Computers and viral diseases. Preliminary bioinformatics studies on the design of a synthetic vaccine and a preventative peptidomimetic antagonist against the SARS-CoV-2 (2019-nCoV, COVID-19) coronavirus

A potential allosteric inhibitor of SARS-CoV-2 main protease (Mpro) identified through metastable state analysis

Anti-COVID19 drugs, such as nirmatrelvir, have been developed targeting the SARS-CoV-2 main protease, Mpro, based on the critical requirement of its proteolytic processing of the viral polyproteins into functional proteins essential for viral replication. However, the emergence of SARS-CoV-2 variants with Mpro mutations has raised the possibility of developing resistance against these drugs, likely due to therapeutic targeting of the Mpro catalytic site. An alternative to these drugs is the development of drugs that target an allosteric site distant from the catalytic site in the protein that may reduce the chance of the emergence of resistant mutants. Here, we combine computational analysis with in vitro assay and report the discovery of a potential allosteric site and an allosteric inhibitor of SARS-CoV-2 Mpro. Specifically, we identified an Mpro metastable state with a deformed catalytic site harboring potential allosteric sites, raising the possibility that stabilization of this metastable state through ligand binding can lead to the inhibition of Mpro activity. We then performed a computational screening of a library (∼4.2 million) of drug-like compounds from the ZINC database and identified several candidate molecules with high predicted binding affinity. MD simulations showed stable binding of the three top-ranking compounds to the putative allosteric sites in the protein. Finally, we tested the three compounds in vitro using a BRET-based Mpro biosensor and found that one of the compounds (ZINC4497834) inhibited the Mpro activity. We envisage that the identification of a potential allosteric inhibitor of Mpro will aid in developing improved anti-COVID-19 therapy.

Searching possible SARS-CoV-2 main protease inhibitors in constituents from herbal medicines using in silico studies

The largest threat to civilization since the Second World War is the spread of the new coronavirus disease (COVID-19). Therefore, there is an urgent need for innovative therapeutic medicines to treat COVID-19. Reusing bio-actives is a workable and efficient strategy in the battle against new epidemics because the process of developing new drugs is time-consuming. This research aimed to identify which herbal remedies had the highest affinity for the receptor and assess a variety of them for potential targets to suppress the SARS-CoV-2 Mpro. The use of AutoDock Vina for structure-based virtual screening was done first due to the importance of protein interactions in the development of drugs. Molecular docking was used in the comparative study to assess 89 different chemicals from medicinal herbs. To anticipate their effectiveness against the primary protease of SARS-CoV-2, more analysis was done on the ADMET profile, drug-likeness, and Lipinski's rule of five. The next step involved three replicas of 100 ns-long molecular dynamics simulations on the potential candidates, which were preceded by calculations of the binding free energy of MM-GBSA. The outcomes showed that Achyrodimer A, Cinchonain Ib, Symphonone F, and Lupeol acetate all performed well and had the highest 6LU7 binding affinities. Using RMSD, RMSF, and protein-ligand interactions, the stability of the protein-ligand complex was assessed. The studies indicate that bioactive substances obtained from herbal medicines may function as a COVID-19 therapeutic agent, necessitating additional wet lab research to confirm their therapeutic potential, efficacy, and pharmacological capacity against the condition.Communicated by Ramaswamy H. Sarma.

An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein-peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide-protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides.

In Silico Design and Evaluation of a Novel Therapeutic Agent Against the Spike Protein as a Novel Treatment Strategy for COVID-19 Treatment

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that is associated with severe damage to other human organs. It causes by a novel coronavirus, and it is spreading all over the world. To date, there is some approved vaccine or therapeutic agent which could be effective against this disease. But their effectiveness against mutated strains is not studied completely. The spike glycoprotein on the surface of the coronaviruses gives the virus the ability to bind to host cell receptors and enter cells. Inhibition of attachment of these spikes can lead to virus neutralization by inhibiting viral entrance.

AIMS

In this study, we tried to use the virus entrance strategy against itself by utilizing virus receptor (ACE-2) in order to design an engineered protein consisting of a human Fc antibody fragment and a part of ACE-2, which reacts with virus RBD, and we also evaluated this interaction by computational methods and in silico methods. Subsequently, we have designed a new protein structure to bind with this site and inhibit the virus from attaching to its cell receptor, mechanically or chemically.

METHODS

Various in silico software, bioinformatics, and patent databases were used to retrieve the requested gene and protein sequences. The physicochemical properties and possibility of allergenicity were also examined. Three-dimensional structure prediction and molecular docking were also performed to develop the most suitable therapeutic protein.

RESULTS

The designed protein consisted of a total of 256 amino acids with a molecular weight of 28984.62 and 5.92 as a theoretical isoelectric point. Instability and aliphatic index and grand average of hydropathicity are 49.99, 69.57 and -0.594, respectively.

CONCLUSIONS

In silico studies can provide a good opportunity to study viral proteins and new drugs or compounds since they do not need direct exposure to infectious agents or equipped laboratories. The suggested therapeutic agent should be further characterized in vitro and in vivo.

Introduction and effect of natural selection analysis at common mutations of SARS-CoV-2 spike gene in Iran

The epidemic of coronavirus disease 2019 (COVID-19) was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spike (S) protein of SARS-Cov-2 is composed of two subunits, S1 and S2. This study aimed to describe SARS-CoV-2 haplotypes in Iranians based on the S gene, which plays a key role in the receptor recognition and cell membrane fusion proses. 95 positive saliva samples for SARS-CoV-2 were amplified and sequenced for the S gene. The sequences were classified into 35 haplotypes, which 11 haplotypes were new (H1, H2, H3, H4, H6, H7, H11, H13, H15, H16, H25) and have not been reported so far. Amino acid substitutions were found at 40 positions that 23 were located at S1 subunit and 16 were at S2 subunit and one was at cleavage loop (P681H/R), thus polymorphisms at S1 subunit were found to be higher than S2. The neutrality index (NI) analyses showed a negative departure from the neutral substitution patterns (NI > 1) for S1 and S2 subunit in the studied sequences. The co-occurrence of B-cell epitopes and mutation sites were found in seven positions with more probably to be exposed the immune system pressure. In conclusion, the results provide the significant data to design an effective vaccine based on this protein.

Julia for biologists

Major computational challenges exist in relation to the collection, curation, processing and analysis of large genomic and imaging datasets, as well as the simulation of larger and more realistic models in systems biology. Here we discuss how a relative newcomer among programming languages-Julia-is poised to meet the current and emerging demands in the computational biosciences and beyond. Speed, flexibility, a thriving package ecosystem and readability are major factors that make high-performance computing and data analysis available to an unprecedented degree. We highlight how Julia's design is already enabling new ways of analyzing biological data and systems, and we provide a list of resources that can facilitate the transition into Julian computing.

