The paper demonstrates the analysis of Corona Virus Disease based on a CNN probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed Convolution neural network structure. The Study is validated on 2002 chest X-ray images with 60 confirmed positive covid19 cases and (650 bacterial – 412 viral -880 normal) x-ray images. The proposed CNN compared with traditional classifiers with proposed CHFS feature extraction model. The experimental study has done with real data demonstrates the feasibility and potential of the proposed approach for the said cause. The result of proposed CNN structure has been successfully done to achieve 98.20% accuracy of covid19 potential cases with comparable of traditional classifiers.
Total 40 natural compounds were selected to perform the molecular docking studies to screen and identify the potent antiviral agents specifically for Severe Acute Respiratory Syndrome Coronavirus 2 that causes coronavirus disease 2019 (COVID-19). The key targets of COVID-19, protease (PDB ID: 6M0K, 6Y2F and 7BQY) and RNA polymerase (PDB ID: 7bV2) were used to dock our target compounds by Molecular Operating Environment (MOE) version 2014.09. After an extensive screening analysis, 20 compounds exhibit good binding affinities to one or more of the COVID-19 targets. 7 out of 20 compounds were predicted to overcome the activity of the 4 drug targets. The top 7 hits are compounds; Flacourticin (3), Sagerinic acid (16), Hordatine A (23), Hordatine B (24), N-feruloyl tyramine dimer (25), Bisavenanthramides B-5 (29) and Vulnibactins (40). According to our results, all these top hits was found to have a better binding scores than Remdesivir, the native ligand in RNA polymerase target (PDB ID: 7bV2). Hordatines are phenolic compounds present in barley, were found to exhibit the highest binding affinity to both protease and polymerase through forming strong hydrogen bonds with the catalytic residues, as well as significant interactions with other receptor-binding residues. These results probably provided an excellent lead candidate for the development of therapeutic drugs against COVID-19. Eventually, animal experiment and accurate clinical trials are needed to confirm the preventive potentials of these compounds.
Severe acute respiratory syndrome corona virus (SARS-CoV)-2 obtained from patients infected despite being fully vaccinated with either BNT162b2 (Pfizer/BioNTech), mRNA-1273 (Moderna), or JNJ-78436735 (Janssen) showed increased mutations rates in the N-terminal domain (NTD) and receptor-binding domain (RBD) of the spike glycoprotein when compared with virus from unvaccinated controls (1). These changes are associated with immune evasion and diagnostic failures, prominent characteristics of variants of concern (2). Variants of concern (VOC) appeared to be overrepresented in numerous breakthrough infections of fully vaccinated people in the United States and (1, 3-6), and Israel (7). These findings confirm concerns about the relation of SARS-CoV-2 variants with vaccine breakthrough, and urged us to attempt clarifying the likely link between vaccination with the currently used vaccines and VOC emergence.
The article explains why Cytotoxic T cells still recognize SARS-CoV-2 Omicron variant even when antibodies cannot, and reveals the danger of powerful T cell killing.
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