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. Graphical abstract

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 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 Atazanavir (DrugBank ID – DB01072) would probably be one of the most effective drugs 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. Author summary: Why was this study done?: The recent outbreak of novel coronavirus disease (COVID-19) is now considered to be a pandemic threat to the global population. The new coronavirus, 2019-nCoV has now affected more than 200 countries with over 17,83,941 cases confirmed and 1,09,312 deaths reported all over the world [as on 12 April 2020]. There is an urgent need for the development of drugs or vaccine which can save people worldwide. However, the vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease. Recently, Artificial Intelligence (AI) technology have been utilized to revolutionize the drug development process. Can we use AI based repurposing of existing drugs for accelerated clinical trial in the treatment of COVID-19? What did the researchers do and find?: Here, we report the Machine Learning (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 Atazanavir (DrugBank ID – DB01072) would probably be one of the most effective drugs based on the selected criterions. What do these findings mean?: The present approach will save a lot of resources and time for synthesizing novel drugs and thus will be useful for a vast majority of medical research community.

In vivo molecular imaging tools are crucially important for elucidating how cells move through complex biological systems; however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upward of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a statistical tracking algorithm (PEPT-EM) to achieve a sensitivity of 4 becquerel per cell and a streamlined workflow to reliably label single cells with over 50 becquerel per cell of ¹⁸ F-fluorodeoxyglucose (FDG). To demonstrate the potential of the method, we tracked the fate of more than 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.

Piper betle leaves possess several ethnomedicinal properties and are immensely used in traditional medicinal practices in regions of Asian and African subcontinents. However, their effects in treating skeletal complications are least known. In this study, we evaluated cellular and molecular effects of betel leaf extract (BLE) and its major phytoconstituent, hydroxychavicol (HCV) in promoting osteogenesis in vitro and alleviating glucocorticoid induced osteoporosis (GIO) in vivo. Both BLE and HCV markedly stimulated osteoblast differentiation of C3H10T1/2 cells with increased expression of RUNX2 and osteopontin through the GSK-3β/β-catenin-signaling pathway. Also, oral administration of BLE and HCV in GIO rats resulted in restoration of bone mass and tissue microarchitecture. Thus, with our findings we conclude that BLE and HCV promote osteogenesis of C3H10T1/2 cells via the GSK-3β/β-catenin pathway and alleviate GIO in rats.

Cell tracking is crucial for understanding the complex patterns of cellular migration that underlie many physiological, pathological, and therapeutic processes. Positron emission particle tracking (PEPT) is a method that uses list-mode positron emission tomography (PET) data to localize moving particles non-invasively inside opaque systems. However, while the application of this method to in vivo cell tracking has previously been evoked, its implementation has been limited to tracking one cell at a time. This study investigates the feasibility of tracking multiple cells simultaneously using a recently developed expectation maximization (EM) algorithm called PEPT-EM. The primary challenge to the translation of this algorithm towards biomedical applications is the low radioactivity of the cells being tracked. We experimentally demonstrated the performance of the PEPT-EM algorithm using a preclinical PET scanner for tracking droplets and cells with activities ranging from tens to hundreds of Bq, in phantoms and in a murine model. We found that while background and multiplexing effects impact static source tracking, sensitivity is critical for dynamic tracking of moving sources. We successfully localized multiple single cells in a murine model, moving at speeds up to 25 mm/s, marking the first use of PEPT-EM for such applications. Our findings highlight the exciting potential of PEPT for real-time, high throughput tracking of multiple single cells in vivo, paving the way for studying cell tracking in biological systems.