• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2191-2208
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• 2022

Motion blur in PET (Positron emission tomography) images induced by respiratory motion will reduce the quality of imaging. Although exiting methods have positive performance for respiratory motion correction in medical practice, there are still many aspects that can be improved. In this paper, an improved 3D unsupervised framework, Res-Voxel based on U-Net network was proposed for the motion correction. The Res-Voxel with multiple residual structure may improve the ability of predicting deformation field, and use a smaller convolution kernel to reduce the parameters of the model and decrease the amount of computation required. The proposed is tested on the simulated PET imaging data and the clinical data. Experimental results demonstrate that the proposed achieved Dice indices 93.81%, 81.75% and 75.10% on the simulated geometric phantom data, voxel phantom data and the clinical data respectively. It is demonstrated that the proposed method can improve the registration and correction performance of PET image.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.449-457
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• 2023

The purpose of this descriptive research study was to investigate health beliefs and self-efficacy in respiratory infection management as factors that affect the respiratory infection prevention behavior of college students. The subjects were 178 students attending a university in K city of Gyeongsangbuk-do. Data were collected with a structured questionnaire from September 1st to October 16th of 2020. The results of this study are as follows; Health belief was significantly different from participant's gender (t=-2.86, p=.005), major classification (F=2.95, p=.034), and taking any medications (t=2.18, p=.030). Self-efficacy in respiratory infection management was significantly different from university students' gender (t=-3.56, p=<.001) and major classification (F=4.59, p=.004). Health belief (r=.276, p<.001) and self-efficacy in respiratory infection management (r=.660, p<.001) had a positive correlation with respiratory infection preventive behavior. Multiple regression analysis results show that self-efficacy in respiratory infection management (β=.66, p<.001) significantly affected respiratory infection preventive behavior. The model had an explanatory power of 43%. The findings demonstrate that the major factor influencing the respiratory infection preventive behavior of university students is self-efficacy in respiratory infection management. Therefore, in order to promote behavior to prevent respiratory infection in college students, a program that can strengthen self-efficacy in respiratory infection management should be developed.

• The Journal of Internal Korean Medicine
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• v.44 no.3
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• pp.418-438
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• 2023

Objective: This study was conducted to review the effects of herbal medicine on respiratory diseases induced by the treatment of particulate matter in in vivo animal models. Methods: Literature searches were performed in seven databases (Pubmed, Embase, Cochrane Library, KISS, KTKP, OASIS, and ScienceON). After the searched studies were screened based on the inclusion/exclusion criteria, the publication date, origin, used animals, induction of particulate matter models, herbal medicine used for intervention, study design, outcome measure, and results of studies were analyzed. Results: Among a total of 972 studies primarily searched, 34 studies were finally included in our study. Of this number, 29 studies induced animal models by using only particulate matter, and 5 studies induced animal models with respiratory diseases, such as asthma and chronic obstructive pulmonary disease, by using particulate matter and other materials. In the selected studies, the treatments of herbal medicine in particulate matter models suppressed oxidative stress and inflammation in lung tissue, bronchoalveolar lavage fluid, and blood as well as lung injury in histological analysis. Conclusion: The results of this study suggest that herbal medicine is effective in treating respiratory diseases induced by particulate matter. These results are also expected to be useful data for designing further studies. However, more systematically designed in vivo studies related to particulate matter are needed.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4327-4330
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• 2012

