• 대한의생명과학회지
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• 제29권4호
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• pp.344-354
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• 2023

This study emphasizes the importance of early diagnosis and response to COVID-19, leading to the development of a rapid diagnostic kit using quantum dots. The research focuses on finely tuning bioconjugation with quantum dots to enhance the accuracy and sensitivity of COVID-19 diagnosis. We have developed a COVID-19 rapid diagnostic kit that exhibits a sensitivity more than 50 times higher than existing COVID-19 diagnostic kits. Quantum dots enable the accurate detection of COVID-19 viral antigens even at low concentrations, providing a rapid response in the early stages of infection. The COVID-19 quantum dot diagnostic kit offers quick analysis time, utilizing the quantum properties of particles to swiftly measure COVID-19 infection for immediate response and isolation measures. Additionally, this diagnostic kit allows for multiple analyses with ease, as multiple quantum dots can detect various antigens and antibodies simultaneously in a single experiment. This efficiency enhances testing, reduces sample requirements, and lowers experimental costs. The application of this diagnostic technology is anticipated in the future for early diagnosis and monitoring of other infectious diseases.

• International Journal of Computer Science & Network Security
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• 제24권9호
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• pp.127-134
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• 2024

The acute respiratory infection known as a coronavirus (COVID-19) may present with a wide range of clinical manifestations, ranging from no symptoms at all to severe pneumonia and even death. Expert medical systems, particularly those used in the diagnostic and monitoring phases of treatment, have the potential to provide beneficial results in the fight against COVID-19. The significance of healthcare mobile technologies, as well as the advantages they provide, are quickly growing, particularly when such applications are linked to the internet of things. This research work presents a knowledge-based smart system for the primary diagnosis of COVID-19. The system uses symptoms that manifest in the patient to make an educated guess about the severity of the COVID-19 infection. The proposed inference system can assist individuals in self-diagnosing their conditions and can also assist medical professionals in identifying the ailment. The system is designed to be user-friendly and easy to use, with the goal of increasing the speed and accuracy of COVID-19 diagnosis. With the current global pandemic, early identification of COVID-19 is essential to regulate and break the cycle of transmission of the disease. The results of this research demonstrate the feasibility and effectiveness of using a knowledge-based smart system for COVID-19 diagnosis, and the system has the potential to improve the overall response to the COVID-19 pandemic. In conclusion, these sorts of knowledge-based smart technologies have the potential to be useful in preventing the deaths caused by the COVID-19 pandemic.

• 대한예방한의학회지
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• 제26권2호
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• pp.25-35
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• 2022

Objectives : This study aims to analyze the patents filed in the clinical diagnosis sector where technologies have been actively developed since the advent of the 4th industrial revolution. Methods : The analysis has been conducted in two ways - the period from 2016 to 2021 and the time points before and after COVID-19 - by visualizing based on the word cloud method. Results : Over two thirds of patents has been filed in the A61B sector (71.8%) and cure, sensor, self diagnosis, control, and breakdown have been observed in the period above. During the overall period (2016~2021), 'ultrasound'(7.5%), 'image'(5.1%), 'skin'(4.0%), 'treatment'(3.4%), and 'artificial intelligence(2.5%)' were the frequently patent applications technologies. In addition, 'ultrasound'(6.2%), 'image'(5.5%), 'skin'(4.0%), 'treatment' (3.7%), and 'portable'(1.7%) appeared most frequently before COVID-19 whereas 'ultrasound(5.5%)', 'artificial intelligence(4.2%)', 'diagnostic device'(1.9%), 'dimentia'(1.6%), and 'diagnostic kit'(1.4%) emerged the most after COVID-19. Conclusion : This study is meaningful in that it showed the technological development trend in the digital diagnosis sector and it was found that the Korean medicine field should contribute to this field more actively in the future.

• Childhood Kidney Diseases
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• 제26권2호
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• pp.74-79
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• 2022

Purpose: Children with nephrotic syndrome may experience disease relapse or aggravation triggered by various viral infections. Limited studies on the clinical implications of the coronavirus disease 2019 (COVID-19) pandemic in children with nephrotic syndrome have been published worldwide. Therefore, this study aimed to investigate the effects of COVID-19 on the clinical course of nephrotic syndrome in children. Methods: The medical records of 59 patients with idiopathic nephrotic syndrome who visited our hospital between February and June 2022 were retrospectively analyzed. Results: Twenty of the total 59 patients with nephrotic syndrome were diagnosed with COVID-19 during the study period. The mean age at the time of the diagnosis of nephrotic syndrome and COVID-19 in all 20 patients was 4.6±3.5 and 8.9±3.9 years, respectively. Three patients (15%) were diagnosed with nephrotic syndrome relapse during COVID-19 and the relapse rate was similar to them without COVID-19 (20.5%, 8/39 patients). At the time of the COVID-19 diagnosis, fever (85%) and cough (40%) were the most common symptoms. After the diagnosis of COVID-19, all patients showed improvement with symptomatic treatment, including antipyretic analgesics and cold medicine. None of the critical patients required hospitalization or oral antiviral medications. Conclusions: Despite the use of immunosuppressants, the clinical manifestations of COVID-19 in children with nephrotic syndrome were not severe and are expected to be similar to that in the general population. The relapse rate of nephrotic syndrome in children with COVID-19 was also not different from them without COVID-19.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Safety and Health at Work
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• 제14권4호
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• pp.467-475
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• 2023

Background: To mitigate the spread of Covid-19, nurses infected with the virus were required to isolate themselves from their families and community. Isolated patients were reported to have experienced mental distress, posttraumatic stress disorder symptoms, and suicide. Though studies have reported the psychological impact of the Covid-19 pandemic, less is known about the lived experiences of nurses who survived Covid-19 infection in sub-Saharan Africa. Methods: A descriptive phenomenological approach was used to study the lived experiences of registered nurses who survived Covid-19 disease. In-depth interviews were conducted among nurses diagnosed with Covid-19 from two hospitals in Kenya between March and May, 2021. Purposive and snowball sampling were used to recruit registered nurses. Data were analyzed using Giorgi's steps of analysis. Results: The study included ten nurses between 29 and 45 years of age. Nurses' experiences encompassed three themes: diagnosis reaction, consequences, and coping. Reactions to the diagnosis included fear, anxiety, and sadness. The consequence of the diagnosis and isolation was stigma, isolation, and loneliness. Nurses coping mechanisms included acceptance, creating routines, support, and spirituality. Conclusion: Our findings aid in understanding how nurses experienced Covid-19 infection as patients and will provide evidence-based content for supporting nurses in future pandemics. Moreover, as we acknowledge the heroic contribution of frontline healthcare workers during the Covid-19 pandemic, it is prudent to recognize the considerable occupational risk as they balance their duty to care, and the risk of infection to themselves and their families.

• 한국인공지능학회지
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• 제8권1호
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• pp.7-9
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• 2020

In this paper, a study was conducted to find a self-diagnosis method to prevent the spread of COVID-19 based on machine learning. COVID-19 is an infectious disease caused by a newly discovered coronavirus. According to WHO(World Health Organization)'s situation report published on May 18th, 2020, COVID-19 has already affected 4,600,000 cases and 310,000 deaths globally and still increasing. The most severe problem of COVID-19 virus is that it spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, which occurs in everyday life. And also, at this time, there are no specific vaccines or treatments for COVID-19. Because of the secure diffusion method and the absence of a vaccine, it is essential to self-diagnose or do a self-diagnosis questionnaire whenever possible. But self-diagnosing has too many questions, and ambiguous standards also take time. Therefore, in this study, using SVM(Support Vector Machine), Decision Tree and correlation analysis found two vital factors to predict the infection of the COVID-19 virus with an accuracy of 80%. Applying the result proposed in this paper, people can self-diagnose quickly to prevent COVID-19 and further prevent the spread of COVID-19.

• 대한영상의학회지
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• 제81권6호
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• pp.1334-1347
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• 2020

코로나바이러스감염증-19 (coronavirus disease 2019; 이하 COVID-19)는 전 세계적 대유행 질환으로 인류 보건을 위협하고 있다. 흉부 CT 및 흉부X선사진은 COVID-19의 표준 진단검사인 역전사 중합효소 연쇄반응에 더하여 COVID-19 진단 및 중증도 평가에서 중요한 역할을 하고 있다. 본 종설에서는 흉부 CT 및 흉부X선사진의 COVID-19 폐렴에 대한 현재 역할에 대하여 살펴보고 인공지능을 적용한 대표적 초기 연구들과 저자들의 경험을 소개함으로써 향후 활용가치에 대해 살펴보고자 한다.

• Korean Journal of Radiology
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• 제23권1호
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• pp.150-152
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• 2022

• 셀메드
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• 제10권3호
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• pp.21.1-21.8
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• 2020

Over a span of few decades, the world has seen the emergence of new viruses that have posed serious problems to global health .COVID-19 is a major pathogenic threat to the modern world that primarily shoots the respiratory system of human beings. Wuhan which is the capital city of Hubei, China was the first place in the world where first cases of COVID-19 emerged and the scores of cases significantly increased at an immense rate leading to city isolation and establishment of new specially designed hospitals. SARS-CoV had emerged from bats in china (2002) and MERS-CoV from camels transmitted via bats in Middle East (2012) where the previous versions of COVID-19 took place. Infections with SARS-CoV-2 are now widespread, like Nuclear Chain Reaction (NRC). In this review we will discuss the COVID-19 origin, transmission, incubation, diagnosis and therapies available at the present scenario.