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RISS 인기검색어
- 검색키워드
- 저자 : Yan-Sub Cho (검색결과 3 건)
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Therapeutic effects of lysophosphatidylcholine in experimental sepsis
Yan, Ji-Jing,Jung, Jun-Sub,Lee, Jung-Eun,Lee, Jongho,Huh, Sung-Oh,Kim, Hee-Sung,Jung, Kyeong Cheon,Cho, Jae-Young,Nam, Ju-Suk,Suh, Hong-Won,Kim, Yung-Hi,Song, Dong-Keun Nature Publishing Group 2004 Nature medicine Vol.10 No.2
Sepsis represents a major cause of death in intensive care units. Here we show that administration of lysophosphatidylcholine (LPC), an endogenous lysophospholipid, protected mice against lethality after cecal ligation and puncture (CLP) or intraperitoneal injection of Escherichia coli. In vivo treatment with LPC markedly enhanced clearance of intraperitoneal bacteria and blocked CLP-induced deactivation of neutrophils. In vitro, LPC increased bactericidal activity of neutrophils, but not macrophages, by enhancing H<SUB>2</SUB>O<SUB>2</SUB> production in neutrophils that ingested E. coli. Incubation with an antibody to the LPC receptor, G2A, inhibited LPC-induced protection from CLP lethality and inhibited the effects of LPC in neutrophils. G2A-specific antibody also blocked the inhibitory effects of LPC on certain actions of lipopolysaccharides (LPS), including lethality and the release of tumor necrosis factor-α (TNF-α) from neutrophils. These results suggest that LPC can effectively prevent and treat sepsis and microbial infections.
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Extended latex proteome analysis deciphers additional roles of the lettuce laticifer
Cho, Won-Kyong,Chen, Xiong-Yan,Rim, Yeong-Gil,Chu, Hyo-Sub,Jo, Yeon-Hwa,Kim, Su-Wha,Park, Zee-Yong,Kim, Jae-Yean The Korean Society of Plant Biotechnology 2010 Plant biotechnology reports Vol.4 No.4
Lettuce is an economically important leafy vegetable that accumulates a milk-like sap called latex in the laticifer. Previously, we conducted a large-scale lettuce latex proteomic analysis. However, the identified proteins were obtained only from lettuce ESTs and proteins deposited in NCBI databases. To extend the number of known latex proteins, we carried out an analysis identifying 302 additional proteins that were matched to the NCBI non-redundant protein database. Interestingly, the newly identified proteins were not recovered from lettuce EST and protein databases, indicating the usefulness of this hetero system in MudPIT analysis. Gene ontology studies revealed that the newly identified latex proteins are involved in many processes, including many metabolic pathways, binding functions, stress responses, developmental processes, protein metabolism, transport and signal transduction. Application of the non-redundant plant protein database led to the identification of an increased number of latex proteins. These newly identified latex proteins provide a rich source of information for laticifer research.
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Machine learning-based risk factor analysis for periodontal disease from a Korean National Survey
손호선,최은선,Yan-Sub Cho,차은종,강태건,김경아 충북대학교 동물의학연구소 2022 Journal of Biomedical and Translational Research Vol.23 No.1
Periodontal disease is a chronic but treatable condition which often does not cause pain during the initial stages of the illness. Lack of awareness of symptoms can delay initiation of treatment and worsen health. The aim of this study was to develop and compare different risk prediction models for periodontal disease using machine learning algorithms. We obtained information on risk factors for periodontal disease from the Korea National Health and Nutrition Examination Survey (KNHANES) dataset. Principal component analysis and an auto-encoder were used to extract data on risk factors for periodontal disease. A synthetic minority oversampling technique algorithm was used to solve the problem of data imbalance. We used a combination of logistic regression analysis, support vector machine (SVM) learning, random forest, and AdaBoost to classify and compare risk prediction models for periodontal disease. In cases where we used principal component analysis (PCA) to extract risk factors, the recall was higher than the feature selection method in the logistic regression and support-vector machine learning models. AdaBoost’s recall was 0.98, showing the highest performance of both feature selection and PCA. The F1 score showed relatively high performance in AdaBoost, logistic regression, and SVM learning models. By using the risk factors extracted from the research results and the predictive model based on machine learning, it will be able to help in the prevention and diagnosis of periodontal disease, and it will be used to study the relationship with various diseases related to periodontal disease.
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