O desenvolvimento tecnológico proporciona uma melhoria na qualidade de vida devido à facilidade, rapidez e flexibilidade no acesso à informação. Na área biomédica, a tecnologia é reconhecidamente uma importante aliada, permitindo o rápido desenvolvimento de métodos e técnicas que auxiliam o profissional na atenção à saúde. Recentes avanços na análise computadorizada de imagens médicas contribuem para o diagnóstico precoce de uma série de doenças. Nesse trabalho é apresentada uma metodologia para o desenvolvimento de um sistema computacional para caracterização de padrões em imagens pulmonares, baseado em técnicas de redes neurais artificiais. No estudo, buscou-se verificar a utilização de redes neurais auto-organizáveis como ferramenta de extração de atributos e redução de dimensionalidade de imagens radiográficas de tórax, objetivando a caracterização de lesões intersticiais de pulmão. Para a redução de dimensionalidade e extração de atributos, implementou-se um algoritmo baseado nos mapas auto-organizáveis (SOM), com algumas variações, obtendo-se uma redução dos cerca de 3 milhões de pixels que compõe uma imagem, para 240 elementos. Para a classificação dos padrões, utilizou-se uma rede Perceptron multi-camadas (MLP), validada com a metodologia leave-one-out. Com uma base contendo 79 exemplos de padrão linear, 37 exemplos de padrão nodular, 30 exemplos de padrão misto, e 72 exemplos de padrão normal, o classificador obteve a média de 89,5% de acerto, sendo 100% de classificação correta para o padrão linear, 67,5% para o padrão nodular, 63,3% para o padrão misto, e 100% para o padrão normal. Os resultados obtidos comprovam a validade da metodologia. / The technological development provides an improvement in the quality of life due to easiness, speed and flexibility in the access to the information. In the biomedical area, the technology is admitted as an important allied, allowing the fast development of methods and techniques that assist the professional in the health care. Recent advances in the computerized analysis of medical images contribute for the precocious diagnosis of a series of diseases. In this work a methodology for the development of a computational system for characterization of patterns in pulmonary images, based in techniques of artificial neural networks is presented. In the study, has searched for the verification the use of self-organizing neural networks as a feature extraction and dimensionality reduction tool of chest radiographs, willing to characterize interstitial lung disease. For the dimensionality reduction and feature extraction, an algorithm based on Self-Organizing Maps (SOM) was implemented, with some variations, getting a reduction of about 3 million pixels that it composes an image, for 240 elements. For the pattern classification, a Multilayer Perceptron (MLP) was used, validated with the leave-one-out methodology. With a database containing 79 samples of linear pattern, 37 samples of nodular pattern, 30 samples of mixed pattern, and 72 samples of normal pattern, the classifier provided an average result of 89.5% of right classification, with 100% of right classification for linear pattern, 67.5% for nodular pattern, 63.3% for mixed pattern, and 100% for normal pattern. The results prove the validity of the methodology.

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Fibroproliferative disorders are the leading cause of morbidity and mortality worldwide, with one specific group of fibroproliferative disorders being interstitial lung diseases (ILD). Idiopathic pulmonary fibrosis is the most common ILD; however its pathogenesis is not entirely understood. What is known is that there is repetitive cellular injury preceding the fibrotic remodeling in the lungs that contributes to the irreversible deposition of extracellular matrix (ECM) proteins. Myofibroblasts that accumulate at the site of injury are thought to be the key drivers of ECM deposition and are often associated in the disease. Although it is poorly understood how these immune cells differentiate in the lung, one hypothesis suggests the role of alternatively activated profibrotic macrophages in this process. The data presented in this thesis suggest that there are a presence of UPR and macrophage proteins in the lungs of IPF patients and the UPR may be necessary in the polarization of alternatively activated macrophages. / Thesis / Master of Science (MSc)

