Background The pandemic situation of SARS-CoV-2 infection has sparked global concern due to the disease COVID-19 caused by it. Since the first cluster of confirmed cases in China in December 2019, the infection has been reported across the continents and inflicted upon a substantial number of populations. Method This study is focused on immunoinformatics analyses of the SARS-CoV-2 spike glycoprotein (S protein) which is key for the viral attachment to human host cells. Computational analyses were carried out for the prediction of B-cell and T-cell (MHC class I and II) epitopes of S protein and the analyses were extended further for the prediction of their immunogenic properties. The interaction and binding affinity of T-cell epitopes with HLA-B7 were also investigated by molecular docking. Result Three distinct epitopes for vaccine design were predicted from the sequence of S protein. The potential B-cell epitope was KNHTSPDVDLG possessing the highest antigenicity score of 1.4039 among other B-cell epitopes. T-cell epitope for human MHC class I was VVVLSFELL with an antigenicity score of 1.0909 and binding ability to 29 MHC-I alleles. The predicted T-cell epitope for human MHC class II molecule was VVIGIVNNT with a corresponding 1.3063 antigenicity score, less digesting enzymes, and 7 MHC-II alleles binding ability. All these three peptides were predicted to be highly antigenic, non-allergenic, and non-toxic. Analyses of the physiochemical properties of these predicted epitopes indicate their stable nature for plausible vaccine design. Furthermore, molecular docking investigation between the MHC class-I epitopes and human HLA-B7 reflects the stable interaction with high affinity among them. Conclusion The present study posits three potential epitopes of S protein of SARS-CoV-2 predicted by immunoinformatic methods based on their immunogenic properties and interactions with the host counterpart that can facilitate the development of vaccine against SARS-CoV-2. This study can act as the springboard for the future development of the COVID-19 vaccine.

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Nanoarchitectured mesoporous metal alloy films integrating the intrinsic catalytic capabilities of their constituent metals to create a suitable surface morphology as well as different signal transduction and catalytic capabilities. As...

Background: Betel quid (BQ) chewing is a common habit and a means of social interaction among the northeastern peoples of Bangladesh. Though this habit integrating in their daily life without knowing its toxic effect. Areca nut, which is one of the main components of BQ and may responsible for this addiction. Here, we assess to see how BQ chewing habit influence hyperglycemia among diabetic patients with respect to their lifestyle. Methodology: Random blood sugar (RBS) test was evaluated from a total of 961 diabetic patients. Behavioral data associated with their daily lifestyle were collected from August 2018 to February 2019 from Sylhet Diabetic Hospital, Bangladesh. Student's t-test, ANOVA and Fisher's exact test were used to assess the RBS status between BQ chewer and non-chewer patients. Results: Higher RBS was found in BQ chewer patients than non-chewer (mean ± SEM, 263.3 ± 4.768 vs. 251.0 ± 5.915mg/dl). Interestingly, it is significantly higher in raw areca nut user than dry nut (mean ± SEM, 278.0 ± 8.790 vs. 252.1 ± 6.835 mg/dl) only from BQ chewer group, suggesting that the habit of chewing raw nut may contribute to more hyperglycemic effect among diabetic patients. BQ habit enhances higher RBS level among smoker, non-smoker and patient's having walking habit. In addition, BQ habit significantly influence to have high RBS in patients with family history with diabetes. Lack of awareness being diabetes have also been observed significantly in BQ chewer patients, while a higher level of RBS was seen in BQ group, who work in different sectors with sitting activities. Conclusions: Diabetic patients who chew betel quid are more prone to keep higher hyperglycemic. Utmost attention should be taken to discourage the use of BQ for proper management of diabetes control. Keywords: Betel quid, Areca nut, Hyperglycemia, Diabetes mellitus, and RBS.

COVID-19 pandemic keeps pressing onward and effective treatment option against it is still far-off. Since the onslaught in 2020, 13 different variants of SARS-CoV-2 have been surfaced including 05 different variants of concern. Success in faster pandemic handling in the future largely depends on reinforcing therapeutics along with vaccines. As a part of RNAi therapeutics, here we developed a computational approach for predicting siRNAs, which are presumed to be intrinsically active against two crucial mRNAs of SARS-CoV-2, the RNA-dependent RNA polymerase (RdRp), and the nucleocapsid phosphoprotein gene (N gene). Sequence conservancy among the alpha, beta, gamma, and delta variants of SARS-CoV-2 was integrated in the analyses that warrants the potential of these siRNAs against multiple variants. We preliminary found 13 RdRP-targeting and 7 N gene-targeting siRNAs using the siDirect V.2.0. These siRNAs were subsequently filtered through different parameters at optimum condition including macromolecular docking studies. As a result, we selected 4 siRNAs against the RdRP and 3 siRNAs against the N-gene as RNAi candidates. Development of these potential siRNA therapeutics can significantly synergize COVID-19 mitigation by lessening the efforts, furthermore, can lay a rudimentary base for the in silico design of RNAi therapeutics for future emergencies.