1.Eruption Stage of Permanent Teeth Using Diagnostic Model Analysis in Kyung Hee Dental Hospital
Taejun OH ; Okhyung NAM ; Misun KIM ; Hyo seol LEE ; Kwangchul KIM ; Sungchul CHOI
Journal of Korean Academy of Pediatric Dentistry 2019;46(1):10-20
Individual dental age is used as an index of chronological age estimation and is an important indicator of the child's growth stage. Dental age does change greatly over time, but it changes constantly. And updating information about this change is important. The purpose of this study was to provide information about tooth eruption stage using diagnostic model analysis and to investigate tooth eruption sequence and estimate chronological age based on this information.Tooth eruption stages were measured on a diagnostic model from 488 patients in 5 – 13 year old children. Based on the information on eruption stage, eruption sequence in maxilla was first permanent molar, central incisor, lateral incisor, first premolar, canine, second premolar and second permanent molar. Eruption sequence in mandible was first permanent molar, central incisor, lateral incisor, canine, first premolar, second premolar and second permanent molar. There were significant differences between males and females in the eruption stage of canine, first and second premolar, and second molar at several ages. The chronological age of male and female was estimated by the coefficient of determination of 0.816, 0.826 respectively.
Bicuspid
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Child
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Female
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Humans
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Incisor
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Male
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Mandible
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Maxilla
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Molar
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Tooth Eruption
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Tooth
2.Establishment of High Throughput Screening System Using Human Umbilical Cord-derived Mesenchymal Stem Cells.
Eu Gene PARK ; Taejun CHO ; Keunhee OH ; Soon Keun KWON ; Dong Sup LEE ; Seung Bum PARK ; Jaejin CHO
International Journal of Oral Biology 2012;37(2):43-50
The use of high throughput screening (HTS) in drug development is principally for the selection new drug candidates or screening of chemical toxicants. This system minimizes the experimental environment and allows for the screening of candidates at the same time. Umbilical cordderived stem cells have some of the characteristics of fetal stem cell and have several advantages such as the ease with which they can be obtained and lack of ethical issues. To establish a HTS system, optimized conditions that mimic typical cell culture conditions in a minimal space such as 96 well plates are needed for stem cell growth. We have thus established a novel HTS system using human umbilical cord derived-mesenchymal stem cells (hUC-MSCs). To determine the optimal cell number, hUC-MSCs were serially diluted and seeded at 750, 500, 200 and 100 cells per well on 96 well plates. The maintenance efficiencies of these dilutions were compared for 3, 7, 9, and 14 days. The fetal bovine serum (FBS) concentration (20, 10, 5 and 1%) and the cell numbers (750, 500 and 200 cells/well) were compared for 3, 5 and 7 days. In addition, we evaluated the optimal conditions for cell cycle block. These four independent optimization experiments were conducted using an MTT assay. In the results, the optimal conditions for a HTS system using hUC-MSCs were determined to be 300 cell/well cultured for 8 days with 1 or 5% FBS. In addition, we demonstrated that the optimal conditions for a cell cycle block in this culture system are 48 hours in the absence of FBS. In addition, we selected four types of novel small molecule candidates using our HTS system which demonstrates the feasibility if using hUC-MSCs for this type of screen. Moreover, the four candidate compounds can be tested for stem cell research application.
Cell Count
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Cell Culture Techniques
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Cell Cycle
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Fetal Stem Cells
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Humans
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Hydrazines
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Mass Screening
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Mesenchymal Stromal Cells
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Seeds
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Stem Cell Research
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Stem Cells
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Umbilical Cord
3.Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study
Sang Won PARK ; Na Young YEO ; Seonguk KANG ; Taejun HA ; Tae-Hoon KIM ; DooHee LEE ; Dowon KIM ; Seheon CHOI ; Minkyu KIM ; DongHoon LEE ; DoHyeon KIM ; Woo Jin KIM ; Seung-Joon LEE ; Yeon-Jeong HEO ; Da Hye MOON ; Seon-Sook HAN ; Yoon KIM ; Hyun-Soo CHOI ; Dong Kyu OH ; Su Yeon LEE ; MiHyeon PARK ; Chae-Man LIM ; Jeongwon HEO ; On behalf of the Korean Sepsis Alliance (KSA) Investigators
Journal of Korean Medical Science 2024;39(5):e53-
Background:
Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department.
Methods:
This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP).
Results:
Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results.
Conclusion
Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.