• Journal of Korean Academy of Nursing
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• v.53 no.4
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• pp.468-479
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• 2023

Purpose: This study aimed to evaluate the use of the respiratory rate oxygenation (ROX) index, ROX-heart rate (ROX-HR) index, and saturation of percutaneous oxygen/fraction of inspired oxygen ratio (SF ratio) to predict weaning from high-flow nasal cannula (HFNC) in patients with respiratory distress in a pediatric intensive care unit. Methods: A total of 107 children admitted to the pediatric intensive care unit were enrolled in the study between January 1, 2017, and December 31, 2021. Data on clinical and personal information, ROX index, ROX-HR index, and SF ratio were collected from nursing records. The data were analyzed using an independent t-test, χ² test, Mann-Whitney U test, and area under the curve (AUC). Results: Seventy-five (70.1%) patients were successfully weaned from HFNC, while 32 (29.9%) failed. Considering specificity and sensitivity, the optimal cut off points for predicting treatment success and failure of HFNC oxygen therapy were 6.88 and 10.16 (ROX index), 5.23 and 8.61 (ROX-HR index), and 198.75 and 353.15 (SF ratio), respectively. The measurement of time showed that the most significant AUC was 1 hour before HFNC interruption. Conclusion: The ROX index, ROX-HR index, and SF ratio appear to be promising tools for the early prediction of treatment success or failure in patients initiated on HFNC for acute hypoxemic respiratory failure. Nurses caring for critically ill pediatric patients should closely observe and periodically check their breathing patterns. It is important to continuously monitor three indexes to ensure that ventilation assistance therapy is started at the right time.

• The Korean Journal of Pain
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• v.36 no.1
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• pp.113-127
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• 2023

Background: Resting-state functional connectivity (rs-FC) may aid in understanding the link between painmodulating brain regions and the descending pain modulatory system (DPMS) in fibromyalgia (FM). This study investigated whether the differences in rs-FC of the primary somatosensory cortex in responders and non-responders to the conditioned pain modulation test (CPM-test) are related to pain, sleep quality, central sensitization, and the impact of FM on quality of life. Methods: This cross-sectional study included 33 females with FM. rs-FC was assessed by functional magnetic resonance imaging. Change in the numerical pain scale during the CPM-test assessed the DPMS function. Subjects were classified either as non-responders (i.e., DPMS dysfunction, n = 13) or responders (n = 20) to CPM-test. A generalized linear model (GLM) and a receiver operating characteristic (ROC) curve analysis were performed to check the accuracy of the rs-FC to differentiate each group. Results: Non-responders showed a decreased rs-FC between the left somatosensory cortex (S1) and the periaqueductal gray (PAG) (P < 0.001). The GLM analysis revealed that the S1-PAG rs-FC in the left-brain hemisphere was positively correlated with a central sensitization symptom and negatively correlated with sleep quality and pain scores. ROC curve analysis showed that left S1-PAG rs-FC offers a sensitivity and specificity of 85% or higher (area under the curve, 0.78, 95% confidence interval, 0.63-0.94) to discriminate who does/does not respond to the CPM-test. Conclusions: These results support using the rs-FC patterns in the left S1-PAG as a marker for predicting CPM-test response, which may aid in treatment individualization in FM patients.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1057-1065
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• 2023

Non-invasive liver fibrosis diagnosis is crucial for patients with chronic liver diseases. Many patients cannot undergo liver tissue biopsy, so predicting the degree of liver fibrosis early through meaningful methods can reduce complications related to chronic liver diseases, such as liver cell carcinoma and cirrhosis. This study compared and analyzed the quantitative measurement of liver fibrosis using shear wave elastography in conjunction with liver ultrasound findings and their associations with serum biomarkers (p<0.05). The results showed that the shear wave elastography measurement in the normal group was 4.55 ± 0.69 kPa, while the abnormal contrast group with echogenic patterns had a measurement of 8.27 ± 1.83 kPa. The hepatitis B carrier group exhibited higher shear wave elastography measurements, and among serum biomarkers, AST, ALT, GGT, and PT showed statistically significant positive correlations with fibrosis severity according to SWE categories (p<0.05), while ALP and TB did not demonstrate statistically significant differences (p=0.163, p=0.567). Conversely, Albumin and PLT showed significant negative correlations (p<0.05). Clinically, utilizing shear wave elastography measurements through liver ultrasound in the tracking and repeat testing of liver fibrosis in chronic hepatitis B patients without cirrhosis can assist in achieving more objective diagnoses among healthcare providers.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.167-172
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• 2024

