecho internacional de los derechos humanos, este derecho se ve afectado por otras normas, como las del Comercio Internacional, entre las que se encuentran los tratados multilaterales y bilaterales de inversión -TBI. Estos últimos contienen cláusulas que muy frecuentemente colisionan con las normas internacionales de protección del derecho al agua y al saneamiento, como el Pacto Internacional de Derechos Económicos, Sociales y Culturales. Los conflictos que surgen en la práctica entre empresas afectadas por TBI y estados se resuelven ante tribunales arbitrales, que no incorporan en sus decisiones las categorías y principios del derecho humano al agua potable y al saneamiento. Este texto aborda estos problemas, y propone ciertas recomendaciones

MedVir: an interactive representation system of multidimensional medical data applied to Traumatic Brain Injury's rehabilitation prediction

Clinicians could model the brain injury of a patient through his brain activity. However, how this model is defined and how it changes when the patient is recovering are questions yet unanswered. In this paper, the use of MedVir framework is proposed with the aim of answering these questions. Based on complex data mining techniques, this provides not only the differentiation between TBI patients and control subjects (with a 72% of accuracy using 0.632 Bootstrap validation), but also the ability to detect whether a patient may recover or not, and all of that in a quick and easy way through a visualization technique which allows interaction.

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

DUI program as OEF/OIF Veterans Outreach: Descriptive Data from Two Community-Based Outpatient Clinics

Although initially conceived as providing simply the preventive portion of an extended continuum of care for veterans, the Driving Under the Influence (DUI) program has turned out to be an important outreach service for active duty or recently discharged OEF/OIF (Operation Enduring Freedom/Operation Iraqi Freedom) veterans. Veterans receive empirically-based, state-mandated education and therapy under the only Department of Veterans Affairs (VA) - sponsored DUI program in the State of Colorado, with the advantage of having providers who are sensitive to symptoms of Post-Traumatic Stress Disorder (PTSD) and other relevant diagnoses specific to this population, including Traumatic Brain Injury (TBI). In this paper, the rapid growth of this program is described, as well as summary data regarding the completion, discontinuation, and augmentation of services from the original referral concern. Key results indicated that for nearly one third (31.9%) of the OEF/OIF veterans who were enrolled in the DUI program, this was their initial contact with the VA health care system. Furthermore, following their enrollment in the DUI program, more than one fourth (27.6%) were later referred to and attended other VA programs including PTSD rehabilitation and group therapy, anger management, and intensive inpatient or outpatient dual diagnosis programs. These and other findings from this study suggest that the DUI program may be an effective additional pathway for providing treatment that is particularly salient to the distinctive OEF/OIF population; one that may also result in earlier intervention for problem drinking and other problems related to combat. Relevant conclusions discussed herein primarily aim to improve providers' understanding of effective outreach, and to enhance the appropriate linkages between OEF/OIF veterans and existing VA services.

The Role of Hope in Rehabilitation and Long-term Outcomes among patients with Comorbid Spinal Cord and Traumatic Brain Injuries.

Spinal cord injury (SCI) and traumatic brain injury (TBI) are two potentially devastating conditions alone; when they co-occur in an individual they can be doubly so. The role of hope in rehabilitating oneself and recovering emotionally is examined in this paper. More specifically, Snyder's Model of Hope (1991) is examined as a tool that can aid in the rehabilitative process and help treatment providers, their patients, and the families of patients keep hope alive during a time of physical and emotional upheaval. This paper further examines the roles of hope in a rehabilitation program at Craig Hospital, a private, non-profit hospital dedicated exclusively to the rehabilitation of SCIs and TBIs and designated as a TBI and SCI Model Systems Center.

