Sound E-Learning of STEM in Malaysian Higher Education Institutions

The economy globally has impacted livelihood and organizations because of the COVID-19 pandemic. It has also impacted the 4th Industrial Revolution Digitalization in education, carried many programs with STEM-based courses that entail hands-on methods to online manner. This study was carried out to ensure that STEM education at Malaysian higher education institutions was well-integrated through e-learning (HEIs). Data were collected via questionnaires and semi-structured interviews with a mixed-methods research design. Respondents were STEM educators in HEIs within the Klang Valley. Educators between the ages of 30-35 are found to be more adept at using various software suites, browsing websites, and handling multimedia tools for e-learning. 83 percent of the sample stated that their institution has no STEM e-learning procedure, contributing to extra challenges in STEM education. A sound e-learning integration of STEM requires educators with specialization in the courses being taught, able to process knowledge of digital applications, cultivate skills and values to problem resolve, and integrate collaboration in the technological curriculum. HEIs must involve stakeholders to develop resistance plan modification and to portion the integration of e-learning in segments pertaining to course learning outcomes for achieving strategic objectives in STEM education. © 2023, Auricle Global Society of Education and Research. All rights reserved.

Virtual screening and activity study of antiviral compounds targeting inosine 5'-monophosphate dehydrogenase

Inosine 5'-monophosphate dehydrogenase (IMPDH) is a key enzyme catalyzing the rate-limiting step of de novo nucleotide synthesis in vivo. In recent years, it has become a therapeutic target for anti-virus, antibacterial, anti-cancer, anti-parasitic and other diseases. IMPDH inhibitors have been shown to inhibit viral proliferation in host cells by depleting guanosine 5'-monophosphate (GMP), the raw material required for viral replication in host cells, with broad-spectrum antiviral properties. In order to find novel anti-coronavirus drugs, this study screened 22 potential IMPDH inhibitors from 70 000 natural small molecule libraries based on IMPDH protein structure using molecular docking and ROC calculation for virtual screening. With ribavirin as the positive control drug, Huh7 cell and H460 cell models were used to verify the anti-coronavirus HCoV-229E and HCoV-OC43 activities of 22 selected target compounds. Among them, compounds 11, 12, 15 and 16 showed inhibitory activity against coronavirus HCoV-229E. The compounds 4, 12, 13 and 15 showed inhibitory activities against coronavirus HCoVOC43. 12 and 15 showed significant inhibitory activity against both two coronaviruses, and their efficacy was similar to ribavirin at the same dose, which can be further studied as a lead compound for IMPDH inhibitors. Copyright © 2022, Chinese Pharmaceutical Association. All rights reserved.

A vertical laminar airflow system to prevent aerosol transmission of SARS-CoV-2 in building space: Computational fluid dynamics (CFD) and experimental approach

Respiratory aerosol particles carrying the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) are a primary cause of the long-distance infection, and insufficient ventilation systems make building occupants highly prone to the coronavirus disease-2019 (COVID-19). As a preventive measure of the aerosolized viable SARS-CoV-2 suspension in building space, we seek to propose an optimal design of the upper-room/drop-ceiling aeration grid system generating vertical laminar airflow (VLAF) as an aerosol barrier. On a test plan (6.1 × 4.7 m) representing the standard hospital patient room in South Korea (a 2.7 m-height room of 77.4 m3 in volume), we investigated the air-terminal size, spacing and air speed that shape uniform downflow of fresh air, minimizing horizontal spread. Our simulation results using the Taguchi method and computational fluid dynamics (CFD) in presence of indoor human expiration indicated that a steady vertical air supply of 0.3 m/s through 0.04m-diameter air diffusers deployed by 0.5 m spacing is the most effective to form VLAF. Physical particle detection tests at the height of 1.5 m in mockup setting of the optimal system design, revealed that expiratory aerosols produced by a single person were almost entirely removable in 20 s. Investigations also confirmed that the proposed design could minimize stagnant airflow regions and would potentially satisfy the indoor air-speed condition for occupant thermal comfort.

