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Thesis (Ph. D.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains vii, 141 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references.

Diss. (sammanfattning) Göteborg : Göteborgs universitet, 2008. / Härtill 4 uppsatser.

Thesis (Ed.D.)--Teachers College, Columbia University, 1974. / Typescript; issues also on microfilm. Sponsor: Marie M. Seedor. Dissertation Committee: Georgie Labadie, Mary McCann. Includes tables. Includes bibliographical references (leaves 97-103).

Thesis (M.S.)--Boston University

This investigator studied the right upper lobe of the lung in 16 autopsy cases with 11 different systemic and pulmonary pathologies. Four of the lungs were diagnosed as normal, six as emphysematous, and six had other recognizable pathology. Portions of each lung were fixed in Bouin's solution, embedded in paraffin, and sectioned at five microns. They were stained with the Periodic Acid Stain and observed under the phase contrast microscope. The alveolar-capillary basement membrane appeared blue under phase contrast in comparison to the surrounding tissue which was red. There was no statistical difference found in the alveolar-capillary basement membrane width of normal lung versus the emphysematous lung or the lung of a case with any other pulmonary pathology that was studied. The phase contrast microscope proved to be a valuable tool for studying the basement membrane in the lung.Ball State UniversityMuncie, IN 47306

>Magister Scientiae - MSc / The infants of smoking mothers (compared to non-smoking mothers) have been shown to have a lower birth mass, a lower brain mass, an increased perinatal mortality rate as well as a predisposition to respiratory abnormalities in later life. Evidence suggests that one of the reasons for the latter is abnormal lung structure due to changes in the connective tissue skeleton. This study evaluated the in vivo effects of maternal nicotine exposure (lmg/kg/day subcutaneously - designated the experimental group), which is equivalent to smoking 32 cigarettes per day, on the connective tissue status of the neonatal (7, 14 and 21 day old) wistar rat lung. The control group received sterile saline as a placebo. The specific aspects investigated were: (1) the morphological changes in lung structure and connective tissue (collagen, elastic tissue and reticulin) distribution by means of light microscopy. (2) the quantities of collagen and Emphysema-like morphological changes are present at all ages. The histochemical appearance of collagen is not affected while reticular fibres appear to be abnormal in structure. On day 7 there appears to be no elastic tissue in the nicotine-exposed lung compared to the control lung. This difference is notelastic tissue in the lung. (3) the ultrastructure of the lung connective tissue skeleton by means of scanning electron microscopy. noticeable on days 14 and 21. Biochemical quantitation indicated that, for the three age groups studied, there was no significant difference in collagen content between experimental and control animals. Elastic tissue was significantly higher in 7 day old experimental lungs than in the control group, contradictory to the results of the histochemical studies. This difference was not significant for 14 and 21 day old lungs Ultrastructural studies of the lung connective tissue skeletons hoed abnormal fibres in the experimental group. Changes included fibre breaks, a beaded appearance of certain fibres and a deficiency in normal fibre arrangement due to the direct or indirect effects of nicotine The effects of nicotine on neonatal rat lung after maternal nicotine exposure is described. The direct mechanisms for these events are still not known but speculation as to this are presented here. Further studies which could explain these mechanisms are also suggested.

Emphysema is a progressive disease characterized by irreversible tissue destruction and airspace enlargement, which manifest as low attenuation area (LAA) on CT images. Previous studies have shown that inflammation, protease imbalance, extracellular matrix remodeling and mechanical forces are collectively playing a role in the progression of emphysema. Elastic spring network models have been applied to investigate the pathogenesis of emphysema from the mechanical perspective. However, all existing models include random removal of springs to mimic the initial locations of LAA clusters from which emphysema progression is initiated. This approach is generically lacking patient specificity of CT scans that precisely reflect the location of LAA in an emphysematous lung. The aim of this work is to develop a novel approach that provides an optimal spring network representation of emphysematous lungs based on apparent density in CT images. The results suggest that the personalized elastic spring network can be used to predict the propagation of structural destruction during emphysema progression. Thus, our approach has the potential to predict disease progression that should be verified by clinical data

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Pulmonary emphysema contributes to the chronic airflow limitation characteristic of chronic obstructive pulmonary disease (COPD), which is a leading cause of morbidity and mortality worldwide. Computed tomography (CT) has enabled in vivo assessment of pulmonary emphysema at the macroscopic level, and is commonly used to identify and assess the extent of the disease. During the past decade, the availability of CT imaging data has increased rapidly, while the image quality has continued to improve. High-resolution CT is extremely valuable both for patient diagnosis and for studying diseases at the population level. However, visual assessment of these large data sets is subjective, inefficient, and expensive. This has increased the demand for objective, automatic, and reproducible image analysis methods. For the assessment of pulmonary emphysema on CT, computational models usually aim either to give a measure of the extent of the disease, or to categorize the emphysema subtypes apparent in a scan. The standard methods for quantitating emphysema extent are widely used, but they remain sensitive to changes in imaging protocols and patient inspiration level. For computational subtyping of emphysema, the methods remain at a developmental stage, and one of the main challenges is the lack of reliable label data. Furthermore, the classic emphysema subtypes were defined on autopsy before the availability of CT and could be considered outdated. There is also no consensus on how to match the subtypes on autopsy to the varying emphysema patterns present on CT. This work presents two methodological improvements for analyzing emphysema on CT. For the assessment of emphysema extent, a novel probabilistic approach is introduced and evaluated on a longitudinal data set with varying imaging protocols. The presented model is shown to improve significantly compared to standard methods, particularly at the presence of differing noise levels. The approach is also applied on quantifying emphysema on a large data set of cardiac CT scans, and is shown to improve the prediction of emphysema extent on subsequent full-lung CT scans. The second major contribution of this work applies unsupervised learning to recognizing patterns of emphysema on CT. Instead of trying to reproduce the classic subtypes, the novel approach aims to capture the most dominant variations of lung structure pertaining to emphysema. While removing the reliance on visually assigned labels, the learned patterns are shown to represent different manifestations of emphysema with distinct appearances and regular spatial distributions. The clinical significance of the patterns is also demonstrated, along with high-level performance in the application of content-based image retrieval. The contributions of this work advance the analysis of emphysema on CT by applying novel machine learning approaches to increase the value of the available imaging data. Probabilistic methods improve from the crude standard methods that are currently used to quantitate emphysema, and the value of learning disease patterns directly from image data is demonstrated. The common framework relying on replicating visually assigned labels of outdated subtypes has not achieved widespread acceptance. The methodology presented in this work may have a substantial impact on how emphysema subtypes on CT are recognized and defined in the future.