Antitarget, Anti-SARS-CoV-2 Leads, Drugs, and the Drug Discovery-Genetics Alliance Perspective

The most advanced antiviral molecules addressing major SARS-CoV-2 targets (Main protease, Spike protein, and RNA polymerase), compared with proteins of other human pathogenic coronaviruses, may have a short-lasting clinical efficacy. Accumulating knowledge on the mechanisms underlying the target structural basis, its mutational progression, and the related biological significance to virus replication allows envisaging the development of better-targeted therapies in the context of COVID-19 epidemic and future coronavirus outbreaks. The identification of evolutionary patterns based solely on sequence information analysis for those targets can provide meaningful insights into the molecular basis of host-pathogen interactions and adaptation, leading to drug resistance phenomena. Herein, we will explore how the study of observed and predicted mutations may offer valuable suggestions for the application of the so-called "synthetic lethal" strategy to SARS-CoV-2 Main protease and Spike protein. The synergy between genetics evidence and drug discovery may prioritize the development of novel long-lasting antiviral agents.

Amentoflavone derivatives against SARS-CoV-2 main protease (MPRO): An in silico study

 Globally, novel coronavirus (nCoV19) outbreak is a great concern to humanity owing to the unavailability of effective medication or vaccine to date. Therefore, the development of drugs having anti-COVID-19 potential is a need of time. In this milieu, in-silico studies have proven to be rapid, inexpensive and effective as compared to other experimental studies. Evidently, natural products have shown significant potential in drug development to curtail different ailments, which have opened a new horizon in the screening of anti-COVID-19 agents. In this study, in-silico analysis were performed on derivatives of amentoflavone (4′, 4′′′-Dimethylamentoflavone, 4′′′, 7-Di-O-Methylamentoflavone, 4′′′′′′-methylamentoflavone, 4′-Monomethylamentoflavone, 7,4′-Dimethylamentoflavone, 7′-O-Methylamentoflavone, 7-O-methylamentoflavone, Heveaflavone, kayaflavone, and Sciadopitysin) and FDA approved anti-viral drug (camostatmesylate). All the derivatives of amentoflavone and FDA-approved anti-viral drugs were docked against SARS-CoV2 main protease (MPRO). The ten derivatives of amentoflavone showed strong interactions with the MPRO protein. In all cases, derivatives of amentoflavone showed good interaction with the targeted protein and better binding/docking score (–9.0351, –8.8566, –8.8509, –8.7746, –8.6192, –8.2537, –8.0876, –7.9501, –7.6429, and –7.6248 respectively) than FDA approved anti-viral drug. Therefore, derivatives of amentoflavone may be potent leads in drug discovery to combat HCoVs, such as SARS-CoV2. Moreover, to support the outcomes of this study further in-vivo investigations are required.

Forecasting the COVID-19 with Interval Type-3 Fuzzy Logic and the Fractal Dimension

In this article, the prediction of COVID-19 based on a combination of fractal theory and interval type-3 fuzzy logic is put forward. The fractal dimension is utilized to estimate the time series geometrical complexity level, which in this case is applied to the COVID-19 problem. The main aim of utilizing interval type-3 fuzzy logic is for handling uncertainty in the decision-making occurring in forecasting. The hybrid approach is formed by an interval type-3 fuzzy model structured by fuzzy if then rules that utilize as inputs the linear and non-linear values of the dimension, and the forecasts of COVID-19 cases are the outputs. The contribution is the new scheme based on the fractal dimension and interval type-3 fuzzy logic, which has not been proposed before, aimed at achieving an accurate forecasting of complex time series, in particular for the COVID-19 case. Publicly available data sets are utilized to construct the interval type-3 fuzzy system for a time series. The hybrid approach can be a helpful tool for decision maker in fighting the pandemic, as they could use the forecasts to decide immediate actions. The proposed method has been compared with previous works to show that interval type-3 fuzzy systems outperform previous methods in prediction.

Computational and comparative investigation of hydrophobic profile of spike protein of SARS-CoV-2 and SARS-CoV

The hydrophobic force is one of the most dominant factors in protein folding. A protein becomes functional only when it achieves its three-dimensional structure and stability upon folding. For a better understanding of the hydrophobic effects and their function in protein folding, quantitative measurement of the hydrophobicity of amino acid side chains is crucial. Spike protein is the primary structural protein in SARS-CoV-2 and SARS-CoV. This study explores how protein sequences in SARS-CoV-2 and SARS-CoV spike proteins encode hydrophobic interactions. Computational tools/techniques have been utilized to investigate the protein sequences of the spike proteins of SARS-CoV-2 and SARS-CoV. Investigations provided an estimate of hydrophobic distribution and its relative strength, indicating a hydrophobic pattern. Analysis of the spike protein's hydrophobic profile may help identify and treat the virus-caused disease; additionally, it can give an insight into the transmissibility and pathogenicity of the virus.

Impact of the New Coronavirus Infection on the Immune System of Children and Adolescents in the Region of the Russian Federation

The emergence of COVID-19 (SARS-CoV-2) has presented public health professionals with new challenges in the diagnosis of the disease and treatment of patients. Nowadays, the epidemiology, clinical features, prevention and treatment of the disease are studied poorly due to continuous mutation of the pathogen. One of the consequences of the new coronavirus infection could be changes in the immune system of the human population. A detailed analysis of the immunological status of different racial groups under the influence of the new coronavirus infection is currently studied insufficiently, making this work of particular relevance. There is also a reluctance among some Russian residents to be vaccinated, including the population of Perm Krai, due to a lack of research on possible deviations in cellular immunity due to SARS-CoV-2 vaccination. At the start of the third wave caused by the new coronavirus infection, only 40% of the Russian population had been vaccinated, which was insufficient to acquire collective immunity. In the autumn of 2021, a QR code measure was introduced for vaccinated residents, which resulted in exceeding the necessary barrier for acquiring collective immunity. Due to the high growth and severity of the disease, we analysed the immunograms of children and adolescents, aged from 5 months to 17 years, in Perm Krai during the pandemic years 2020-2021. The patients' immunological status results were divided into three categories. Laboratory diagnosis of the human immune system was carried out using serological and flow cytophotometric analyses. A total of 247 samples were analysed. The aim of this work was to investigate changes in the immune system of children and adolescents during the pandemic caused by the new coronavirus infection. The methodology was based on the analysis of immunograms, including biochemical studies, immune status and flow cytophotometric analysis. The immunograms were pre-sorted by IgA, IgM, IgG immunoglobulin status into four categories: absence of disease-k₁ in which IgA, IgM, IgG immunoglobulin values were within the reference interval, active disease stage-k₂ in which IgA, IgM immunoglobulins had gone beyond the reference interval, passive disease stage-k₃ characterised by IgG and IgM immunoglobulin status, and patient recovery process-k₄. In the immunograms, three immune status indicators were selected for further investigation: phagocytosis absolute value, phagocytic number and phagocytic index and five flow cytometry indices: leukocytes, lymphocytes, NK cells (CD16+CD56+), T helpers (CD3+CD4+) and CD4+/CD8+ immunoregulation index. A quantitative analysis of the deviations of these indicators from the reference intervals was performed in the three studied age groups of children and adolescents living in Perm Krai of the Russian Federation during the pandemic of 2020-2021.

An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.

