A bioinformatics approach to characterize a hypothetical protein Q6S8D9_SARS of SARS-CoV

Characterization as well as prediction of the secondary and tertiary structure of hypothetical proteins from their amino acid sequences uploaded in databases by in silico approach are the critical issues in computational biology. Severe acute respiratory syndrome-associated coronavirus (SARS-CoV), which is responsible for pneumonia alike diseases, possesses a wide range of proteins of which many are still uncharacterized. The current study was conducted to reveal the physicochemical characteristics and structures of an uncharacterized protein Q6S8D9_SARS of SARS-CoV. Following the common flowchart of characterizing a hypothetical protein, several sophisticated computerized tools e.g., ExPASy Protparam, CD Search, SOPMA, PSIPRED, HHpred, etc. were employed to discover the functions and structures of Q6S8D9_SARS. After delineating the secondary and tertiary structures of the protein, some quality evaluating tools e.g., PROCHECK, ProSA-web etc. were performed to assess the structures and later the active site was identified also by CASTp v.3.0. The protein contains more negatively charged residues than positively charged residues and a high aliphatic index value which make the protein more stable. The 2D and 3D structures modeled by several bioinformatics tools ensured that the proteins had domain in it which indicated it was functional protein having the ability to trouble host antiviral inflammatory cytokine and interferon production pathways. Moreover, active site was found in the protein where ligand could bind. The study was aimed to unveil the features and structures of an uncharacterized protein of SARS-CoV which can be a therapeutic target for development of vaccines against the virus. Further research are needed to accomplish the task.

Clinico-epidemiological Characteristics of Asymptomatic and Symptomatic COVID-19-Positive Patients in Bangladesh.

As of August 15, 2020, Bangladesh lost 3591 lives since the first Coronavirus disease 2019 (COVID-19) case announced on March 8. The objective of the study was to report the clinical manifestation of both symptomatic and asymptomatic COVID-19-positive patients. An online-based cross-sectional survey was conducted for initial recruitment of participants with subsequent telephone interview by the three trained physicians in 237 adults with confirmed COVID-19 infection in Bangladesh. The study period was 27 April to 26th May 2020. Consent was ensured before commencing the interview. Collected data were entered in a pre-designed case record form and subsequently analyzed by SPSS 20.0. The mean±SD age at presentation was 41.59±13.73 years and most of the cases were male (73.0%). A total of 90.29% of patients reside in urban areas. Among the positive cases, 13.1% (n=31) were asymptomatic. Asymptomatic cases were significantly more common in households with 2 to 4 members (p=0.008). Both symptomatic and asymptomatic patients shared similar ages of presentation (p=0.23), gender differences (p=0.30) and co-morbidities (p=0.11). Only 5.3% of patients received ICU care during their treatment. The most frequent presentation was fever (88.3%), followed by cough (69.9%), chest pain (34.5%), body ache (31.1%), and sore throat (30.1%). Thirty-nine percent (n=92) of the patients had co-morbidities, with diabetes and hypertension being the most frequently observed. There has been an upsurge in COVID-19 cases in Bangladesh. Patients were mostly middle-aged and male. Typical presentations were fever and cough. Maintenance of social distancing and increased testing are required to meet the current public health challenge.

COVID-19 Pandemic: Animal Cross Talk and Comparison Between nSARS-CoV-2 and Animal Coronaviruses

