• Tuberculosis and Respiratory Diseases
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• v.82 no.2
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• pp.158-165
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• 2019

Background: A recent study reported that mesenchymal stem cells possess potential cellular therapeutic properties for treating patients with chronic obstructive pulmonary disease, which is characterized by emphysema. We examined the potential therapeutic effect of Wharton's Jelly-derived mesenchymal stem cells (WJMSCs), following pretreatment with pioglitazone, in lung regeneration mouse emphysema models. Methods: We used two mouse emphysema models, an elastase-induced model and a cigarette smoke-induced model. We intravenously injected WJMSCs ($1{\times}10^4/mouse$) to mice, pretreated or not, with pioglitazone for 7 days. We measured the emphysema severity by mean linear intercepts (MLI) analysis using lung histology. Results: Pioglitazone pretreated WJMSCs (pioWJMSCs) were associated with greater lung regeneration than non-augmented WJMSCs in the two mouse emphysema models. In the elastase-induced emphysema model, the MLIs were $59.02{\pm}2.42{\mu}m$ (n=6), $72.80{\pm}2.87{\mu}m$ (n=6), for pioWJMSCs injected mice, and non-augmented WJMSCs injected mice, respectively (p<0.01). Both pioWJMSCs and non-augmented WJMSCs showed regenerative effects in the cigarette smoke emphysema model (MLIs were $41.25{\pm}0.98$ [n=6] for WJMSCs and $38.97{\pm}0.61{\mu}m$ [n=6] for pioWJMSCs) compared to smoking control mice ($51.65{\pm}1.36{\mu}m$, n=6). The mean improvement of MLI appeared numerically better in pioWJMSCs than in non-augmented WJMSCs injected mice, but the difference did not reach the level of statistical significance (p=0.071). Conclusion: PioWJMSCs may produce greater lung regeneration, compared to non-augmented WJMSCs, in a mouse emphysema model.

• Health Policy and Management
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• v.25 no.4
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• pp.323-332
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• 2015

Background: Antibiotic resistance has been becoming serious challenge to human beings. Overuse of antibiotics, especially, for infants is concerned, but studies are very few for the prescribing pattern of antibiotic use for infants. This study analyzes prescribing patterns of antibiotics in outpatients of preschool children with acute respiratory tract infections in South Korea. Methods: Data are used from 2011 Health Insurance Review & Assessment Services-pediatric patients sample. Inclusion criteria is outpatient children (0 to 5 years) with top five frequent diseases. Prescription rates are analyzed by types of disease, provider, specialty, region, and ages. Binary or multinomial logit models are used to analyze determinants of providers' prescription pattern. Results: The main findings are as follows. First, distributions of prescription rates are shown as L-shape or M-shape depending on the types of disease. Second, the prescription variation is so large among providers, where providers are polarized as a group with low prescription rates and the other group with high prescription rates, though the shapes are shown diversified across types of disease. Third, prescription rates appear to be lower in pediatrics and higher in ENT (ear-nose-throat). Fourth, broad spectrum antibiotics are widely used among children. Finally, the logit analysis shows similar results with descriptive statistics, but partly different results across types of disease. Conclusion: Antibiotics for respiratory tract infections of infants are used excessively with a large variation among providers, and especially broad spectrum antibiotics are used. The prescription guideline for antibiotics should be provided for each specific disease to reduce antibiotic resistance in the future.

• The Plant Pathology Journal
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• v.26 no.1
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• pp.37-48
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• 2010

This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of $240\;m{\times}240\;m$ based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast, sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.6
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• pp.2491-2494
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• 2014

Objective: To explore the mechanism and significance of tumor angiogenesis by observing changes of microvessel density (MVD) and expression of vascular endothelial growth factor (VEGF) in residual tumor tissues after cryoablation. Materials and Methods: A total of 18 nude mice xenograft models with transplanted lung adenocarcinoma cell line A549 were established and randomly divided into 3 groups when the maximum diameter of tumor reached 1 cm: control, cisplatin (DDP) and cryoablation. The nude mice were sacrificed after 21-d cryoablation to obtain the tumor tissues. Then immunohistochemistry was applied to determine MVD and the expression of VEGF in tumor tissues. Results: The tumor volumes of control group, DDP group and cryoablation group were $1.48{\pm}0.14cm^3$, $1.03{\pm}0.12cm^3$ and $0.99{\pm}0.06cm^3$ respectively and the differences were significant (P<0.01), whereas MVD values were $21.1{\pm}0.86$, $24.7{\pm}0.72$ and $29.2{\pm}0.96$ (P<0.01) and the positive expression rates of VEGF were $36.2{\pm}1.72%$, $39.0{\pm}1.79%$ and $50.8{\pm}2.14%$ (P<0.01), respectively, showing that MVD was proportional to the positive expression of VEGF (r=0.928, P<0.01). Conclusions: Cryoablation can effectively inhibit tumor growth, but tumor angiogenesis significantly increases in residual tumors, with high expression of VEGF playing an important role in the residual tumor angiogenesis.

