• Implantology
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• v.18 no.2
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• pp.120-138
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• 2014

Purpose: The purpose of this study was to review the outcomes of a series of studies on tissue regeneration conducted in multiple institutions including the Department of Periodontology, College of Dentistry, Yonsei University. Materials and Methods: Studies were performed divided into the following three subjects; 1) Development of three-dimensional nano-hydroxyapatite (n-HA) scaffold for facilitating drug release and cell adhesion. 2) Synergistic effects of bone marrow-derived mesenchymal stem cells (BMMSC) application simultaneously with platelet-rich plasma (PRP) on HA scaffolds. 3) The efficacy of silk scaffolds coated with n-HA. Also, all results were analyzed by subjects. Results: Hollow hydroxyapatite spherical granules were found to be a useful tool for the drug release and avidin-biotin binding system for cell attachment. Also, BMMSC simultaneously with PRP applied in an animal bone defect model was seen to be more synergistic than in the control group. But, the efficacy of periodontal ligament cells and dental pulp cells with silk scaffolds could not be confirmed in the initial phase of bone healing. Conclusion: The ideal combination of three elements of tissue engineering-scaffolds, cells and signaling molecules could be substantiated due to further investigations with the potentials and limitations of the suggested list of studies.

• Journal of The Korean Association For Science Education
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• v.43 no.6
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• pp.573-582
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• 2023

This study aimed to explore the potential use of artificial intelligence in science education for gifted students by analyzing the structure of abstracts written by students at a gifted science academy and comparing the performance of various elements extracted using AI. The study involved an analysis of 263 graduation theses from S Science High School over five years (2017-2021), focusing on the frequency and types of background, objectives, methods, results, and discussions included in their abstracts. This was followed by an evaluation of their accuracy using AI classification methods with fine-tuning and prompts. The results revealed that the frequency of elements in the abstracts written by gifted students followed the order of objectives, methods, results, background, and discussions. However, only 57.4% of the abstracts contained all the essential elements, such as objectives, methods, and results. Among these elements, fine-tuned AI classification showed the highest accuracy, with background, objectives, and results demonstrating relatively high performance, while methods and discussions were often inaccurately classified. These findings suggest the need for a more effective use of AI, through providing a better distribution of elements or appropriate datasets for training. Educational implications of these findings were also discussed.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.539-547
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• 2023

In recent years, university education has emphasized a learner-centered education model with a change in educational paradigm. This study aims to explore students' diverse opinions and improve the quality of education by analyzing the open-ended responses of university lecture evaluations using topic modeling. To this end, a total of 45,001 open-ended responses based on the results of lecture evaluations from 2017 to 2022 in non-metropolitan universities were divided into majors and liberal arts, and a short-form optimized Biterm Topic Modeling (BTM) analysis was conducted. As a result of the analysis, major lectures were divided into "attitude toward non-face-to-face classroom experience", "attitude toward questions and discussions", "attitude toward attendance and grading", "attitude toward practical activities and presentations", and "attitude toward communication and collaboration", while liberal arts lectures were divided into "attitude toward non-face-to-face classroom experience", "attitude toward grades and evaluations", "attitude toward attendance and syllabus", "attitude toward academic knowledge and interest", and "attitude toward communication and questions". The results of this study, which analyzed various feedback from students, provide insights that can be used to compare the characteristics of majors and liberal arts courses and improve teaching and learning experiences.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.679-684
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• 2023

