• Imaging Science in Dentistry
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• v.53 no.3
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• pp.229-238
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• 2023

Purpose: Ectopic eruption can be defined as the emergence of a tooth in an abnormal location, where the tooth does not follow its typical eruption pathway. While ectopic eruption within the dentate region is well-documented in the literature, ectopic eruption in non-dentate regions is relatively rare. This study aimed to report 6 cases of ectopic teeth and present a systematic review of the English-language literature on ectopic teeth, emphasizing demographic characteristics, radiographic features, potential complications, and treatment options. Materials and Methods: A literature search was conducted using the PubMed, Medline, Web of Science, and Cochrane databases. The demographic data and radiographic findings of patients presenting with ectopic teeth were recorded. Results: The literature review yielded 61 cases of ectopic teeth, with patients ranging in age from 3 to 74 years. The findings from these previously reported cases demonstrated that the most common location for ectopic teeth was the maxillary sinus, which is consistent with this case series. The Pearson chi-square test was performed to evaluate the correlation between age and location of ectopic teeth, and the results were found to be statistically significant (P<0.05). However, no statistically significant relationship was observed between sex and the location of ectopic teeth. Conclusion: The distinct features of these cases warrant reporting. This study presents the first case of supernumerary teeth in the condyle without any associated pathosis. Another notable characteristic is the pre-eruptive resorption of 2 inverted supernumerary teeth ectopically located in the palate, which predisposes to sinus opacification.

• Genomics & Informatics
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• v.21 no.1
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• pp.3.1-3.10
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• 2023

Characterization as well as prediction of the secondary and tertiary structure of hypothetical proteins from their amino acid sequences uploaded in databases by in silico approach are the critical issues in computational biology. Severe acute respiratory syndrome-associated coronavirus (SARS-CoV), which is responsible for pneumonia alike diseases, possesses a wide range of proteins of which many are still uncharacterized. The current study was conducted to reveal the physicochemical characteristics and structures of an uncharacterized protein Q6S8D9_SARS of SARS-CoV. Following the common flowchart of characterizing a hypothetical protein, several sophisticated computerized tools e.g., ExPASy Protparam, CD Search, SOPMA, PSIPRED, HHpred, etc. were employed to discover the functions and structures of Q6S8D9_SARS. After delineating the secondary and tertiary structures of the protein, some quality evaluating tools e.g., PROCHECK, ProSA-web etc. were performed to assess the structures and later the active site was identified also by CASTp v.3.0. The protein contains more negatively charged residues than positively charged residues and a high aliphatic index value which make the protein more stable. The 2D and 3D structures modeled by several bioinformatics tools ensured that the proteins had domain in it which indicated it was functional protein having the ability to trouble host antiviral inflammatory cytokine and interferon production pathways. Moreover, active site was found in the protein where ligand could bind. The study was aimed to unveil the features and structures of an uncharacterized protein of SARS-CoV which can be a therapeutic target for development of vaccines against the virus. Further research are needed to accomplish the task.

• Journal of Korean Academy of Nursing
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• v.54 no.2
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• pp.119-138
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• 2024

Purpose: This study aimed to identify research trends related to emotional leadership among nurse managers by conducting a systematic literature review and meta-analysis. This study sought to derive insights that could contribute to improving emotional leadership in nursing practice. Methods: A systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and Meta-Analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. Databases including PubMed, Cumulative Index to Nursing and Allied Health Literature, Scopus, Web of Science, Research Information Sharing Service, Koreanstudies Information Service System, Korean Medical Database, KoreaMed, ScienceON, and DBpia were searched to obtain papers published in English and Korean. Literature searches and screenings were conducted for the period December 1, 2023 to December 17, 2023. The effect size correlation (ESr) was calculated for each variable and the meta-analysis was performed using the statistical software SPSS 29.0, R 4.3.1. Results: Twenty-five (four personal, six job, and fifteen organizational) relevant variables were identified through the systematic review. The results of the meta-analysis showed that the total overall effect size was ESr = .33. Job satisfaction (ESr = .40) and leader-member exchange (ESr = .75) had the largest effect size among the job and organizational-related factors. Conclusion: Emotional leadership helps promote positive changes within organizations, improves organizational effectiveness, and increases member engagement and satisfaction. Therefore, it is considered an important strategic factor in improving organizational performance.

• Dementia and Neurocognitive Disorders
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• v.23 no.3
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• pp.127-135
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• 2024

Background and Purpose: To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Methods: Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. Results: The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. Conclusions: The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.

