Hidetoshi Nakamura

Reversal of elastase-induced pulmonary emphysema and promotion of alveolar epithelial cell proliferation by simvastatin in mice

Besides lowering cholesterol, statins exert multiple effects, such as anti-inflammatory activity and improvement of endothelial cell function. We examined whether simvastatin (SS) protects against the development of elastase-induced pulmonary emphysema in mice by using mean linear intercepts of alveoli (Lm) as a morphometric parameter of emphysema. After injection of intratracheal elastase on day 0, C57BL/6 mice were treated daily with SS (SS+ group) or PBS (SS- group) for 2 wk. A 21% decrease in Lm on day 7 was observed in the SS+ group vs. the SS- group. Anti-inflammatory effects of SS were observed as a decrease in percentage of neutrophils up to day 3, and in hydroxyproline concentration on day 3, in bronchoalveolar lavage fluid (BALF). SS also increased the number of proliferating cell nuclear antigen (PCNA)-positive alveolar epithelial cells between days 3 and 14. To confirm the role of statins in promoting proliferation of alveolar cells, mice were treated with SS (SS+) vs. PBS (SS-) for 12 days, starting 3 wk after elastase administration. After SS treatment, Lm decreased by 52% and PCNA-positive alveolar epithelial cells increased compared with the SS- group. Concentrations of vascular endothelial growth factor in BALF and endothelial nitric oxide synthase protein expression in pulmonary vessels tended to be higher in the SS+ group vs. the SS- group in this protocol. In conclusion, SS inhibited the development of elastase-induced pulmonary emphysema in mice. This therapeutic effect was due not only to anti-inflammation but also to the promotion of alveolar epithelial cell regeneration, partly mediated by restoring endothelial cell functions.

Genetics of COPD

Previous family studies suggested that genetic variation contributes to COPD susceptibility. The only gene proven to influence COPD susceptibility is SERPINA1, encoding α1-antitrypsin. Most studies on COPD candidate genes except SERPINA1, have not been consistently replicated. However, longitudinal studies of decline in lung function, meta-analyses of candidate gene studies, and family-based linkage analyses suggested that variants in EPHX1, GST, MMP12, TGFB1, and SERPINE2 were associated with susceptibility to COPD. A genome-wide association (GWA) study has recently demonstrated that CHRNA3/5 in 15q25 was associated with COPD compared with control smokers. It was of interest that the CHRNA3/5 locus was associated with nicotine dependence and lung cancer as well. The associations of HHIP on 4q31 and FAM13A on 4q22 with COPD were also suggested in GWA studies. Another GWA study has shown that BICD1 in 12p11 was associated with the presence or absence of emphysema. Although every genetic study on COPD has some limitations including heterogeneity in smoking behaviors and comorbidities, it has contributed to the progress in elucidating the pathogenesis of COPD. Future studies will make us understand the mechanisms underlying the polygenic disease, leading to the development of a specific treatment for each phenotype.

A neural network based computer-aided diagnosis of emphysema using CT lung images

Chronic Obstructive Pulmonary Disease (COPD) is a disease in which the airways and tiny air sacs (alveoli) inside the lungs are partially obstructed or destroyed. The result is labored breathing. There are varying degrees of this illness, and different names for them, but it all comes back to damaged airways and air sacs. Emphysema is what occurs as more and more of the walls between air sacs get destroyed. Instead of having lots of little sacs, the sacs break up and what is left are larger sacs. These bigger sacs have less surface area for the exchange of oxygen and carbon dioxide than the tiny ones. Poor exchange of oxygen and carbon dioxide causes shortness of breath. At present, diagnosis of emphysema is done by using spirometry, X-rays, spiral chest CT-scan, bronchoscopy, pulse oximetry and arterial blood gas sampling. This paper proposes a computer-aided diagnostic system for emphysema that segments the lungs into multiple square regions and classifies the segmented regions into 5 classes of severity. The proposed algorithm is divided into three stages: 1. digital image processing, 2. feature extraction, and 3. classification using neural network (NN). The aim of this paper is to analyze the severity of the lungs region by region along with NN classification.

Common functional polymorphisms in the cathepsin S promoter in Japanese subjects: Possible contribution to pulmonary emphysema

Background and objective:u2003 Cathepsin S is involved in the pathogenesis of COPD in murine models overexpressing interferon (IFN)‐γ and IL‐13. It is widely accepted that genetic factors partly influence susceptibility to COPD; however, the association of genetic polymorphisms in the cathepsin S gene with COPD has not been reported previously. In this study, functional polymorphisms in the 5′‐flanking region of the human cathepsin S gene were identified and their association with COPD phenotypes was investigated.

Evaluation of a new fiber-grating vision sensor for assessing pulmonary functions in healthy and COPD subjects

Spirometry is practically the only tool to evaluate pulmonary functions. Other automatic systems comparable to spirometry are expected. A fiber-grating (FG) vision sensor is a non-contact respiratory monitoring system to detect changes in volumes by measuring the movement of laser spots on the body surface. We examined the contributions of the FG sensor to evaluating pulmonary functions. The FG sensor showed a linear correlation with spirometry in tidal volumes (TV) obtained from five controls (R = 0.98, P < 0.0001). We also showed agreement of TV between the two devices using Bland-Altman analysis. TV measured by the FG sensor were reproducible and applicable to distinct subjects. To detect airway obstruction, we performed forced expiration in controls (n = 16) and chronic obstructive pulmonary disease (COPD) patients (n = 18) with the FG sensor and spirometry. Forced expiratory volume in 1 s (FEV(1)) and FEV(1)/forced vital capacity in COPD patients were lower than those in controls by the FG sensor. In addition, prolonged expiration in natural breathing by the FG sensor was related to airflow limitation by spirometry. The FG sensor was helpful to measure volume changes and to evaluate pulmonary functions in controls and patients with COPD. Its upcoming clinical applications are promising for simplicity and feasibility.

