Structural changes in the epithelium of the small intestine and immune cell infiltration of enteric ganglia following acute mucosal damage and local inflammation

An acute enteritis is commonly followed by intestinal neuromuscular dysfunction, including prolonged hyperexcitability of enteric neurons. Such motility disorders are associated with maintained increases in immune cells adjacent to enteric ganglia and in the mucosa. However, whether the commonly used animal model, trinitrobenzene sulphonate (TNBS)-induced enteritis, causes histological and immune cell changes similar to human enteric neuropathies is not clear. We have made a detailed study of the mucosal damage and repair and immune cell invasion following intralumenal administration of TNBS. Intestines from untreated, sham-operated and TNBS-treated animals were examined at 3 h to 56 days. At 3 h, the mucosal surface was completely ablated, by 6 h an epithelial covering was substantially restored and by 1 day there was full re-epithelialisation. The lumenal epithelium developed from a squamous cell covering to a fully differentiated columnar epithelium with mature villi at about 7 days. Prominent phagocytic activity of enterocytes occurred at 1-7 days. A surge of eosinophils and T lymphocytes associated with the enteric nerve ganglia occurred at 3 h to 3 days. However, elevated immune cell numbers occurred in the lamina propria of the mucosa until 56 days, when eosinophils were still three times normal. We conclude that the disruption of the mucosal surface that causes TNBS-induced ileitis is brief, a little more than 6 h, and causes a transient immune cell surge adjacent to enteric ganglia. This is much briefer than the enteric neuropathy that ensues. Ongoing mucosal inflammatory reaction may contribute to the persistence of enteric neuropathy.

Determination of the numerical parameters of a continuous damage model for the structural analysis of clay brick masonry

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Automatic detection of thermal damage in grinding process by artificial neural network

Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado) e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

Structural alterations in the seminiferous tubules of rats treated with immunosuppressor tacrolimus

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Automation in fault detection using neural network and model updating

In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.

Creep and damage models for the service behaviour of structural members

Deformability is often a crucial to the conception of many civil-engineering structural elements. Also, design is all the more burdensome if both long- and short-term deformability has to be considered. In this thesis, long- and short-term deformability has been studied from the material and the structural modelling point of view. Moreover, two materials have been handled: pultruded composites and concrete. A new finite element model for thin-walled beams has been introduced. As a main assumption, cross-sections rigid are considered rigid in their plane; this hypothesis replaces that of the classical beam theory of plane cross-sections in the deformed state. That also allows reducing the total number of degrees of freedom, and therefore making analysis faster compared with twodimensional finite elements. Longitudinal direction warping is left free, allowing describing phenomena such as the shear lag. The new finite-element model has been first applied to concrete thin-walled beams (such as roof high span girders or bridge girders) subject to instantaneous service loadings. Concrete in his cracked state has been considered through a smeared crack model for beams under bending. At a second stage, the FE-model has been extended to the viscoelastic field and applied to pultruded composite beams under sustained loadings. The generalized Maxwell model has been adopted. As far as materials are concerned, long-term creep tests have been carried out on pultruded specimens. Both tension and shear tests have been executed. Some specimen has been strengthened with carbon fibre plies to reduce short- and long- term deformability. Tests have been done in a climate room and specimens kept 2 years under constant load in time. As for concrete, a model for tertiary creep has been proposed. The basic idea is to couple the UMLV linear creep model with a damage model in order to describe nonlinearity. An effective strain tensor, weighting the total and the elasto-damaged strain tensors, controls damage evolution through the damage loading function. Creep strains are related to the effective stresses (defined by damage models) and so associated to the intact material.

Association between cam-type deformities and magnetic resonance imaging-detected structural hip damage: a cross-sectional study in young men

Objective Femoroacetabular impingement may be a risk factor for hip osteoarthritis in men. An underlying hip deformity of the cam type is common in asymptomatic men with nondysplastic hips. This study was undertaken to examine whether hip deformities of the cam type are associated with signs of hip abnormality, including labral lesions and articular cartilage damage, detectable on magnetic resonance imaging (MRI). Methods In this cross-sectional, population-based study in asymptomatic young men, 1,080 subjects underwent clinical examination and completed a self-report questionnaire. Of these subjects, 244 asymptomatic men with a mean age of 19.9 years underwent MRI. All MRIs were read for cam-type deformities, labral lesions, cartilage thickness, and impingement pits. The relationship between cam-type deformities and signs of joint damage were examined using logistic regression models adjusted for age and body mass index. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were determined. Results Sixty-seven definite cam-type deformities were detected. These deformities were associated with labral lesions (adjusted OR 2.77 [95% CI 1.31, 5.87]), impingement pits (adjusted OR 2.9 [95% CI 1.43, 5.93]), and labral deformities (adjusted OR 2.45 [95% CI 1.06, 5.66]). The adjusted mean difference in combined anterosuperior femoral and acetabular cartilage thickness was −0.19 mm (95% CI −0.41, 0.02) lower in those with cam-type deformities compared to those without. Conclusion Our findings indicate that the presence of a cam-type deformity is associated with MRI-detected hip damage in asymptomatic young men.

