• Journal of Physiology & Pathology in Korean Medicine
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• v.19 no.1
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• pp.167-173
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• 2005

To investigate the possible molecular mechanism (s) of bee venom as a candidate of anti-cancer drug, we examined the effects of the compound on the growth of human lung carcinoma cell line A549. Bee venom treatment declined the cell growth and viability of A549 cells in a concentration-dependent manner, which was associated with induction of apoptotic cell death. Bee venom down-regulated the levels of anti-apoptotic genes such as Bcl-2 and Bcl-XS/L, however, the levels of Bax, a pro-apoptotic gene, were up-regulated. Bee venom treatment induced not only tumor suppressor p53 but also cyclin-dependent kinase inhibitor p21WAF1/CIP1 expression in a dose-dependent manner. Furthermore, bee venom treatment induced the down-regulation of telomerase reverse transcriptase mRNA and telomeric repeat binding factor expression of A549 cells, however, the levels of telomerase-associated protein-1 and c-myc were not affected. Taken together, these findings suggest that bee venom-induced inhibition of human lung cancer cell growth is associated with the induction of apoptotic cell death via regulation of several major growth regulatory gene products, and bee venom may have therapeutic potential in human lung cancer.

• Tuberculosis and Respiratory Diseases
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• v.39 no.5
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• pp.443-447
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• 1992

Bronchial adenomas are a group of rare tumors representing less than 1% of primary lung neoplasms. Mucoepidermoid carcinoma of the lung is a very rare subtype of bronchial adenoma. We experienced a case of 20 year old patient with mucoepidermoid carcinoma of the lung, so we present this case of mucoepidermoid carcinoma with literature review.

• 한국전자현미경학회:학술대회논문집
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• 2004.11a
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• pp.116-116
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• 2004

• Tuberculosis and Respiratory Diseases
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• v.67 no.1
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• pp.14-20
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• 2009

Background: The pathophysiologic mechanisms of radiation-induced lung injury should be elucidated to enhance the therapeutic efficacy of radiotherapy and to manage patients exposed to serious radiation by accident. It has been suggested that pro-inflammatory cytokines play an important role in radiation-induced effect on the lung. This study was aimed to investigate changes in pro-inflammatory cytokines such as TNF-$\alpha$, MIP-2, IL-1$\beta$ and HMGB1, a newly recognized inflammatory mediator. Methods: The chests of BALB/c mice were selectively irradiated with single fraction of 20 Gy and then sacrificed at indicated times. Pathologic changes in the lung were examined after H&E staining. The expression level of pro-inflammatory cytokines was evaluated by ELISA kits in lung homogenate and in serum. Results: Radiation induced inflammatory changes and mild fibrosis in lung. Biphasic increase of TNF-$\alpha$ and IL-1$\beta$ was found in lung homogenate at 4 hours and at 3 weeks after radiation. The elevation in the second phase tended to be more intense. However, there was no similar change in serum. MIP-2 level was slightly increased in lung homogenate at 4 hours, but not at 3 weeks. HMGB1 was increased at 3 weeks in serum while there was no significant change in lung homogenate. Conclusion: Radiation induced a biphasic increase in TNF-$\alpha$ and IL-1$\beta$. The effective control of second phase cytokine elevation should contribute to preventing severe lung fibrosis caused by radiation.

• Tuberculosis and Respiratory Diseases
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• v.77 no.2
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• pp.85-89
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• 2014

Intrathoracic involvement of immunoglobulin G4 (IgG4)-related disease has recently been reported. However, a subset of the disease presenting as interstitial lung disease is rare. Here, we report a case of a 35-year-old man with IgG4-related lung disease with manifestations similar to those of interstitial lung disease. Chest computed tomography showed diffuse ground glass opacities and rapidly progressive pleural and subpleural fibrosis in both upper lobes. Histological findings showed diffuse interstitial lymphoplasmacytic infiltration with an increased number of IgG4-positive plasma cells. Serum levels of IgG and IgG4 were also increased. The patient was diagnosed with IgG4-related lung disease, treated with anti-inflammatory agents, and showed improvement. Lung involvement of IgG4-related disease can present as interstitial lung disease and, therefore, should be differentiated when evaluating interstitial lung disease.

• Tuberculosis and Respiratory Diseases
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• v.77 no.6
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• pp.258-261
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• 2014

Anaplastic lymphoma kinase (ALK) rearrangement, is a kind of driver mutation, accounts for 3%-5% of non-small cell lung cancer (NSCLC). NSCLC patients harboring ALK fusion genes have distinct clinical features and good response to ALK inhibitors. Metastasis from lung cancer to the ovary has rarely been known. We report a case of a 54-year-old woman with bilateral ovarian metastases from ALK rearranged NSCLC. She underwent bilateral salpingo-oophorectomy for ovary masses, which were progressed after cytotoxic chemotherapy although primary lung mass was decreased. Histopathological examination of the ovary tumor showed characteristic adenocarcinoma patterns of the lung and ALK rearrangement.

• Tuberculosis and Respiratory Diseases
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• v.39 no.3
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• pp.266-270
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• 1992

Pulmonary lymphangioleiomyomatosis is a rare disease, prevalent in women of reproductive age, and characterized by proliferation of atypical smooth muscle in the lung. A 44-year-old women with pulmonary lymphangioleiomyomatosis was presented. Chest radiographs showed diffuse increased reticulonoular infiltrations on the almost entire lung and high resolution CT illustrated multiple, well defined cystic lesions which were distributed diffusely through the lung. Histologic confirmation of lymphangioleiomyomatosis was made upon open lung biopsy. Treatment with methylprogesterone was initated.

• Journal of Yeungnam Medical Science
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• v.36 no.1
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• pp.8-15
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• 2019

Connective tissue diseases (CTDs) can affect all compartments of the lungs, including airways, alveoli, interstitium, vessels, and pleura. CTD-associated lung diseases (CTD-LDs) may present as diffuse lung disease or as focal lesions, and there is significant heterogeneity between the individual CTDs in their clinical and pathological manifestations. CTD-LDs may presage the clinical diagnosis a primary CTD, or it may develop in the context of an established CTD diagnosis. CTD-LDs reveal acute, chronic or mixed pattern of lung and pleural manifestations. Histopathological findings of diverse morphological changes can be present in CTD-LDs airway lesions (chronic bronchitis/bronchiolitis, follicular bronchiolitis, etc.), interstitial lung diseases (nonspecific interstitial pneumonia/fibrosis, usual interstitial pneumonia, lymphocytic interstitial pneumonia, diffuse alveolar damage, and organizing pneumonia), pleural changes (acute fibrinous or chronic fibrous pleuritis), and vascular changes (vasculitis, capillaritis, pulmonary hemorrhage, etc.). CTD patients can be exposed to various infectious diseases when taking immunosuppressive drugs. Histopathological patterns of CTD-LDs are generally nonspecific, and other diseases that can cause similar lesions in the lungs must be considered before the diagnosis of CTD-LDs. A multidisciplinary team involving pathologists, clinicians, and radiologists can adequately make a proper diagnosis of CTD-LDs.

• Biomolecules & Therapeutics
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• v.30 no.2
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• pp.179-183
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• 2022

Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy ("no tumor") was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.

• Proceedings of the Korean Society of Veterinary Pathology Conference
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• 2004.10a
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• pp.4-4
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• 2004