• The Journal of Internal Korean Medicine
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• v.41 no.6
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• pp.1089-1099
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• 2020

Objective: The purpose of this study was to investigate the effect of cinnamon on the treatment of Parkinson's disease (PD) and to introduce its use in Korea. Method: We searched the experimental studies in electronic databases (PubMed, CNKI, Wanfang, CiNii, J-STAGE, Science ON, and OASIS) using the key search terms "cinnamic acid", "cinnamon", "cinnamomum", "Parkinson's disease", "Parkinson disease", "Parkinsonism", and "dopamine". This study only involved experimental studies (in vivo and in vitro) that adopted cinnamon as a single administration and measured indicators relating to Parkinson's disease, including parkin, tyrosine hydroxylase (TH), and dopamine. Results: A Total of 11 literature studies were selected, and they all showed that treatment with cinnamon has a neuroprotective effect. Cinnamon activated neuroprotective factors and restored neurotransmitters and it reduced the rate of oxidative stress and inflammation in neurons. As a result, cell viability was upregulated, while cell apoptosis and neurodegeneration were downregulated. Five in vivo studies, through behavioral tests, also confirmed that cinnamon recovers locomotor function in PD models. Conclusion: We identified that cinnamon is an effective neural protector and improves motor performance in behavioral testing in the experimental PD studies.

• Journal of Ginseng Research
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• v.46 no.1
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• pp.11-22
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• 2022

Spinal cord injury (SCI) is defined as damage to the spinal cord that temporarily or permanently changes its function. There is no definite treatment established for neurological complete injury patients. This study investigated the effect of ginseng extract and ginsenosides on neurological recovery and antioxidant efficacies in rat models following SCI and explore the appropriate dosage. Searches were done on PubMed, Embase, and Chinese databases, and animal studies matches the inclusion criteria were selected. Pair-wise meta-analysis and subgroup analysis were performed. Ten studies were included, and the overall methodological qualities were low quality. The result showed ginseng extract and ginsenosides significantly improve neurological function, through the Basso, Beattie, and Bresnahan (BBB) locomotor rating scale (pooled MD = 4.40; 95% CI = 3.92 to 4.88; p < 0.00001), significantly decrease malondialdehyde (MDA) (n = 290; pooled MD = -2.19; 95% CI = -3.16 to 1.22; p < 0.0001) and increase superoxide dismutase (SOD) levels (n = 290; pooled MD = 2.14; 95% CI = 1.45 to 2.83; p < 0.00001). Both low (<25 mg/kg) and high dosage (25 mg/kg) showed significant improvement in the motor function recovery in SCI rats. Collectively, this review suggests ginseng extract and ginsenosides has a protective effect on SCI, with good safety and a clear mechanism of action and may be suitable for future clinical trials and applications.

• Smart Media Journal
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• v.11 no.5
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• pp.48-55
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• 2022

Tomato crops are easy to expose to disease and spread in a short period of time, so late measures against disease are directly related to production and sales, which can cause damage. Therefore, there is a need for a service that enables early prevention by simply and accurately diagnosing tomato diseases in the field. In this paper, we construct a system that applies a deep learning-based model in which ImageNet transition is learned in advance to classify and serve nine classes of tomatoes for disease and normal cases. We use the input of MobileNet, ResNet, with a deep learning-based CNN structure that builds a lighter neural network using a composite product for the image set of leaves classifying tomato disease and normal from the Plant Village dataset. Through the learning of two proposed models, it is possible to provide fast and convenient services using MobileNet with high accuracy and learning speed.

• IMMUNE NETWORK
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• v.20 no.6
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• pp.51.1-51.17
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• 2020

Respiratory syncytial virus (RSV) causes severe pulmonary disease in infants, young children, and the elderly. Formalin inactivated RSV (FI-RSV) vaccine trials failed due to vaccine enhanced respiratory disease, but the underlying immune mechanisms remain not fully understood. In this study, we have used wild type C57BL/6 and CD4 knockout (CD4KO) mouse models to better understand the roles of the CD4 T cells and cellular mechanisms responsible for enhanced respiratory disease after FI-RSV vaccination and RSV infection. Less eosinophil infiltration and lower pro-inflammatory cytokine production were observed in FI-RSV vaccinated CD4KO mice after RSV infection compared to FI-RSV vaccinated C57BL/6 mice. NK cells and cytokine-producing CD8 T cells were recruited at high levels in the airways of CD4KO mice, correlating with reduced respiratory disease. Depletion studies provided evidence that virus control was primarily mediated by NK cells whereas CD8 T cells contributed to IFN-γ production and less eosinophilic lung inflammation. This study demonstrated the differential roles of effector CD4 and CD8 T cells as well as NK cells, in networking with other inflammatory infiltrates in RSV disease in immune competent and CD4-deficient condition.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9673-9678
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• 2014

Background: Breast cancer is a fatal disease and the most frequently diagnosed cancer in women with an increasing pattern worldwide. The burden is mostly attributed to metastatic cancers that occur in one-third of patients and the treatments are palliative. It is of great interest to determine factors affecting time from cancer diagnosis to secondary metastasis. Materials and Methods: Cure rate models assume a Poisson distribution for the number of unobservable metastatic-component cells that are completely deleted from the non-metastasis patient body but some may remain and result in metastasis. Time to metastasis is defined as a function of the number of these cells and the time for each cell to develop a detectable sign of metastasis. Covariates are introduced to the model via the rate of metastatic-component cells. We used non-mixture cure rate models with Weibull and log-logistic distributions in a Bayesian setting to assess the relationship between metastasis free survival and covariates. Results: The median of metastasis free survival was 76.9 months. Various models showed that from covariates in the study, lymph node involvement ratio and being progesterone receptor positive were significant, with an adverse and a beneficial effect on metastasis free survival, respectively. The estimated fraction of patients cured from metastasis was almost 48%. The Weibull model had a slightly better performance than log-logistic. Conclusions: Cure rate models are popular in survival studies and outperform other models under certain conditions. We explored the prognostic factors of metastatic breast cancer from a different viewpoint. In this study, metastasis sites were analyzed all together. Conducting similar studies in a larger sample of cancer patients as well as evaluating the prognostic value of covariates in metastasis to each site separately are recommended.