Designing novel peptides for detecting the Omicron variant, specifying SARS-CoV-2, and simultaneously screening coronavirus infections.

In this study in silico a candidate diagnostic peptide-based tool was designed in four stages including diagnosis of coronavirus diseases, simultaneously identifying of COVID-19 and SARS from other members of this family, specific identification of SARS-CoV2, and diagnosis of COVID-19 Omicron. Designed candidate peptides consist of four immunodominant peptides from the proteins of the SARS-CoV-2 spike (S) and membrane (M). The tertiary structure of each peptide was predicted. The stimulation ability of the humoral immunity for each peptide was evaluated. Finally, in silico cloning was performed to develop an expression strategy for each peptide. These four peptides have suitable immunogenicity, appropriate construct, and the ability to be expressed in E.coli. These results must be experimentally validated in vitro and in vivo to ensure the immunogenicity of the kit.Communicated by Ramaswamy H. Sarma.

The effectiveness of Colchicine as an anti-inflammatory drug in the treatment of coronavirus disease 2019: Meta-analysis.

A recently discovered coronavirus, SARS-CoV-2, caused a global respiratory disease pandemic called COVID-19. Many studies have shown the excessive activation of the innate immune response that leads to the adverse outcomes of COVID-19, and anti-inflammatory drugs are very useful in the treatment and management of this infection. The activities of Colchicine, one of the anti-inflammatory drugs, target several pathways related to excessive inflammation of COVID-19. This study aimed to evaluate the efficacy of Colchicine in the treatment of COVID-19 using a meta-analysis approach. Scopus, Pubmed, Google scholars, Web of Science, and Science direct were used to search all the randomized controlled trials, case-control, and cross-sectional studies that have evaluated the efficacy of Colchicine as a treatment for COVID-19 (up to 28 May 2021). The overall effect of Colchicine versus the control group was determined using a random-effects model meta-analysis where we compared changes (i.e. mean differences-Colchicine group vs Control group) between the two conditions in test scores indicative of hospitalization time (day) and mortality rate. The results illustrated Colchicine therapy is associated with a decreased mortality rate in COVID-19 patients and associated with a decrease in hospitalization time (day) in COVID-19 patients. Present preliminary data shows that Colchicine has a beneficial effect on coronavirus disease care in 2019. Therefore, Colchicine can be a good suggestion in the management of COVID-19.

FCOD: Fast COVID-19 Detector based on deep learning techniques.

The sudden COVID-19 pandemic has caused a serious global concern due to infections and mortality rates. It is a hazardous disease that has recently become the biggest crisis in the modern era. Due to the limitation of test kits and the need for screening and rapid diagnosis of patients, it is essential to perform a self-operating detection model as a fast recognition system to detect COVID-19 infection and prevent the spread among the people. In this paper, we propose a novel technique called Fast COVID-19 Detector (FCOD) to have a fast detection of COVID-19 using X-ray images. The FCOD is a deep learning model based on the Inception architecture that uses 17 depthwise separable convolution layers to detect COVID-19. Depthwise separable convolution layers decrease the computation costs, time, and they can have a reducing role in the number of parameters compared to the standard convolution layers. To evaluate FCOD, we used covid-chestxray-dataset, which contains 940 publicly available typical chest X-ray images. Our results show that FCOD can provide accuracy, F1-score, and AUC of 96%, 96%, and 0.95%, respectively in classifying COVID-19 during 0.014 s for each case. The proposed model can be employed as a supportive decision-making system to assist radiologists in clinics and hospitals to screen patients immediately.

Design an Efficient Multi-Epitope Peptide Vaccine Candidate Against SARS-CoV-2: An in silico Analysis.

BACKGROUND: To date, no specific vaccine or drug has been proven to be effective against SARS-CoV-2 infection. Therefore, we implemented an immunoinformatic approach to design an efficient multi-epitopes vaccine against SARS-CoV-2. RESULTS: The designed-vaccine construct consists of several immunodominant epitopes from structural proteins of spike, nucleocapsid, membrane, and envelope. These peptides promote cellular and humoral immunity and interferon-gamma responses. Also, these epitopes have a high antigenic capacity and are not likely to cause allergies. To enhance the vaccine immunogenicity, we used three potent adjuvants: Flagellin of Salmonella enterica subsp. enterica serovar Dublin, a driven peptide from high mobility group box 1 as HP-91, and human beta-defensin 3 protein. The physicochemical and immunological properties of the vaccine structure were evaluated. The tertiary structure of the vaccine protein was predicted and refined by Phyre2 and Galaxi refine and validated using RAMPAGE and ERRAT. Results of ElliPro showed 246 sresidues from vaccine might be conformational B-cell epitopes. Docking of the vaccine with toll-like receptors (TLR) 3, 5, 8, and angiotensin-converting enzyme 2 approved an appropriate interaction between the vaccine and receptors. Prediction of mRNA secondary structure and in silico cloning demonstrated that the vaccine can be efficiently expressed in Escherichia coli. CONCLUSION: Our results demonstrated that the multi-epitope vaccine might be potentially antigenic and induce humoral and cellular immune responses against SARS-CoV-2. This vaccine can interact appropriately with the TLR3, 5, and 8. Also, it has a high-quality structure and suitable characteristics such as high stability and potential for expression in Escherichia coli .