Coronavirus disease 2019 (COVID-19) is caused by novel coronavirus Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first time reported in December 2019 in Wuhan, China and thereafter quickly spread across the globe. Till September 19, 2020, COVID-19 has spread to 216 countries and territories. Severe infection of SARS-CoV-2 cause extreme increase in inflammatory chemokines and cytokines that may lead to multi-organ damage and respiratory failure. Currently, no specific treatment and authorized vaccines are available for its treatment. Renin angiotensin system holds a promising role in human physiological system specifically in regulation of blood pressure and electrolyte and fluid balance. SARS-CoV-2 interacts with Renin angiotensin system by utilizing angiotensin-converting enzyme 2 (ACE2) as a receptor for its cellular entry. This interaction hampers the protective action of ACE2 in the cells and causes injuries to organs due to persistent angiotensin II (Ang-II) level. Patients with certain comorbidities like hypertension, diabetes, and cardiovascular disease are under the high risk of COVID-19 infection and mortality. Moreover, evidence obtained from several reports also suggests higher susceptibility of male patients for COVID-19 mortality and other acute viral infections compared to females. Analysis of severe acute respiratory syndrome coronavirus (SARS) and Middle East respiratory syndrome coronavirus (MERS) epidemiological data also indicate a gender-based preference in disease consequences. The current review addresses the possible mechanisms responsible for higher COVID-19 mortality among male patients. The major underlying aspects that was looked into includes smoking, genetic factors, and the impact of reproductive hormones on immune systems and inflammatory responses. Detailed investigations of this gender disparity could provide insight into the development of patient tailored therapeutic approach which would be helpful in improving the poor outcomes of COVID-19.

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In this work, a new nanocomposite (Ag@S-GQDs) have been synthesized using one-step facile synthesis process and their antibacterial as well as cytotoxicity properties were investigated systematically.

Background The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2. Methods and findings We perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID–DB00701) would probably be the most effective drug based on the selected criterions. Conclusions Our study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.

For the first time is reported a facile in situ synthesis of folic acid-conjugated sulfur-doped graphene quantum dots (FA-SGQDs) through simple pyrolysis of citric acid (CA), 3-mercaptopropionic acid (MPA), and FA. The as-prepared FA-SGQDs were extensively characterized to confirm the synthesis and incidence of FA molecule on the surface of SGQDs through advanced characterization techniques. Upon excitation at 370-nm wavelength, FA-SGQDs exhibited blue fluorescence with an emission band at 455 nm. While exhibiting relatively high quantum yield (~78%), favorable biocompatibility, excellent photostability, and desirable optical properties, the FA-SGQDs showed suitability as a fluorescent nanoprobe to distinguish the folate receptor (FR)-positive and FR-negative cancer cells. The experimental studies revealed that FA-SGQDs aptly entered into FR-positive cancer cells via a non-immunogenic FR-mediated endocytosis process. Additionally, the FA-SGQDs exhibited excellent free radical scavenging activity. Hence, these FA-SGQDs hold high promise to serve as efficient fluorescent nanoprobes for the prediagnosis of cancer through targeted bioimaging and other pertinent biological studies.

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The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2. The research includes the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time.Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naïve Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we suggest that the antiretroviral drug Saquinavir (DrugBank ID -DB01232) would probably be one of the most effective drugs based on the selected criterions. Our study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.

Background Diagnosis of TB in pediatric population poses several challenges. A novel initiative was implemented in several major cities of India aimed at providing upfront access to free-ofcost Xpert MTB/RIF to presumptive pediatric TB cases. This paper aims to describe the experience of implementing this large initiative and assess feasibility of the intervention in high TB burden settings. Methods Data were drawn from the pediatric TB project implemented in 10 major cities of India between April 2014 and March 2018. In each city, providers, both public and private, were engaged and linked with a high throughput Xpert MTB/RIF lab (established in that city) through rapid specimen transportation and electronic reporting system. Rates and proportions were estimated to describe the characteristics of this cohort. Results Of the total 94,415 presumptive pediatric TB cases tested in the project, 6,270 were diagnosed positive for MTB (6.6%) on Xpert MTB/RIF (vs 2% on smear microscopy). Among MTB positives, 545 cases were rifampicin resistant (8.7%). The median duration between