Receptor-binding domain-anchored peptides block binding of severe acute respiratory syndrome coronavirus 2 spike proteins with cell surface angiotensin-converting enzyme 2

Background

The COVID-19 pandemic has killed over 6 million people worldwide. Despite the accumulation of knowledge about the causative pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the pathogenesis of this disease, cures remain to be discovered. We searched for certain peptides that might interfere with spike protein (S protein)-angiotensin-converting enzyme 2 (ACE2) interactions.

Methods

Phage display (PhD)-12 peptide library was screened against recombinant spike trimer (S-trimer) or receptor-binding domain (S-RBD) proteins. The resulting enriched peptide sequences were obtained, and their potential binding sites on S-trimer and S-RBD 3D structure models were searched. Synthetic peptides corresponding to these and other reference sequences were tested for their efficacy in blocking the binding of S-trimer protein onto recombinant ACE2 proteins or ACE2-overexpressing cells.

Results

After three rounds of phage selections, two peptide sequences (C2, DHAQRYGAGHSG; C6, HWKAVNWLKPWT) were enriched by S-RBD, but only C2 was present in S-trimer selected phages. When the 3D structures of static monomeric S-RBD (6M17) and S-trimer (6ZGE, 6ZGG, 7CAI, and 7CAK, each with different status of S-RBDs in the three monomer S proteins) were scanned for potential binding sites of C2 and C6 peptides, C6 opt to bind the saddle of S-RBD in both 6M17 and erected S-RBD in S-trimers, but C2 failed to cluster there in the S-trimers. In the competitive S-trimer-ACE2-binding experiments, synthetic C2 and C6 peptides inhibited S-trimer binding onto 293T-ACE2hR cells at high concentrations (50 μM) but not at lower concentrations (10 μM and below), neither for the settings of S-trimer binding onto recombinant ACE2 proteins.

Conclusion

Using PhD methodology, two peptides were generated bearing potentials to interfere with S protein-ACE2 interaction, which might be further exploited to produce peptidomimetics that block the attachment of SARS-CoV-2 virus onto host cells, hence diminishing the pathogenesis of COVID-19.

In silico detection of potential inhibitors from vitamins and their derivatives compounds against SARS-CoV-2 main protease by using molecular docking, molecular dynamic simulation and ADMET profiling

COVID-19 is a new infectious disease caused by SARS-COV-2 virus of the coronavirus Family. The identification of drugs against this serious infection is a significant requirement due to the rapid rise in the positive cases and deaths around the world. With this concept, a molecular docking analysis for vitamins and their derivatives (28 molecules) with the active site of SARS-CoV-2 main protease was carried out. The results of molecular docking indicate that the structures with best binding energy in the binding site of the studied enzyme (lowest energy level) are observed for the compounds; Folacin, Riboflavin, and Phylloquinone oxide (Vitamin K1 oxide). A Molecular Dynamic simulation was carried out to study the binding stability for the selected vitamins with the active site of SARS-CoV-2 main protease enzyme. Molecular Dynamic shows that Phylloquinone oxide and Folacin are quite unstable in binding to SARS-CoV-2 main protease, while the Riboflavin is comparatively rigid. The higher fluctuations in Phylloquinone oxide and Folacin indicate that they may not fit very well into the binding site. As expected, the Phylloquinone oxide exhibits small number of H-bonds with protein and Folacin does not form a good interaction with protein. Riboflavin exhibits the highest number of Hydrogen bonds and forms consistent interactions with protein. Additionally, this molecule respect the conditions mentioned in Lipinski's rule and have acceptable ADMET proprieties which indicates that Riboflavin (Vitamin B2) could be interesting for the antiviral treatment of COVID-19.

Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches

An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.

Principles of Quantum Mechanics for Artificial Intelligence in medicine. Discussion with reference to the Quantum Universal Exchange Language (Q-UEL)

This paper reviews some basic principles of Quantum Mechanics, Quantum Computing, and Artificial Intelligence in terms of a specific unifying theme. This theme relates to the hyperbolic or split-complex imaginary numbers and their equivalent matrices, rediscovered by Dirac, and the underlying mathematics of the previously described Q-UEL language based on them. Hyperbolic imaginary numbers h have the property hh = +1: contrast the more familiar i such that ii = -1. Examples of analogous matrices include that for the Hadamard gate as used in quantum computing and the Pauli spin matrices, and all Hermitian matrices of interest in quantum computing can readily be derived from these. They also relate to Dirac dualization, spinor projectors of Quantum Field Theory, the non-wave-like part of quantum theory, collapse of the wave function, and a dualized form of classical probability theory that has advantages in automated reasoning for medicine.

Systems pharmacology-based drug discovery and active mechanism of natural products for coronavirus pneumonia (COVID-19): An example using flavonoids

BACKGROUND

Recently, the value of natural products has been extensively considered because these resources can potentially be applied to prevent and treat coronavirus pneumonia 2019 (COVID-19). However, the discovery of nature drugs is problematic because of their complex composition and active mechanisms.

METHODS

This comprehensive study was performed on flavonoids, which are compounds with anti-inflammatory and antiviral effects, to show drug discovery and active mechanism from natural products in the treatment of COVID-19 via a systems pharmacological model. First, a chemical library of 255 potential flavonoids was constructed. Second, the pharmacodynamic basis and mechanism of action between flavonoids and COVID-19 were explored by constructing a compound-target and target-disease network, targets protein-protein interaction (PPI), MCODE analysis, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment.

RESULTS

In total, 105 active flavonoid components were identified, of which 6 were major candidate compounds (quercetin, epigallocatechin-3-gallate (EGCG), luteolin, fisetin, wogonin, and licochalcone A). 152 associated targets were yielded based on network construction, and 7 family proteins (PTGS, GSK3β, ABC, NOS, EGFR, and IL) were included as central hub targets. Moreover, 528 GO items and 178 KEGG pathways were selected through enrichment of target functions. Lastly, molecular docking demonstrated good stability of the combination of selected flavonoids with 3CL Pro and ACEⅡ.

CONCLUSION

Natural flavonoids could enable resistance against COVID-19 by regulating inflammatory, antiviral, and immune responses, and repairing tissue injury. This study has scientific significance for the selective utilization of natural products, medicinal value enhancement of flavonoids, and drug screening for the treatment of COVID-19 induced by SARS-COV-2.

Cognition and Social Behaviors Related to COVID-19 Among Students in Medical Colleges: A Cross-Sectional Study in Guangdong Province of China

Background

The COVID-19 pandemic is a public health emergency of international concern. This study aimed to describe the cognition and social behaviors related to COVID-19 among medical college students in China and to explore the relevant factors that have affected individual social behaviors. The study could enrich practical research on the social behaviors of college students during the COVID-19 pandemic.

Methods

From February to April 2020, online questionnaire survey was conducted meticulously. Based on their majors, the students were divided into a medical student group (249 cases) and a near-peer medical student group (397 cases). Descriptive statistics was used to elaborate the cognition related to the pandemic and the status quo of social behaviors among these students. A multiple linear regression model was established to analyze the relevant factors affecting individual social behaviors from various perspectives during the pandemic.