Das Sjögren-Syndrom (SjS) stellt eine mögliche autoimmune Ursache einer interstitiellen Lungenerkrankung dar. Die Abklärung in Richtung SjS ist aber im Vergleich zu anderen systemischen Autoimmunerkrankungen bisher kaum standardisiert. Die subjektive Sicca-Symptomatik, die Anti-SS-A/Ro-Antikörper und selbst die ANA-Diagnostik als Suchtest haben alle relevante Einschränkungen in ihrer Sensitivität und/oder Spezifität. Vor diesem Hintergrund haben wir in einer interdisziplinären Diskussion einen Konsens für die SjS-Abklärung entwickelt, den wir hier für die breitere Diskussion vorstellen. Neben ANA sollten sowohl Anti-SS-A/Ro-Antikörper als auch Antikörper gegen α‑Fodrin bestimmt werden. Wichtig ist die Objektivierung der Trockenheit mittels Schirmer- und Saxon-Test und bei fehlenden typischen Autoantikörpern die Speicheldrüsenbiopsie. / Sjögren’s syndrome (SjS) is a possible autoimmune cause of interstitial lung disease. The diagnostic pathway for SjS, however, is largely undefined in comparison to other systemic autoimmune diseases. Subjective sicca symptoms, anti-SS-A/Ro antibodies and even ANA as screening tests all have relevant limitations in sensitivity and/or specificity. Against this background, in an interdisciplinary discussion we have developed a consensus for the clarification of SjS, which is presented here for broader discussion. In addition to ANA and anti-SS-A/Ro antibodies, antibodies against alpha-fodrin should be included. Objective measures of dryness, such a Schirmer and Saxon tests are important, as is a salivary gland biopsy in the absence of typical autoantibodies.

Cette recherche vise à développer un système de diagnostic assisté par ordinateur pour la détection automatique et la classification des pathologies du parenchyme pulmonaire telles que les pneumonies interstitielles idiopathiques et l'emphysème, en tomodensitométrie multicoupe. L’approche proposée repose sur morphologie mathématique 3-D, analyse de texture et logique floue, et peut être divisée en quatre étapes : (1) un schéma de décomposition multi-résolution basé sur un filtre 3-D morphologique exploitée pour discriminer les régions pulmonaires selon différentes échelles d’analyse. (2) Un partitionnement spatial supplémentaire du poumon basé sur la texture du tissu pulmonaire a été introduit afin de renforcer la séparation spatiale entre les motifs extraits au même niveau résolution dans la pyramide de décomposition. Puis, (3) une structure d'arbre hiérarchique a été construite pour décrire la relation d’adjacence entre les motifs à différents niveaux de résolution, et pour chaque motif, six fonctions d'appartenance floue ont été établies pour attribuer une probabilité d'association avec un tissu normal ou une cible pathologique. Enfin, (4) une étape de décision exploite les classifications par la logique floue afin de sélectionner la classe cible de chaque motif du poumon parmi les catégories suivantes : normal, emphysème, fibrose/rayon de miel, et verre dépoli. La validation expérimentale du système développé a permis de définir des spécifications relatives aux valeurs recommandées pour le nombre de niveaux de résolution NRL = 12, et le protocole d'acquisition comportant le noyau de reconstruction “LUNG” / ”BONPLUS” et des collimations fines (1.25 mm ou moins). Elle souligne aussi la difficulté d'évaluer quantitativement la performance de l'approche proposée en l'absence d'une vérité terrain, notamment une évaluation volumétrique, la sélection large des bords de la pathologie, et la distinction entre la fibrose et les structures (vasculaires) de haute densité / This research aims at developing a computer-aided diagnosis (CAD) system for fully automatic detection and classification of pathological lung parenchyma patterns in idiopathic interstitial pneumonias (IIP) and emphysema using multi-detector computed tomography (MDCT). The proposed CAD system is based on 3-D mathematical morphology, texture and fuzzy logic analysis, and can be divided into four stages: (1) a multi-resolution decomposition scheme based on a 3-D morphological filter was exploited to discriminate the lung region patterns at different analysis scales. (2) An additional spatial lung partitioning based on the lung tissue texture was introduced to reinforce the spatial separation between patterns extracted at the same resolution level in the decomposition pyramid. Then, (3) a hierarchic tree structure was exploited to describe the relationship between patterns at different resolution levels, and for each pattern, six fuzzy membership functions were established for assigning a probability of association with a normal tissue or a pathological target. Finally, (4) a decision step exploiting the fuzzy-logic assignments selects the target class of each lung pattern among the following categories: normal (N), emphysema (EM), fibrosis/honeycombing (FHC), and ground glass (GDG). The experimental validation of the developed CAD system allowed defining some specifications related with the recommendation values for the number of the resolution levels NRL = 12, and the CT acquisition protocol including the “LUNG” / ”BONPLUS” reconstruction kernel and thin collimations (1.25 mm or less). It also stresses out the difficulty to quantitatively assess the performance of the proposed approach in the absence of a ground truth, such as a volumetric assessment, large margin selection, and distinguishability between fibrosis and high-density (vascular) regions