In major wars of the 21st century, drones are expanding beyond surveillance and reconnaissance to include land and air as well as sea and underwater for purposes such as precision strikes, suicide attacks, and cognitive warfare. These drones will perform multi-domain operations, and to this end, they will continue to develop by improving the level of autonomy and strengthening scalability based on the High-Low Mix concept. Recently, drones have been used as a major means in major wars around the world, and there seems to be a good chance that they will evolve into game changers in the future. North Korea has also been making significant efforts to operate reconnaissance and attack drones for a long time. North Korea has recently continued to engage in provocations using drones, and its capabilities are gradually becoming more sophisticated. In addition, with the recent emergence of new strategic Drones, wartime and peacetime threats such as North Korea's use of these to secure surveillance, reconnaissance and early warning capabilities against South Korea and new types of provocations are expected to be strengthened. Through this study, we hope to provide implications by analyzing the capabilities of North Korea's strategic Drones, predicting their operation patterns, and conducting active follow-up research on the establishment of a comprehensive strategy, such as our military's drone deployment and counter-drone system solutions.

• Korean Journal of Radiology
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• v.23 no.10
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• pp.949-958
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• 2022

Objective: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA). Materials and Methods: Forty patients (24 male and 16 female; median age, 62.5 years) with dysfunctional native AVF were prospectively recruited. Digital sounds from the AVF shunt were recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA), and the audio files were subsequently converted to mel spectrograms, which were used to construct various deep convolutional neural network (DCNN) models (DenseNet201, EfficientNetB5, and ResNet50). The performance of these models for diagnosing ≥ 50% AVF stenosis was assessed and compared. The ground truth for the presence of ≥ 50% AVF stenosis was obtained using digital subtraction angiography. Gradient-weighted class activation mapping (Grad-CAM) was used to produce visual explanations for DCNN model decisions. Results: Eighty audio files were obtained from the 40 recruited patients and pooled for the study. Mel spectrograms of "pre-PTA" shunt sounds showed patterns corresponding to abnormal high-pitched bruits with systolic accentuation observed in patients with stenotic AVF. The ResNet50 and EfficientNetB5 models yielded an area under the receiver operating characteristic curve of 0.99 and 0.98, respectively, at optimized epochs for predicting ≥ 50% AVF stenosis. However, Grad-CAM heatmaps revealed that only ResNet50 highlighted areas relevant to AVF stenosis in the mel spectrogram. Conclusion: Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.21-32
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• 2023

The Environmental Impact Assessment (EIA) plays a pivotal role in predicting the potential environmental impacts of proposed developments and planning appropriate mitigation measures to minimize effects on species. However, as concerns over biodiversity loss rise, there's ongoing debate about the efficacy of these mitigation plans. In this study, we utilized data from EIAs and post-environmental impact surveys to understand the trends in biodiversity during construction and operation phases. By examining 30 urban development projects, we categorized species richness indices of mammals, birds, amphibians, and reptiles into pre-construction, during construction, and post-construction operational stages. The biodiversity trends were analyzed based on the rate of change in these indices. The results revealed three distinct biodiversity change patterns: (A) An initial increase in biodiversity indices post-development, followed by a gradual decline over time; (B) a sustained increase in biodiversity as a result of mitigation measures; and (C) a continuous decline in biodiversity post-development. Furthermore, all species exhibited a higher rate of biodiversity decline during the construction phase compared to the operational phase, with mammals showing the most significant rate of change. Notably, the biodiversity change rate during operation was generally lower than during construction. In particular, mammals seemed to be most influenced by mitigation measures, displaying the smallest rate of change. This study provides empirical evidence on the efficacy of mitigation measures and deliberates on ways to enhance their effectiveness in minimizing the adverse impacts of urban development on biodiversity. These findings can serve as foundational data for addressing terrestrial biodiversity reduction.