Coronaviruses (CoVs) belong to a large family (Coronaviridae), have a global distribution, and cause respiratory and intestinal infections in animals, birds, and humans. Usually, these viruses cause common cold, which is typically mild in humans, although rarer forms such as severe acute respiratory syndrome and Middle East respiratory syndrome can be lethal. CoVs cause an upper respiratory disease in chickens, but diarrhea in cows and pigs. The newly emerging pandemic, the coronavirus disease 2019 (COVID-19), is caused by the novel severe acute respiratory syndrome coronavirus 2 (nSARS-CoV-2), which first appeared in Wuhan, China, in December 2019 and thereafter spread throughout the globe and declared as a pandemic disease by the World Health Organization. It has been postulated that the virus was transmitted to humans from bats through an evolutionary process termed as ‘host jump’, resulting in a cross talk about animal-human interface and zoonotic links of nSARS-CoV-2 and urging an intensive investigation of the involvement of animals or birds. Later, several animals such as dogs, cats, tigers, pangolins, ferrets, and minks were found to be naturally infected with nSARS-CoV-2. Additionally, laboratory animals such as mice, ferrets, and monkeys were successfully infected with the virus. Animal CoVs share some common features with nSARS-CoV-2. Although nSARS-CoV-2 is of animal origin, the roles of animals in the course of the pandemic are still elusive. This chapter discusses the predicted roles of animals in the COVID-19 pandemic, along with comparisons of nSARS-CoV-2 with other animal CoVs. © 2022 Elsevier Inc. All rights reserved.

Do-it-yourself rheometry

We describe the structure and outcomes of a course project for do-it-yourself (DIY) rheometry. Although the project was created in response to the shelter-in-place orders of the COVID-19 pandemic, the student learning outcomes were so positive that we have continued implementing the project even when students have access to laboratory rheometers. Students select an interesting complex fluid, collect qualitative visual evidence of key rheological phenomena, and then produce their own readily available flows that they quantitatively analyze to infer rheological properties, such as yield stress, extensional viscosity, or shear viscosity. We provide an example rubric, present example student project outcomes, and discuss learning outcomes that are achieved with DIY measurements.& nbsp;Published under an exclusive license by AIP Publishing.& nbsp;

PSYCHOMETRIC EVALUATION OF THE MODIFIED “FEAR OF COVID-19” SCALE

This study was designed to modify the recently developed “Fear of COVID-19” scale (FCV-19S) as a diagnostic criterion and to evaluate its psychometric properties and potential to predict risk of psychological problems. Through an e-questionnaire, data for this study were collected from 1,317 university students from 49 universities in Bangladesh. The modified “Fear of COVID-19” scale (MFCV-19S) showed good internal consistency (ω =.867) and concurrent validity;there was significant association with anxiety and depression. The unidimensionality was confirmed by an acceptable average variance extracted (0.49) and construct reliability (.87). The MFCV-19S differentiates fairly between persons with and without anxiety disorder, using an optimized cut score of ≥ 8 (93% sensitivity and 78% specificity). The multivariate analysis also suggested that MFCV-19S can significantly predict risk of mental health problems. The results indicated that the MFCV-19S is an efficient and valid psychometric tool for screening fear of COVID-19 among students and could be used for general people © Copyright: The Author(s). All articles are licensed under the terms and conditions of the Creative Commons Attribution 4.0 International License (CC-BY 4.0 <http://creativecommons.org/licenses/by/4.0/>)

Deep Learning-Based Drug Screening for COVID-19 and Case Studies

Coronavirus infectious disease (COVID-19), caused by deadly severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been declared as a pandemic by the World Health Organization. This disease has become the world’s worst infectious disease, killing about 1.05 million lives as of September 2020. The absence of vaccines and effective drugs is a key trouble responsible for the ineffective management of this pandemic. Considering this emergency situation, several trials have been made to identify repurposing on-market drugs known for their antiviral behavior. Several modern technologies such as deep machine learning are used to combat this deadly disease with faster prediction and greater accuracy. More interestingly, these studies have provided clues for the antiviral properties and are believed to help in effective control of this pandemic. The drugs identified by deep learning-based virtual screening will help in unraveling molecular mechanisms of therapeutic and antiviral properties and will pave the way for designing artificial drugs. Hence we focus in this chapter on the integrated applications of deep learning models as a pipeline for drug and vaccine discovery which has implications in therapeutic drug targeting for COVID-19.