• Journal of agricultural medicine and community health
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• v.28 no.2
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• pp.155-169
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• 2003

Objectives: The purpose of this study is to provide data for the improvement of hypertension management of community health practitioner posts through the study on hypertension management in community health practitioner posts. Methods: A questionnaire was mailed to 700 community health practitioners and 205 of them responded during the period from March 13, 2003 to May 13. The survey results were analyzed using SPSS program, version 11. Results: The results are as follows; 1. There are two major activities in a hypertension prevention project for community: health education and early detection. About 57% of community health care practitioners perform a health education for community people four times a year. The 64.5% of them used the materials for health education provided from a community health center and 22.1% of them performed a post-evaluation. The main method of early detection of hypertension was measurement of blood pressure of person to visit, which was 96.1%. Other methods included home visiting(89.3%), a referral from community hospitals and other resources(49.1%), health promotion events(39.5%), and a review of medical records(35.7%). 2. For the registration and management of patients with hypertension, about 36% of community health centers used a special form and more than 50% of them have registered patients who were managed by other health care institutions in the community. A computerized program was used for the management of patients with hypertension in 68.5% of them. More than 60% of them responded that it was used for report, treatment, and follow-up of patients with hypertension.

• Korean Journal of Clinical Laboratory Science
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• v.39 no.2
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• pp.113-121
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• 2007

Hearing loss is a common congenital disorder that is frequently associated with mutations in the Cx26 gene (GJB2). Recently, the mutation analysis of GJB2 has been used in a newborn screening test for the detection of hearing impairment. Population-based studies should be performed before the application of genetic testing for the identification of deaf newborns. In this study, 8 positions of GJB2 mutations-including 35delG, 167delT, 235delC, V27I, V37I, M34T, E114G, and I203T-were analyzed using PCR-direct sequencing in a total of 437 healthy Korean neonates. DNAs from dried blood spots were extracted using a commercial DNA extraction kit. The PCR-amplified products (783 bps) of the GJB2 gene were detected using 2% agarose gel electrophoresis and subjected to direct sequencing. The sequences were compared with those in the GenBank database by using the BLAST program. In this study, 5 GJB2 mutations -including V27I (79G>A), V37I (109G>A), E114G (341A>G), I203T (608T>C), and 235delC- were found. Of the 437 neonate samples, 301 subjects showed GJB2 mutations (68.9%, 301/437). The V27I mutation was found in 271 subjects and was the most frequent (62.0%, 271/437). The E114G, I203T and V37I mutations were shown in 146, 17 and 14 subjects, respectively. The 235delC mutation was found in 1 subject. The E114G mutation was frequently accompanied by the V27I mutation. V27I/E114G (97.2%, 143/147) was the most common double mutation and 3 subjects had the double mutation V27I/I203T. A triple mutation, V27I/E114G/I203T, was found in 1 subject. In conclusion, PCR-direct sequencing is a convenient tool for the rapid detection of GJB2 mutations and this data might provide information for the genetic counseling of the GJB2 gene.

• Tuberculosis and Respiratory Diseases
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• v.43 no.3
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• pp.388-402
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• 1996

Background : To compare the diagnostic accuracies of High-resolution CT(HRCI) and chest radiography in the diagnosis of diffuse infiltrative lung disease(DILD). Methods : This study included ninety-nine patients with a diagnosis of acute or chronic DILD, representing 20 different diseases. Twelve normal subjects were included as control. The disease state was confirmed either pathologically or clinically. Radiographs and CT scans were evaluated separately by three independent observers without knowledge of clinical and pathologic results. The observers listed three most likely diagnoses and recorded degree of confidence. Results : The sensitivity of HRCT in the detection of DILD was 98.9% compared to 97.9% of chest radiography. Overall, a correct first-choice diagnosis was made in 48% using chest radiographs and in 60% using HRCT images. The correct diagnosis was among the top-three choices in 64% when chest radiographs were used, and in 75% when HRCT images were reviewed. Overally a confident diagnosis was reached more often with HRCT(55%) than with chest radiography(26%). The correct first-choice diagnosis increased remarkably when the HRCT was used in usual interstitial pneumonia, miliary tuberculosis, diffuse panbronchiolitis and lymphangitic carcinomatosis. Conclusion : HRCT is confirmed to be superior to conventional radiography in the detection and accurate diagnosis of DILD. HRCT is especially valuable in the diagnosis of usual interstitial pneumonia, miliary tuberculosis, diffuse panbronchiolitis, and lymphangitic carcinomatosis.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Pediatric Infection and Vaccine
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• v.28 no.2
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• pp.92-100
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• 2021

