COVID-19 Prognosis from Chest X-ray Images by using Deep Learning Approaches: A Next Generation Diagnostic Tool

Global public health is overwhelmed due to the ongoing Corona Virus Disease (COVID-19). As of October 2022, the causative virus SARS-CoV-2 and its multiple variants have infected more than 600 million confirmed cases and nearly 6.5 million fatalities globally. The main objective of this reported study is to understand the COVID-19 infection better from the chest X-ray (CXR) image database of COVID-19 cases from the dataset of CXR of normal, pneumonia and COVID-19 patients. Deep learning approaches like VGG-16 and LSTM models were used to classify images as normal, pneumonia and COVID-19 impacted by extracting the features. It has been observed during the COVID-19 pandemic peaks that large number of patients could not avail medical beds and were seen stranded outdoors. To address such health emergency situations with limited available bed and scarcity of expert physicians, computer-aided analysis could save precious lives through early screening and appropriate care. Such computer-based deep-learning strategy could help during future pandemics, especially when the available health resources and the need for preventive measures to take do not match the burden of a disease. [ FROM AUTHOR] Copyright of Journal of Pure & Applied Microbiology is the property of Dr. M. N. Khan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Role of available COVID-19 vaccines in reducing deaths and perspective for next generation vaccines and therapies to counter emerging viral variants: an update.

The COVID-19 disease wreaked havoc all over the world causing more than 6 million deaths out of over 519 million confirmed cases. It not only disturbed the human race health-wise but also caused huge economic losses and social disturbances. The utmost urgency to counter pandemic was to develop effective vaccines as well as treatments that could reduce the incidences of infection, hospitalization and deaths. The most known vaccines that could help in managing these parameters are Oxford-AstraZeneca (AZD1222), Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273) and Johnson & Johnson (Ad26.COV2.S). The effectiveness of AZD1222 vaccine in reducing deaths is 88% in the age group 40-59 years, touching 100% in the age group 16-44 years & 65-84 years. BNT162b2 vaccine also did well in reducing deaths due to COVID-19 (95% in the age group 40-49 years and 100% in the age group 16-44 years. Similarly, mRNA-1273 vaccine showed potential in reducing COVID-19 deaths with effectiveness ranging from 80.3 to 100% depending upon age group of the vaccinated individuals. Ad26.COV2.S vaccine was also 100% effective in reducing COVID-19 deaths. The SARS-CoV-2 emerging variants have emphasized the need of booster vaccine doses to enhance protective immunity in vaccinated individuals. Additionally, therapeutic effectiveness of Molnupiravir, Paxlovid and Evusheld are also providing resistance against the spread of COVID-19 disease as well as may be effective against emerging variants. This review highlights the progress in developing COVID-19 vaccines, their protective efficacies, advances being made to design more efficacious vaccines, and presents an overview on advancements in developing potent drugs and monoclonal antibodies for countering COVID-19 and emerging variants of SARS-CoV-2 including the most recently emerged and highly mutated Omicron variant.

Omicron (B.1.1.529) variant and its subvariants and lineages may lead to another COVID-19 wave in the world? -An overview of current evidence and counteracting strategies.

The highly contagious Omicron variant of SARS-CoV-2 is a recent cause of concern during the COVID-19 pandemic. The World Health Organization (WHO) has classified SARS-CoV-2 variants into variants of concern (VOCs), variants of interest (VOIs), and variants under monitoring (VUMs). VOCs were categorized as Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2). Omicron (B.1.1.529) was a further modified strain that has a short incubation period; it was called VOC by the WHO, and it became fifth on the list of variants. Omicron has spread faster than any other variant since its emergence in late 2021. Omicron is currently the only circulating VOC. The various subvariants of Omicron are BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), BA.3 (B.1.1.529.3), BA.4, BA.5, and descendent lineages. More recently, identified Omicron subvariants and sublineages BQ.1, BQ.1.1, BA.4.6, BF.7, BA.2.75.2, XBB.1, and BF.7 have also attracted global attention. The BA.5 strain of Omicron is the most contagious and dominant subvariant globally. Recent spikes in cases in China are due to the BF.7 subvariant. With the large increase in the number of cases, there has been an increase in hospitalisations in countries worldwide. In many countries, the lifting of infection prevention protocols, such as the use of masks and physical distancing, contributes to the spread of the virus. This article highlights the potential impacts of SARS-CoV-2 variants and subvariants, which have made the pandemic far from over. Effective vaccination remains the safest option to kerb transmission of these variants. Therefore, people must be vaccinated, wear masks, perform regular hand hygiene, and observe social distancing. Additionally, genome sequencing of positive samples can help detect various virus variants; thus, mapping cases in a particular area can be performed.

