Peter T. Habib

The pandemic of COVID-19 has caused a global crisis. Today, everybody focuses on COVID-19 infection prevention, preparation, and discussion of physical health effects issues. It is important to understand, however, that a few will face life-threatening negative effects on physical health, but that all people will face the negative impact of the pandemic on mental health. COVID-19 hospitals are established in different locations to address the physical health implications of the pandemic. However, it is necessary to understand the effects of infections on mental health more effectively to prevent the negative consequences of infection. Here, we try to find out how the infection could affect mental health. We identify motifs in SARS-CoV-2 that are predicted to interact with human transcription factors (TF). Those TFs regulating behavior and mental health. Our results show that SARS-CoV-2 infection may lead to overactivation or inhibition of critical genes already known to affect behavior and mental health. This study is still limited to in silico limits so, clinical investigation needs to be addressed to assess our hypothesis.

Coronavirus, the current pandemic that takes lives of 1,936,632 until the time of the paper. Scientists over the world exhibited all efforts to fight the virus. Many supplements developed to fight coronavirus by supporting the immune system which is the strongest worrier against it for now. One of the neglected strategies to enhance immunity is music therapy. Music therapy can drive immunity into different conditions. Not only able to boost and suppress immunity but also many physiological and psychological diseases are treated with music therapy. It is more than just entertainment, and research shows that music therapy may be helpful and cost-effective to support standard therapy. This systemic review tries to explore the effect of music therapy and arrives at a conclusion suggesting routine procedures to support the patient with music therapy. However, the idea of using music therapy in improving the immune system must undergo clinical and medical confirmation to be used

The infections with the Nipah virus (NiV) are highly infectious and may lead to severe febrile encephalitis. High rates of mortality in southeastern Asia including Bengal, Malaysia, Papua New Guinea, Vietnam, Cambodia, Indonesia, Madagascar, the Philippines, Thailand, and India have been reported in NiV outbreaks. Considering the high risk of an epidemic, NiV was declared a priority pathogen by the World Health Organization (WHO). However, for the treatment of this infection, there is no effective therapy or approved FDA medicines. RNA-dependent polymerase RNA (RdRp) plays an important role in viral replication among the nine well-known proteins of NiV. Therefore, fourteen antiviral molecules have been computerized for NiV RNA-dependent RNA polymerase and demonstrated a potential inhibition effect against coronavirus (NiV-RdRp). A multi-step molecular docking process, followed by extensive analyzes of molecular binding interactions, binding energy estimates, synthetic accessibility assessments, and toxicity tests. Analysis reveals that Uprifosbuvir is the most suitable inhibitor for RdRp of Nipah Virus regarding the binding affinity and binding in the target cavity. Although, such studies need clinical confirmation.

Nipah virus (NiV) is a zoonotic paramyxovirus of the Henipavirus genus first identified in Malaysia in 1998. Henipavirus have bat reservoir hosts and have been isolated from fruit bats found across Oceania, Asia, and Africa. Bat-to-human transmission is thought to be the primary mode of human NiV infection, although multiple intermediate hosts are described. Human infections with NiV were originally described as a syndrome of fever and rapid neurological decline following contact with swine. More recent outbreaks describe a syndrome with prominent respiratory symptoms and human-to-human transmission. Nearly annual outbreaks have been described since 1998 with case fatality rates reaching greater than 90%. To prevent the spreading of the Nipah virus and turning it into a new pandemic, we must be armed with a ready-made vaccine to save the time consuming that vaccine takes until production. Here we in this paper, we analyzed the whole Nipah virus proteome to find out the most antigenic, non-allergic, and immune inducing epitopes to construct different vaccines that undergone deep investigation to reveal the most appropriate vaccine to immunize humanity from this probably pandemic.

The SARS Coronavirus-2 (SARS-CoV-2) pandemic has become a global epidemic that has increased the scientific community's concern about developing and finding a counteraction against this lethal virus. So far, hundreds of thousands of people have been infected by the pandemic due to contamination and spread. This research was therefore carried out to develop potential epitope-based vaccines against the SARS-CoV-2 virus using reverse vaccinology and immunoinformatics approaches. Three potential vaccine constructs were designed after intensive computational experimentation, and one vaccine model was chosen as the best vaccine based on a molecular docking analysis that is intended to work efficiently against SARS-CoV-2. In order to verify biological stability and find an appropriate mass production technique for the chosen vaccine, molecular dynamics simulation, and silico codon adaptation studies were subsequently carried out. This study should help to maintain current research efforts to secure a definitive preventive measure against this contagious disease.

COVID-19, a new pandemic of coronavirus (CoV), was reported in Wuhan, China, in 2019. No specific drugs are available and investigations regarding COVID-19 treatment are proceeding. Lan et al. (2020) successfully crystallized the COVID-19 spike receptor-binding domain bounding to the ACE2 receptor, which is a potential drug target. The present study aimed to assess 716 bioactive compounds found in the South Atlantic Ocean as potential COVID-19 Spike inhibitors, using a molecular docking study. Molecular docking was performed using Autodock Vina to analyze the probability of docking. COVID-19 Spike was docked with several compounds, and docking was virtually screened by Chimera, Pymol, and Biovia Discovery Studio and test for draggability using SWISSADME. The analysis shows that 11 NPs out of 716 are predicted to be Spike inhibitors by blocking the amino acids responsible for binding Spike to ACE2. However, further findings are necessary to experimentally investigated for their potential medicinal use.