Results

Regarding the cognition situation: 76.32% of those surveyed had good pandemic awareness, and the average general cognition score was 30.55 ± 3.17 points. In terms of social behaviors, the average scores for purposive rational actions and affective actions during the outbreak were relatively high, scoring 8.85 ± 1.72 points (>10 points) and 4.32 ± 1.41 points (>6 points), respectively, while the average value rational actions score was relatively low at 5.95 ± 1.90 points (>10 points). The results of the multiple linear regression model showed that urban college students had higher scores for purposive rational actions; college students with the CCP membership had higher value rational actions scores; school and major were also significant factors affecting affective actions scoring. The COVID-19 cognition score had a significant effect on the social behavior score in all dimensions (P < 0.001).

Conclusions

The cognition of COVID-19 among students in Chinese medical colleges was good, and pandemic cognition was an important factor that affected individual social behaviors. Universities and colleges should strengthen the publicity and education of knowledge related to COVID-19, guide students to internalize their knowledge of the pandemic into positive behaviors, and help to win the battle of pandemic prevention and control.

Synthesis, spectroscopic, and computational studies on molecular charge-transfer complex of 2-((2-hydroxybenzylidene) amino)-2-(hydroxymethyl) propane-1, 3-diol with chloranilic acid: Potential antiviral activity simulation of CT-complex against SARS-CoV-2

An innovative charge-transfer complex between the Schiff base 2-((2-hydroxybenzylidene) amino)-2-(hydroxymethyl) propane-1,3-diol [SAL-THAM] and the π-acceptor, chloranilic acid (CLA) within the mole ratio (1:1) was synthesized and characterized aiming to investigate its electronic transition spectra in acetonitrile (ACN), methanol (MeOH) and ethanol (EtOH) solutions. Applying Job`s method in the three solvents supported the 1:1 (CLA: SAL-THAM) mole ratio complex formation. The formation of stable CT- complex was shown by the highest values of charge-transfer complex formation constants, K_CT, calculated using minimum-maximum absorbance method, with the sequence, acetonitrile > ethanol > methanol DFT study on the synthesized CT complex was applied based on the B3LYP method to evaluate the optimized structure and extract geometrical and reactivity parameters. Based on TD-DFT theory, the electronic properties, ¹H and ¹³C NMR, IR, and UV-Vis spectra of the studied system in different solvents showing good agreement with the experimental studies. MEP map described the possibility of hydrogen bonding and charge transfer in the studied system. Finally, a computational approach for screening the antiviral activity of CT - complex towards SARS-CoV-2 coronavirus protease via molecular docking simulation was conducted and confirmed with molecular dynamic (MD) simulation.

Peptides for Vaccine Development

This review discusses peptide epitopes used as antigens in the development of vaccines in clinical trials as well as future vaccine candidates. It covers peptides used in potential immunotherapies for infectious diseases including SARS-CoV-2, influenza, hepatitis B and C, HIV, malaria, and others. In addition, peptides for cancer vaccines that target examples of overexpressed proteins are summarized, including human epidermal growth factor receptor 2 (HER-2), mucin 1 (MUC1), folate receptor, and others. The uses of peptides to target cancers caused by infective agents, for example, cervical cancer caused by human papilloma virus (HPV), are also discussed. This review also provides an overview of model peptide epitopes used to stimulate non-specific immune responses, and of self-adjuvanting peptides, as well as the influence of other adjuvants on peptide formulations. As highlighted in this review, several peptide immunotherapies are in advanced clinical trials as vaccines, and there is great potential for future therapies due the specificity of the response that can be achieved using peptide epitopes.

Amyloid and Hydrogel Formation of a Peptide Sequence from a Coronavirus Spike Protein

We demonstrate that a conserved coronavirus spike protein peptide forms amyloid structures, differing from the native helical conformation and not predicted by amyloid aggregation algorithms. We investigate the conformation and aggregation of peptide RSAIEDLLFDKV, which is a sequence common to many animal and human coronavirus spike proteins. This sequence is part of a native α-helical S2 glycoprotein domain, close to and partly spanning the fusion sequence. This peptide aggregates into β-sheet amyloid nanotape structures close to the calculated pI = 4.2, but forms disordered monomers at high and low pH. The β-sheet conformation revealed by FTIR and circular dichroism (CD) spectroscopy leads to peptide nanotape structures, imaged using transmission electron microscopy (TEM) and probed by small-angle X-ray scattering (SAXS). The nanotapes comprise arginine-coated bilayers. A Congo red dye UV–vis assay is used to probe the aggregation of the peptide into amyloid structures, which enabled the determination of a critical aggregation concentration (CAC). This peptide also forms hydrogels under precisely defined conditions of pH and concentration, the rheological properties of which were probed. The observation of amyloid formation by a coronavirus spike has relevance to the stability of the spike protein conformation (or its destabilization via pH change), and the peptide may have potential utility as a functional material. Hydrogels formed by coronavirus peptides may also be of future interest in the development of slow-release systems, among other applications.

Combating Infectious Diseases with Synthetic Biology

Over the past decades, there have been numerous outbreaks, including parasitic, fungal, bacterial, and viral infections, worldwide. The rate at which infectious diseases are emerging is disproportionate to the rate of development for new strategies that could combat them. Therefore, there is an increasing demand to develop novel, specific, sensitive, and effective methods for infectious disease diagnosis and treatment. Designed synthetic systems and devices are becoming powerful tools to treat human diseases. The advancement in synthetic biology offers efficient, accurate, and cost-effective platforms for detecting and preventing infectious diseases. Herein we focus on the latest state of living theranostics and its implications.

Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance

Coronavirus disease, Covid19, pandemic has a great effect on human heath worldwide since it was first detected in late 2019. A clear understanding of the structure of the available Covid19 datasets might give the healthcare provider a better understanding of identifying some of the cases at an early stage. In this article, we will be looking into a Covid19 Mexican Patients' Dataset (Covid109MPD), and we will apply number of machine learning algorithms on the dataset to select the best possible classification algorithm for the death and survived cases in Mexico, then we will study the performance of the enhancement of the specified classifiers in term of their features selection in order to be able to predict sever, and or death, cases from the available dataset. Results show that J48 classifier gives the best classification accuracy with 94.41% and RMSE = 0.2028 and ROC = 0.919, compared to other classifiers, and when using feature selection method, J48 classifier can predict a surviving Covid19MPD case within 94.88% accuracy, and by using only 10 out of the total 19 features.

Animal models for SARS-CoV-2 and SARS-CoV-1 pathogenesis, transmission and therapeutic evaluation

There is a critical need to develop animal models to alleviate vaccine and drug development difficulties against zoonotic viral infections. The coronavirus family, which includes severe acute respiratory syndrome coronavirus 1 and severe acute respiratory syndrome coronavirus 2, crossed the species barrier and infected humans, causing a global outbreak in the 21^st century. Because humans do not have pre-existing immunity against these viral infections and with ethics governing clinical trials, animal models are therefore being used in clinical studies to facilitate drug discovery and testing efficacy of vaccines. The ideal animal models should reflect the viral replication, clinical signs, and pathological responses observed in humans. Different animal species should be tested to establish an appropriate animal model to study the disease pathology, transmission and evaluation of novel vaccine and drug candidates to treat coronavirus disease 2019. In this context, the present review summarizes the recent progress in developing animal models for these two pathogenic viruses and highlights the utility of these models in studying SARS-associated coronavirus diseases.

