• Research in Plant Disease
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• v.30 no.1
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• pp.99-102
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• 2024

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

• Genomics & Informatics
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• v.18 no.1
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• pp.6.1-6.9
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• 2020

Acute leukemia represents the most common pediatric malignancy comprising diverse subtypes with varying prognosis and treatment outcomes. New and targeted treatment options are warranted for this disease. Patient-derived xenograft (PDX) models are increasingly being used for preclinical testing of novel treatment modalities. A novel approach involving targeted error-corrected RNA sequencing using ArcherDX HemeV2 kit was employed to compare 25 primary pediatric acute leukemia samples and their corresponding PDX samples. A comparison of the primary samples and PDX samples revealed a high concordance between single nucleotide variants and gene fusions whereas other complex structural variants were not as consistent. The presence of gene fusions representing the major driver mutations at similar allelic frequencies in PDX samples compared to primary samples and over multiple passages confirms the utility of PDX models for preclinical drug testing. Characterization and tracking of these novel cryptic fusions and exonal variants in PDX models is critical in assessing response to potential new therapies.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.200-219
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• 2008

The effective management of uncertainty is one of the most fundamental problems in medical decision making. According to the literatures review, most medical decision models rely on point estimates for input parameters. However, it is natural that they should be interested in the relationship between changes in those values and subsequent changes in model output. Therefore, the purpose of this study is to identify the ranges of numerical values for which each option will be most efficient with respect to the input parameters. The Nonhomogeneous Poisson Process(NHPP) was used for describing the behavior of aging chronic diseases. Three kind of failure models (linear, exponential, and power law) were considered, and each of these failure models was studied under the assumptions of unknown scale factor and known aging rate, known scale factor and unknown aging rate, and unknown scale factor and unknown aging rate, respectively. In addition, this study illustrated developed method with an analysis of data from a trial of immunotherapy in the treatment of chronic Granulomatous disease. Finally, the proposed design of Bayesian value of information analysis facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert's opinions and the sampling information which will furnish decision makers with valuable support for quality medical decision making.

• Journal of the Korean Applied Science and Technology
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• v.30 no.4
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• pp.622-634
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• 2013

The multi-origin of obesity and its associated diseases made it's a complex area of biomedical science research and severe health disorder. From the 1970s to onwards this health problem turned to an epidemic without having any report of declining yet and it created a red alert to the health sector. Meanwhile, many animal models have been developed to study the lethal effect of obesity. In consequence, many drugs, therapies and strategies have already been adopted based on the findings of those animal models. However, many complicated things based on molecular and generic mechanism has not been clarified to the date. Thus, it is important to develop a need based animal model for the better understanding and strategic planning to eliminate/avoid the obesity disorder. Therefore, the present review would unveil the pros and cons of presently established animal models for obesity research. In addition, it would indicate the required turning direction for further obesity and obesity based disease research.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.7
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• pp.909-924
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• 2016

The gut is the largest organ that helps with the immune function. Gut health, especially in young pigs has a significant benefit to health and performance. In an attempt to maintain and enhance intestinal health in pigs and improve productivity in the absence of in-feed antibiotics, researchers have evaluated a wide range of feed additives. Some of these additives such as zinc oxide, copper sulphate, egg yolk antibodies, mannan-oligosaccharides and spray dried porcine plasma and their effectiveness are discussed in this review. One approach to evaluate the effectiveness of these additives in vivo is to use an appropriate disease challenge model. Over the years, researchers have used a number of challenge models which include the use of specific strains of enterotoxigenic Escherichia coli, bacteria lipopolysaccharide challenge, oral challenge with Salmonella enteric serotype Typhimurium, sanitation challenge, and Lawsonia intercellularis challenge. These challenge models together with the criteria used to evaluate the responses of the animals to them are also discussed in this review.

