ABSTRACT
Ebola virus disease (EVD) is a rare but highly contagious and lethal disease that occurs predominantly in African countries, with a case-fatality rate of 30–90%. The causative viral pathogens of EVD are within the genus Ebolavirus in the family Filoviridae. The primary route of human-to-human transmission is through direct contact with blood, bodily fluids and secretions from infected individuals. Direct contact with virally contaminated objects and sexual transmission have also been reported. Management of EVD is aggressive supportive care with possibly new therapeutic options. On 20 September 2022, an EVD outbreak was declared in Uganda, caused by Sudan ebolavirus. As of 7 November 2022, a total of 136 confirmed cases, 53 confirmed deaths have been reported, including 18 cases with seven deaths among healthcare workers. In the Democratic Republic of Congo (DRC), an EVD outbreak was also declared on 22 August 2022 (which ended on 27 September 2022);with only one case, a middle-aged woman. At the time when most countries in the world have been occupied with the coronavirus disease 2019 (COVID-19) pandemic and the recent human monkeypox outbreak, these two outbreaks of EVD have the potential to significantly add to the burden on global health. Authorities need to augment their multi-faceted response, including stringent contact tracing and border control, to avoid the catastrophe of the 2014–2016 EVD epidemic. © 2022, School of Medicine, Universitas Syiah Kuala. All rights reserved.
ABSTRACT
The year 2020 has seen the world being traumatized with the COVID-19 pandemic. COVID-19 virus had infected more than 100 million people and 2 million deaths worldwide. Many researchers race against time in producing vaccines and also used the latest technology in data analytics and artificial intelligence to help curb the pandemic. Deep features have shown to be an emerging area of research in various fields. Most recent deep works in the lung area focused on Convolutional Neural Networks (CNN). However, these have a drawback of over-classifying and not reflective of the real-world. Therefore, this article presented a cloud-based lung disease classification system, where medical practitioners can upload their patients' chest X-ray onto the cloud, and the system will classify if the disease is absent (normal) or present (abnormal). To test the disease, the system will then classify the lung infection as COVID-19 and non-COVID. Overall, the proposed system has obtained fairly good accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The year 2020 has seen the world being traumatized with the COVID-19 pandemic. COVID-19 virus had infected more than 100 million people and 2 million deaths worldwide. Many researchers race against time in producing vaccines and also used the latest technology in data analytics and artificial intelligence to help curb the pandemic. Deep features have shown to be an emerging area of research in various fields. Most recent deep works in the lung area focused on Convolutional Neural Networks (CNN). However, these have a drawback of over-classifying and not reflective of the real-world. Therefore, this article presented a cloud-based lung disease classification system, where medical practitioners can upload their patients' chest X-ray onto the cloud, and the system will classify if the disease is absent (normal) or present (abnormal). To test the disease, the system will then classify the lung infection as COVID-19 and non-COVID. Overall, the proposed system has obtained fairly good accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Background: Hesitancy towards COVID-19 vaccination and its rejection remains a major public health concern worldwide, especially in the Middle East and North African countries. The current study aimed to assess the intentions to get COVID-19 vaccines and its determinants among the general public in Algeria. Methods: A self-administered online survey was distributed during August-September 2021 using a convenience-based sampling approach. Data were collected anonymously and analyzed using IBM SPSS v22.0 software. Results: The study sample comprised a total of 656 participants, with 51.1% being in favor of COVID-19 vaccines while 18.5% and 30.5% were against or hesitant respectively. Only 38.6% among the study participants got vaccinated. Factors associated with higher odds of acceptance were: male sex, healthcare profession, the belief in natural origin of the pandemic and previous COVID-19 infection in family. The most common cited reasons for COVID-19 vaccine acceptance were the belief that vaccination is the only way to fight COVID-19 and the fear of getting infected by the virus;while the most common reasons of rejection were lack of trust in proper vaccine testing and fear of side effects. Conclusion: The overall level of COVID-19 vaccine acceptance in this study was below the levels required to achieve population immunity. Besides certain socio-demographic characteristics, the determinants of COVID-19 vaccine rejection included the embrace of conspiratorial ideas regarding the virus and its vaccination. This should be considered in implementation of interventional measures aiming to promote COVID-19 vaccination in the country.
ABSTRACT
Affected by COVID-19, Chinese Ministry of Education issued an important deployment named 'Classes Suspended while Learning Continues' during Spring Semester, 2020. Online teaching, therefore, has been carried out in all of Chinese universities between March and June, 2020. College teachers should adjust their teaching behaviors to adapt to large scale online education situation and they might meet some challenges during the implementation of online teaching. This study aimed to investigate college teachers' TPACK level during the epidemic situation. 91 teachers from Chu Kochen Honors College, Zhejiang University, were randomly invited to fill in the survey. The findings of the study indicated that: (1) the scores of teachers in content knowledge (CK) dimension were the highest, while it was lowest in technology knowledge (TK) dimension, in addition, technology-related knowledge was kept at a low level relatively;(2) gender and age had no significant impact on surveyed teachers' TPACK level while online teaching experience had a significant impact on TK dimension. Based on the findings, the researchers put forward several suggestions including perspectives from teacher training, comprehensive utilization of emerging technology to improve teachers' TPACK level. © 2021 IEEE.
ABSTRACT
INTRODUCTION: Since April 2020, multiple reports emerged from Europe and later from New York City of a presentation of a multi-system inflammatory syndrome in children (MIS-C) with different clinical patterns that occurs 1-6 weeks following SARS-CoV2 infection. MIS-C is associated with variable degrees of cardiac involvement. The median age of affected children is 8.5-10 years. METHODS: A previously healthy 16-month-old patient who presented with febrile seizure, diarrhea and generalized skin rash. Twelve hours later, patient had persistent fever (41C), skin rash and diarrhea with prominent physical findings of periorbital edema, hepatomegaly, skin rash and inguinal lymphadenopathy. Shortly after admission, patient developed vasoplegic shock and was therefore intubated and vasopressor support was initiated. Broad spectrum antibiotics were started. The diagnosis of MIS-C was established with high inflammatory markers, anemia, thrombocytopenia, markedly elevated ferritin level, coagulopathy with significantly elevated D-dimers and positive anti-SARS-CoV-2 IgG. BNP was also elevated. Troponin level was within normal limit. Electrocardiography showed nonspecific T-wave changes and cardiac echocardiography showed mild atrioventricular (AV) valves' regurgitation that had no clinical significance and ultimately resolved. Patient received methylprednisolone with a maximum dose of 30mg/kg/day, anakinra with a maximum dose of 12m/kg/day, 2 doses of 1g/kg of intravenous immunoglobulins (IVIG) and Low molecular weight heparin. Patient remained intubated, mechanically ventilated and on vasopressors support for 3 days after which signs of hemodynamic stability were established and was subsequently extubated. RESULTS: We successfully treated this patient who presented with shock and reversible AV valves regurgitation secondary to MIS-C in an uncommon age group compared to the recent reports. Our management approach was parallel to most institutions with IVIG, glucocorticoids, and interleukins receptor antagonists. The good outcome of this patient with MIS-C highlights the importance of early recognition of shock state, proper and judicious fluid resuscitation, early establishment of invasive monitoring, intubation and mechanical ventilation, and appropriate initiation of inotropes and vasopressors.