Facemask Detection Based on Double Convolutional Neural Networks

Background: The study on facemask detection is of great significance because facemask detection is difficult, and the workload is heavy in places with a large number of people during the COVID-19 outbreak. Objective: The study aims to explore new deep learning networks that can accurately detect facemasks and improve the network's ability to extract multi-level features and contextual information. In addition, the proposed network effectively avoids the interference of objects like masks. The new network could eventually detect masks wearers in the crowd. Methods: A Multi-stage Feature Fusion Block (MFFB) and a Detector Cascade Block (DCB) are proposed and connected to the deep learning network for facemask detection. The network's ability to obtain information improves. The network proposed in the study is Double Convolutional Neural Networks (CNN) called DCNN, which can fuse mask features and face position information. During facemask detection, the network extracts the featural information of the object and then inputs it into the data fusion layer. Results: The experiment results show that the proposed network can detect masks and faces in a complex environment and dense crowd. The detection accuracy of the network improves effectively. At the same time, the real-time performance of the detection model is excellent. Conclusion: The two branch networks of the DCNN can effectively obtain the feature and position information of facemasks. The network overcomes the disadvantage that a single CNN is susceptible to the interference of the suspected mask objects. The verification shows that the MFFB and the DCB can improve the network's ability to obtain object information, and the proposed DCNN can achieve excellent detection performance. © 2022 Bentham Science Publishers.

[Research on COVID-19 vaccination strategies in Singapore].

As of December 31, 2021, Singapore reported that 4 758 601 had completed at least one dose of COVID-19 vaccination, 4 714 655 had completed two doses of COVID-19 vaccination, and 2 207 341 had received one booster shot of COVID-19 vaccine. This article analyses the current performance of COVID-19 vaccination in Singapore, interprets the content of Singapore's National Vaccination Programme, and systematically introduces specific measures of COVID-19 vaccination in Singapore, such as door-to-door vaccination, vaccination differentiated management, and self-payment of medical expenses for those who refuse to be vaccinated, to provide reference for the COVID-19 vaccination in China.

DARNet: Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 via CT Images

The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a serious threat to public health and the economy. Rapid and accurate diagnosis of COVID-19 is essential to prevent the further spread of the disease and reduce its mortality. Chest Computed tomography (CT) is an effective tool for the early diagnosis of lung diseases including pneumonia. However, detecting COVID-19 from CT is demanding and prone to human errors as some early-stage patients may have negative findings on images. Recently, many deep learning methods have achieved impressive performance in this regard. Despite their effectiveness, most of these methods underestimate the rich spatial information preserved in the 3D structure or suffer from the propagation of errors. To address this problem, we propose a Dual-Attention Residual Network (DARNet) to automatically identify COVID-19 from other common pneumonia (CP) and healthy people using 3D chest CT images. Specifically, we design a dual-attention module consisting of channel-wise attention and depth-wise attention mechanisms. The former is utilized to enhance channel independence, while the latter is developed to recalibrate the depth-level features. Then, we integrate them in a unified manner to extract and refine the features at different levels to further improve the diagnostic performance. We evaluate DARNet on a large public CT dataset and obtain superior performance. Besides, the ablation study and visualization analysis prove the effectiveness and interpretability of the proposed method. © 2021 IEEE.

The lung ultrasound characteristics and its predicting value for COVID-19 in the early stage