• Molecules and Cells
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• v.23 no.2
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• pp.132-137
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• 2007

With the advance of stem cell transplantation research, in vivo cell tracking techniques have become increasingly important in recent years. Magnetic resonance imaging (MRI) may provide a unique tool for non-invasive tracking of transplanted cells. Since the initial findings on the stem cell migration by MRI several years ago, there have been numerous studies using various animal models, notably in heart or brain disease models. In order to develop more reliable and clinically applicable methodologies, multiple aspects should be taken into consideration. In this review, we will summarize the current status and future perspectives of in vivo cell tracking technologies using MRI. In particular, use of different MR contrast agents and their detection methods using MRI will be described in much detail. In addition, various cell labeling methods to increase the sensitivity of signals will be extensively discussed. We will also review several key experiments, in which MRI techniques were utilized to detect the presence and/or migration of transplanted stem cells in various animal models. Finally, we will discuss the current problems and future directions of cell tracking methods using MRI.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.295-308
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• 2015

We have experienced a substantial improvement in and cost-drop for genotyping that enables genetic epidemiological studies with large-scale genetic data. Genome-wide association studies have identified more than ten thousand causal variants. Many statistical methods based on linear mixed models have been developed for various goals such as estimating heritability and identifying disease susceptibility locus. Empirical results also repeatedly stress the importance of linear mixed models. Therefore, we review the statistical methods related with to linear mixed models and illustrate the meaning of their estimates.

• Genomics & Informatics
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• v.20 no.2
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• pp.22.1-22.9
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• 2022

The rise of newer coronavirus disease 2019 (COVID-19) variants has brought a challenge to ending the spread of COVID-19. The variants have a different fatality, morbidity, and transmission rates and affect vaccine efficacy differently. Therefore, the impact of each new variant on the spread of COVID-19 is of interest to governments and scientists. Here, we proposed mathematical SEIQRDVP and SEIQRDV3P models to predict the impact of the Omicron variant on the spread of the COVID-19 situation in South Korea. SEIQEDVP considers one vaccine level at a time while SEIQRDV3P considers three vaccination levels (only one dose received, full doses received, and full doses + booster shots received) simultaneously. The omicron variant's effect was contemplated as a weighted sum of the delta and omicron variants' transmission rate and tuned using a hyperparameter k. Our models' performances were compared with common models like SEIR, SEIQR, and SEIQRDVUP using the root mean square error (RMSE). SEIQRDV3P performed better than the SEIQRDVP model. Without consideration of the variant effect, we don't see a rapid rise in COVID-19 cases and high RMSE values. But, with consideration of the omicron variant, we predicted a continuous rapid rise in COVID-19 cases until maybe herd immunity is developed in the population. Also, the RMSE value for the SEIQRDV3P model decreased by 27.4%. Therefore, modeling the impact of any new risen variant is crucial in determining the trajectory of the spread of COVID-19 and determining policies to be implemented.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.1-9
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• 2024

Predicting the spread of COVID-19 remains a challenge due to the complexity of the disease and its evolving nature. This study presents an integrated approach using the classic SIR model for infectious diseases, enhanced by the chemical master equation (CME). We employ a Monte Carlo method (SSA) to solve the model, revealing unique aspects of the SARS-CoV-2 virus transmission. The study, a first of its kind in Korea, adopts a step-by-step and complementary approach to model prediction. It starts by analyzing the epidemic's trajectory at local government levels using both basic and stochastic SIR models. These models capture the impact of public health policies on the epidemic's dynamics. Further, the study extends its scope from a single-infected individual model to a more comprehensive model that accounts for multiple infections using the jump SIR prediction model. The practical application of this approach involves applying these layered and complementary SIR models to forecast the course of the COVID-19 epidemic in small to medium-sized local governments, particularly in Gangnam-gu, Seoul. The results from these models are then compared and analyzed.

• International Journal of Stem Cells
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• v.17 no.1
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• pp.38-50
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• 2024

Induced pluripotent stem cell (iPSC) technology has revolutionized various fields, including stem cell research, disease modeling, and regenerative medicine. The evolution of iPSC-based models has transitioned from conventional two-dimensional systems to more physiologically relevant three-dimensional (3D) models such as spheroids and organoids. Nonetheless, there still remain challenges including limitations in creating complex 3D tissue geometry and structures, the emergence of necrotic core in existing 3D models, and limited scalability and reproducibility. 3D bioprinting has emerged as a revolutionary technology that can facilitate the development of complex 3D tissues and organs with high scalability and reproducibility. This innovative approach has the potential to effectively bridge the gap between conventional iPSC models and complex 3D tissues in vivo. This review focuses on current trends and advancements in the bioprinting of iPSCs. Specifically, it covers the fundamental concepts and techniques of bioprinting and bioink design, reviews recent progress in iPSC bioprinting research with a specific focus on bioprinting undifferentiated iPSCs, and concludes by discussing existing limitations and future prospects.