• Clinical and Experimental Pediatrics
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• v.64 no.5
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• pp.239-246
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• 2021

Background: The consequences of severe acute respiratory syndrome corona virus 2 on mother and fetus remain unknown due to a lack of robust evidence from prospective studies. Purpose: This study evaluated the effect of coronavirus disease 2019 (COVID-19) on neonatal outcomes and the scope of vertical transmission. Methods: This ambispective observational study enrolled pregnant women with COVID-19 in North India from April 1 to August 31, 2020 to evaluate neonatal outcomes and the risk of vertical transmission. Results: A total of 44 neonates born to 41 COVID-19-positive mothers were evaluated. Among them, 28 patients (68.3%) (2 sets of twins) were delivered within 7 days of testing positive for COVID-19, 23 patients (56%) (2 sets of twins) were delivered by cesarean section; 13 newborns (29.5%) had low birth weight; 7 (15.9%) were preterm; and 6 (13.6%) required neonatal intensive care unit admission, reflecting an increased incidence of cesarean delivery and low birth weight but zero neonatal mortality. Samples of cord blood, placental membrane, vaginal fluid, amniotic fluid, peritoneal fluid (in case of cesarean section), and breast milk for COVID-19 reverse transcription-polymerase chain reaction tested negative in 22 prospective delivery cases. Nasopharyngeal swabs of 2 newborns tested positive for COVID-19: one at 24 hours and the other on day 4 of life. In the former case, biological samples were not collected as the mother was asymptomatic and her COVID-19 report was available postdelivery; hence, the source of infection remained inconclusive. In the latter case, all samples tested negative, ruling out the possibility of vertical transmission. All neonates remained asymptomatic on follow-up. Conclusion: COVID-19 does not have direct adverse effects on the fetus per se. The possibility of vertical transmission is almost negligible, although results from larger trials are required to confirm our findings.

• Journal of Korean Academy of Dental Administration
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• v.9 no.1
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• pp.44-50
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• 2021

Owing to the Corona virus (COVID-19) crisis, virtual education has been expanded. Accordingly, this study was conducted to determine the direction of oral health education by examining participants' satisfaction with virtual education and educational media preferences. This study collected data from a Naver Form online survey targeting 290 university students across the country, from May 10 to 31, 2021. The collected data were analyzed using IBM SPSS 20.0. According to the data, satisfaction with virtual classes was 3.36 points in 5-point Likert scale, satisfaction factors were reduced commuting time and money expenditure, and the highest dissatisfaction factor was a decrease in lecture concentration. The media platform that most interested students pursing oral health education was YouTube. The oral health education that participants wished to receive through virtual education included how to prevent tooth decay, how to prevent gum disease, and how to brush teeth. In conclusion, it is necessary to develop various media like Zoom, YouTube, and virtual reality programs so that students feel motivated to utilize oral health education and improve oral health.

• Journal of IKEEE
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• v.24 no.1
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• pp.293-301
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• 2020

Consultation with the patient and doctor is very important in the examination. However, if the consultation cannot be done directly, such as corona virus, it is difficult for the doctor to determine the patient's condition more accurately. Recently, an image counseling system has been developed based on the Internet, but in the case of heart disease, remote medical counseling cannot be performed because it is not possible to stethoscope the heart sounds remotely. In order to solve this problem, it is necessary to develop an interactive mobile robot capable of remote medical consultation, and a doctor and a patient should be able to set a planting sound during consultation and transmit it in real time. In this paper, we developed a robot that can remotely control a medical counseling robot to move to a hospital room where patients are hospitalized, and to consult a patient in the room remotely from a doctor's office. A remote medical imaging stethoscope system for real-time heart sound transmission is presented. The proposed system is a kind of P2P communication that transmits video information, audio information, and control signal independently through webRTC platform, so that there is no data loss. Consults and sees doctors in real time and finds it more effective than traditional methods for patient security. The system implemented in this paper will be able to perform remote medical care in the place where the spread of diseases between humans like the recent corona 19 as well as the remote medical care of heart disease patients in the future.

• Journal of Fashion Business
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• v.27 no.1
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• pp.1-15
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• 2023

This study divided articles into two time periods, from 2012 to 2022, with the aim of using big data analysis to look at patterns in the ecosystem of fashion start-ups. The research method extracted top keywords based on TF(Term Frequency) and TF-IDF(Term Frequency-Inverse Document Frequency), analyzed the network, and derived centrality values. As a result of comparing the first and second fashion startup ecosystems, elements of policy, support, market, finance, and human capital were derived in the first period. In addition, in the second period, elements of policy, support, market, finance, and culture were derived. In the first period, the fashion startup ecosystem focused on fostering new designer startups by emphasizing support, finance, and human capital factors and focusing on policies. Meanwhile, in the second period, online-based fashion platform startups and fashion tech startups appeared with the support of digital transformation and fulfillment services triggered by COVID-19(Corona Virus Disease 19), private finances were emphasized, and cultural factors were derived along with success stories of fashion startups. This study is meaningful in that it helps in developing strategies for fashion startups to grow into sustainable companies.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.66-74
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• 2022