Purpose: Rapid detection of etiologic organisms is crucial for initiating appropriate therapy in patients with central nervous system (CNS) infection. This study aimed to evaluate the diagnostic value of the BioFire^® Meningitis/Encephalitis (ME) panel in detecting etiologic organisms in cerebrospinal fluid (CSF) samples from febrile infants. Methods: CSF samples from infants aged <90 days who were evaluated for fever were collected between January 2016 and July 2019 at the Seoul National University Children's Hospital. We performed BioFire^® ME panel testing of CSF samples that had been used for CSF analysis and conventional tests (bacterial culture, Xpert^® enterovirus assay, and herpes simplex virus-1 and -2 polymerase chain reaction) and stored at -70℃ until further use. Results: In total, 72 (24 pathogen-identified and 48 pathogen-unidentified) CSF samples were included. Using BioFire^® ME panel testing, 41 (85.4%) of the 48 pathogen-unidentified CSF samples yielded negative results and 22 (91.7%) of the 24 pathogen-identified CSF samples yielded the same results (enterovirus in 19, Streptococcus agalactiae in 2, and Streptococcus pneumoniae in 1) as those obtained using the conventional tests, thereby resulting in an overall agreement of 87.5% (63/72). Six of the 7 pathogen-unidentified samples were positive for human parechovirus (HPeV) via BioFire^® ME panel testing. Conclusions: Compared with the currently available etiologic tests for CNS infection, BioFire^® ME panel testing demonstrated a high agreement score for pathogen-identified samples and enabled HPeV detection in young infants. The clinical utility and cost-effectiveness of BioFire^® ME panel testing in children must be evaluated for its wider application.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.497-510
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• 2022

Agricultural reservoirs are essential structures for water supplies during dry period in the Korean peninsula, where water resources are temporally unequally distributed. For efficient water management, systematic and effective monitoring of medium-small reservoirs is required. Synthetic Aperture Radar (SAR) provides a way for continuous monitoring of those, with its capability of all-weather observation. This study aims to evaluate the applicability of SAR in monitoring medium-small reservoirs using Sentinel-1 (10 m resolution) and Capella X-SAR (1 m resolution), at Chari (CR), Galjeon (GJ), Dwitgol (DG) reservoirs located in Ulsan, Korea. Water detected results applying Z fuzzy function-based threshold (Z-thresh) and Chan-vese (CV), an object detection-based segmentation algorithm, are quantitatively evaluated using UAV-detected water boundary (UWB). Accuracy metrics from Z-thresh were 0.87, 0.89, 0.77 (at CR, GJ, DG, respectively) using Sentinel-1 and 0.78, 0.72, 0.81 using Capella, and improvements were observed when CV was applied (Sentinel-1: 0.94, 0.89, 0.84, Capella: 0.92, 0.89, 0.93). Boundaries of the waterbody detected from Capella agreed relatively well with UWB; however, false- and un-detections occurred from speckle noises, due to its high resolution. When masked with optical sensor-based supplementary images, improvements up to 13% were observed. More effective water resource management is expected to be possible with continuous monitoring of available water quantity, when more accurate and precise SAR-based water detection technique is developed.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.2
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• pp.51-58
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• 2019

