A Comparative Study of Rapid SARS-Cov-2 Antigen Detection Assay against RT-PCR Assay for Diagnosis of COVID-19 in a Tertiary Hospital of Kathmandu.

Background The Coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) has spread worldwide since its first recorded case in the city of Wuhan, China, in December 2019. SARS-CoV-2 infection causes asymptomatic to sever pneumonia. Severe cases may develop acute respiratory disease symdrome (ARDS), with an average mortality rate of 6.9%. Real Time Reverse Transcriptase Polymerase Chain Reaction (rRT-PCR) assay is the current reference standard laboratory method for the diagnosis of SARS-CoV-2 infection. However, it takes around 6-8 hours to get the result and is time consuming. Therefore, rapid and accurate tests for SARS-CoV-2 screening are essential to expedite disease prevention and control. Lateral flow immunoassay using monoclonal anti SARS-CoV-2 antibodies which target for SARS-CoV-2 antigen can be complimentary screening test if their accuracy were comparable to that of the real time reverse transcriptionpolymerase chain reaction (RT-PCR) assay. Objective To find the sensitivity and specificity of a rapid antigentest kit in comparison to reverse transcription-polymerase chain reaction (RT-PCR). Method A cross-sectional hospital based study was carried out at Shree Birendra Army Hospital, Kathmandu for a period of four months. Result Our finding shows sensitivity and specificity of rapid diagnostic tests (RDT) Ag kit as 60.6% and 96.4% respectively. Positive and negative predictive value was 83.7% and 89.0%. Likewise, positive and negative likelihood ratio was 17.0 and 0.4. The overall accuracy of the antigen kit was 88.1% in comparison to reverse transcriptionpolymerase chain reaction (RT-PCR) as the gold standard. Conclusion Our study concluded the use of rapid antigen kit is mainly useful for screening purposes.

A Comparative Study of Rapid SARS-Cov-2 Antigen Detection Assay against RT-PCR Assay for Diagnosis of COVID-19 in a Tertiary Hospital of Kathmandu

Background The Coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) has spread worldwide since its first recorded case in the city of Wuhan, China, in December 2019. SARS-CoV-2 infection causes asymptomatic to sever pneumonia. Severe cases may develop acute respiratory disease symdrome (ARDS), with an average mortality rate of 6.9%. Real Time Reverse Transcriptase Polymerase Chain Reaction (rRT-PCR) assay is the current reference standard laboratory method for the diagnosis of SARS-CoV-2 infection. However, it takes around 6-8 hours to get the result and is time consuming. Therefore, rapid and accurate tests for SARS-CoV-2 screening are essential to expedite disease prevention and control. Lateral flow immunoassay using monoclonal anti SARS-CoV-2 antibodies which target for SARS-CoV-2 antigen can be complimentary screening test if their accuracy were comparable to that of the real time reverse transcription-polymerase chain reaction (RT-PCR) assay. Objective To find the sensitivity and specificity of a rapid antigentest kit in comparison to reverse transcription-polymerase chain reaction (RT-PCR). Method A cross-sectional hospital based study was carried out at Shree Birendra Army Hospital, Kathmandu for a period of four months. Result Our finding shows sensitivity and specificity of rapid diagnostic tests (RDT) Ag kit as 60.6% and 96.4% respectively. Positive and negative predictive value was 83.7% and 89.0%. Likewise, positive and negative likelihood ratio was 17.0 and 0.4. The overall accuracy of the antigen kit was 88.1% in comparison to reverse transcription-polymerase chain reaction (RT-PCR) as the gold standard. Conclusion Our study concluded the use of rapid antigen kit is mainly useful for screening purposes. Copyright © 2022, Kathmandu University. All rights reserved.

Insight and Perspective on Omicron's Development, Behaviour, and Vaccine Breakthrough: Next Sequelae of COVID-19

Background: The Severe Acute Respiratory Coronavirus (SARS-CoV-2) has emerged in a variety of forms since its first appearance in early December 2019. The Omicron variation (B.1.1.529) was recently confirmed as a relatively new Variant of Concern (VOC). There are several mutations in this S-protein, making it an exclusively lethal version of the protein. Omicron variants feature multiple mutations clustered in a region of S protein that is the principal target of antibodies, and these mutations may have an impact on the binding affinities of antibodies to the S protein, as demonstrated by structural analysis. Materials and Methods: Google, Sciencedirect, Web of science, and ResearchGate databases have been explored for potentially existing research to obtain the most emerging trends and up-to-date metadata on various perspectives of Omicron variants. Conclusion: There is evidence that the Omicron variant's mutations may interfere with antibody binding in people who have been exposed to the SARS-CoV-2 virus in the past. At the moment, there is very little information on the Omicron version. Therefore, mutation dispersion evaluations, evolutionary links to previous variants, and putative structural effects on antibody binding effects are all explored in this work. Results: In the current state of pandemic crises, the comprehension of Omicron will pave a path for healthcare professionals to treat infectious conditions very well.