Towards faster response against emerging epidemics and prediction of variants of concern

The author, the journal, Computers in Biology and Medicine (CBM), and Elsevier Press more generally, played a helpful very early role in responding to COVID-19. Within a few days of the appearance of the "Wuhan Seafood isolate" genome on GenBank, a bioinformatics study was posted by the present author in ResearchGate in January 2020, "Preliminary Bioinformatics Studies on the Design of Synthetic Vaccines and Preventative Peptidomimetic Antagonists against the Wuhan Seafood Market Coronavirus. Possible Importance of the KRSFIEDLLFNKV Motif" DOI: 10.13140/RG.2.2.18275.09761. On February 2nd^, 2020, a more thorough analysis was submitted to CBM, e-published on February 26, and formally published in April 2020, at about the same time as the virus named as 2019n-CoV was identified as essentially SARS and renames SARS-COV-2. This was followed by four further papers describing in more detail some previously unreported aspects of the early investigation. The speed of research and writing of the papers was made possible by knowledge-gathering tools. Based on this and earlier experiences with fast responses to emerging epidemics such as HIV and Mad Cow Disease, it is possible to envisage the nature of a speedier response to emerging epidemics and new variants of concern in established epidemics.

Applying deep learning-based multi-modal for detection of coronavirus

Amidst the global pandemic and catastrophe created by 'COVID-19', every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus. The experimental results on the datasets available at National Centre for Biotechnology Information, GitHub, and Kaggle repositories show that it is successful in detecting the genome of 'SARS-CoV-2' in the host genome with an accuracy of 99.27% and screening of chest radiographs into COVID-19, non-COVID pneumonia and healthy with a sensitivity of 95.47%. Thus, it may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of 'COVID-19'.

A systematic review of RdRp of SARS-CoV-2 through artificial intelligence and machine learning utilizing structure-based drug design strategy

Since the coronavirus disease has been declared a global pandemic, it had posed a challenge among researchers and raised common awareness and collaborative efforts towards finding the solution. Caused by severe acute respiratory coronavirus syndrome-2 (SARS-CoV-2), coronavirus drug design strategy needs to be optimized. It is understandable that cognizance of the pathobiology of COVID-19 can help scientists in the development and discovery of therapeutically effective antiviral drugs by elucidating the unknown viral pathways and structures. Considering the role of artificial intelligence and machine learning with its advancements in the field of science, it is rational to use these methods which can aid in the discovery of new potent candidates in silico. Our review utilizes similar methodologies and focuses on RNA-dependent RNA polymerase (RdRp), based on its importance as an essential element for virus replication and also a promising target for COVID-19 therapeutics. Artificial neural network technique was used to shortlist articles with the support of PRISMA, from different research platforms including Scopus, PubMed, PubChem, and Web of Science, through a combination of keywords. "English language", from the year "2000" and "published articles in journals" were selected to carry out this research. We summarized that structural details of the RdRp reviewed in this analysis will have the potential to be taken into consideration when developing therapeutic solutions and if further multidisciplinary efforts are taken in this domain then potential clinical candidates for RdRp of SARS-CoV-2 could be successfully delivered for experimental validations.

In Silico Screening of Natural Products as Potential Inhibitors of SARS-CoV-2 Using Molecular Docking Simulation

Objective

To explore potential natural products against severe acute respiratory syndrome coronavirus (SARS-CoV-2) via the study of structural and non-structural proteins of human coronaviruses.

Methods

In this study, we performed an in-silico survey of 25 potential natural compounds acting against SARS-CoV-2. Molecular docking studies were carried out using compounds against 3-chymotrypsin-like protease (3CL^PRO), papain-like protease (PL^PRO), RNA-dependent RNA polymerase (RdRp), non-structural protein (nsp), human angiotensin converting enzyme 2 receptor (hACE2R), spike glycoprotein (S protein), abelson murine leukemia viral oncogene homolog 1 (ABL1), calcineurin-nuclear factor of activated T-cells (NFAT) and transmembrane protease serine 2.

Results

Among the screened compounds, amentoflavone showed the best binding affinity with the 3CL^PRO, RdRp, nsp13, nsp15, hACE2R. ABL1 and calcineurin-NFAT; berbamine with hACE2R and ABL1; cepharanthine with nsp10, nsp14, nsp16, S protein and ABL1; glucogallin with nsp15; and papyriflavonol A with PL^PRO protein. Other good interacting compounds were juglanin, betulinic acid, betulonic acid, broussooflavan A, tomentin A, B and E, 7-methoxycryptopleurine, aloe emodin, quercetin, tanshinone I, tylophorine and furruginol, which also showed excellent binding affinity towards a number of target proteins. Most of these compounds showed better binding affinities towards the target proteins than the standard drugs used in this study.

Conclusion

Natural products or their derivatives may be one of the potential targets to fight against SARS-CoV-2.

Electronic Supplementary Material

Supplementary materials (Appendixes 1–6) are available in the online version of this article at DOI: 10.1007/s11655-021-3504-5

Mining real-world high dimensional structured data in medicine and its use in decision support. Some different perspectives on unknowns, interdependency, and distinguishability

There are many difficulties in extracting and using knowledge for medical analytic and predictive purposes from Real-World Data, even when the data is already well structured in the manner of a large spreadsheet. Preparative curation and standardization or "normalization" of such data involves a variety of chores but underlying them is an interrelated set of fundamental problems that can in part be dealt with automatically during the datamining and inference processes. These fundamental problems are reviewed here and illustrated and investigated with examples. They concern the treatment of unknowns, the need to avoid independency assumptions, and the appearance of entries that may not be fully distinguished from each other. Unknowns include errors detected as implausible (e.g., out of range) values that are subsequently converted to unknowns. These problems are further impacted by high dimensionality and problems of sparse data that inevitably arise from high-dimensional datamining even if the data is extensive. All these considerations are different aspects of incomplete information, though they also relate to problems that arise if care is not taken to avoid or ameliorate consequences of including the same information twice or more, or if misleading or inconsistent information is combined. This paper addresses these aspects from a slightly different perspective using the Q-UEL language and inference methods based on it by borrowing some ideas from the mathematics of quantum mechanics and information theory. It takes the view that detection and correction of probabilistic elements of knowledge subsequently used in inference need only involve testing and correction so that they satisfy certain extended notions of coherence between probabilities. This is by no means the only possible view, and it is explored here and later compared with a related notion of consistency.

