• Natural Product Sciences
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• v.29 no.2
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• pp.83-90
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• 2023

To our knowledge, this is the first study aiming to optimize the extraction conditions of total phenolic compounds (TPC) and total flavonoids contents (TFC) from Argania spinosa leaves using Response Surface Methodology (RSM) with a Box-Behnken design (BBD). The optimal conditions obtained were 5% (w/v) solvent-to-solid ratio, 72.33% ethanol concentration, and 10h ours as an extraction time, which resulted in an extract with maximum TPC (131.63 mg GAE/g dw) and TFC (10.66 mg QE/g dw). Under the optimal extraction conditions, the antioxidant activity of the extracts of leaves of argan tree showed a moderate antiradical capacity of DPPH (IC₅₀ = 0,130 mg/mL) and ABTS (IC₅₀ = 0.198 mg/mL). However, the leaves of argan tree showed a very interesting reducing power of Iron (IC₅₀ = 0.448 mg/ml) which is similar to that of the ascorbic acid (IC₅₀ = 0.371 mg/mL).

• Journal of Applied Biological Chemistry
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• v.64 no.3
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• pp.269-275
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• 2021

The in vitro anti-obesity activity of 12 species of tree sprouts in differentiated 3T3-L1 cells and the mechanisms underlying their activity were evaluated. (+)-Catechin and quercetin concentrations in the sprouts were analyzed by HPLC/UV at 270 and 254 nm, respectively. Euonymus alatus (EAT) and Fraxinus mandschuria (FMS) extracts at doses of 50 and 100 ㎍/mL inhibited the accumulation of lipid droplets in differentiated 3T3-L1 cells. Moreover, EAT and FMS downregulated the expression of the CCAAT/enhancer-binding protein-α, adipogenesis-related proteins peroxisome proliferator-activated receptor-γ, and adipocyte P-2α in differentiated 3T3-L1 cells. Tree sprouts with an abundant flavonoid content exerted the highest anti-obesity activity. Concentrations of total flavonoids were the highest in FMS (24.281 mg/g DW) sprouts. These findings could be used to develop health-promoting functional foods or supplements derived from tree sprouts.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.8-13
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• 2019

Despite technical advances in healthcare, the rates of hospital-acquired pressure injury (HAPI) are still high although many are potentially preventable. The purpose of this study was to determine whether tree-based prediction modeling is suitable for assessing the risk of HAPI in ICU patients. Retrospective cohort study has been carried out. A decision tree model was constructed with Age, Weight, eTube, diabetes, Braden score, Isolation, and Number of comorbid conditions as decision nodes. We used RStudio for model training and testing. Correct prediction rate of the final prediction model was 92.4 and the Area Under the ROC curve (AUC) was 0.699, which means there is about 70% chance that the model is able to distinguish between HAPI and non-HAPI. The results of this study has limited generalizability as the data were from a single academic institution. Our research finding shows that the data-driven tree-based prediction modeling may potentially support ICU sensitive risk assessment for HAPI prevention.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.883-902
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• 2023

Water is critical to the health and productivity of fruit trees. Efficient monitoring of water stress is essential for optimizing irrigation practices and ensuring sustainable fruit production. Short-range sensing can be reliable, rapid, inexpensive, and used for applications based on well-developed and validated algorithms. This paper reviews the recent advancement in fruit tree water stress detection via short-range sensing, which can be used for irrigation scheduling in orchards. Thermal imagery, near-infrared, and shortwave infrared methods are widely used for crop water stress detection. This review also presents research demonstrating the efficacy of short-range sensing in detecting water stress indicators in different fruit tree species. These indicators include changes in leaf temperature, stomatal conductance, chlorophyll content, and canopy reflectance. Short-range sensing enables precision irrigation strategies by utilizing real-time data to customize water applications for individual fruit trees or specific orchard areas. This approach leads to benefits, such as water conservation, optimized resource utilization, and improved fruit quality and yield. Short-range sensing shows great promise for potentially changing water stress monitoring in fruit trees. It could become a useful tool for effective fruit tree water stress management through continued research and development.

• Environmental Analysis Health and Toxicology
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• v.29
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• pp.3.1-3.7
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• 2014

Objectives This study was conducted to determine the depositional characteristics of several tree barks, including Ginkgo (Ginkgo biloba), Pine (Pinus densiflora), Platanus (Platanus), and Metasequoia (Metasequoia glyptostroboides). These were used as passive air sampler (PAS) of atmospheric polybrominated diphenyl ethers (PBDEs). Methods Tree barks were sampled from the same site. PBDEs were analyzed by high-resolution gas chromatography/high-resolution mass spectrometer, and the lipid content was measured using the gravimetric method by n-hexane extraction. Results Gingko contained the highest lipid content (7.82 mg/g dry), whereas pine (4.85 mg/g dry), Platanus (3.61 mg/g dry), and Metasequoia (0.97 mg/g dry) had relatively lower content. The highest total PBDEs concentration was observed in Metasequoia (83,159.0 pg/g dry), followed by Ginkgo (53,538.4 pg/g dry), Pine (20,266.4 pg/g dry), and Platanus (12,572.0 pg/g dry). There were poor correlations between lipid content and total PBDE concentrations in tree barks ($R^2$=0.1011, p =0.682). Among the PBDE congeners, BDE 206, 207 and 209 were highly brominated PBDEs that are sorbed to particulates in ambient air, which accounted for 90.5% (84.3-95.6%) of the concentration and were therefore identified as the main PBDE congener. The concentrations of particulate PBDEs deposited on tree barks were dependent on morphological characteristics such as surface area or roughness of barks. Conclusions Therefore, when using the tree barks as the PAS of the atmospheric PBDEs, samples belonging to same tree species should be collected to reduce errors and to obtain reliable data.