Venous puncture is widely used to obtain blood samples for pathological examination. Because the invasive venipuncture method using a needle is repeatedly performed, the pain suffered by the patient increases, so our research team pre-developed a miniaturized near-infrared (NIR) imaging system in advance. To improve the image quality of the acquired NIR images, this study aims to model the non-local means (NLM) algorithm, which is well known to be efficient in noise reduction, and analyze its applicability in the system. The developed NIR imaging system is based on the principle that infrared rays pass through dichroic and long-pass filters and are detected by a CMOS sensor module. The proposed NLM algorithm is modeled based on the principle of replacing the pixel from which noise is to be removed with a value that reflects the distances between surrounding pixels. After acquiring an NIR image with a central wavelength of 850 nm, the NLM algorithm was applied to segment the final vein area through histogram equalization. As a result, the coefficient of variation of the NIR image of the vein using the NLM algorithm was 0.247 on average, which was an excellent result compared to conventional filtering methods. In addition, the dice similarity coefficient value of the NLM algorithm was improved by 62.91 and 9.40%, respectively, compared to the median filter and total variation methods. In conclusion, we demonstrated that the NLM algorithm can acquire accurate segmentation of veins acquired with a NIR imaging system.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.911-917
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• 2023

The method of observing nodular changes on the liver surface using clinical ultrasonography is useful for diagnosing cirrhosis. However, the speckle noise that inevitably occurs in ultrasound images makes it difficult to identify changes in the liver surface and echo patterns, which has a negative impact on the diagnosis of cirrhosis. The purpose of this study is to model the median modified Wiener filter (MMWF), which can efficiently reduce noise in cirrhotic ultrasound images, and confirm its applicability. Ultrasound images were acquired using an ACR phantom and an actual cirrhotic patient, and the proposed MMWF algorithm and conventional noise reduction algorithm were applied to each image. Coefficient of variation (COV) and edge rise distance (ERD) were used as quantitative image quality evaluation factors for the acquired ultrasound images. We confirmed that the MMWF algorithm improved both COV and ERD values compared to the conventional noise reduction algorithm in both ACR phantom and real ultrasound images of cirrhotic patients. In conclusion, the proposed MMWF algorithm is expected to contribute to improving the diagnosis rate of cirrhosis patients by reducing the noise level and improving spatial resolution at the same time.

• Journal of Soil and Groundwater Environment
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• v.28 no.6
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• pp.71-89
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• 2023

A groundwater potential map (GPM) was built for the Nakdonggang River Basin based on ten variables, including hydrogeologic unit, fault-line density, depth to groundwater, distance to surface water, lineament density, slope, stream drainage density, soil drainage, land cover, and annual rainfall. To integrate the thematic layers for GPM, the criteria were first weighted using the Analytic Hierarchical Process (AHP) and then overlaid using the Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) model. Finally, the groundwater potential was categorized into five classes (very high (VH), high (H), moderate (M), low (L), very low (VL)) and verified by examining the specific capacity of individual wells on each class. The wells in the area categorized as VH showed the highest median specific capacity (5.2 m³/day/m), while the wells with specific capacity < 1.39 m³/day/m were distributed in the areas categorized as L or VL. The accuracy of GPM generated in the work looked acceptable, although the specific capacity data were not enough to verify GPM in the studied large watershed. To create GPMs for the determination of high-yield well locations, the resolution and reliability of thematic maps should be improved. Criterion values for groundwater potential should be established when machine learning or statistical models are used in the GPM evaluation process.

• Journal of Environmental Health Sciences
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• v.49 no.6
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• pp.334-343
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• 2023