The mouse double minute 2 (MDM2) gene plays a key role in the p53 pathway, and the SNP 309T/G single-nucleotide polymorphism in the promoter region of MDM2 has been shown to be associated with increased risk of cancer. However, no consistent results were found concerning the relationships between the polymorphism and prostate cancer risk. This meta-analysis, covering 4 independent case-control studies, was conducted to better understand the association between MDM2-SNP T309G and prostate cancer risk focusing on overall and subgroup aspects. The analysis revealed, no matter what kind of genetic model was used, no significant association between MDM2-SNP T309G and prostate cancer risk in overall analysis (GT/TT: OR = 0.84, 95%CI = 0.60-1.19; GG/TT: OR = 0.69, 95%CI = 0.43-1.11; dominant model: OR = 0.81, 95%CI= 0.58-1.13; recessive model: OR = 1.23, 95%CI = 0.95-1.59). In subgroup analysis, the polymorphism seemed more likely to be a protective factor in Europeans (GG/TT: OR = 0.52, 95%CI = 0.31-0.87; recessive model: OR = 0.58, 95%CI = 0.36-0.95) than in Asian populations, and a protective effect of the polymorphism was also seen in hospital-based studies in all models (GT/TT: OR = 0.74, 95%CI = 0.57-0.97; GG/TT: OR = 0.55, 95%CI = 0.38-0.79; dominant model: OR = 0.69, 95%CI = 0.54-0.89; recessive model: OR = 0.70, 95%CI = 0.51-0.97). However, more primary studies with a larger number of samples are required to confirm our findings.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4493-4497
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• 2014

Background: Epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) can predict the clinical response to tyrosine kinase inhibitor (TKI) therapy. However, EGFR mutations may be different in primary tumors (PT) and metastatic lymph nodes (MLN). The aim of this study was to compare EGFR mutations between PT and the corresponding MLN in NSCLC patients, and provide some guidelines for clinical treatment using TKI therapy. Materials and Methods: A systematic review and meta-analysis was performed with several research databases. Relative risk (RR) with the 95% confidence interval (CI) were used to investigate the EGFR mutation status between PT and the corresponding MLN. A random-effects model was used. Results: 9 publications involving 707 patients were included in the analysis. It was found that activation of EGFR mutations identified in PT and the corresponding MLN was 26.4% (187/707) and 19.9% (141/707), respectively. The overall discordance rate in our meta-analysis was 12.2% (86/707). The relative risk (RR) for EGFR mutation in PT relative to MLN was 1.33 (95%CI: 1.10-1.60; random-effects model). There was no significant heterogeneity between the studies ($I^2$=5%, p=0.003). Conclusions: There exists a considerable degree of EGFR mutation discrepancy in NSCLC between PT and corresponding MLN, suggesting that tumor heterogeneity might arise at the molecular level during the process of metastasis.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.373-384
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• 2014

Data mining is to clarify pattern or correlation of mass data of complicated structure and to predict the diverse outcomes. This technique is used in the fields of finance, telecommunication, circulation, medicine and so on. In this paper, we selected risk factors of respiratory diseases in the field of medicine. The data we used was divided into respiratory diseases group and health group from the Gyeongsangbuk-do database of Community Health Survey conducted in 2012. In order to select major risk factors, we applied data mining techniques such as neural network, logistic regression, Bayesian network, C5.0 and CART. We divided total data into training and testing data, and applied model which was designed by training data to testing data. By the comparison of prediction accuracy, CART was identified as best model. Depression, smoking and stress were proved as the major risk factors of respiratory disease.

• Korean Journal of Veterinary Service
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• v.41 no.4
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• pp.263-269
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• 2018

Developing drugs targeting respiratory pathogen is essential to control respiratory diseases. Many experiments have been performed under in vivo situation. However, in vivo experiments have economical and ethical issues. The objective of this study was to determine the possibility of developing an ex vivo lung culture system with possible application for respiratory infection studies. After isolating lungs from naïve pigs, agarose-inflated lung tissues were prepared and sliced manually. These sliced lung tissues were then subsequently placed on 24-well plates. Eight different combinations of media were used to determine the optimum ex vivo lung culture condition. In addition, lung tissues were infected with porcine reproductive and respiratory syndrome (PRRS) virus at a titer of $1{\times}10^4\;TCID_{50}/mL$. Virus growth was confirmed by titration in MARC-145 cells at 2, 4, 6 days post infection (dpi). We found that ex vivo lung culture in physiological environment was not media specific based on histopathology and cytotoxicity. However, under virus-infected condition, thickened alveolar walls in the lung tissues and stable virus titers at 2, 4, 6 dpi were shown in F12K medium suggesting that it was useful for tissue maintenance and virus infection using PRRS virus infected lung tissues. The present study shows the possibility of using porcine ex vivo lung model for respiratory infection studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.296-302
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• 2021