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is a global pandemic. Early diagnosis is crucial for prognosis of patients, and lung ultrasound (LUS) may be a promising technique that can be performed at the bedside.We aimed to describe the LUS characteristics and explore its predicting value in assessing the severity and prognosis of COVID-19 patients. METHODS: We conducted a retrospective, observational study on patients confirmed with COVID-19 in Wuhan Hankou hospital. The patients who underwent LUS examination within 24 hours after being admitted to isolation wards were enrolled. The clinical and LUS data were collected and analyzed. Patients were divided into three groups (moderate, severe, and critical group). LUS characteristics and its scores were compared in different group of patients and between survivors and non-survivors. RESULTS: 42 patients were included, of whom 37 (88.1%) were discharged and 5 (11.9%) died in hospital. With the aggravation of lung injury, LUS showed a significantly reduced A-lines and increased coalescent B-lines or consolidation. The survivors had 60% of normal aeration with presence of lung sliding with A-lines (survivors 44.3% vs non-survivors 4.0%, P=0.000) or less than three isolated B-lines (survivors 16.2% vs non-survivors 2.0%, P=0.000), while non-survivors lost 90% of lung aeration resulting from coalescent B lines (non-survivors 84.0% vs survivors 18.1%, P=0.000) or lung consolidation (non-survivors 6.0% vs survivors 2.7%, P=0.193). The global LUS scores were significantly higher in non-survivors than survivors (19.00±3.54 vs 6.32±4.96, P=0.000), and in critical ill patients comparing with moderate and severe ill patients (18.5±3.39 vs 8.82±3.89 vs 1.71±1.77, P=0.000). The LUS scores cutoff of 4.5 and 15.0 could identify diffdifferent type of patients with excellent sensitivity, specificity and area under the curve (AUC). In addition, LUS scores more than 17.5 points could predict mortality of COVID-19 patients with AUC 0.975 (95%CI 0.922-1.028), sensitivity 80% and specificity 100%. CONCLUSIONS: LUS has a great value for rapid assessment of the severity of lung injury for COVID-19 patients at presentation in the early stage. The semiquantitative analysis based on LUS has high diagnostic ability to reflect clinical classification and predict prognosis.

Late Breaking Abstract - Multidisciplinary medical team in severe COVID-19 treatment

Background: The pandemic of COVID-19 is emerging. Concomitant comorbidities are major cause of death after SARS-Cov2 infection. Multidisciplinary groups involved intervention should be a great potential for better outcomes in severe COVID-19. Method: A multidisciplinary medical team was allocated from our hospital to assist Wuhan at an emergency. According the local policy and guidelines, a centralized ward with severe COVID-19 patients were charged by this team. Cardiovascular, endocrine, respiratory, nutrition and psychology disorders, as well as infection control and rehabilitation were systematically evaluated and treated. The data of interventional effect were retrospectively reviewed. Results: A total of 90 severe COVID-19 patients were evaluated with high incidence of comorbidities and effectively treated by these comprehensive interventions (Table 1). In the 50 consecutive mission days, most of them survived (n=86, 96%), discharged (n=72, 80%) or transferred into wards where mild cases were treated. Only 2 were transferred into critical care. Conclusion: The astonishing outcomes achieved by the comprehensive intervention warrant more concerns about multidisciplinary treatment in COVID-19 fight policy.

Directly Acting Antivirals for COVID-19: Where Do We Stand?

The outbreak of a novel coronavirus (SARS-CoV-2) in Wuhan, China in December 2019 has now become a pandemic with no approved therapeutic agent. At the moment, the genomic structure, characteristics, and pathogenic mechanisms of SARS-CoV-2 have been reported. Based upon this information, several drugs including the directly acting antivirals have been proposed to treat people with coronavirus disease 2019 (COVID-19). This rapid review aims to describe the directly acting antivirals that have been examined for use in the management of COVID-19. Searches were conducted in three electronic databases, supplemented with a search on arXiv, bioRxiv, medRxiv, ChinaXiv, ClinicalTrials.gov, and Chinese Clinical Trial Registry for studies examining the use of antivirals in COVID-19 to identify for case reports, case series, observational studies, and randomized controlled studies describing the use of antivirals in COVID-19. Data were extracted independently and presented narratively. A total of 98 studies were included, comprising of 38 published studies and 60 registered clinical trials. These drugs include the broad spectrum antivirals such as umifenovir, protease inhibitors such as lopinavir/ritonavir as well as the RNA-dependent RNA polymerase inhibitors, remdesivir, and favipiravir. Other drugs that have been used include the nucleosidase inhibitors and polymerase acidic endonuclease inhibitors which are currently approved for prevention of influenza infections. While some of the drugs appear promising in small case series and reports, more clinical trials currently in progress are required to provide higher quality evidence.