This study identified the impact of information literacy, risk perception, crisis communication on preventive behaviors related to COVID-19 among nursing students. Data were collected from 187 nursing students from 25 June 2020 to 3 July 2020, and analyzed using the SPSS/WIN 26.0 program. As a result of regression analysis, the factors influencing prevention behaviors were crisis communication(β=0.30, p<.001), information literacy(β=0.29, p<.001), and risk perception(β=0.19, p=.004). The explanatory power of the model was 27%. This study suggests that the focus should be on improving the activating crisis communication process among individual, family and society, increasing information literacy and risk perception on crisis when developing program to improve COVID-19 preventive behaviors of nursing students experiencing clinical practice.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Safety and Health at Work
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• v.13 no.3
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• pp.269-278
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• 2022

Background: PTSD (Post-traumatic stress disorder, PTSD) had a great impact on health care workers during the COVID-19 (Corona Virus Disease 2019, COVID-19). Better knowledge of the prevalence of PTSD and its risk factors is a major public health problem. This study was conducted to assess the prevalence and important risk factors of PTSD among medical staff during the COVID-19. Methods: The databases were searched for studies published during the COVID-19, and a PRISMA (preferred reporting items for systematic review and meta-analysis) compliant systematic review (PROSPERO-CRD 42021278970) was carried out to identify articles from multiple databases reporting the prevalence of PTSD outcomes among medical staff. Proportion random effect analysis, I² statistic, quality assessment, subgroup analysis, and sensitivity analysis were carried out. Results: A total of 28 cross-sectional studies and the PTSD results of doctors and nurses were summarized from 14 and 27 studies: the prevalences were 31% (95% CI [confidence interval, CI]: 21%-40%) and 38% (95% CI: 30%-45%) in doctors and nurses, respectively. The results also showed seven risks (p < 0.05): long working hours, isolation wards, COVID-19 symptoms, nurses, women, fear of infection, and pre-existing mental illness. Two factors were of borderline significance: higher professional titles and married. Conclusion: Health care workers have a higher prevalence of PTSD during COVID-19. Health departments should provide targeted preventive measures for medical staff away from PTSD.

• Journal of East-West Nursing Research
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• v.27 no.1
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• pp.14-21
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• 2021

Purpose: This study aims to examine the levels of posttraumatic stress disorder (PTSD), depression and social stigma among nurses caring for Corona Virus Disease-19 (COVID-19) patients. Methods: 169 nurses caring for COVID-19 patients participated in this study. Data collection was conducted at university hospitals from March 1 to August 31, 2020. Data analysis was performed for descriptive statistics, t-test, ANOVA, and Pearson correlation coefficients using SPSS/WIN 24.0 program. Results: The mean scores of PTSD, depression and social stigma were 25.16±16.80, 17.26±8.63 and 5.83±2.84, respectively. The PTSD scores were significantly different between the department (F=2.89, p=.037). Depressive scores were significantly different between the marital status (t=2.27, p=.024) and the department (F=4.91, p=.003). Social stigma scores were significantly different between age (F=6.49, p=.002), marital status (t=-3.30, p=.008), having or not having children (t=3.82, p=.001), department (F=5.82, p=.001) and clinical experience (F=7.43, p=.001). Positive correlations were found between PTSD and depression (r=.70, p<.001) and social stigma (r=.22, p<.004). Conclusion: Integrated assessment and management are required to address the psychological and emotional problems faced by nurses caring for COVID-19 patients, and active follow-up measures should be considered.

• Korean Journal of Culture and Social Issue
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• v.27 no.4
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• pp.629-659
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• 2021

This study compared the influences of Korean psycho-social experiences on emotional-distress(stress, depression, anxiety, anger) of Koreans between two-periods during COVID-19. First, an online survey was conducted among 600 participants between April 13, 2020 and 21, while WHO had declared the pandemic, and Daegu-Gyungbuk were declared as a special-disaster area. Second, an online survey was conducted among 482 participants out of 600 study participants from the first study during August 21 to September 2, while COVID-19 re-spreaded around the world, and total confirmed cases were over 1,000 for a week in Seoul-Gyeonggi province. Hierarchical-regression analysis was used to determine the influence of personal characteristics, fear and social constraints, relationship conflict and income-decreasing factors on stress, depression, anxiety, anger in the two-time points. Results suggest that gender, quality-of-life, 'frequent information-checking about COVID-19', 'fear of unpredictability' and 'difficulties on hospital treatment access' predicted distress(stress, depression, anxiety, anger) at both Time1 and 2. 'Difficulties with official schedule' predicted distress at Time 1, and age, vulnerability to infection and difficulties with personal schedules predicted distress(stress, depression, anxiety, anger) at Time 2. Based on the reseults, implications and recommendations were presented.

• ETRI Journal
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• v.46 no.2
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• pp.290-306
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• 2024

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transferlearning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses.