• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.316-321
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• 2022

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly, infecting millions of people worldwide. On March 11, 2020, the World Health Organization declared coronavirus disease (COVID-19) a pandemic owing to the worldwide spread of SARS-CoV-2, which created an unprecedented burden on the global healthcare system. In this context, there are increasing concerns regarding co-infections with other respiratory viruses, such as the influenza virus. In this study, clinical data of patients infected with SARS-CoV-2 and other respiratory viruses were compared with patients infected with SARS-CoV-2 alone. The hematology and blood biochemistry results of 178 patients infected with SARS-CoV-2 , who were tested on admission, were retrospectively reviewed. In patients with SARS-CoV-2 and adenovirus co-infection, C-reactive protein levels were elevated on admission, whereas lactate dehydrogenase (LDH), prothrombin time, international normalized ratio, activated partial thromboplastin clotting time, and bilirubin values were all within the normal range. Moreover, patients with SARS-CoV-2 and human bocavirus co-infection had low LDH and high bilirubin levels on admission. These findings reveal the clinical features of respiratory virus and SARS-CoV-2 co-infections and support the development of appropriate approaches for treating patients with SARS-CoV-2 and other respiratory virus co-infections.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.539-541
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• 2023

COVID-19 is a viral pandemic disease that spreads widely all around the world. The only way to identify COVID-19 patients at an early stage is to stop the spread of the virus. Different approaches are used to diagnose, such as RT-PCR, Chest X-rays, and CT images. However, these are time-consuming and require a specialized lab. Therefore, there is a need to develop a time-efficient diagnosis method to detect COVID-19 patients. The proposed machine learning (ML) approach predicts the presence of coronavirus based on clinical symptoms. The clinical dataset is collected from the Israeli Ministry of Health. We used different ML classifiers (i.e., XGB, DT, RF, and NB) to diagnose COVID-19. Later, classifiers are optimized with the Bayesian hyperparameter optimization approach to improve the performance. The optimized RF outperformed the others and achieved an accuracy of 97.62% on the testing data that help the early diagnosis of COVID-19 patients.

• Journal of Ginseng Research
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• 제46권6호
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• pp.722-730
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• 2022

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogenic virus that causes coronavirus disease 2019 (COVID-19), with major symptoms including hyper-inflammation and cytokine storm, which consequently impairs the respiratory system and multiple organs, or even cause death. SARS-CoV-2 activates inflammasomes and inflammasome-mediated inflammatory signaling pathways, which are key determinants of hyperinflammation and cytokine storm in COVID-19 patients. Additionally, SARS-CoV-2 inhibits inflammasome activation to evade the host's antiviral immunity. Therefore, regulating inflammasome initiation has received increasing attention as a preventive measure in COVID-19 patients. Ginseng and its major active constituents, ginsenosides and saponins, improve the immune system and exert anti-inflammatory effects by targeting inflammasome stimulation. Therefore, this review discussed the potential preventive and therapeutic roles of ginseng in COVID-19 based on its regulatory role in inflammasome initiation and the host's antiviral immunity.

• IMMUNE NETWORK
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• 제21권1호
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• pp.7.1-7.24
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• 2021

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 has severely impacted global health and economy. There is currently no effective approved treatment for COVID-19; although vaccines have been granted emergency use authorization in several countries, they are currently only administered to high-risk individuals, thereby leaving a gap in virus control measures. The scientific and clinical communities and drug manufacturers have collaborated to speed up the discovery of potential therapies for COVID-19 by taking advantage of currently approved drugs as well as investigatory agents in clinical trials. In this review, we stratified some of these candidates based on their potential targets in the progression of COVID-19 and discuss some of the results of ongoing clinical evaluations.

• Journal of Microbiology and Biotechnology
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• 제32권9호
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• pp.1073-1085
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• 2022

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has continued for over 2 years, following the outbreak of coronavirus-19 (COVID-19) in 2019. It has resulted in enormous casualties and severe economic crises. The rapid development of vaccines and therapeutics against SARS-CoV-2 has helped slow the spread. In the meantime, various mutations in the SARS-CoV-2 have emerged to evade current vaccines and therapeutics. A better understanding of SARS-CoV-2 pathogenesis is a prerequisite for developing efficient, advanced vaccines and therapeutics. Since the outbreak of COVID-19, a tremendous amount of research has been conducted to unveil SARS-CoV-2 pathogenesis, from clinical observations to biochemical analysis at the molecular level upon viral infection. In this review, we discuss the molecular mechanisms of SARS-CoV-2 propagation and pathogenesis, with an update on recent advances.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.39-46
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• 2022

The spread of Omicron, a mutated version of COVID-19 across several countries is leading to the discussion of lockdown once again for curbing the spread of the new virus. In this context, this research is showing the impact of lockdown for the successful control of the COVID-19 pandemic in Saudi Arabia. The outbreak of the COVID-19 pandemic around the globe has affected Saudi Arabia with around 2,37,803 confirmed cases within the initial 4 months of transmission. Saudi Arabia has announced a 21-day lockdown from March 23, 2020, to reduce the transmission of the COVID-19 pandemic. Machine Learning-based, Multinomial logistic regression was applied to understand the relationship between daily COVID-19 confirmed cases and lockdown in the 17 most-affected cities of KSA. We used secondary published data from the Ministry of Health, KSA daily dataset of COVID-19 confirmed case counts. These 17 cities were categorized into 4 classes based on lockdown dates. A total of three scenarios such as night lockdown, full lockdown, and no lockdown have been analyzed with the total number of confirmed cases with 4 classes. 15 out of 17 cities have shown a strong correlation with a confidence interval of 95%. These findings provide evidence that the COVID-19 pandemic may be partially suppressed with lockdown measures.