Structural Insights on the SARS-CoV-2 Variants of Concern Spike Glycoprotein: A Computational Study With Possible Clinical Implications

Coronavirus disease 2019 (COVID-19) pandemic has been attributed to SARS-CoV-2 (SARS2) and, consequently, SARS2 has evolved into multiple SARS2 variants driving subsequent waves of infections. In particular, variants of concern (VOC) were identified to have both increased transmissibility and virulence ascribable to mutational changes occurring within the spike protein resulting to modifications in the protein structural orientation which in-turn may affect viral pathogenesis. However, this was never fully elucidated. Here, we generated spike models of endemic HCoVs (HCoV 229E, HCoV OC43, HCoV NL63, HCoV HKU1, SARS CoV, MERS CoV), original SARS2, and VOC (alpha, beta, gamma, delta). Model quality check, structural superimposition, and structural comparison based on RMSD values, TM scores, and contact mapping were all performed. We found that: 1) structural comparison between the original SARS2 and VOC whole spike protein model have minor structural differences (TM > 0.98); 2) the whole VOC spike models putatively have higher structural similarity (TM > 0.70) to spike models from endemic HCoVs coming from the same phylogenetic cluster; 3) original SARS2 S1-CTD and S1-NTD models are structurally comparable to VOC S1-CTD (TM = 1.0) and S1-NTD (TM > 0.96); and 4) endemic HCoV S1-CTD and S1-NTD models are structurally comparable to VOC S1-CTD (TM > 0.70) and S1-NTD (TM > 0.70) models belonging to the same phylogenetic cluster. Overall, we propose that structural similarities (possibly ascribable to similar conformational epitopes) may help determine immune cross-reactivity, whereas, structural differences (possibly associated with varying conformational epitopes) may lead to viral infection (either reinfection or breakthrough infection).

A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction

Currently, the novel COVID-19 coronavirus has been widely spread as a global pandemic. The COVID-19 pandemic has a major influence on human life, healthcare systems, and the economy. There are a large number of methods available for predicting the incidence of the virus. A complex and non-stationary problem such as the COVID-19 pandemic is characterized by high levels of uncertainty in its behavior during the pandemic time. The fuzzy logic, especially Type-2 Fuzzy Logic, is a robust and capable model to cope with high-order uncertainties associated with non-stationary time-dependent features. The main objective of the current study is to present a novel Deep Interval Type-2 Fuzzy LSTM (DIT2FLSTM) model for prediction of the COVID-19 incidence, including new cases, recovery cases, and mortality rate in both short and long time series. The proposed model was evaluated on real datasets produced by the world health organization (WHO) on top highly risked countries, including the USA, Brazil, Russia, India, Peru, Spain, Italy, Iran, Germany, and the U.K. The results confirm the superiority of the DIT2FLSTM model with an average area under the ROC curve (AUC) of 96% and a 95% confidence interval of [92-97] % in the short-term and long-term. The DIT2FLSTM was applied to a well-known standard benchmark, the Mackey-Glass time-series, to show the robustness and proficiency of the proposed model in uncertain and chaotic time series problems. The results were evaluated using a 10-fold cross-validation technique and statistically validated through the t-test method. The proposed DIT2FLSTM model is promising for the prediction of complex problems such as the COVID-19 pandemic and making strategic prevention decisions to save more lives.

Vaccine design and delivery approaches for COVID-19

COVID-19 is still a deadly disease that remains yet a major challenge for humans. In recent times, many large pharmaceutical and non-pharmaceutical companies have invested a lot of time and cost in fighting this disease. In this regard, today's scientific knowledge shows that designing and producing an effective vaccine is the best possible way to diminish the disease burden and dissemination or even eradicate the disease. Due to the urgent need, many vaccines are now available earlier than scheduled. New technologies have also helped to produce much more effective vaccines, although the potential side effects must be taken into account. Thus, in this review, the types of vaccines and vaccine designs made against COVID-19, the vaccination programs, as well as the delivery methods and molecules that have been used to deliver some vaccines that need a carrier will be described.

Testing machine learning techniques for general application by using protein secondary structure prediction. A brief survey with studies of pitfalls and benefits using a simple progressive learning approach

Many researchers have recently used the prediction of protein secondary structure (local conformational states of amino acid residues) to test advances in predictive and machine learning technology such as Neural Net Deep Learning. Protein secondary structure prediction continues to be a helpful tool in research in biomedicine and the life sciences, but it is also extremely enticing for testing predictive methods such as neural nets that are intended for different or more general purposes. A complication is highlighted here for researchers testing their methods for other applications. Modern protein databases inevitably contain important clues to the answer, so-called "strong buried clues", though often obscurely; they are hard to avoid. This is because most proteins or parts of proteins in a modern protein data base are related to others by biological evolution. For researchers developing machine learning and predictive methods, this can overstate and so confuse understanding of the true quality of a predictive method. However, for researchers using the algorithms as tools, understanding strong buried clues is of great value, because they need to make maximum use of all information available. A simple method related to the GOR methods but with some features of neural nets in the sense of progressive learning of large numbers of weights, is used to explore this. It can acquire tens of millions and hence gigabytes of weights, but they are learned stably by exhaustive sampling. The significance of the findings is discussed in the light of promising recent results from AlphaFold using Google's DeepMind.

A new fuzzy fractal control approach of non-linear dynamic systems: The case of controlling the COVID-19 pandemics

This article is presenting a first attempt on a proposed fuzzy fractal control method for efficiently controlling nonlinear dynamic systems. The main goal is to combine the main advantages of fractal theoretical concepts and fuzzy logic theory for achieving efficient control of nonlinear dynamic systems. The concept coming from Fractal theory, known as the fractal dimension, can be utilized to measure the complexity of the dynamic behavior of a non-linear plant. On the other hand, fuzzy logic theory can be used to represent and capture the expert knowledge in controlling a plant. In addition, fuzzy logic enables to manage the uncertainty involved in the decision-making process for achieving efficient control of a non-linear plant. We illustrate the proposed fuzzy fractal control method with the current worldwide situation that requires achieving an efficient control of the COVID-19 pandemics.

In-silico strategies to combat COVID-19: A comprehensive review

The novel coronavirus SARS-CoV-2 since its emergence at Wuhan, China in December 2019 has been creating global health turmoil despite extensive containment measures and has resulted in the present pandemic COVID-19. Although the virus and its interaction with the host have been thoroughly characterized, effective treatment regimens beyond symptom-based care and repurposed therapeutics could not be identified. Various countries have successfully developed vaccines to curb the disease-transmission and prevent future outbreaks. Vaccination-drives are being conducted on a war-footing, but the process is time-consuming, especially in the densely populated regions of the world. Bioinformaticians and computational biologists have been playing an efficient role in this state of emergency to escalate clinical research and therapeutic development. However, there are not many reviews available in the literature concerning COVID-19 and its management. Hence, we have focused on designing a comprehensive review on in-silico approaches concerning COVID-19 to discuss the relevant bioinformatics and computational resources, tools, patterns of research, outcomes generated so far and their future implications to efficiently model data based on epidemiology; identify drug targets to design new drugs; predict epitopes for vaccine design and conceptualize diagnostic models. Artificial intelligence/machine learning can be employed to accelerate the research programs encompassing all the above urgent needs to counter COVID-19 and similar outbreaks.