This paper proposes an approach to the classification of respiratory states of humans based on visual information. An ultra-wide-band radar sensor acquired respiration signals, and the respiratory states were classified based on two-dimensional (2D) images instead of one-dimensional (1D) vectors. The 1D vector-based classification of respiratory states has limitations in cases of various types of normal respiration. The deep neural network model was employed for the classification, and the model learned the 2D images of respiration signals. Conventional classification methods use the value of the quantified respiration values or a variation of them based on regression or deep learning techniques. This paper used 2D images of the respiration signals, and the accuracy of the classification showed a 10% improvement compared to the method based on a 1D vector representation of the respiration signals. In the classification experiment, the respiration states were categorized into three classes, normal-1, normal-2, and abnormal respiration.

• The Journal of Internal Korean Medicine
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• v.44 no.3
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• pp.294-312
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• 2023

Objective: The purpose of this study is to evaluate the effects of GGX on an ovalbumin (OVA)-induced asthma mice model. Methods: Balb/c mice were challenged with OVA and then treated with three concentrations of GGX (100, 200, and 400 mg/kg). After sacrifice, the bronchoalveolar lavage fluid (BALF) or lungs of the mice were analyzed by fluorescence-activated cell sorting, ELISA, real-time PCR, H&E, Masson's trichrome, PAS and AB-PAS staining, and immunohistofluorescence staining. Results: GGX significantly inhibited the increase of total cells, immune cells (lymphocyte, neutrophils, macrophage, CD4+, CD8+, CD4+CD69+, CD62L-CD44high+, Gr-1+SiglecF-), and the expression of cytokines (IL-4, IL-5, IL-13, IFN-γ) in BALF. It also significantly inhibited the increase of total cells, immune cells (lymphocyte, neutrophils, eosinophil/macrophage, CD3+, CD19+, CD3+CD193+, CD4+, CD8+, CD4+CD69+, CD62L-CD44high+, and Gr-1+SiglecF-), and the expression of IL-13, TARC, and MCP-1 in lung tissue. GGX decreased the severity of histological lung injury and the expressions of STAT3 and GATA3. Conclusion: This study suggests the probability of using GGX for the treatment of asthma by inhibiting inflammatory immune response.

• The Journal of Internal Korean Medicine
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• v.40 no.4
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• pp.567-581
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• 2019

Objective: This study aimed to evaluate the inhibitory effects of SGX01 on the lung injuries of COPD mice model. Materials and Methods: This study was carried out in two ways: in vitro and in vivo. In vitro, L929 cells were challenged with LPS, and then treated with six concentrations of SGX01 (10, 30, 50, 100, 300, and $500{\mu}g/ml$) and analyzed by ELISA. In vivo, C57BL/6 mice were challenged with LPS and cigarette smoking solution (CSS), and then treated with a vehicle only (control group), dexamethasone 3 mg/kg (dexa group), or a SGX01 200 mg/kg (SGX01 group). After sacrifice, the BALF or lung tissue was analyzed with Cytospin, FACS, ELISA, real-time PCR and H&E, and Masson's trichrome staining. Results: SGX01 significantly decreased NO, $TNF-{\alpha}$, and IL-6 on L929 cells challenged with LPS. In the COPD model, SGX01 significantly inhibited the increase of neutrophils, $TNF-{\alpha}$, IL-17A, CXCL-1, MIP2, CD8+ cells in BALF, and $TNF-{\alpha}$, $IL-1{\beta}$ mRNA expression in lung tissue. It also decreased the severity of the histological lung injury. Conclusion: This study suggests the usability of SGX01 for COPD patients by controlling lung tissue injury.