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1.
Bangladesh Journal of Medical Science ; 21(4):731-743, 2022.
Article in English | EMBASE | ID: covidwho-2065437

ABSTRACT

Objective: At the start of the Covid-19 pandemic, educational establishments, including universities, were closed. Educators in high-income countries quickly shifted all education online, building on available infrastructures and approaches. However, there were concerns in developing countries regarding the necessary skills among students and faculty as well as financial support for equipment and the internet. Consequently, a pilot was undertaken in Bangladesh to determine the impact of Covid-19 on the non-medical education system, building on similar research with healthcare professionals. Material(s) and Method(s): A purposively designed questionnaire was disseminated among eight non-medical healthcare educators in private and public universities in Bangladesh. Results and Discussion: Private university educators reported their universities readily adopted e-learning systems and resumed classes more quickly than public universities. Both private and public university educators shared similar challenges, including a lack of training on e-learning initially, variable internet connections, affordability of internet bundles, concerns with available devices, as well as mental stress of faculty and students. Private universities reduced their tuition fees, extended submission deadlines, and shared class recordings to address challenges. Public universities arranged student loans, established Covid-19 testing centers, and the trained students in biosafety practices and molecular tests to volunteer in testing facilities. Conclusion(s): Lessons learned from the pandemic emphasize introducing hybrid education systems with full technological and financial support, alongside biosafety education in the curriculum. Copyright © 2022, Ibn Sina Trust. All rights reserved.

2.
Malaysian Journal of Medicine and Health Sciences ; 18(8):43-49, 2022.
Article in English | Scopus | ID: covidwho-1965189

ABSTRACT

Introduction: Educational sector in Malaysia had been severely affected by COVID-19 pandemic. Due to the erratic nature of COVID-19 cases in Malaysia for the past two years, teaching style has shifted back and forth between home-based teaching and learning (PdPR) sessions and face-to-face teaching. Teachers must be prepared for any unanticipated shifts that occurred throughout the pandemic due to the implementation of movement control orders that resulted in school closures. Thus, this study aims to measure the depression, anxiety, stress, and quality of life among Malaysian teachers during the COVID-19 pandemic. Methods: Between March 21st and June 1st, 2021, 391 teachers completed Google form questionnaires containing the DASS-21, SF-36, and socio-demographic data, which were distributed online via WhatsApp, Telegram, Twitter, and Facebook. Results: : According to the findings of this study, most teachers (55.5 %) were anxious, followed by depression (39.9 %) and stress (27.6 %). Depression, anxiety, and stress were all statistically related to age (p<0.05), marital status (p<0.01), and the number of children (p<0.05). When it came to quality of life, teachers had the highest physical functioning score about 86 but the lowest vitality at 62.3. All domains of quality of life were found negatively correlated with depression, anxiety, and stress (p<0.05). Conclusion: The COVID-19 pandemic affected the depression, anxiety, and stress among the Malaysian teachers. To improve teachers’ well-being and mental health, effective policies, guidelines, and planning, as well as massive resources and support from administrative authorities, would be necessary. © 2022 UPM Press. All rights reserved.

3.
Journal of Applied Pharmaceutical Science ; 12(1):001-028, 2022.
Article in English | Scopus | ID: covidwho-1703120

ABSTRACT

The novel human coronavirus (CoV) 2019, similar to previous severe acute respiratory syndrome corona virus-1 outbreaks, has posed the unprecedented challenges that have shaped global action on preventive and easy to employ measures and policies, including regular disinfection. There is an indiscriminate use of antimicrobial agents, which may pose toxicity to humans, environmental hazards, and, in some cases, development antiviral drug resistance. This review comprehensively highlights the physical and chemical countermeasures applied to prevent various CoV infections and their potential toxicity on humans and the environment, as well as the danger of developing drug resistance. Literature information was sourced from PubMed, ScienceDirect, Embase, MEDLINE, and China National Knowledge Infrastructure databases using Google Scholars and Free Full PDF as search engines. Articles written in the English language were retrieved and included in the study. Researches covering the literature on physical and chemical severe acute respiratory syndrome corona virus-2 preventive measures, their toxicity, and possible ways of developing drug resistance were also discussed. The literature review reveals that physical inactivation under the influence of temperature, humidity, and light, especially ultraviolet-C radiation, has proven effective in reducing the spread of CoV infections. Similarly, chemical countermeasures such as the use of alcohol- and iodine-based disinfecting agents have demonstrated inhibitory potentials of the viruses on surfaces depending on nature, dose, and exposure time. The inactivation occurs through the interference of these agents with the lipid envelope, thereby disrupting the viral activity. A vast majority of the antimicrobial agents are reported to contain corrosive chemicals that are toxic to humans, especially children, and the environment. The toxicity is due to the unhealthy accumulation and pollution caused by the inappropriate disposal of biomedical waste. This study showed that chemicals might have long-term effects on public health, such as reproductive disorders, chronic obstructive pulmonary disease, cancers, skin damage, and central nervous system impairment. Therefore, further research on long-term preventive alternatives such as the formulation of these agents with natural products as active ingredients is necessary to mitigate the effects of alcohol- and iodine-based chemicals on humans and the environment. © 2022. Sani Yahaya Najib et al.