• Physical Therapy Rehabilitation Science
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• v.11 no.3
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• pp.285-295
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• 2022

Objective: The purpose of this study is to use logistic regression and decision tree analysis to identify the factors that affect the success or failurein the national physical therapy examination; and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 76,727 subjects from the physical therapy national examination data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was pass or fail, and the input variables were gender, age, graduation status, and examination area. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In the logistic regression analysis, subjects in their 20s (Odds ratio, OR=1, reference), expected to graduate (OR=13.616, p<0.001) and from the examination area of Jeju-do (OR=3.135, p<0.001), had a high probability of passing. In the decision tree, the predictive factors for passing result had the greatest influence in the order of graduation status (x²=12366.843, p<0.001) and examination area (x²=312.446, p<0.001). Logistic regression analysis showed a specificity of 39.6% and sensitivity of 95.5%; while decision tree analysis showed a specificity of 45.8% and sensitivity of 94.7%. In classification accuracy, logistic regression and decision tree analysis showed 87.6% and 88.0% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. Additionally, whether actual test takers passed the national physical therapy examination could be determined, by applying the constructed prediction model and prediction rate.

• The Korean Journal of Health Service Management
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• v.10 no.3
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• pp.63-74
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• 2016

Objectives : There is a need to develop a data quality management algorithm to improve the quality of healthcare data using a data quality management system. In this study, we developed a data quality control algorithms associated with diseases related to hypertension in patients with diabetes mellitus. Methods : To make a data quality algorithm, we extracted the 2011 and 2012 discharge damage survey data from diabetes mellitus patients. Derived variables were created using the primary diagnosis, diagnostic unit, primary surgery and treatment, minor surgery and treatment items. Results : Significant factors in diabetes mellitus patients with hypertension were sex, age, ischemic heart disease, and diagnostic ultrasound of the heart. Depending on the decision tree results, we found four groups with extreme values for diabetes accompanying hypertension patients. Conclusions : There is a need to check the actual data contained in the Outlier (extreme value) groups to improve the quality of the data.

• Information Systems Review
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• v.9 no.1
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• pp.45-65
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• 2007

In medical industry, health insurance bill audit is unique and essential process in general hospitals. The health insurance bill audit process is very important because not only for hospital's profit but also hospital's reputation. Particularly, at the large general hospitals many related workers including analysts, nurses, and etc. have engaged in the health insurance bill audit process. This paper introduces a case of health insurance bill audit for finding reducible health insurance bill cases using decision tree induction techniques at a large general hospital in Korea. When supervised learning methods had been tried to be applied, one of major problems was data imbalance problem in the health insurance bill audit data. In other words, there were many normal(passing) cases and relatively small number of reduction cases in a bill audit dataset. To resolve the problem, in this study, well-known methods for imbalanced data sets including over sampling of rare cases, under sampling of major cases, and adjusting the misclassification cost are combined in several ways to find appropriate decision trees that satisfy required conditions in health insurance bill audit situation.

• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.935-948
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• 2014

As chronic diseases have become more prevalent and problematic, effective cares for major chronic diseases have been a locus of the healthcare policy. In this regard, this study examines how region-specific characteristics affect the prevalence of hypertension in South Korea. To analyze, we combined a unique multi-year data set including key indicators of health conditions and health behaviors at the 237 small administrative districts. The data are collected from the Annual Community Health Survey between 2009 and 2011 by Korea Centers for Disease Control and Prevention and other government organizations. For the purpose of investigating regional variations, we estimated using Geographically Weighted Regression (GWR) and decision tree model. Our finding first suggests that using the multi-year data is more legitimate than using the single-year data for the geographical analysis of chronic diseases, because the significant annual differences are observed in most variables. We also find that the prevalence of hypertension is more likely to be positively associated with the prevalence of diabetes and obesity but to be negatively associated with population density. More importantly, noticeable geographical variations in these factors are observed according to the results from the GWR. In line with this result, additional findings from the decision tree model suggest that primary influential factors that affect the hypertension prevalence are indeed heterogeneous across regional groups. Taken as a whole, accounting for geographical variations of health conditions, health behaviors and other socioeconomic factors is very important when the regionally customized healthcare policy is implemented to mitigate the hypertension prevalence. In short, our study sheds light on possible ways to manage the chronic diseases for policy makers in the local government.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1191-1199
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• 2010

Knowing what factors into a player's ability to affect the outcome of a sports game is crucial. This knowledge helps determine the relative degree of contribution by each team member as well as sets appropriate annual salaries. This study uses statistical analysis to investigate how much the outcome of a professional baseball game is influenced by the records of individual players. We used the Lotte Giants' data on 252 games played between 2007 and 2008 that included environmental data(home or away games and opponents) as well as pitchers' and batters' data. Using a SAS Enterprise Miner, we performed a logistic regression analysis and decision tree analysis on the data. The results obtained through the two analytic methods are compared and discussed.