Segmentation of airway trees from multislice CT using fuzzy logic

The segmentation and reconstruction of the human airway tree from volumetric computed tomography (CT) images facilitates many clinical applications and physiological investigations. The main problem with standard automated region-growing segmentation algorithms is leakage into the extra-luminal regions due to thinness of the airway wall during the process of segmentation. This phenomenon causes regions of lung parenchyma to be wrongly identified as airways. Main previous solutions to this problem include region of interest modification-based techniques, morphology-based method and fuzzy connectivity based method in which early leaks are detected and avoided. In this paper, an airway segmentation focusing on 2D line profile based evaluation of the degree of existence of airway wall using fuzzy logic is presented. New features are proposed and the usefulness of the features are evaluated. Comparison with a commonly used region-growing segmentation algorithm shows that the proposed method retrieves a significantly higher count of airway branches and less leaks. Our algorithm provides a way for fast realization of the major 3D airway trees. The algorithm succeeds in segmenting airways that have moderate to obvious airway walls in 2D slices. It provides a structure for follow-up branch growing algorithm.

Automated Extraction and Diagnosis of Lung Emphysema from Lung CT Images Using Artificial Neural Network

Emphysema is characterized by loss of elasticity of the lung tissue; destruction of structures supporting the alveoli; the destruction of capillaries feeding the alveoli. The result is that the small airways collapse during expiration, leading to an obstructive form of lung disease (air is trapped in the lungs in obstructive lung diseases). The scientific definition of emphysema is: permanent destructive enlargement of the airspaces distal to the terminal bronchioles without obvious fibrosis. Hence, the definite diagnosis is made by a pathologist. At present, diagnosis of emphysema is done by using spirometry, X-rays, spiral chest CT-scan, bronchoscopy, blood tests, pulse oximetry and arterial blood gas sampling. Although emphysema is an irreversible degenerative condition, early prognosis and treatment are very important for optimizing the patients quality of life. This paper proposes an automated computed-aided diagnosis algorithm for extracting enlarged airways from lung CT image automatically using an image matching method, and consequently classifying the lung condition artificial neural network (ANN) by supplying 30 network inputs obtained from texture analysis of the lung CT image and calculations of the feature properties of extracted enlarged airways to the trained ANN. Our research aims to produce an automated system which has higher objectivity in the diagnosis of lung emphysema

An automated three-dimensional visualization and classification of emphysema using neural network

Chronic obstructive pulmonary disease (COPD) is a disease in which the airways and tiny air sacs (alveoli) inside the lungs are partially obstructed or destroyed. Emphysema is what occurs as more and more of the walls between air sacs get destroyed. Computed tomography (CT) image has been a useful modality for assessing diffuse lung diseases, particularly, emphysema. At present, diagnosis of emphysema is done by using spirometry, X-rays, spiral chest computed tomography (CT)-scan, bronchoscopy, blood tests and pulse oximetry. In this study, we extracted the two-dimensional emphysematous lung tissues in the lung CT automatically using digital image processing techniques, then we visualized the extracted emphysematous lung tissues by implementing a three-dimensional (3D) lung model which was computed using 55 pre-processed CT images, and finally we divided the lung model into eight sub-volumes and classified each sub-volume into five classes of emphysema related severity using an artificial neural network. The performance of the classifier was assessed using the leave-one-out method on 120 sub-volumes of the lungs generated from 15 COPD-verified patients CT data sets.

A Study of Computer Aided Visualization and Quantification of Emphysema

Emphysema is a lung disease that occurs as more and more of the walls between air sacs in the lungs get destroyed. Computed tomography (CT) image of the human thorax has been a useful modality for assessing emphysema. Out goal in this paper is to automatically visualize bullaes (continuous low-attenuated region in CT which represents the air-filled region in the CT and therefore the emphysematous lesion in the lung) in the lungs in three dimensions and quantify the emphysema severity of each bullae based on its size and the distance of the bullae from the center of the lungs using fuzzy logic. From the computed emphysema severity score of bullae in the lung, we calculate the overall emphysema severity of the lung by summing up the emphysema severity score of all bullaes in the lung. For the visualization part, we first compute a transparent three-dimensional lung model and from there, we cluster the bullaes using K-means clustering method to see how the bullaes are distributed in groups in the lung. Besides, we compress the three-dimensional lung model along x-, y- and z-axis by assigning the value of every bullae pixel as one and adding up the pixel intensity along x-, y- and z-axis allowing the visualization of the compressed lung from the front, side, and top view, respectively. Consequently, we color the compressed image using continuous multi-valued color code for indicating the severity of the emphysematous destruction in the lung. Our visualization techniques can be used as a medical assistant visualization tool for radiologists.