• 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.

• 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.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1080-1099
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• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.2
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• pp.155-159
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• 2022

Smart livestock, which combines information and communication technology (ICT) with livestock, can be said to be an effective solution to existing livestock problems such as productivity improvement, odors, and diseases. So far, it has hardly been universalized; thus, it is necessary to develop automation devices to reduce labor by localizing automation devices to expand the distribution of ICT technology to farms, and to advance precise specifications and health management technology using biometric information. Weighing scales currently being used in livestock farms are to prevent the spread of diseases by diagnosis and preparation for AI and other diseases in advance, using information on the growing weight of duck breeding. However, accurate values cannot be obtained due to poor breeding conditions. In this paper, we developed a separate data transmission system kit for the weighing scale and placed the sensor on top of the weighing scale so that the sensor wire is not affected by pollutants or ducks on the floor. A display function was provided, and a method of receiving and analyzing the serial port data of the weighing device, and then transmitting them to the data collection server was implemented.

• Electronics and Telecommunications Trends
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• v.39 no.4
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• pp.54-62
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• 2024

The emergence and resurgence of novel respiratory infectious diseases since the turn of the millennium, including SARS, H1N1 flu, MERS, and COVID-19, have posed a significant global health threat. Efforts to combat these threats have involved various approaches, however, continued research and development are crucial to prepare for the possibility of emerging viruses and viral variants. Direct detection methods for viral pathogens include molecular diagnostic techniques and immunodiagnostic methods, while indirect diagnostic methods involve detecting changes in the condition of infected patients through imaging diagnostics, gas analysis, and biosignal measurement. Molecular diagnostic techniques, utilizing advanced technologies such as gene editing, are being developed to enable faster detection than traditional PCR methods, and research is underway to improve the efficiency of diagnostic devices. Diagnostic technologies for infectious diseases continue to evolve, and several key trends are expected to emerge in the future. Automation will facilitate widespread adoption of rapid and accurate diagnostics, portable diagnostic devices will enable immediate on-site diagnosis by healthcare professionals, and advancements in AI-based deep learning diagnostic models will enhance diagnostic accuracy.

• Journal of Microbiology
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• v.44 no.1
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• pp.42-49
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• 2006

In this study, we describe our newly-developed sensitive two-stage PCR procedure for the detection of 13 common mycoplasmal contaminants (M. arthritidis, M. bovis, M. fermentans, M. genitalium, M. hominis, M. hyorhinis, M. neurolyticum, M. orale, M. pirum, M. pneumoniae, M. pulmonis, M. salivarium, U. urealyticum). For primary amplification, the DNA regions encompassing the 16S and 23S rRNA genes of 13 species were targeted using general mycoplasma primers. The primary PCR products were then subjected to secondary nested PCR, using two different primer pair sets, designed via the multiple alignment of nucleotide sequences obtained from the 13 mycoplasmal species. The nested PCR, which generated DNA fragments of 165-353 bp, was found to be able to detect 1-2 copies of the target DNA, and evidenced no cross-reactivity with the generated DNA of related microorganisms or of human cell lines, thereby confirming the sensitivity and specificity of the primers used. The identification of contaminated species was' achieved via the performance of restriction fragment length polymorphism (RFLP) coupled with Sau3AI digestion. The results obtained in this study furnish evidence suggesting that the employed assay system constitutes an effective tool for the disagnosis of mycoplasmal contamination in cell culture systems.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1197-1201
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• 2012

Objective: To study the relationship between clinical pathologic characteristics, treatment modalities and prognostic factors in HER-2 (Human Epidermal growth factor Receptor-2) overexpressed breast carcinoma. Materials and Methods: Major clinico-pathological factors including therapeutic modalities and survival status of 371 breast cancer patients with HER2 over-expression, teated at Yantai Yuhuangding Hospital from March of 2002 to December of 2010 were retrospectively studied, with special attention focused on survival-related factors. Results: The median age of the total 371 patients in this study was 48 years at time of diagnosis, among which, the leading pathological type was infiltrating ductal carcinoma (92.5%); 62.8% presented with a primary tomor larger than 2 cm in diameter at diagnosis, 51.0% had axillary lymph node (ALN) metastases; ER (Estrogen receptor)/PR (Progesterone receptor) double negative occured in 52.8% of cases, and PCNA (proliferation cell nuclear antigen) (+++) was found in 55.1%. HER-2 overexpressed patients were usually in advanced stage when the diagnosis was made (72.8% at stages IIA~IIIC). The prognosis and survival were assessed in 259 patients with complete follow-up data. 5-year DFS (disease-free survival) and OS (overall survival) rate was 68.0% and 78.0% respectively. Univariate analysis revealed that age, tumor size, ALN metastases, LVSI (lymph-vascular space involvement), PCNA status, hormonal therapy, chemotherapy cycles, and HER-2 overexpression, correlated closely with the prognosis. ALN metastases, LVSI, PCNA status and chemotherapy cycles were independent predictors of survival. Conclusions: HER-2 overexpressed breast cancer has special clinical and pathological characteristics, with advanced clinical stages and high rate of ER/PR double negative. Lymph node metastases, LVSI, PCNA and chemotherapy cycles are independent predictors of prognosis.

• 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