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Multivariate classification of urine metabolome profiles for breast cancer diagnosis
Kim, Younghoon,Koo, Imhoi,Jung, Byung Hwa,Chung, Bong Chul,Lee, Doheon BioMed Central 2010 BMC bioinformatics Vol.11 No.suppl2
<P><B>Background</B></P><P>Diagnosis techniques using urine are non-invasive, inexpensive, and easy to perform in clinical settings. The metabolites in urine, as the end products of cellular processes, are closely linked to phenotypes. Therefore, urine metabolome is very useful in marker discoveries and clinical applications. However, only univariate methods have been used in classification studies using urine metabolome. Since multiple genes or proteins would be involved in developments of complex diseases such as breast cancer, multiple compounds including metabolites would be related with the complex diseases, and multivariate methods would be needed to identify those multiple metabolite markers. Moreover, because combinatorial effects among the markers can seriously affect disease developments and there also exist individual differences in genetic makeup or heterogeneity in cancer progressions, single marker is not enough to identify cancers.</P><P><B>Results</B></P><P>We proposed classification models using multivariate classification techniques and developed an analysis procedure for classification studies using metabolome data. Through this strategy, we identified five potential urinary biomarkers for breast cancer with high accuracy, among which the four biomarker candidates were not identifiable by only univariate methods. We also proposed potential diagnosis rules to help in clinical decision making. Besides, we showed that combinatorial effects among multiple biomarkers can enhance discriminative power for breast cancer.</P><P><B>Conclusions</B></P><P>In this study, we successfully showed that multivariate classifications are needed to precisely diagnose breast cancer. After further validation with independent cohorts and experimental confirmation, these marker candidates will likely lead to clinically applicable assays for earlier diagnoses of breast cancer.</P>
Osteokinematic analysis during shoulder abduction using the C-arm
Lee, Seung Hoo,Kim, Younghoon,Lee, Dong Geon,Lee, Kyeong-Bong,Lee, Gyu Chang korean Academy of Physical Therapy Rehabilitation 2017 Physical therapy rehabilitation science Vol.6 No.4
Objective: Despite reliable evidence of abnormal scapular motions increases, there is not yet sufficient evidence of abnormal humeral translations. This study aims to analyze the motion of the humeral head toward the scapula when the shoulder is actively abducted using the C-arm. Design: A case report. Methods: The participant was a healthy man without any limitation and pain during shoulder movement. The participant's shoulder was abducted; this movement in the frontal plane was measured using a C-arm (anterior-posterior view) and was analyzed with computer-aided design. The starting posture was $15^{\circ}$, and as the participant abducted his shoulder measurements were taken and analyzed at $30^{\circ}$, $60^{\circ}$, $90^{\circ}$, $120^{\circ}$, $150^{\circ}$, and ending at $165^{\circ}$. A line was drawn perpendicularly to the line connecting the humeral head axis to the glenoid, and another line was drawn perpendiculary to the line connecting the scapular axis to the glenoid. The distance between the two lines measured is defined as the e value. Results: At the starting posture ($15^{\circ}$), the central axis of the humeral head was located 1.92 mm inferior to the central axis of the scapula. The humeral head was superiorly translated from the starting posture to $120^{\circ}$, and then, showed an inferior translation to the ending posture ($165^{\circ}$). Conclusions: The results of this study showed that the humeral head moved upward from the starting posture ($15^{\circ}$) up to $120^{\circ}$ indicating, superior translation, and it moved downward when the posture was past $120^{\circ}$, indicating inferior translation.
Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification.