Perspectives on the Technological Aspects and Biomedical Applications of Virus-Like Particles/Nanoparticles in Reproductive Biology: Insights on the Medicinal and Toxicological Outlook

Increasing data on the infection indicate that maternal infections are severe. Under the realms of vaccine development, virus-like particles (VLP)/nanoparticles (NPs) hold the promise of targeted control of therapeutics transfer across the placental barrier with the potential to trigger innate immune responses. Though the placenta is known to act as a barrier against exogenous materials, viruses exploit the transport systems and overcome the barrier properties. VLPs can be strategically designed to obtain the necessary mechanisms for navigation across the placenta and immune response. However, several knowledge gaps on the chemical, viral transmission strategies and the host defense response exist owing to the highly dynamic etiology of the placental barrier. This further complicates the toxicological analysis of the developed therapeutics. Herein, placental physiology and functions are discussed in significance with chemical toxicology, viral infections, and the host defense. Further, the promising applications of VLPs and perspective on their design to overcome the placental gatekeeper to gain the necessary immune response or therapy are provided. Finally, a holistic approach to various bioengineering models for studying chemical toxicants, viral infections, and effects of VLPs is provided to facilitate better translation of these VLPs to clinical applications.

Diagnosis of COVID-19 based on chest X-ray images using pre-trained deep convolutional neural networks

The novel coronavirus (COVID-19) that emerged and transmitted from China (Wuhan City) had a staggering effect on public health and the world economy. The early diagnosis of COVID-19 has become more important for its treatment and for controlling its spread due to its highly transmissible nature. In addition, the restricted supply of test kits calls for an alternative system for diagnosis. Since radiological images of chest of patients with COVID-19 show abnormalities, it is possible to diagnose COVID-19 utilizing chest X-ray images. Therefore, by applying deep convolution neural network (CNN), we have presented a diagnosis of COVID-19 based on chest X-ray images in this paper. For the diagnosis of COVID-19, an exhaustive comparative performance analysis of 16 state-of-the-art models is presented. Moreover, each model is trained with three approaches: transfer learning, fine tuning and scratch learning. The experiments were conducted on the dataset that comprises of 127 images of COVID-19, 500 images of Pneumonia and 500 images of normal cases. We have performed the experiments in two scenarios: binary classification (COVID-19 vs. Normal) and multiclass classification (COVID-19 vs. Pneumonia vs. Normal). Further, we have applied cost-sensitive learning technique to handle the class imbalance issue. In this study, InceptionResNetV2 model with fine-tuning approach achieved highest classification accuracy of 99.20% in binary classification and Xception model achieved classification accuracy of 89.33% in multiclass classification among all considered models. To validate our approach, we have presented the performance of our model on three other datasets and achieved adequate classification accuracy. Hence, the promising results demonstrate that the fine-tuning of deep CNN models is an effective way for diagnosis of COVID-19 and therefore, it can be deployed in diagnostic centers to assist radiologist after its validation with more prominent datasets.

The short-term economic effects of COVID-19 on low-income households in rural Kenya: an analysis using weekly financial household data. (Special Section: Pandemics and sustainability.)

This research assesses how low-income households in rural Kenya coped with the immediate economic consequences of the COVID-19 pandemic. It uses granular financial data from weekly household interviews covering six weeks before the first case was detected in Kenya to five weeks after during which various containment measures were implemented. Based on household-level fixed-effects regressions, our results suggest that income from work decreased with almost one-third and income from gifts and remittances reduced by more than one-third after the start of the pandemic. Nevertheless, household expenditures on food remained at pre-COVID levels. We do not find evidence that households coped with reduced income through increased borrowing, selling assets or withdrawing savings. Instead, they gave out less gifts and remittances themselves, lent less money to others and postponed loan repayments. Moreover, they significantly reduced expenditures on schooling and transportation, in line with the school closures and travel restrictions. Thus, despite their affected livelihoods, households managed to keep food expenditures at par, but this came at the cost of reduced informal risk-sharing and social support between households.

Fact Check Using Multinomial Naive Bayes

An easy access to social media platforms has made information available effortlessly and thus has increased the intricacies to distinguish between true and falsified information. The credibility or reliability on social media platforms is also at stake. It is of utmost necessary to address this as a severe issue and act on it promptly. The extensive spread of counterfeit news has the potential for creating negative impacts on vast audience. Therefore, fake news detection on social media has become a very critical agenda in today’s world. This paper proposes a prototype to detect whether a news is fake or real using the multinomial Naive Bayes algorithm and its various architectures. Furthermore, the proposed prototype is capable of handling the unstructured data as the news can be in the form of images. In addition to this, the use of Django which is a high-level Python framework that allows the development of UI very easily with multiple designing options. As there was a high need of a 24/7 working server, the system has been deployed on Amazon Web Services EC2 Server as it gave less downtime and is highly reliable. Experimentation was done on the synthetic COVID news dataset created by collecting COVID news on social platforms. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.