• Journal of Korean Neurosurgical Society
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• 제65권2호
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• pp.269-275
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• 2022

Objective : The coronavirus disease 2019 (COVID-19) pandemic is affecting the characteristics of patients with head injuries. This study aimed to evaluate the effect of the COVID-19 pandemic on patients with head injuries at a regional emergency medical center in South Korea. Methods : From April 2019 to November 2020, 350 patients with head injuries were admitted to our hospital. The study period was divided into the pre-COVID-19 (n=169) and COVID-19 (n=181) eras (10 months each). Patients with severe head injuries requiring surgery (n=74) were categorized into those who underwent surgery (n=41) and those who refused surgery (n=33). Results : Head injuries in pediatric patients (<3 years) were more frequent in the COVID-19 era than in the pre-COVID-19 era (8.8% vs. 3.6%, p=0.048). More patients refused surgery in the COVID-19 era than in the pre-COVID-19 era (57.9% vs. 30.6%, p=0.021). Refusal of surgery was associated with old age (67.7±14.5 vs. 52.4±19.1, p<0.001), marital status (married, 84.8% vs. 61.0%, p=0.037), unemployment (42.4% vs. 68.3%, p=0.034), COVID-19 era (66.7% vs. 39.0%, p=0.021), and lower Glasgow coma scale scores (6.12±3.08 vs. 10.6±3.80, p<0.001). Multivariable logistic regression analysis revealed that refusal of surgery was independently associated with old age (adjusted odds ratio [OR], 1.084; 95% confidence interval [CI], 1.030-1.140; p=0.002), COVID-19 era (adjusted OR, 6.869; 95% CI, 1.624-29.054; p=0.009), and lower Glasgow coma scale scores (adjusted OR, 0.694; 95% CI, 0.568-0.848; p<0.001). Conclusion : We observed an increased prevalence of head injuries in pediatric patients (<3 years) during the COVID-19 pandemic. Additionally, among patients with severe head injuries requiring surgery, more patients refused to undergo surgery during the COVID-19 pandemic.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.192-200
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• 2021

In the mid of the December 2019, the virus has been started to spread from China namely Corona virus. It causes fatalities globally and WHO has been declared as pandemic in the whole world. There are different methods which can fit such types of values which obtain peak and get flattened by the time. The main aim of the paper is to find the best or nearly appropriate modeling of such data. The three different models has been deployed for the fitting of the data of Coronavirus confirmed patients in Pakistan till the date of 20^th November 2020. In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Logistic model, Gompertz model and Auto-Regressive Integrated Moving Average Model (ARIMA) model. The fitted models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.247-253
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• 2021

The outbreak of the deadly virus COVID-19 is said to infect 17.3Cr people around the globe since 2019. This outbreak is continuously affecting a lot of new people till this day and, most of it is said to under control. However, vaccines introduced around the world can help mitigate the risk of the virus. Apart from medical professionals, prediction models are also said to combinedly help predict the risk of infection based on given datasets. This paper is based on publication of a machine learning approach using regression models to predict the output based on dataset which have indictors grouped based on active, tested, recovered and critical cases along with regions and cities covering most of it from Dubai. Hence, the active cases are tested based on the other indicators and other attributes. The coefficient of the determination (r²) is 0.96, which is considered promising. This model can be used as an frame work, among others, to predict the resources related to the dangerous outbreak.

• 한국컴퓨터정보학회논문지
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• 제27권8호
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• pp.135-142
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• 2022

본 논문에서는 위급한 상황(예 : COVID-19)에서 바이러스 검사를 빠르게 진행하기 위한 그래프 기반 사용자 경로 제어와 이것을 도시 맵에서 시뮬레이션을 할 수 있는 프레임워크를 제안한다. 가상환경에서 많이 활용되는 길찾기(Pathfinding) 알고리즘인 A^*나 네비게이션 메쉬 자료구조는 정해진 정적 이동 경로만을 안내하기 때문에 가상환경에서 에이전트를 제어하는 CS(Computer science)문제에 적용할 할 경우 효율적이다. 하지만, 실제 COVID-19 환경에 적용하여 문제를 풀기에는 충분하지 않다. 특히, 빠른 바이러스 검사를 받기 위해서는 짧은 거리만을 이용하는 게 아닌, 실제 도로 교통상황, 병원의 크기, 환자 이동 수, 환자 처리 시간 등 고려해야 할 상황들이 많다. 본 논문에서는 위에서 언급한 다양한 속성들과 이를 이용한 최적화 함수를 모델링하여, 실제 도시 맵에서 바이러스 검사를 빠르고 효율적으로 처리할 수 있고, 다양한 상황을 디지털 트윈 방식으로 시뮬레이션을 할 수 있는 프레임워크를 제안한다.