4.
Bangladesh Journal of Medical Science ; 20(5):131-139, 2021.
Article in English | EMBASE | ID: covidwho-1448712

ABSTRACT

Background: With the drastic spread of COVID-19 and mass mortality of people globally, detection of the progression of this disease has stood out to be a necessity. Hence, we set out to identify the prevalence of COVID-19 antibodies in Bangladesh using the in-house rapid pan-immunoglobulin dot-blot test kit and evaluate the performance of this kit. Methods: In this cross-sectional study, we tested serum collected between mid-May and mid-June 2020 for COVID-19 antibodies by using the in-house rapid pan-immunoglobulin dot-blot test kit in RT-PCR confirmed patients with symptoms for 1-7 days (Group Ia;n =100) and 8-14 days (Group Ib;n = 100);symptomatic RT-PCR negative patients (Group II;n = 100) and convalescent patients (Group III;n = 109) while comparing with pre-pandemic sera samples collected prior two years to December-2019 (Group IV;n = 100). Results: Our kit detected that almost 70% of the convalescent patients produced antibodies against COVID-19 compared to other groups. However, the group with individuals at the end phase of COVID-19 exhibited the second-highest percentage of seroprevalence (41%). We also observed that though Group II was RT-PCR negative, 20% of them showed COVID-19 antibodies. Conclusion: With a specificity of 96% in our kit, we can say that our kit will be a potential device for the detection of SARS-CoV-2 antibodies and to understand herd immunity in Bangladesh.

5.
6th International Conference on Application of Science and Mathematics, SCIEMATHIC 2020 ; 2355, 2021.
Article in English | Scopus | ID: covidwho-1246473

ABSTRACT

COVID-19 pandemic has caused nearly all face-to-face classes in all schools and institutions of higher education around the world to stop temporarily, including Malaysia to curb the spread of the virus. As an initiative to that, the Ministry of Education (MOE) Malaysia has ordered all schools and higher education institutions to conduct e-learning sessions to practice the new normal and to obey the Movement Control Order (MCO) implemented in Malaysia. Therefore, this study aims to investigate the preparedness factors of the open and distance learning (ODL) among the lecturers in Universiti Teknologi MARA (Pahang). A simple random sampling technique was used to distribute the questionnaires to 126 lecturers on demographic characteristics and how ODL did affect them positively or negatively. A descriptive statistics was used in representing the demographic characteristics of the lecturer and a new analysis model was built based on the determination of the preparedness factors using logistic regression analysis. Based on the finding, five factors influenced the preparedness of ODL among the lecturers;1) teaching experience, 2) number of family members, 3) enough number of devices, 4) enough internet data and 5) convenience in ODL. Besides, the overall model explained further that 93.65% of the sample was classified correctly. © 2021 American Institute of Physics Inc.. All rights reserved.

6.
IOP Conf. Ser. Earth Environ. Sci. ; 620, 2021.
Article in English | Scopus | ID: covidwho-1078795

ABSTRACT

The erosion in Malaysia has brought attention to many authorities especially the coastline in the eastern part of Peninsular Malaysia. Although the erosion in the northern part of Peninsular Malaysia does not receive as much attention as the eastern part of Peninsular Malaysia, however, the issue should not be neglected. High spatial resolution satellite imageries were used for the extraction of coastline and classification level of erosion rate along with the Pulau Tuba. The coastline data was extracted using two different methods known as Maximum Likelihood (ML) and On-Screen Digitizing (OSD) in the determination of the best approach of coastline detection from the Sentinel-2 data of the year 2016 and 2019. Furthermore, the level of erosion is made based on the physical and economic parameters outlined by the National Coastal Erosion Study 2015 (NCES). Due to some inevitable constraints of Movement Control Order by the Malaysian government due to the COVID-19 pandemic, physical observation data of Pulau Tuba were collected via Google Maps. The information acquired includes type of coastal geomorphology, land use, development on the area, activities conducted, and adaptation of erosion if any. These data were utilized to determine the erosion rate and categories using the proposed model by NCES for five divided management units (MU) of the Pulau Tuba areas utilizing Erdas Imagine and ArcGIS software. The analysis found that the ML approach has under-detected the coastline length between 3.19% to 45.0% as compared to OSD for both years of 2016 and 2019. Rate of erosion for Pulau Tuba based on the NCES approach found that the highest erosion rate occurred at the MU1 (Pulau Dayang Bunting- Pulau Tuba causeway) with 2.91% and classified as K1 (critical erosion category) with a value of 4.39 m/yr-1and the highest accretion rate at the MU3 with 3.06%. The critical erosion category was associated with the MU that has significant development and on-going activities that occurred in the area especially in MU 4 (Pulau Tuba) and MU 5 (Teluk Berembang). Other than that, the high number of erosions occurred in that section is due to the exposure of waves, wind, currents, and tides. © 2021 Institute of Physics Publishing. All rights reserved.

7.
Int. Conf. Cyber IT Serv. Manag., CITSM ; 2020.
Article in English | Scopus | ID: covidwho-1015447

ABSTRACT

An extraordinary outbreak of pneumonia in Wuhan City, China, was subsequently termed as COVID-19 emerged in December 2019. The virus is also known as an infectious disease inherited from a novel coronavirus. This study exposed the beginning of the unprecedented COVID-19 confirmed cases spike exponentially in the United States and 200 countries globally. Epidemiologists usually utilize conventional spread prediction via the classic clustering method. A suspected patient is likely to blow out the disease to a potential agglomerative of cases grouped in place and time. In the era of cutting edge, outbreak prediction can also generate accurate techniques to utilize unsupervised machine learning methods. We apply two prominent unsupervised learning methods, namely K-means clustering and correlation on a set Coronavirus Outbreak COVID-19 data collection dated March 27 and August 16, 2020. The K-means automatically search for unknown clusters of many countries infected with the COVID-19 rapidly. It shows that a group of $m = 5$ produces an accuracy of about 97% with [The United States and Italy], [Iran, France], [Spain, German], [Indonesia, Malaysia, Philippine] as clusters. At the same time, it predicts a pertinent relationship between the total deaths and critical patients' attributes of 0.85 while correlating COVID-19 characteristics. © 2020 IEEE.

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