Detection of Internal Defects in Concrete Members Using Global Vibration Characteristics

Rock-pocket and honeycomb defects impair overall stiffness, accelerate aging, reduce service life, and cause structural problems in hardened concrete members. Traditional methods for detecting such deficient volumes involve visual observations or localized nondestructive methods, which are labor-intensive, time-consuming, highly sensitive to test conditions, and require knowledge of and accessibility to defect locations. The authors propose a vibration response-based nondestructive technique that combines experimental and numerical methodologies for use in identifying the location and severity of internal defects of concrete members. The experimental component entails collecting mode shape curvatures from laboratory beam specimens with size-controlled rock pocket and honeycomb defects, and the numerical component entails simulating beam vibration response through a finite element (FE) model parameterized with three defect-identifying variables indicating location (x, coordinate along the beam length) and severity of damage (alpha, stiffness reduction and beta, mass reduction). Defects are detected by comparing the FE model predictions to experimental measurements and inferring the low number of defect-identifying variables. This method is particularly well-suited for rapid and cost-effective quality assurance for precast concrete members and for inspecting concrete members with simple geometric forms.

Imaging structural abnormalities in the hip joint: instability and impingement as a cause of osteoarthritis

Osteoarthritis is thought to be caused by a combination of intrinsic vulnerabilities of the joint, such as anatomic shape and alignment, and environmental factors, such as body weight, injury, and overuse. It has been postulated that much of osteoarthritis is due to anatomic deformities. Advances in surgical techniques such as the periacetabular osteotomy, safe surgical dislocation of the hip, and hip arthroscopy have provided us with effective and safe tools to correct these anatomical problems. The limiting factor in treatment outcome in many mechanically compromised hips is the degree of cartilage damage which has occurred prior to treatment. In this regard, the role of imaging, utilizing plain radiographs in conjunction with magnetic resonance imaging, is becoming vitally important for the detection of these anatomic deformities and pre-radiographic arthritis. In this article, we will outline the plain radiographic features of hip deformities that can cause instability or impingement. Additionally, we will illustrate the use of MRI imaging to detect subtle anatomic abnormalities, as well as the use of biochemical imaging techniques such as dGEMRIC to guide clinical decision making.

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.

Multifunctional cement composites strain and damage sensors applied on reinforced concrete (RC) structural elements

In this research, strain-sensing and damage-sensing functional properties of cement composites have been studied on a conventional reinforced concrete (RC) beam. Carbon nanofiber (CNFCC) and fiber (CFCC) cement composites were used as sensors on a 4 m long RC beam. Different casting conditions (in situ or attached), service location (under tension or compression) and electrical contacts (embedded or superficial) were compared. Both CNFCC and CFCC were suitable as strain sensors in reversible (elastic) sensing condition testing. CNFCC showed higher sensitivities (gage factor up to 191.8), while CFCC only reached gage factors values of 178.9 (tension) or 49.5 (compression). Furthermore, damage-sensing tests were run, increasing the applied load progressively up to the RC beam failure. In these conditions, CNFCC sensors were also strain sensitive, but no damage sensing mechanism was detected for the strain levels achieved during the tests. Hence, these cement composites could act as strain sensors, even for severe damaged structures near to their collapse.

RANDOMDEC--mathematical background and application to detection of structural deterioration in bridges /

Mode of access: Internet.

Immunochemical detection of UV-induced DNA damage and repair

The application of an antiserum to ultraviolet radiation (UVR)-damaged DNA is presented. A novel experimental system was employed to ascertain the limits of detection for this antiserum. Using a DNA standard containing a known amount of dimer, the limits of detection were found to be 0.9 fmol of dimer. This was compared to a limit of 20-50 fmol dimer using gas chromatography-mass spectrometry (GC-MS). Induction of thymine dimers in DNA following UVR exposure, as assessed using this antiserum in an enzyme-linked immunosorbent assay (ELISA), was compared with GC-MS measurements. The ELISA method successfully demonstrated the induction of lesions in DNA irradiated either with UVC or UVB, although despite high sensitivity, no discernible binding was seen to UVA-irradiated DNA. The antiserum was also shown to be applicable to immunocytochemistry, localising damage in the nuclei of UVR exposed keratinocytes in culture. The ability of the antiserum to detect DNA damage in skin biopsies of individuals exposed to sub-erythemal doses of UVR was also demonstrated. Moreover, the subsequent removal of this damage, as evidenced by a reduction in antiserum staining, was noted in sections of biopsies taken in the hours following irradiation. © 2003 Elsevier B.V. All rights reserved.

Immunochemical detection of glyoxal DNA damage

The relevance of reactive oxygen species (ROS) in the pathogenesis of inflammatory diseases is widely documented. Immunochemical detection of ROS DNA adducts has been developed, however, recognition of glyoxal-DNA adducts has not previously been described. We have generated a polyclonal antibody that has shown increased antibody binding to ROS-modified DNA in comparison to native DNA. In addition, dose-dependent antibody binding to DNA modified with ascorbate alone was shown, with significant inhibition by desferrioxamine, catalase, and ethanol. Minimal inhibition was observed with uric acid, 1,10-phenanthroline and DMSO. However, antibody binding in the presence of EDTA increased 3500-fold. The involvement of hydrogen peroxide and hydroxyl radical in ascorbate-mediated DNA damage is consistent with ascorbate acting as a reducing agent for DNA-bound metal ions. Glyoxal is known to be formed during oxidation of ascorbate. Glyoxylated DNA, that previously had been proposed as a marker of oxidative damage, was recognised in a dose dependent manner using the antibody. We describe the potential use of our anti-ROS DNA antibody, that detects predominantly Fenton-type mediated damage to DNA and report on its specificity for the recognition of glyoxal-DNA adducts.