Revealing the structural and molecular interaction landscape of the favipiravir-RTP and SARS-CoV-2 RdRp complex through integrative bioinformatics: Insights for developing potent drugs targeting SARS-CoV-2 and other viruses.

BACKGROUND: The global research community has made considerable progress in therapeutic and vaccine research during the COVID-19 pandemic. Several therapeutics have been repurposed for the treatment of COVID-19. One such compound is, favipiravir, which was approved for the treatment of influenza viruses, including drug-resistant influenza. Despite the limited information on its molecular activity, clinical trials have attempted to determine the effectiveness of favipiravir in patients with mild to moderate COVID-19. Here, we report the structural and molecular interaction landscape of the macromolecular complex of favipiravir-RTP and SARS-CoV-2 RdRp with the RNA chain. METHODS: Integrative bioinformatics was used to reveal the structural and molecular interaction landscapes of two macromolecular complexes retrieved from RCSB PDB. RESULTS: We analyzed the interactive residues, H-bonds, and interaction interfaces to evaluate the structural and molecular interaction landscapes of the two macromolecular complexes. We found seven and six H-bonds in the first and second interaction landscapes, respectively. The maximum bond length is 3.79 Å. In the hydrophobic interactions, five residues (Asp618, Asp760, Thr687, Asp623, and Val557) were associated with the first complex and two residues (Lys73 and Tyr217) were associated with the second complex. The mobilities, collective motion, and B-factor of the two macromolecular complexes were analyzed. Finally, we developed different models, including trees, clusters, and heat maps of antiviral molecules, to evaluate the therapeutic status of favipiravir as an antiviral drug. CONCLUSIONS: The results revealed the structural and molecular interaction landscape of the binding mode of favipiravir with the nsp7-nsp8-nsp12-RNA SARS-CoV-2 RdRp complex. Our findings can help future researchers in understanding the mechanism underlying viral action and guide the design of nucleotide analogs that mimic favipiravir and exhibit greater potency as antiviral drugs against SARS-CoV-2 and other infectious viruses. Thus, our work can help in preparing for future epidemics and pandemics.

A novel mutation-proof, next-generation vaccine to fight against upcoming SARS-CoV-2 variants and subvariants, designed through AI enabled approaches and tools, along with the machine learning based immune simulation: A vaccine breakthrough.

Emerging SARS-CoV-2 variants and subvariants are great concerns for their significant mutations, which are also responsible for vaccine escape. Therefore, the study was undertaken to develop a mutation-proof, next-generation vaccine to protect against all upcoming SARS-CoV-2 variants. We used advanced computational and bioinformatics approaches to develop a multi-epitopic vaccine, especially the AI model for mutation selection and machine learning (ML) strategies for immune simulation. AI enabled and the top-ranked antigenic selection approaches were used to select nine mutations from 835 RBD mutations. We selected twelve common antigenic B cell and T cell epitopes (CTL and HTL) containing the nine RBD mutations and joined them with the adjuvants, PADRE sequence, and suitable linkers. The constructs' binding affinity was confirmed through docking with TLR4/MD2 complex and showed significant binding free energy (-96.67 kcal mol-1) with positive binding affinity. Similarly, the calculated eigenvalue (2.428517e-05) from the NMA of the complex reveals proper molecular motion and superior residues' flexibility. Immune simulation shows that the candidate can induce a robust immune response. The designed mutation-proof, multi-epitopic vaccine could be a remarkable candidate for upcoming SARS-CoV-2 variants and subvariants. The study method might guide researchers in developing AI-ML and immunoinformatics-based vaccines for infectious disease.

A comprehensive analysis of the mutational landscape of the newly emerging Omicron (B.1.1.529) variant and comparison of mutations with VOCs and VOIs.

The Omicron variant is spreading rapidly throughout several countries. Thus, we comprehensively analyzed Omicron's mutational landscape and compared mutations with VOC/VOI. We analyzed SNVs throughout the genome, and AA variants (NSP and SP) in VOC/VOI, including Omicron. We generated heat maps to illustrate the AA variants with high mutation prevalence (> 75% frequency) of Omicron, which demonstrated eight mutations with > 90% prevalence in ORF1a and 29 mutations with > 75% prevalence in S-glycoprotein. A scatter plot for Omicron and VOC/VOI's cluster evaluation was computed. We performed a risk analysis of the antibody-binding risk among four mutations (L452, F490, P681, D614) and observed three mutations (L452R, F490S, D614G) destabilized antibody interactions. Our comparative study evaluated the properties of 28 emerging mutations of the S-glycoprotein of Omicron, and the ΔΔG values. Our results showed K417N with minimum and Q954H with maximum ΔΔG value. Furthermore, six important RBD mutations (G339D, S371L, N440K, G446S, T478K, Q498R) were chosen for comprehensive analysis for stabilizing/destabilizing properties and molecular flexibility. The G339D, S371L, N440K, and T478K were noted as stable mutations with 0.019 kcal/mol, 0.127 kcal/mol, 0.064 kcal/mol, and 1.009 kcal/mol. While, G446S and Q498R mutations showed destabilizing results. Simultaneously, among six RBD mutations, G339D, G446S, and Q498R mutations increased the molecular flexibility of S-glycoprotein. This study depicts the comparative mutational pattern of Omicron and other VOC/VOI, which will help researchers to design and deploy novel vaccines and therapeutic antibodies to fight against VOC/VOI, including Omicron.