Purpose In in-vitro laboratories of nuclear medicine department, when the reagent lot or reagent lot changes Comparability test or parallel test is performed to determine whether the results between lots are reliable. The most commonly used standard domestic laboratories is to obtain %difference from the difference in results between two lots of reagents, and then many laboratories are set the standard to less than 20% at low concentrations and less than 10% at medium and high concentrations. If the range is deviated from the standard, the test is considered failed and it is repeated until the result falls within the standard range. In this study, several tests are selected that are performed in nuclear medicine in-vitro laboratories to analyze parallel test results and to establish criteria for customized percent difference for each test. Materials and Methods From January to November 2018, the result of parallel test for reagent lot change is analyzed for 7 items including thyroid-stimulating hormone (TSH), free thyroxine (FT4), carcinoembryonic antigen (CEA), CA-125, prostate-specific antigen (PSA), HBs-Ab and Insulin. The RIA-MAT 280 system which adopted the principle of IRMA is used for TSH, FT4, CEA, CA-125 and PSA. TECAN automated dispensing equipment and GAMMA-10 is used to measure insulin test. For the test of HBs-Ab, HAMILTON automated dispensing equipment and Cobra Gamma ray measuring instrument are used. Separate reagent, customized calibrator and quality control materials are used in this experiment. Results 1. TSH [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(low concentration) [14.8 / 4.4 / 3.7 / 0.0 ] C-2(middle concentration) [10.1 / 4.2 / 3.7 / 0.0] 2. FT4 [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(low concentration) [10.0 / 4.2 / 3.9 / 0.0] C-2(high concentration) [9.6 / 3.3 / 3.1 / 0.0 ] 3. CA-125 [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(middle concentration) [9.6 / 4.3 / 4.3 / 0.3] C-2(high concentration) [6.5 / 3.5 / 4.3 / 0.4] 4. CEA [%diffrence Max / Mean / median] (P-value by t-test > 0.05) C-1(low concentration) [9.8 / 4.2 / 3.0 / 0.0] C-2(middle concentration) [8.7 / 3.7 / 2.3 / 0.3] 5. PSA [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(low concentration) [15.4 / 7.6 / 8.2 / 0.0] C-2(middle concentration) [8.8 / 4.5 / 4.8 / 0.9] 6. HBs-Ab [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(middle concentration) [9.6 / 3.7 / 2.7 / 0.2] C-2(high concentration) [8.9 / 4.1 / 3.6 / 0.3] 7. Insulin [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(middle concentration) [8.7 / 3.1 / 2.4 / 0.9] C-2(high concentration) [8.3 / 3.2 / 1.5 / 0.1] In some low concentration measurements, the percent difference is found above 10 to nearly 15 percent in result of target value calculated at a lower concentration. In addition, when the value is measured after Standard level 6, which is the highest value of reagents in the dispensing sequence, the result would have been affected by a hook effect. Overall, there was no significant difference in lot change of quality control material (p-value>0.05). Conclusion Variations between reagent lots are not large in immunoradiometric assays. It is likely that this is due to the selection of items that have relatively high detection rate in the immunoradiometric method and several remeasurements. In most test results, the difference was less than 10 percent, which was within the standard range. TSH control level 1 and PSA control level 1, which have low concentration target value, exceeded 10 percent more than twice, but it did not result in a value that was near 20 percent. As a result, it is required to perform a longer period of observation for more homogenized average results and to obtain laboratory-specific acceptance criteria for each item. Also, it is advised to study observations considering various variables.

• Journal of Microbiology and Biotechnology
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• v.17 no.12
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• pp.1955-1964
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• 2007

A recent report showed that analysis of CD154 expression in the presence of the secretion inhibitor Brefeldin A (Bref A) could be used to assess the entire repertoire of antigen-specific $CD4^+\;T$ helper cells. However, the capacity of intracellular CD154 expression to identify antigen-specific $CD8^+\;T$ cells has yet to be investigated. In this study, we compared the ability of intracellular CD154 expression to assess antigen-specific $CD8^+\;T$ cells with that of accepted standard assays, namely intracellular cytokine IFN-${\gamma}$ staining (ICS) and MHC class I tetramer staining. The detection of intracellular CD154 molecules in the presence of Bref A reflected the kinetic trend of antigen-specific $CD8^+\;T$ cell number, but unfortunately showed less sensitivity than ICS and tetramer staining. However, ICS levels peaked and saturated 8 h after antigenic stimulation in the presence of Bref A and then declined, whereas intracellular CD154 expression peaked by 8 h and maintained the saturated level up to 24 h post-stimulation. Moreover, intracellular CD154 expression in antigen-specific $CD8^+\;T$ cells developed in the absence of $CD4^+\;T$ cells changed little, whereas the number of IFN-${\gamma}$-producing $CD8^+\;T$ cells decreased abruptly. These results suggest that intracellular CD154 could aid the assessment of antigen-specific $CD8^+\;T$ cells, but does not have as much ability to identify heterogeneous $CD4^+\;T$ helper cells. Therefore, the combined analytical techniques of ICS and tetramer staining together with intracellular CD154 assays may be able to provide useful information on the accurate phenotype and functionality of antigen-specific $CD8^+\;T$ cells.