Background: South Korea's Act on Registration and Evaluation, etc. of Chemicals (known as K-REACH) was established to protect public health and the environment from hazardous chemicals. 4,4'-Methylenedianiline (MDA), which is used as a major intermediate in industrial polymer production and as a vulcanizing agent in South Korea, is classified as a toxic substance under the K-REACH act. Although MDA poses potential ecological risks due to industrial emissions and hazards to aquatic ecosystems, no ecological risk assessment has been conducted. Objectives: The aim of this study is to assess the ecological risk of MDA by identifying the actual exposure status based on the K-REACH act. Methods: Various toxicity data were collected to establish predicted no effect concentrations (PNECs) for water, sediment, and soil. Using the SimpleBox Korea v2.0 model with domestic release statistical data and EU emission factors, predicted environmental concentrations (PECs) were derived for ten sites, each referring to an MDA-using company. Hazard quotient (HQ) was calculated by ratio of the PECs and PNECs to characterize the ecological risk posed by MDA. To validate the results of modeling-based assessment, concentration of MDA was measured using in-site freshwater samples (two to three samples per site). Results: PNECs for water, sediment, and soil were 0.000525 mg/L, 4.36 mg/kg dw, and 0.1 mg/kg dw, respectively. HQ for surface water and sediment at several company sites exceeded 1 due to modeling data showing markedly high PEC in each environmental compartment. However, in the results of validation using in-site surface water samples, MDA was not detected. Conclusions: Through an ecological risk assessment conducted in accordance with the K-REACH act, the risk level of MDA emitted into the environmental compartments in South Korea was found to be low.

• Korean Journal of Environmental Biology
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• v.41 no.3
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• pp.204-214
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• 2023

This study aimed to conduct a comprehensive assessment of the potential impact of deforestation and forest restoration on carbon storage in North Korea until 2050, employing rigorous analyses of trends of land use change in the past periods and projecting future land use change scenarios. We utilized the CA-Markov model, which can reflect spatial trends in land use changes, and verified the impact of forest restoration strategies on carbon storage by creating land use change scenarios (reforestation and non-reforestation). We employed two distinct periods of land use maps (2000 to 2010 and 2010 to 2020). To verify the overall terrestrial carbon storage in North Korea, our evaluation included estimations of carbon storage for various elements such as above-ground, below-ground, soil, and debris (including litters) for settlement, forest, cultivated, grass, and bare areas. Our results demonstrated that effective forest restoration strategies in North Korea have the potential to increase carbon storage by 4.4% by the year 2050, relative to the carbon storage observed in 2020. In contrast, if deforestation continues without forest restoration efforts, we predict a concerning decrease in carbon storage by 11.5% by the year 2050, compared to the levels in 2020. Our findings underscore the significance of prioritizing and continuing forest restoration efforts to effectively increase carbon storage in North Korea. Furthermore, the implications presented in this study are expected to be used in the formulation and implementation of long-term forest restoration strategies in North Korea, while fostering international cooperation towards this common environmental goal.

• Information Systems Review
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• v.24 no.1
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• pp.21-37
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• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• Pediatric Infection and Vaccine
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• v.31 no.1
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• pp.55-63
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• 2024

Purpose: A 2-dose varicella vaccination strategy has been introduced in many countries worldwide, aiming to increase vaccine effectiveness (VE) against varicella infection. In this network meta-analysis, we aimed to provide a comprehensive evaluation and an overall estimated effect of varicella vaccination strategies, via a Bayesian model. Methods: For each eligible study, we collected trial characteristics, such as: 1-dose vs. 2-dose, demographic characteristics, and outcomes of interest. For studies involving different doses, we aggregated the data for the same number of doses delivered into one arm. The preventive effect of 1-dose vs. 2-dose of varicella vaccine were evaluated in terms of the odds ratio (OR) and corresponding equal-tailed 95% confidence interval (95% CI). Results: A total of 903 studies were retrieved during our literature search, and 25 interventional or observational studies were selected for the Bayesian network meta-analysis. A total of 49,265 observed individuals were included in this network meta-analysis. Compared to the 0-dose control group, the OR of all varicella infections were 0.087 (95% CI, 0.046-0.164) and 0.310 (95% CI, 0.198-0.484) for 2-doses and one-dose, respectively, which corresponded to VE of 69.0% (95% CI, 51.6-81.2) and VE of 91.3% (95% CI, 83.6-95.4) for 1- and 2-doses, respectively. Conclusions: A 2-dose vaccine strategy was able to significantly reduce varicella burden. The effectiveness of 2-dose vaccination on reducing the risk of infection was demonstrated by sound statistical evidence, which highlights the public health need for a 2-dose vaccine recommendation.