An Insight into Advances in Developing Nanotechnology Based Therapeutics, Drug Delivery, Diagnostics and Vaccines: Multidimensional Applications in Tuberculosis Disease Management.

Tuberculosis (TB), one of the deadliest contagious diseases, is a major concern worldwide. Long-term treatment, a high pill burden, limited compliance, and strict administration schedules are all variables that contribute to the development of MDR and XDR tuberculosis patients. The rise of multidrug-resistant strains and a scarcity of anti-TB medications pose a threat to TB control in the future. As a result, a strong and effective system is required to overcome technological limitations and improve the efficacy of therapeutic medications, which is still a huge problem for pharmacological technology. Nanotechnology offers an interesting opportunity for accurate identification of mycobacterial strains and improved medication treatment possibilities for tuberculosis. Nano medicine in tuberculosis is an emerging research field that provides the possibility of efficient medication delivery using nanoparticles and a decrease in drug dosages and adverse effects to boost patient compliance with therapy and recovery. Due to their fascinating characteristics, this strategy is useful in overcoming the abnormalities associated with traditional therapy and leads to some optimization of the therapeutic impact. It also decreases the dosing frequency and eliminates the problem of low compliance. To develop modern diagnosis techniques, upgraded treatment, and possible prevention of tuberculosis, the nanoparticle-based tests have demonstrated considerable advances. The literature search was conducted using Scopus, PubMed, Google Scholar, and Elsevier databases only. This article examines the possibility of employing nanotechnology for TB diagnosis, nanotechnology-based medicine delivery systems, and prevention for the successful elimination of TB illnesses.

A sustainable trend in COVID-19 research: An environmental perspective

The COVID-19 pandemic has been the most disrupting phenomenon in this decade. Its potent effects have earned the attention of researchers in different fields around the world. Amongst them, authors from different countries have published numerous research articles based on the environmental concepts of COVID-19. The environment is considered an essential receptor in the COVID-19 pandemic, and it is academically significant to look into publications to follow the pathway of hot subjects and upcoming trends in studies. Reviewing the literature, therefore, can provide valuable information, regarding the strengths and weaknesses of facing the COVID-19 pandemic, considering the environmental viewpoint. The present study categorizes the understanding caused by environmental and COVID-19-related published papers in the Scopus metadata from 2020 to 2021. VOSviewer is a promising bibliometric tool used in the present study to analyze the publications with both "COVID-19*” and "Environment” keywords. Then, a narrative evaluation is utilized to delineate the most interesting research topics. Cooccurrence analysis is applied in this research, which further characterizes different thematic clusters. The published literature mainly focused on four central cluster environmental concepts: air pollution, epidemiology and virus transmission, water and wastewater, and environmental policy. It also reveals that environmental policy has gained worldwide interest with the main keyword "management” and gathers keywords like waste management, sustainability, governance, ecosystem and climate change. Although these keywords could also appear in other environmental policy-related research, the importance of the COVID-19 pandemic requires such comprehensive research. The fourth cluster involves governance and management concerns during the pandemic. Mapping the research topics in different clusters will pave the way for the researchers to view future potential ideas and studies better. The scope for further research needs from the perspective of the environmental concepts is reviewed and recommended, which can expand environmental sciences vital role and value in alerting, observing, and COVID-19 prediction for all four clusters. In another word, the research trend would shift from qualitative studies and perspectives to quantitative ones.

Antivirals for COVID-19: A critical review.

No specific drugs have been approved for coronavirus disease 2019 (COVID-19) to date as the development of antivirals usually requires time. Therefore, assessment and use of currently available antiviral drugs is critical for a timely response to the current pandemic. Here, we have reviewed anti-SARS-CoV-2 potencies of available antiviral drug groups such as fusion inhibitors, protease inhibitors, neuraminidase inhibitors, and M2 ion-channel protein blockers. Although clinical trials to assess the efficacy of these antivirals are ongoing, this review highlights important information including docking and modeling analyses, in vitro studies, as well as results from clinical uses of these antivirals against COVID-19 pandemic.