• Research in Community and Public Health Nursing
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• v.5 no.1
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• pp.81-96
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• 1994

The primary purpose of this study was to identify factors affecting health promoting lifestyles of college students on the basis of Pender's Health Promotion Model. The subjects were 1,159 students of one university in Seoul. These data were collected by self reporting questionnaire from April 19 to May 3, 1993. This study examined health promoting lifestyles, cognitive-percep-tual factors: perceived health status, perceived importance of health, health locus of control, and health conception, and modifying factors: sex, grade, major, residence type, and attendance at a health care course. The instruments used in this study were Health Promoting Lifestyle Profile by Walker et al. (1987), Health Value Scale by Wallston et al. (1976), Health Locus of Control by Wallston et al. (1978), and Health Conception Scale by Laffrey(1986). The data were analyzed by Cronbach's $\alpha$, mean, standard deviation, percentage, t-test, ANOVA, Pearson's Correlation Coefficient, and Stepwise Multiple Regression with SPSS PC+ Program. The results were as follows : 1. The means of health promoting lifestyles revealed total 3.33, self actualization 3.74, health responsibility 2.72, exercise 2.80, nutrition 3.55, interpersonal surpport 3.76, and stress management 2.96. Interpersonal support showed the highest score and health responsibility showed the lowest score. 2. No significant differences between sex, grade, major, and residence type, and health promoting lifestyles Were founded(p>.05). Attendants at a health care course showed a significant higher score than nonattendants (p<.001). Male showed a significant higher score in exercise subscale, female showed significant higher scores in health responsibility, nutrition, interpersonal support, stress management subscales(p<.001), residence type showed a significant difference in nutrition subscale(p<.001). 3. No significant differences between perceived health status and sex, grade, major, residence type, and attendance at a health care course were founded(p>.05). Perceived importance of health showed no differences among sex, grade, major, and residence type(p>.05), showed only in attendance at a health care course (p<.001). Attendants at a health care course showed a significant higher score than nonattendants(p<.001). No significant differences between health conception and sex, grade, major, and residence type were founded (p>.05), only significant difference between health conception and attendance at a health care course was founded(p<.05). Nonattendants showed a significant higher score in clinical health conception, attendants showed a significant higher score in wellbeing health conception(p<.05). 4. A significant positive correlation between health promoting lifestyles and perceived health status was founded(r=.2415, p<.001). A significant positive correlation between health promoting lifestyles and perceived importance of health was founded (r=.1475, p<.001). The health promoting lifestyles revealed significant positive correlations in internal and powerful others locus of control (r=.3187, p<.001: r=.1475, p<.001), but revealed a significant negative correlation in chance locus of control(r=-.997, p<.001). A significant positive correlation between health promoting lifestyles and clinical health conception and wellbeing health conception were founded (r=.1241, p<.001 : r=.3047, p<.001). 5. Perceived health status was the highest factor predicting health promoting lifestyles of college students(R=.3415, $R^2=11.62$). Perceived health' status, perceived improtance of health, internal locus of control, wellbeing health conception, powerful others locus of control accounted for 28.19% in health promoting lifestyle patterns. In conclusion, college students who reported more helath promoting lifestyles evaluated their health positively, perceived importance of health highly, perceived their health as affected by theirselves, powerful others but not by chance or luck, and accepted health as high level wellness rather than merely the absence of illness. Those who attending at a health care course had healthier lifestyle patterns. And attendance at a health care course had significant. correlations in these cognitive-perceptual factors. Further studies are required to identify reasons of attending a health care course, and to compare health promoting lifestyles pre-post attending a course related to health care.

