• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2209-2213
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• 2015

DNA methylation of tumor suppressor gene promoters is the most frequent phenomenon leading to inactivation of function, consequently driving malignant cell transformation. Cyclin D2 is implicated in tumor suppression. In our study, we carried out the MSP assay to evaluation the methylation status at CpG islands in the cyclin D2 promoter in breast cancer cases from the Vietnamese population. The results showed that the frequency of methylation reached 62.1% (59 of 95 breast cancer tumors), but was low in non-cancer specimens at 10% (2 of 20 non-cancer specimens). Additionally, with an RR (relative risk) and OR (odd ratios) of 6.21 and 14.8, DNA hypermethylation of cyclin D2 increased the possibility of malignant transformation. Our results confirmed the cyclin D2 hypermethylation could be used as the potential biomarker which could be applied in prognosis and early diagnosis of Vietnamese breast cancer patients.

• Clinical and Experimental Pediatrics
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• v.46 no.4
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• pp.389-392
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• 2003

Netherton's syndrome is an unusual disorder which consists of triad of ichtyosiform dermatosis, multiple defects of hair shaft and an atopic diathesis. The finding of bamboo hair is pathognomic in Netherton's syndrome and the ichthyosiform dermatosis may consist of either ichtyosis linearis circumflexa or congenital ichthyosiform erythroderma. Often, variability in the clinical features leads to a delay in diagnosis in many cases. We report a case of Netherton's syndrome diagnosed in the neonatal period. The patient presented with severe ichthyosis and confirmed microscopically distinctive bamboo hair.

• Journal of Korean Academy of Nursing Administration
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• v.3 no.1
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• pp.107-118
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• 1997

This study was designed to obtain basic data for development of evaluation tool which would be needed to measure the outcome of general quality nursing care of individual patient. The purpose of this study was to analyze and classify the outcome indicators of quality nursing care. The 29 articles of quality nursing care and outcome measures were selected coveniently, and analyzed to classify the outcome indicators of quality nursing care using open coding method. The results of this study were as follows: 1. Quality nursing care was defined as level of excellence of nursing care to achieve good patient outcome. 2. The 6 domains of which were health status, satisfaction, self care, patient progress and prognosis, and compliance were identified in outcome indicators of quality nursing care 3. Seven indicators of health status domain which were perceived health status, quality of life, well-being, daily activities, physical-physiological status, psychoemotional status, and social role functioning were identified. 4. Two indicators of satifaction domain which were patient satisfaction and family satisfaction were identified. 5. Three indicators of self care domain which were skill, knowledge, and home management were identified. 6. Seven indicators of patient progress and prognosis domain which were change of clinical status, resolution of nursing diagnosis and problem, days of stay, dicahrge state, recovery state, survival were identified. 7. compliance with therapeutic direction compliance was identified as an indicator of compliance domain. 8. It was sugested that studies for development of evaluation tools for outcomes of quality nursing the results of this study could be executed

• Structural Engineering and Mechanics
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• v.31 no.3
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• pp.333-347
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• 2009

Due to structural complicacy, structural health monitoring for civil engineering needs more accurate and effectual methods of damage identification. This study aims to import multi-source information fusion (MSIF) into structural damage diagnosis to improve the validity of damage detection. Firstly, the essential theory and applied mathematic methods of MSIF are introduced. And then, the structural damage identification method based on multi-mode information fusion is put forward. Later, on the basis of a numerical simulation of a concrete continuous box beam bridge, it is obviously indicated that the improved modal strain energy method based on multi-mode information fusion has nicer sensitivity to structural initial damage and favorable robusticity to noise. Compared with the classical modal strain energy method, this damage identification method needs much less modal information to detect structural initial damage. When the noise intensity is less than or equal to 10%, this method can identify structural initial damage well and truly. In a word, this structural damage identification method based on multi-mode information fusion has better effects of structural damage identification and good practicability to actual structures.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3997-4000
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• 2012

MicroRNAs (miRNAs) play roles in the clinic, both as diagnostic and therapeutic tools. The identification of relevant microRNAs is critically required for ovarian cancer because of the prevalence of late diagnosis and poor treatment options currently. To identify miRNAs involved in the development or progression of ovarian cancer, we analyzed gene expression profiles downloaded from Gene Expression Omnibus. Comparison of expression patterns between carcinomas and the corresponding normal ovarian tissues enabled us to identify 508 genes that were commonly up-regulated and 1331 genes that were down-regulated in the cancer specimens. Function annotation of these genes showed that most of the up-regulated genes were related to cell cycling, and most of the down-regulated genes were associated with the immune response. When these differentially expressed genes were mapped to MiRTarBase, we obtained a total of 18 key miRNAs which may play important regulatory roles in ovarian cancer. Investigation of these genes and microRNAs should help to disclose the molecular mechanisms of ovarian carcinogenesis and facilitate development of new approaches to therapeutic intervention.

• Advances in pediatric surgery
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• v.6 no.2
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• pp.124-127
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• 2000

Femoral hernia is very rare in children and is easily misdiagnosed. During a period of three years, three children with femoral hernia were treated by one pediatric surgeon at Severance Hospital. Only one case was diagnosed correctly before surgery, and the others were thought to be either an indirect inguinal hernia or groin mass. Curative hernioplasty (McVay hernioplasty) could be done in only one case at the time of first operation. Diagnosis of femoral hernia in children is a challenge because of rarity and similarity of clinical presentation to indirect inguinal hernia. Co-incidental findings of indirect inguinal hernia sac or patent processus vaginalis during surgery can perpetuate the misdiagnosis. In case of absence of expected indirect inguinal hernia or apparent recurrence of indirect inguinal hernia, one should consider the possibility of femoral hernia.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.1
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• pp.11-19
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• 2019

Recently, with the development of the fourth industrial revolution that has been achieved through the fusion of information and communication technology (ICT), the technologies of AI, IOT, BIG-DATA, it is increasing utilization rate by industry and research and development of application technologies are being actively carried out. Especially, in the case of unmanned aerial vehicles, the construction market is expected to be one of the most commercialized areas in the world for the next decade. However, research on utilization of unmanned aerial vehicles in the construction field in Korea is insufficient. In this study, We have developed a quantitative building inspection method using the unmanned aerial vehicle and presented the protocol for it. The proposed protocol was verified by applying it to existing old buildings, and defect information could be quantified by calculating length, width, and area for each defect. Through this technical research, the final goal is to contribute to the development of safety diagnosis technology using unmanned aerial vehicle and risk assessment technology of buildings in case of disaster such as earthquake.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1486-1495
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• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.

• Journal of Platform Technology
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• v.10 no.1
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• pp.20-28
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• 2022

Artificial intelligence-based health data verification has become an essential element not only to help clinical research, but also to develop new treatments. Since the US Food and Drug Administration (FDA) approved the marketing of medical devices that detect mild abnormal diabetic retinopathy in adult diabetic patients using artificial intelligence in the field of medical diagnosis, tests using artificial intelligence have been increasing. In this study, an artificial intelligence model based on image classification was created using a Teachable Machine supported by Google, and a predictive model was completed through learning. This not only facilitates the early detection of cataracts among eye diseases occurring among patients with chronic diseases, but also serves as basic research for developing a digital personal health healthcare app for eye disease prevention as a healthcare program for eye health.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.245-251
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• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.