• Ocean and Polar Research
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• v.26 no.2
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• pp.287-298
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• 2004

As a part of Korea Deep Ocean Study program, we investigated the distribution of planktonic protists in the upper 200 m of the northeast Pacific from $5^{\circ}N$ to $17^{\circ}N$, along $131^{\circ}30'W$. Area of divergence was formed at $9^{\circ}N$ which is boundaries of the north equatorial counter current (NECC) and the north equatorial current (NEC) during this cruise. Chlorophyll-a concentration was higher in NECC than in NEC area. Pico chl-a(<$2\;{\mu}m$) to total chl-a accounted for average 89% in the study area. The contribution of pico chl-a to total chl-a was relatively high in NEC area than in NECC area. Biomass of planktonic protists, ranging from 635.3 to $1077.3\;mgC\;m^{-2}$(average $810\;mgC\;m^{-2}$), was most enhanced in NECC area and showed distinct latitudinal variation. Biomass of HNF ranged from 88.7 to $208.3\;mgC\;m^{-2}$ and comprised 15% of planktonic protists. Biomass of ciliates ranged from 123.6 to $393.0\;mgC\;m^{-2}$ and comprised 25% of planktonic protists. Biomass of HDF ranged from 407.2 to $607.8\;mgC\;m^{-2}$ and comprised 60% of planktonic protists. HDF was the most dominant component in both NECC and NEC areas. Nano-protist biomass accounted for more than 50% of total protists in the both areas. The contribution of nanoprotist to total protists biomass was relatively higher in NEC area than in NECC. The biomass of planktonic protists was significantly correlated with phytoplankton biomass in this study area. The size structure of phytoplankton biomass coincided with that of planktonic protists. This suggested that the structure of the planktonic protists community and the microbial food web were dependent on the size structure of the phytoplankton biomass. However, biomass and size structure of planktonic protist communities might be significantly influenced by physical characteristics of the water column and food concentration in this study area.

• Journal of Korean society of Dental Hygiene
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• v.17 no.3
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• pp.465-478
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• 2017

Objectives: The purpose of this study was to investigate the relationship between the level of subjective stress recognition and oral symptom experiences including toothache, gum diseases, and oral soft tissue diseases in the Korean adolescents. Methods: The subjects were 68,043 adolescents recruited using a web-based survey, National Korean Youth Risk Behavior in 2015 by the Korean Center for Disease Control. For statistical analysis, SPSS 21.0 for Windows was used. Descriptive analysis and a Chi-square test were conducted to determine the factors associated with general characteristics, health behaviors, oral health behaviors, and level of subjective stress recognition. Finally, to investigate the relationship between the level of subjective stress recognition and oral symptom experiences, logistic regression analysis was performed. Results: Toothache related subjective stress recognition level was significantly higher in the moderate group with the score of 1.59 (95% CI; 1.49-1.68), and was greater in the high group with 2.38 (95% CI; 2.24-2.53) compared to the low group. Gum disease related subjective stress recognition level was significantly higher in the moderate group with 1.41 (95% CI; 1.32-1.51), and was greater in the high group with 1.99 (95% CI; 1.86-2.13). Oral soft tissue disease related subjective stress recognition level was significantly higher in the moderate group with 1.59 (95% CI; 1.45-1.74), and was greater in the high group with 2.55 (95% CI; 2.33-2.79). Bad breath related subjective stress recognition level was significantly higher in the moderate group with 1.48 (95% CI; 1.39-1.57), and was greater in the high group with 2.10 (95% CI; 1.97-2.25). Conclusions: Higher subjective stress recognition level was found to affect the oral symptoms experienced. Therefore, the stress management plan should be prepared through the cause identification of the main stress in the adolescents. Practical and systematic education is needed for oral health management in the schools.