Automatic detection of COVID-19 using pruned GLCM-Based texture features and LDCRF classification

Recently, automatic computer-aided detection (CAD) of COVID-19 using radiological images has received a great deal of attention from many researchers and medical practitioners, and consequently several CAD frameworks and methods have been presented in the literature to assist the radiologist physicians in performing diagnostic COVID-19 tests quickly, reliably and accurately. This paper presents an innovative framework for the automatic detection of COVID-19 from chest X-ray (CXR) images, in which a rich and effective representation of lung tissue patterns is generated from the gray level co-occurrence matrix (GLCM) based textural features. The input CXR image is first preprocessed by spatial filtering along with median filtering and contrast limited adaptive histogram equalization to improve the CXR image's poor quality and reduce image noise. Automatic thresholding by the optimized formula of Otsu's method is applied to find a proper threshold value to best segment lung regions of interest (ROIs) out from CXR images. Then, a concise set of GLCM-based texture features is extracted to accurately represent the segmented lung ROIs of each CXR image. Finally, the normalized features are fed into a trained discriminative latent-dynamic conditional random fields (LDCRFs) model for fine-grained classification to divide the cases into two categories: COVID-19 and non-COVID-19. The presented method has been experimentally tested and validated on a relatively large dataset of frontal CXR images, achieving an average accuracy, precision, recall, and F1-score of 95.88%, 96.17%, 94.45%, and 95.79%, respectively, which compare favorably with and occasionally exceed those previously reported in similar studies in the literature.

Recent Applications of Retro-Inverso Peptides

Natural and de novo designed peptides are gaining an ever-growing interest as drugs against several diseases. Their use is however limited by the intrinsic low bioavailability and poor stability. To overcome these issues retro-inverso analogues have been investigated for decades as more stable surrogates of peptides composed of natural amino acids. Retro-inverso peptides possess reversed sequences and chirality compared to the parent molecules maintaining at the same time an identical array of side chains and in some cases similar structure. The inverted chirality renders them less prone to degradation by endogenous proteases conferring enhanced half-lives and an increased potential as new drugs. However, given their general incapability to adopt the 3D structure of the parent peptides their application should be careful evaluated and investigated case by case. Here, we review the application of retro-inverso peptides in anticancer therapies, in immunology, in neurodegenerative diseases, and as antimicrobials, analyzing pros and cons of this interesting subclass of molecules.

Exploring peptide studies related to SARS-CoV to accelerate the development of novel therapeutic and prophylactic solutions against COVID-19

Recent advances in peptide research revolutionized therapeutic discoveries for various infectious diseases. In view of the ongoing threat of the COVID-19 pandemic, there is an urgent need to develop potential therapeutic options. Intense and accomplishing research is being carried out to develop broad-spectrum vaccines and treatment options for corona viruses, due to the risk of recurrent infection by the existing strains or pandemic outbreaks by new mutant strains. Developing a novel medicine is costly and time consuming, which increases the value of repurposing existing therapies. Since, SARS-CoV-2 shares significant genomic homology with SARS-CoV, we have summarized various peptides identified against SARS-CoV using in silico and molecular studies and also the peptides effective against SARS-CoV-2. Dissecting the molecular mechanisms underlying viral infection could yield fundamental insights in the discovery of new antiviral agents, targeting viral proteins or host factors. We postulate that these peptides can serve as effective components for therapeutic options against SARS-CoV-2, supporting clinical scientists globally in selectively identifying and testing the therapeutic and prophylactic agents for COVID-19 treatment. In addition, we also summarized the latest updates on peptide therapeutics against SARS-CoV-2.

Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

Nucleocapsid-specific and PD-L1+CXCR3+ CD8 polyfunctional T-cell abundances are associated with survival of critical SARS-CoV2-infected patients

The importance of the adaptive T cell response in the control and resolution of viral infection has been well established. However, the nature of T cell–mediated viral control mechanisms in life-threatening stages of COVID-19 has yet to be determined. The aim of the present study was to determine the function and phenotype of T cell populations associated with survival or death of patients with COVID-19 in intensive care as a result of phenotypic and functional profiling by mass cytometry. Increased frequencies of circulating, polyfunctional CD4⁺CXCR5⁺HLA-DR⁺ stem cell memory T cells (Tscms) and decreased proportions of granzyme B–expressing and perforin-expressing effector memory T cells were detected in recovered and deceased patients, respectively. The higher abundance of polyfunctional PD-L1⁺CXCR3⁺CD8⁺ effector T cells (Teffs), CXCR5⁺HLA-DR⁺ Tscms, and anti-nucleocapsid (anti-NC) cytokine-producing T cells permitted us to differentiate between recovered and deceased patients. The results from a principal component analysis show an imbalance in the T cell compartment that allowed for the separation of recovered and deceased patients. The paucity of circulating PD-L1⁺CXCR3⁺CD8⁺ Teffs and NC-specific CD8⁺ T cells accurately forecasts fatal disease outcome. This study provides insight into the nature of the T cell populations involved in the control of COVID-19 and therefore might impact T cell–based vaccine designs for this infectious disease.

The Spike of SARS-CoV-2: Uniqueness and Applications

The Spike (S) protein of the SARS-CoV-2 virus is critical for its ability to attach and fuse into the host cells, leading to infection, and transmission. In this review, we have initially performed a meta-analysis of keywords associated with the S protein to frame the outline of important research findings and directions related to it. Based on this outline, we have reviewed the structure, uniqueness, and origin of the S protein of SARS-CoV-2. Furthermore, the interactions of the Spike protein with host and its implications in COVID-19 pathogenesis, as well as drug and vaccine development, are discussed. We have also summarized the recent advances in detection methods using S protein-based RT-PCR, ELISA, point-of-care lateral flow immunoassay, and graphene-based field-effect transistor (FET) biosensors. Finally, we have also discussed the emerging Spike mutants and the efficacy of the Spike-based vaccines against those strains. Overall, we have covered most of the recent advances on the SARS-CoV-2 Spike protein and its possible implications in countering this virus.

Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes

Virus attacks have had devastating effects on mankind. The prominent viruses such as Ebola virus (2012), SARS-CoV or Severe acute respiratory syndrome, Middle East respiratory syndrome-related coronavirus called as the MERS (EMC/2012), Spanish flu (H1N1 virus-1918) and the most recent COVID-19(SARS-CoV-2) are the ones that have created a difficult situation for the survival of the human race. Currently, throughout the world, a global pandemic situation has put economy, livelihood and human existence in a very pathetic situation. Most of the above-mentioned viruses exhibit some similar characteristics and genetic pattern. Analysing such characteristics and genetic pattern can help the researchers to get a deeper insight into the viruses and helps in finding appropriate medicine or cure. To address these issues, this paper proposes an experimental analysis of the above-mentioned viruses data using correlation methods. The virus data considered for the experimental analysis include the distribution of various amino acids, protein sequences, 3D modelling of viruses, pairwise alignment of proteins that comprise the DNA genome of the viruses. Furthermore, this comparative analysis can be used by the researchers and organizations like WHO(World Health Organization), computational biologists, genetic engineers to frame a layout for studying the DNA sequence distribution, percentage of GC (guanine-cytosine) protein which determines the heat stability of viruses. We have used the Biopython to illustrate the gene study of prominent viruses and have derived results and insights in the form of 3D modelling. The experimental results are more promising with an accuracy rate of 96% in overall virus relationship calculation.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

Hypothesis: Designation of Liposomal Scavenger System for Fighting against 2019-nCoV

2019 novel coronavirus (2019-nCoV), also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or COVID-19 virus, is a member of the family Coronaviridae, which is responsible for the current pandemic of disease COVID-19. It is the seventh member of the family Coronaviridae, which infects humans, after 229E, OC43, NL63, HKU1, SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). Fever, dry cough and severe pneumonia are seen as common symptoms at the early stages of COVID-19. Some cases progress to acute respiratory stress syndrome, septic shock, organ failure, and death. The development of an effective treatment or vaccination for treating or preventing this lethal condition is an urgent need in order to fight this crisis. Up to now, some effective vaccines with different efficacy profiles have been introduced. Herein, we have theoretically designed a scavenger system for gathering 2019-nCoVs, breaking them, and re-introducing them to the immune system.