• Radiation Oncology Journal
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• v.25 no.4
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• pp.193-200
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• 2007

Purpose: This study evaluated the pretreatment expression patterns of MDM2, p53, and pRb proteins to determine if the expression patterns could predict the outcome of concurrent chemoradiotherapy (CCRT) for esophageal squamous cell carcinoma and aid in the decisions for the selection of treatment modalities. Materials and Methods: Fifty-one patients that were treated with definitive chemoradiotherapy for stage $I{\sim}IVa$ esophageal squamous cell carcinoma were selected for this study. Radiotherapy was administered with daily $1.8{\sim}2\;Gy$ fractions up to a median dose of 54 Gy for primary tumors, and with four cycles of cisplatin/5-fluorouracil chemotherapy that was administered every 4 weeks, the first two cycles of which were administered concurrently with radiotherapy. Expression of MDM2, p53, and pRb was investigated by immunohistochemical analysis using pretreatment biopsy specimens. Results: MDM2, p53, and pRb were detected with high immunoreactivity in 19.6%, 27.5%, and 66.7% of the patients, respectively. However, there was no significant correlation between expression of these factors and clinical outcome. By the use of multivariate analysis with nine covariates-age, tumor location, tumor length, stage, pathological response, clinical response, MDM2 expression, p53 expression, and pRb expression, only pathological response and stage were significant factors for cause-specific survival. Conclusion: Expression of MDM2, p53, and pRb was not found to be clinically significant for predicting outcomes after CCRT in this study. Further studies with a larger patient population and longer follow-up periods are needed to re-evaluate the expression pattern and to identify new predictors for CCRT response.

• Sleep Medicine and Psychophysiology
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• v.5 no.1
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• pp.71-79
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• 1998

Objectives : This study was to evaluate sleep patterns and daytime sleepiness resulting from rotating shiftwork. The authors, also, tried to find out the relationship between the severity of daytime sleepiness and personality factors. Methods : The subjects consisted of 41 female rotating shiftwork nurses and the control group consisted of 39 female day timeworkers. All of them completed the Sleep questionnaire of Korea University Sleep Disorder Clinic, the Epworth Sleepiness Scale(ESS), the 16 Personality Factors(16PF), the Beck Depression Inventory(BDI) and the State Trait Anxiety Inventory(STAI). Multiple regression analysis of 16PF of the rotating shiftwork nurses was done to find out possible predictors of the severity of daytime sleepiness. Results : The mean duration of deprived sleep due to rotating shiftwork was $64.26\;{\pm}\;14.54\;min$. The frequency of sleep difficulty($1.24\;{\pm}\;1.17\;day/week$ vs $0.67\;{\pm}\;1.31\;day/week$, p < 0.05), time needed to fall asleep($103.05\;{\pm}\;73.48\;min$. vs $70.00\;{\pm}\;60.08\;min$, p < 0.05), sleep duration when having some difficulties in sleep ($204.25\;{\pm}\;79.90\;min$. vs $280.44\;{\pm}\;111.59\;min$., p < 0.001), recent changes in energy($x^2\;=\;4.16$, p < 0.05), worrying about sleep($x^2\;=\;11.08$, p < 0.05), and taking naps($x^2\;=\;4.98$, p < 0.05) showed significant differences between rotating shiftworkers and normal controls. The ESS socre of shiftworkers ($8.68\;{\pm}\;3.04$) was greater than that of normal controls ($6.86\;{\pm}\;3.04$)(p < 0.01). Personality factors such as C factor($R^2\;=0.283$), I factor($R^2\;=0.358$) and G factor($R^2\;=0.470$) were related with the severity of the daytime sleepiness(p < 0.001). Conclusions : The rotating shiftwork nurses had more difficulties in sleep such as having difficulties in falling asleep and in maintaining sleep, and showed lowered energy, decreased senses of well-being and so on. The rotating shiftwork nurses experienced more severe daytime sleepiness than controls did. Personality factors, such as C factor, I factor, and G factor of 16PF were suggested to be useful for predicting the severity of daytime sleepiness resulting from rotating shiftwork.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.