Idiopathic Pulmonary Fibrosis (IPF) is a debilitating respiratory disorder that is characterized by a progressive decline in lung function. Originating through unknown etiology, it is essentially an unchecked wound healing response that causes the build-up of excessive scar tissue in the lung interstitial tissue with a heavy toll on the patient’s respiratory capacity. Pro-fibrotic alternatively activated macrophages (M2) have been linked as an important contributor to the fibrotic remodeling of the lung. Previous Ask research indicates that targeting M2 macrophages is possible through the use of the Dectin-1 receptor, a transmembrane cell surface receptor found in high abundance on M2 macrophages. Activating the Dectin-1 receptor through the use of beta-glucan, a ligand the receptor has a high affinity for, initiates a pro-inflammatory response within the naturally immunosuppressive macrophage and can alter its activity to be less fibrogenic. Our data suggest that M2 polarization of naïve macrophages can be inhibited in vitro by beta-glucan microparticles. Additionally, we have found that polarized M2 macrophages adopt M1-like characteristics when treated with beta-glucan microparticles, in a process that is largely Dectin-1 dependent. M2 cell surface marker CD206, increased levels of which are associated with rapidly progressing IPF, shows significantly decreased frequency of expression in M2 macrophages treated with beta-glucan microparticles. Our assessment for cell-specific uptake of beta-glucan microparticles suggests an important role of the Dectin-1 receptor for significantly increased uptake in murine wild-type M2 macrophages relative to their Dectin-1 knockout counterpart. The use of beta-glucan microparticles as a potential anti-fibrotic therapeutic was assessed in the bleomycin model of fibrotic lung disease. Mice given bleomycin and treated with beta-glucan displayed decreased soluble collagen content and TGFB expression within lung homogenate relative to fibrotic bleomycin control mice. Overall, these results provide insight into the use of beta-glucan as a potential activity modulator of macrophage function in IPF and the possibility of its use as a therapeutic. / Thesis / Master of Science (MSc)

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with worsening respiratory symptoms and physiological impairment. Pulmonary fibrosis is a chronic lung disease characterized by forming scar tissue (fibrosis) in the lungs. Alternatively activated macrophages (M2) known as pro-fibrotic are known to contribute to the fibrotic remodeling of the lung. In addition to the polarization of slices from naïve to pro-fibrotic, the addition of anti-fibrotic therapeutics reprogram slices back to a naïve condition. To polarize the slices, naïve slices are incubated with a previously investigated method in the lab known as the polarization cocktail. The polarization cocktail can be achieved by adding of IL-4, IL-6 and IL-13 to naïve(M0) slices in the Precision Cut lung slice (PCLS) model. For the therapeutic model, slices are incubated with the polarization cocktail and subsequently with the therapeutic. Our results have shown that the precision cut lung slice model can mimic previously investigated in-vivo experiments with the polarization cocktail. Secondly, the addition of therapeutics result in the slices exhibiting lower amounts of M2 markers and arginase activity concluding the model is suitable for the polarization and reprogramming of macrophages. / Thesis / Master of Science in Medical Sciences (MSMS)