Blocking the interactions between human ACE2 and coronavirus spike glycoprotein by selected drugs: a computational perspective

The coronavirus disease of 2019 (COVID-19) has become a global pandemic with rapid rate of transmission and fatalities worldwide. Scientists have been investigating a host of drugs that may be rechanneled to fight this malaise. Thus, in this current computational study we carried out molecular docking experiments to assess the bridging potentials of some commercial drugs such as chloroquine, hydroxychloroquine, lopinavir, ritonavir, nafamostat, camostat, famotidine, umifenovir, nitazoxanide, ivermectin, and fluvoxamine at the interface between human ACE2 and the coronavirus spike glycoprotein complex. This is aimed at ascertaining the ability of these drugs to bridge and prevent the complexing of these two proteins. The crystal structure of human ACE2 and the coronavirus spike glycoprotein complex was retrieved from protein database, while the selected drugs were retrieved from PubChem data base. The proteins and drugs were prepared for docking using Cresset Flare software. The docking was completed via AutoDock Vina module in Python Prescription software. The best hit drugs with each receptor were selected and their molecular interactions were analyzed using BIOVIA’s Discovery Studio 2020. The best hit compounds on the human ACE2 were the lopinavir (-10.1 kcal/mol), ritonavir (-8.9 kcal/mol), and nafamostat (-8.7 kcal/mol). Ivermectin, nafamostat, and camostat with binding energy values -9.0 kcal/mol, -7.8 kcal/mol, and -7.4 kcal/mol respectively were the hit drugs on the coronavirus spike glycoprotein. Nafamostat showed a dual bridging potential against ACE2 and spike glycoprotein, and could therefore be a promising lead compound in the prevention and control of this disease.

Mutations of SARS-CoV-2 spike protein: Implications on immune evasion and vaccine-induced immunity

Responsible for more than 4.9 million deaths so far, COVID-19, caused by SARS-CoV-2, is instigating devastating effects on the global health care system whose impacts could be longer for the years to come. Acquiring a comprehensive knowledge of host-virus interaction is critical for designing effective vaccines and/or drugs. Understanding the evolution of the virus and the impact of genetic variability on host immune evasion and vaccine efficacy is helpful to design novel strategies to minimize the effects of the emerging variants of concern (VOC). Most vaccines under development and/or in current use target the spike protein owning to its unique function of host receptor binding, relatively conserved nature, potent immunogenicity in inducing neutralizing antibodies, and being a good target of T cell responses. However, emerging SARS-CoV-2 strains are exhibiting variability on the spike protein which could affect the efficacy of vaccines and antibody-based therapies in addition to enhancing viral immune evasion mechanisms. Currently, the degree to which mutations on the spike protein affect immunity and vaccination, and the ability of the current vaccines to confer protection against the emerging variants attracts much attention. This review discusses the implications of SARS-CoV-2 spike protein mutations on immune evasion and vaccine-induced immunity and forward directions which could contribute to future studies focusing on designing effective vaccines and/or immunotherapies to consider viral evolution. Combining vaccines derived from different regions of the spike protein that boost both the humoral and cellular wings of adaptive immunity could be the best options to cope with the emerging VOC.

A novel DeepNet model for the efficient detection of COVID-19 for symptomatic patients

The novel Coronavirus (COVID-19) disease has disrupted human life worldwide and put the entire planet on standby. A resurgence of coronavirus infections has been confirmed in most countries, resulting in a second wave of the deadly virus. The infectious virus has symptoms ranging from an itchy throat to Pneumonia, resulting in the loss of thousands of human lives while globally infecting millions. Detecting the presence of COVID-19 as early as possible is critical, as it helps prevent further spread of disease and helps isolate and provide treatment to the infected patients. Recent radiological imaging findings confirm that lung X-ray and CT scans provide an excellent indication of the progression of COVID-19 infection in acute symptomatic carriers. This investigation aims to rapidly detect COVID-19 progression and non-COVID Pneumonia from lung X-ray images of heavily symptomatic patients. A novel and highly efficient COVID-DeepNet model is presented for the accurate and rapid prediction of COVID-19 infection using state-of-the-art Artificial Intelligence techniques. The proposed model provides a multi-class classification of lung X-ray images into COVID-19, non-COVID Pneumonia, and normal (healthy). The proposed systems’ performance is assessed based on the evaluation metrics such as accuracy, sensitivity, precision, and f1 score. The current research employed a dataset size of 7500 X-ray samples. The high recognition accuracy of 99.67% was observed for the proposed COVID-DeepNet model, and it complies with the most recent state-of-the-art. The proposed COVID-DeepNet model is highly efficient and accurate, and it can assist radiologists and doctors in the early clinical diagnosis of COVID-19 infection for symptomatic patients.

DNA vaccination induced protective immunity against SARS CoV-2 infection in hamsterss

The development of efficient vaccines against COVID-19 is an emergent need for global public health. The spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major target for the COVID-19 vaccine. To quickly respond to the outbreak of the SARS-CoV-2 pandemic, a nucleic acid-based vaccine is a novel option, beyond the traditional inactivated virus vaccine or recombinant protein vaccine. Here, we report a DNA vaccine containing the spike gene for delivery via electroporation. The spike genes of SARS-CoV and SARS-CoV-2 were codon optimized for mammalian cell expression and then cloned into mammalian cell expression vectors, called pSARS-S and pSARS2-S, respectively. Spike protein expression was confirmed by immunoblotting after transient expression in HEK293T cells. After immunization, sera were collected for antigen-specific antibody and neutralizing antibody titer analyses. We found that both pSARS-S and pSARS2-S immunization induced similar levels of antibodies against S2 of SARS-CoV-2. In contrast, only pSARS2-S immunization induced antibodies against the receptor-binding domain of SARS-CoV-2. We further found that pSARS2-S immunization, but not pSARS-S immunization, could induce very high titers of neutralizing antibodies against SARS-CoV-2. We further analyzed SARS-CoV-2 S protein-specific T cell responses and found that the immune responses were biased toward Th1. Importantly, pSARS2-S immunization in hamsters could induce protective immunity against SARS-CoV-2 challenge in vivo. These data suggest that DNA vaccination could be a promising approach for protecting against COVID-19.