• Journal of Nutrition and Health
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• v.56 no.4
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• pp.391-403
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• 2023

Purpose: This study evaluated the validity of a physical activity classification table (PACT) based on total energy expenditure (TEE) and physical activity level (PAL) measured using the doubly labeled water (DLW) method in Korean adults and the elderly. Methods: A total of 141 (male 70, female 71) adults and elderly were included. The reference standards TEE_DLW, PAL_DLW were measured over a 14-day period using DLW. A 24-hour physical activity diary was kept for three days (two days during the week and one day on the weekend). PAL_PACT was calculated by classifying the activity type and intensity using the PACT. PAL_PACT was multiplied by resting energy expenditure measured by indirect calorimetry to estimate TEE_PACT. Results: The mean age of the study participants was 50.5 ± 18.8 years, and the mean body mass index was 23.4 ± 3.3 kg/m². A comparison of TEE_DLW and TEE_PACT by sex and age showed no significant differences. The bias, the difference between TEE_DLW and TEE_PACT, was male 17.3 kcal/day and female -4.5 kcal/day. The percentage of accurate predictions (values within ± 10% of the TEE_DLW) of TEE_PACT was 58.6% in males and 54.9% in females, with the highest prediction values in the age group 40-64 years (70.9%) in males and over 65 years (73.9%) in females. The spearman correlation coefficient (r) between TEE_PACT and TEE_DLW was 0.769, indicating a significant positive correlation (p < 0.001). Conclusion: In this study, the use of a new PACT for calculating TEE and PAL was evaluated as valid. A web version of the software program and a smartphone application need to be developed using PACT to make it easier to apply for research purposes.

• Korean Journal of Radiology
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• v.25 no.5
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• pp.414-425
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• 2024

Objective: This study aims to explore the opinions on the insurance coverage of artificial intelligence (AI), as categorized based on the distinct value elements offered by AI, with a specific focus on patient-centered outcomes (PCOs). PCOs are distinguished from traditional clinical outcomes and focus on patient-reported experiences and values such as quality of life, functionality, well-being, physical or emotional status, and convenience. Materials and Methods: We classified the value elements provided by AI into four dimensions: clinical outcomes, economic aspects, organizational aspects, and non-clinical PCOs. The survey comprised three sections: 1) experiences with PCOs in evaluating AI, 2) opinions on the coverage of AI by the National Health Insurance of the Republic of Korea when AI demonstrated benefits across the four value elements, and 3) respondent characteristics. The opinions regarding AI insurance coverage were assessed dichotomously and semi-quantitatively: non-approval (0) vs. approval (on a 1-10 weight scale, with 10 indicating the strongest approval). The survey was conducted from July 4 to 26, 2023, using a web-based method. Responses to PCOs and other value elements were compared. Results: Among 200 respondents, 44 (22%) were patients/patient representatives, 64 (32%) were industry/developers, 60 (30%) were medical practitioners/doctors, and 32 (16%) were government health personnel. The level of experience with PCOs regarding AI was low, with only 7% (14/200) having direct experience and 10% (20/200) having any experience (either direct or indirect). The approval rate for insurance coverage for PCOs was 74% (148/200), significantly lower than the corresponding rates for other value elements (82.5%-93.5%; P ≤ 0.034). The approval strength was significantly lower for PCOs, with a mean weight ± standard deviation of 5.1 ± 3.5, compared to other value elements (P ≤ 0.036). Conclusion: There is currently limited demand for insurance coverage for AI that demonstrates benefits in terms of non-clinical PCOs.

• Journal of the Korean Society of Food Science and Nutrition
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• v.33 no.1
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• pp.114-122
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• 2004

The purpose of this study was to develop a web-based internet program for nutritional counseling and diet management of patient with diabetes mellitus. The program consisted of four parts according to their functions and contents. The first part explained the metabolism of glucose and mechanism of insulin and insulin receptor expressed by flash 6.0, and defined the diabetes mellitus. The second part is to assess the general health status such as body weight, obesity index, basal metabolic rate and total energy requirement by the input of age, sex, height, weight and degree of activity. This part also provides tlne patient with menu lists and one day menu suitable to his weight and activity, and offers the information for food selection, snacks, convenience foods, dine-out, behavioral modification, cooking methods, food exchange lists, dietary education using buffet, and information on energy and nutrients of foods and drinks, and top 20 foods classified by nutrients. The third part is designed to investigate dietary history of patient, that is, to find out his inappropriate dietary habit and give him some suggestions for appropriate dietary behavior. This part also offers on-line counseling, follow-up management and frequently asked questions. The fourth part is evaluating their energy and nutrients intake by comparing with recommended dietary allowance for Koreans or standardized data for patient with diabetes mellitus. In this part, it is also analyzing energy and nutrients of food consumed by food group and meals, and evaluating the status of nutrient intake. These results are finally displayed as tabular forms and graphical forms on the computer screen. Therefore it is expected that the web-based internet program developed in this study will play a role in their health promotion as widely using by diabetic patients.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.