Traumatisme craniocérébral, services de santé mentale et suicide de l'enfance à l'âge adulte : une étude épidémiologique et qualitative de la situation québécoise (1987 à 2013)

Introduction et recension des écrits : Récemment, les suicides de vétérans et d’athlètes professionnels ont attiré l’attention sur l’association entre le TCC et le suicide. Les lignes directrices concernant la prise en charge en santé mentale dans cette population demeurent fragmentaires. Les objectifs de cette thèse sont de 1) déterminer si une association existe entre le TCC subi dans l’enfance et le suicide futur, 2) explorer si les personnes qui se sont suicidées ont consulté un psychiatre dans l’année précédant le suicide et évaluer si cela diffère selon que la personne ait eu un TCC ou non, 3) décrire et qualifier l’offre québécoise de santé mentale offerte en réadaptation aux enfants et aux adultes ayant subi un TCC. Méthodologie : Le volet épidémiologique consiste en une étude de cohorte rétrospective sur un échantillon de 135 703 enfants ayant reçu des services médicaux au Québec en 1987 et suivis jusqu’en 2008. Le volet qualitatif comprend un sondage auprès des gestionnaires des programmes de réadaptation TCC du Québec, des groupes de discussion avec des cliniciens et des entrevues avec des survivants de TCC et leurs proches. Résultats : Notre étude épidémiologique confirme une association significative entre le TCC subi dans l’enfance (HR 1,49 IC95% 1,04- 2,14), dans l’adolescence (HR 1,57, IC 95% 1,09-2,26) et à l’âge adulte (HR 2,53, IC95% 1,79-3,59) et le suicide. Malgré un risque de suicide plus élevé, les personnes avec un TCC et qui se sont suicidées n’ont pas consulté de psychiatre plus fréquemment que les personnes sans TCC (OR 1,29, IC 95% 0,75- 2,24). Par ailleurs, notre étude qualitative révèle que les forces du système actuel incluent une bonne qualité des services, mais qu’il existe des faiblesses au niveau de l’accès aux médecins spécialisés, du dépistage systématique et de l’accès aux services à long terme. Nos recommandations incluent le développement d’une approche coordonnée en santé mentale, l’implication automatique d’un gestionnaire de cas et l’amélioration des mécanismes d’accès après le congé.

Hipotermia terapêutica no traumatismo crânio-encefálico

Enquadramento – A hipotermia terapêutica consiste na redução controlada da temperatura central dos doentes com objetivos terapêuticos pré-definidos, assumindo-se actualmente como uma mais-valia no tratamento clínico de vítimas com TCE. Objetivos – Avaliar a eficácia da HT intra-hospitalar na redução da morbilidade e mortalidade do adulto vítima de TCE. Métodos – Foi realizada uma revisão sistemática da literatura sobre a eficácia da HT intrahospitalar na redução da morbilidade e mortalidade do adulto vítima de TCE. Efetuou-se uma pesquisa na PUBMED, The Cochrane Library, Scielo e Google Académico de estudos publicados entre 1 de janeiro de 2012 a 31 de agosto de 2014. Partindo dos critérios de inclusão previamente definidos, os estudos selecionados foram posteriormente avaliados. Dois revisores avaliaram a qualidade dos estudos a incluir, utilizando a grelha para a avaliação crítica de um estudo de Ascenção et al. (2008). Resultados – Quatro artigos preencheram os critérios de inclusão, tendo-se constituído como corpus amostral. Como metasíntese e seleção da análise dos artigos, inferiu-se que os potenciais benefícios neuroprotetores da hipotermia terapêutica, sem efeitos colaterais negativos, deve ser implementada como parte do quotidiano de tratamento e controlo das vítimas de TCE e não como terapia de resgate. Contudo, a validação das implicações práticas da HT carece de mais estudos, cuja indução seja fator neuroprotetor, independentemente do valor da PIC, individualizando a duração da indução da hipotermia. Conclusão – Os benefícios da HT em adultos vítimas de TCE são ainda alvo de debate, requerendo a realização de mais metanálises e ensaios clínicos, visando a atualização das suas recomendações. Palavras-chave – Hipotermia terapêutica; traumatismo crânio-encefálico; eficácia