• Clinical and Experimental Pediatrics
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• 제64권5호
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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.

• 대한치과의료관리학회지
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• 제9권1호
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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.

• 전기전자학회논문지
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• 제24권1호
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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.

• 패션비즈니스
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• 제27권1호
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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.

• 융합정보논문지
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• 제12권3호
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• pp.66-74
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• 2022

본 연구에서는 간호대학생의 코로나바이러스감염증-19 (Corona Virus Disease-19, COVID-19)에 대한 정보이해력, 위험인식 및 위기소통이 COVID-19 관련 예방행위에 미치는 영향을 확인하기 위해 시도되었다. 본 연구는 간호대학생 3,4학년 187명을 대상으로, 2020년 6월 25일부터 7월 3일까지 구조화된 설문지를 사용하여 자료를 수집하였고, 자료분석은 SPSS/WIN 26.0 프로그램을 이용하였다. 회귀분석 결과, 위기소통(β=0.30 p<.001), 정보이해력(β=0.29, p<.001), 위험인식(β=0.19 p=.004) 순으로 예방행위에 영향을 미치는 것으로 나타났으며, 총 모델 설명력은 27%였다. 본 연구는 간호대학생의 COVID-19 예방행위 향상을 위한 프로그램 개발시에 COVID-19와 관련하여 개인, 가족 및 사회 간의 위기사건에 대한 정보를 교환하는 소통과정을 활성화하고, 정보접근 및 이해력 수준을 향상시킴과 동시에 질병에 대한 위험인식을 높여야함을 시사하고 있다.

• 산업융합연구
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• 제21권1호
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• pp.175-185
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• 2023

최근 COVID-19와 같은 신종 바이러스 감염증이 확산하여 심각한 공중 보건 문제를 제기하고 있다. 특히 이러한 질병은 고령자에게 치명적으로 작용하여, 생명을 위협하고 심각한 사회적, 경제적 손실을 초래하였다. 이에 많은 산업분야에서 사물 인터넷(IoT) 및 인공 지능(AI)을 응용한 원격진료, 헬스케어, 질병예방 등의 애플리케이션이 소개되어 질병 감지, 모니터링 및 검역 성능을 향상하고 있다. 하지만 기존기술은 갑작스러운 전염병의 출현에 신속하고 통합적으로 적용되지 않기 때문에, 사회 속에 감염병이 대규모 감염 및 전국적 확산되는 것을 차단하지 못하였다. 따라서 본 논문에서는 바이러스 질병 정보 수집기를 통해 지역적 한계가 있는 다양한 감염 정보를 수집하고, AI 브로커를 통해 AI 분석 및 심각도 매칭을 수행하여 감염의 확산을 예측하고자 한다. 최종에는 질병관리본부를 통해 고령자에게 위험경보 발령, 확산 차단 문자 발송 및 감염지역 대피정보를 신속하게 제공한다. 현실적인 고령자 지원시스템은 감염자 발생지역 정보와 고령자의 위치정보를 비교하여 증강현실 기반의 스마트폰 애플리케이션으로 직관적인 위험지역(감염지역) 회피기능을 제공하고 감염지역 방문이 확인되면 자동으로 방역관리 서비스를 제공한다. 향후 제안시스템은 위치기반의 사용자 밀집도를 파악함으로써 갑작스런 인파 집중으로 인한 압사 사고를 사전에 예방하는 방법으로도 활용 가능할 것이다.

• Safety and Health at Work
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• 제13권3호
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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.

• 동서간호학연구지
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• 제27권1호
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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.

• 한국심리학회지 : 문화 및 사회문제
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• 제27권4호
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• pp.629-659
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• 2021

본 연구는 코로나 19 팬데믹 시기동안 한국인이 겪는 정서적 디스트레스에 영향을 미치는 심리·사회적 요인의 영향력을 서로 다른 두 시점에서 비교하였다. 1차 조사시기는 WHO에서 코로나 19 팬데믹을 선언하고, 대구 경북지역이 특별재난지역으로 선정되었던 2020년 4월 13일부터 4월 21일까지이며, 연구참여자 600명의 자료를 수집하였다. 2차 조사시기는 전 세계적으로 코로나 19가 재확산되고, 수도권에서 처음으로 집단감염이 확산되면서 일주일동안 수도권에서만 확진자가 1,000명이 넘었던 시점인 2020년 8월 21일부터 9월 2일까지 진행되었다. 1차 조사의 연구참여자 중 482명이 2차 조사에 응하였으며, 모든 자료는 온라인 설문을 통해 수집되었다. 본 연구는 시점별로 개인특성요인, 코로나 19에 대한 두려움과 사회활동 제약요인, 대인관계갈등 및 수입감소 요인이 코로나 19로 인한 디스트레스(스트레스, 우울, 불안, 분노) 경험에 미치는 영향을 알아보기 위해 위계적 회귀분석을 실시하였다. 그 결과, 두시점 모두에서 정서적 디스트레스에 영향을 미치는 요인으로는 성별, 삶의 질, '코로나 19 관련 정보 수시 확인', '코로나 19 이후 삶을 예측할 수 없어 두려움', '병원 이용의 어려움'이 확인되었다. 시점1에서만 유의한 변인으로 '공적일정에 차질', 시점 2에서만 유의한 변인으로는 연령, 감염취약성, '개인일정에 차질'이 확인되었다. 이를 토대로 논의 및 시사점이 제시되었다.

• ETRI Journal
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• 제46권2호
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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.