Introduction: The SARS Coronavirus-2 (SARS-CoV-2) pandemic has become a global epidemic that has increased the scientific community's concern about developing and finding a counteraction against this lethal virus. So far, hundreds of thousands of people have been infected by the pandemic due to contamination and spread. This research was therefore carried out to develop potential epitope-based vaccines against the SARS-CoV-2 virus using reverse vaccinology and immunoinformatics approaches.Material and Methods: The material of SARS-COV2 Surface Glycoprotein (S), Membrane Protein (M), and Envelope Protein (E) were downloaded from the NCBI protein database. Each protein has undergone epitopes prediction for MHC class I epitopes, MHC class II epitopes, and Antibody of B-cell epitopes. Selected epitopes according to their antigenicity score was tested for allergenicity and toxicity. Finally, filtered epitopes were used in vaccine construction. Vaccines were constructed, docked against Toll-like receptor 3, and undergone Molecular Dynamic simulation. The vaccine with the best scores, subjected to immune stimulation and cloning design.Results: Three vaccines were constructed, COVac-1, COVac-2, and COVac-3. Each vaccine was submitted into a deep investigation. The molecular dynamic simulation determines the stability and physical movement of protein atoms and molecules. After Molecular dynamics simulation, COVac-1 was having the best scores. COVac-1 was then subjected to immune simulation analysis to insure the stimulation of innate and adaptive immunity. After passing the immune simulation, COVac-1 was integrated into E.coli pET-30b plasmid using in silico cloning design.Conclusion:Viral pandemics are threatened to face humanity today. The best scenario to fight against any pandemic is utilizing the full power of computational biology, especially immune-informatics, to design and discover in silico new vaccines or molecules that may stimulate the immune system against the invader pathogens or inhibit the pathogen life cycle.

Introduction: The infections with the Nipah virus (NiV) are highly infectious and may lead to severe febrile encephalitis. High mortality rates in southeastern Asia, including Bengal, Malaysia, Papua New Guinea, Vietnam, Cambodia, Indonesia, Madagascar, the Philippines, Thailand, and India, have been reported in NiV outbreaks. Considering the high risk of an epidemic, NiV was declared a priority pathogen by the World Health Organization. However, for the treatment of this infection, there is no effective therapy or approved FDA medicines. RNA-dependent polymerase RNA (RdRp) plays an important role in viral replication among the nine well-known proteins of NiV.Material and Methods: Fourteen antiviral molecules have been computerized for NiV RNA-dependent RNA polymerase and demonstrated a potential inhibition effect against coronavirus (NiV-RdRp). A multi-step molecular docking process, followed by extensive analyzes of molecular binding interactions, binding energy estimates, synthetic accessibility assessments, and toxicity tests.Results: Molecular docking analysis reveals that Uprifosbuvir is the most suitable inhibitor for RdRp of Nipah Virus regarding the binding affinity and binding in the target cavity. Although, such studies need clinical confirmation.Conclusion: The role of anti-viral molecules as a ligand against RNA-dependent RNA polymerase is critical important in the current era. Computational tools such as molecular docking has proven its power in the analysis of molecules interaction. Our analysis reveals the Uprifosbuvir might be a candidate RdRp inhibitor. This study should further investigate the properties of the already identified anti-viral molecules followed by a pharmacological investigation of these in-silico findings in suitable models.

The term chimeric virus was not popular in the last decades. Recently, according to current sequencing efforts in discovering COVID-19 Secrets, the generated information assumed the presence of 6 Coronavirus main strains, but coronavirus diverges into hundreds of sub-strains. the bottleneck is the mutation rate. With two mutation/month, humanity will meet a new sub-strain every month. Tracking new sequenced viruses is urgently needed because of the pathogenic effect of the new substrains. here we introduce COVATOR, A user-friendly and python-based software that identifies viral chimerism. COVATOR aligns input genome and protein that has no known source, against genomes and protein with known source, then gives the user a graphical summary.

COVID-19, caused by SARS-CoV-2 infection, has already reached pandemic proportions in a matter of a few weeks. At the time of writing this manuscript, the unprecedented public health crisis caused more than 2.5 million cases with a mortality range of 5-7%. The SARS-CoV-2, also called novel Coronavirus, is related to both SARS-CoV and bat SARS. Great efforts have been spent to control the pandemic that has become a significant burden on the health systems in a short time. Since the emergence of the crisis, a great number of researchers started to use the AI tools to identify drugs, diagnosing using CT scan images, scanning body temperature, and classifying the severity of the disease. The emergence of variants of the SARS-CoV-2 genome is a challenging problem with expected serious consequences on the management of the disease. Here, we introduce COVIDier, a deep learning-based software that is enabled to classify the different genomes of Alpha coronavirus, Beta coronavirus, MERS, SARS-CoV-1, SARS-CoV-2, and bronchitis-CoV. COVIDier was trained on 1925 genomes, belonging to the three families of SARS retrieved from NCBI Database to propose a new method to train deep learning model trained on genome data using Multi-layer Perceptron Classifier (MLPClassifier), a deep learning algorithm, that could blindly predict the virus family name from the genome of by predicting the statistically similar genome from training data to the given genome. COVIDier able to predict how close the emerging novel genomes of SARS to the known genomes with accuracy 99%. COVIDier can replace tools like BLAST that consume higher CPU and time.