Nam, Hojung,Chung, Bong Chul,Kim, Younghoon,Lee, Kiyoung,Lee, Doheon Oxford University Press 2009 Bioinformatics Vol.25 No.23
<P>MOTIVATION: For the early detection of cancer, highly sensitive and specific biomarkers are needed. Particularly, biomarkers in bio-fluids are relatively more useful because those can be used for non-biopsy tests. Although the altered metabolic activities of cancer cells have been observed in many studies, little is known about metabolic biomarkers for cancer screening. In this study, a systematic method is proposed for identifying metabolic biomarkers in urine samples by selecting candidate biomarkers from altered genome-wide gene expression signatures of cancer cells. Biomarkers identified by the present study have increased coherence and robustness because the significances of biomarkers are validated in both gene expression profiles and metabolic profiles. RESULTS: The proposed method was applied to the gene expression profiles and urine samples of 50 breast cancer patients and 50 normal persons. Nine altered metabolic pathways were identified from the breast cancer gene expression signatures. Among these altered metabolic pathways, four metabolic biomarkers (Homovanillate, 4-hydroxyphenylacetate, 5-hydroxyindoleacetate and urea) were identified to be different in cancer and normal subjects (p <0.05). In the case of the predictive performance, the identified biomarkers achieved area under the ROC curve values of 0.75, 0.79 and 0.79, according to a linear discriminate analysis, a random forest classifier and on a support vector machine, respectively. Finally, biomarkers which showed consistent significance in pathways' gene expression as well as urine samples were identified. CONTACT: dhlee@biosoft.kaist.ac.kr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</P>
Role of Coagulation Factor 2 Receptor during Respiratory Pneumococcal Infections
Shin, Seul Gi,Bong, Younghoon,Lim, Jae Hyang The Korean Society for Microbiology 2016 Journal of Bacteriology and Virology Vol.46 No.4
Coagulation factor 2 receptor (F2R), also well-known as a protease-activated receptor 1 (PAR1), is the first known thrombin receptor and plays a critical role in transmitting thrombin-mediated activation of intracellular signaling in many types of cells. It has been known that bacterial infections lead to activation of coagulation systems, and recent studies suggest that PAR1 may be critically involved not only in mediating bacteria-induced detrimental coagulation, but also in innate immune and inflammatory responses. Community-acquired pneumonia, which is frequently caused by Streptococcus pneumoniae (S. pneumoniae), is characterized as an intra-alveolar coagulation and an interstitial neutrophilic inflammation. Recently, the role of PAR1 in regulating pneumococcal infections has been proposed. However, the role of PAR1 in pneumococcal infections has not been clearly understood yet. In this review, recent findings on the role of PAR1 in pneumococcal infections and possible underlying molecular mechanisms by which S. pneumoniae regulates PAR1-mediated immune and inflammatory responses will be discussed.
( Jonghee Han ),( Su Young Yoon ),( Junepill Seok ),( Jin Young Lee ),( Jin Suk Lee ),( Jin Bong Ye ),( Younghoon Sul ),( Seheon Kim ),( Hong Rye Kim ) 대한외상학회 2023 大韓外傷學會誌 Vol.36 No.4
Purpose: In this study, we aimed to compare the characteristics of patients with trauma by age group in a single center in Korea to identify the clinical characteristics and analyze the risk factors affecting mortality. Methods: Patients aged ≥18 years who visited the Chungbuk National University Hospital Regional Trauma Center between January 2016 and December 2022 were included. The accident mechanism, severity of the injury, and outcomes were compared by classifying the patients into group A (18-64 years), group B (65-79 years), and group C (≥80 years). In addition, logistic regression analysis was performed to identify factors affecting death. Results: The most common injury mechanism was traffic accidents in group A (40.9%) and slipping in group B (37.0%) and group C (56.2%). Although group A had the highest intensive care unit admission rate (38.0%), group C had the highest mortality rate (9.5%). In the regression analysis, 3 to 8 points on the Glasgow Coma Scale had the highest odds ratio for mortality, and red blood cell transfusion within 24 hours, intensive care unit admission, age, and Injury Severity Score were the predictors of death. Conclusions: For patients with trauma, the mechanism, injured body region, and severity of injury differed among the age groups. The high mortality rate of elderly patients suggests the need for different treatment approaches for trauma patients according to age. Identifying factors affecting clinical patterns and mortality according to age groups can help improve the prognosis of trauma patients in the future.