FDA approved drugs with antiviral activity against SARS-CoV-2: From structure-based repurposing to host-specific mechanisms.

The continuing heavy toll of the COVID-19 pandemic necessitates development of therapeutic options. We adopted structure-based drug repurposing to screen FDA-approved drugs for inhibitory effects against main protease enzyme (Mpro) substrate-binding pocket of SARS-CoV-2 for non-covalent and covalent binding. Top candidates were screened against infectious SARS-CoV-2 in a cell-based viral replication assay. Promising candidates included atovaquone, mebendazole, ouabain, dronedarone, and entacapone, although atovaquone and mebendazole were the only two candidates with IC50s that fall within their therapeutic plasma concentration. Additionally, we performed Mpro assays on the top hits, which demonstrated inhibition of Mpro by dronedarone (IC50 18 µM), mebendazole (IC50 19 µM) and entacapone (IC50 9 µM). Atovaquone showed only modest Mpro inhibition, and thus we explored other potential mechanisms. Although atovaquone is Dihydroorotate dehydrogenase (DHODH) inhibitor, we did not observe inhibition of DHODH at the respective SARS-CoV-2 IC50. Metabolomic profiling of atovaquone treated cells showed dysregulation of purine metabolism pathway metabolite, where ecto-5'-nucleotidase (NT5E) was downregulated by atovaquone at concentrations equivalent to its antiviral IC50. Atovaquone and mebendazole are promising candidates with SARS-CoV-2 antiviral activity. While mebendazole does appear to target Mpro, atovaquone may inhibit SARS-CoV-2 viral replication by targeting host purine metabolism.

Identification of FDA Approved Drugs with Antiviral Activity against SARS-CoV-2: A Tale from structure-based drug repurposing to host-cell mechanistic investigation

The continuing heavy toll of the COVID-19 pandemic necessitates development of therapeutic options. We adopted structure-based drug repurposing to screen FDA-approved drugs for inhibitory effects against main protease enzyme (Mpro) substrate-binding pocket of SARS-CoV-2 for non-covalent and covalent binding. Top candidates were screened against infectious SARS-CoV-2 in a cell-based viral replication assay. Promising candidates included atovaquone, mebendazole, ouabain, dronedarone, and entacapone, although atovaquone and mebendazole were the only two candidates with IC50s that fall within their therapeutic plasma concentration. Additionally, we performed Mpro assays on the top hits, which demonstrated inhibition of Mpro by dronedarone (IC50 18 µM), mebendazole (IC50 19 µM) and entacapone (IC50 9 µM). Atovaquone showed only modest Mpro inhibition, and thus we explored other potential mechanisms. Although atovaquone is Dihydroorotate dehydrogenase (DHODH) inhibitor, we did not observe inhibition of DHODH at the respective SARS-CoV-2 IC50. Metabolomic profiling of atovaquone treated cells showed dysregulation of purine metabolism pathway metabolite, showing that ecto-5′-nucleotidase (NT5E) is downregulated by atovaquone at concentrations equivalent to its antiviral IC50. Atovaquone and mebendazole are promising candidates targeting SARS-CoV-2, however atovaquone did not significantly inhibit Mpro at therapeutically meaningful concentrations but may inhibit SARS-CoV-2 viral replication by targeting host purine metabolism. Graphical

Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities.

The increasingly ageing population and the tendency to live alone have led science and engineering researchers to search for health care solutions. In the COVID 19 pandemic, the elderly have been seriously affected in addition to suffering from isolation and its associated and psychological consequences. This paper provides an overview of the RobWell (Robotic-based Well-Being Monitoring and Coaching System for the Elderly in their Daily Activities) system. It is a system focused on the field of artificial intelligence for mood prediction and coaching. This paper presents a general overview of the initially proposed system as well as the preliminary results related to the home automation subsystem, autonomous robot navigation and mood estimation through machine learning prior to the final system integration, which will be discussed in future works. The main goal is to improve their mental well-being during their daily household activities. The system is composed of ambient intelligence with intelligent sensors, actuators and a robotic platform that interacts with the user. A test smart home system was set up in which the sensors, actuators and robotic platform were integrated and tested. For artificial intelligence applied to mood prediction, we used machine learning to classify several physiological signals into different moods. In robotics, it was concluded that the ROS autonomous navigation stack and its autodocking algorithm were not reliable enough for this task, while the robot's autonomy was sufficient. Semantic navigation, artificial intelligence and computer vision alternatives are being sought.

Data Analysis and Forecasting of the COVID-19 Spread: A Comparison of Recurrent Neural Networks and Time Series Models.

To understand and approach the spread of the SARS-CoV-2 epidemic, machine learning offers fundamental tools. This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. The methods compared are linear, polynomial, and generalized logistic regression models to describe the growth of COVID-19 incidents in Mexico. Additionally, machine learning and time series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with the mobility rates obtained from Google's Mobility Reports and climate variables acquired from the Weather Online API. The results suggest that the logistic growth model fits best the pandemic's behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term memory network can be exploited for predicting daily cases. Given this, we propose a model to predict daily cases and fatalities for SARS-CoV-2 using time series data, mobility, and weather variables.

Systematic review of experiences and perceptions of key actors and organisations at multiple levels within health systems internationally in responding to COVID-19.

BACKGROUND: COVID-19 has presented challenges to healthcare systems and healthcare professionals internationally. After one year of the pandemic, the initial evidence on health system responses begins to consolidate, and there is a need to identify and synthesise experiences of responding to COVID-19 among healthcare professionals and other health system stakeholders. This systematic review of primary qualitative studies depicts the experiences and perceptions of organisations and actors at multiple levels of health systems internationally in responding to COVID-19. METHODS: Six main databases of biomedical information, public health and health administration research were searched over the period October 1, 2019, to October 21, 2020. Information extracted from included studies was analysed thematically. RESULTS: Thirty-four studies were eligible for data extraction. Nine of those studies, of lower methodological quality, were removed from the thematic analysis of study results. Considering the professional level experiences, predominant themes of the studies consisted of the new roles and responsibilities of healthcare workers, burnout and distress, recognition of ´unseen´ healthcare workers, and positive changes and emergent solutions amid the crisis. Organisational level findings of the studies included provision of psychological support, COVID-19 as "catalyst" for change, and exercise of more "open" leadership by managers and health authorities. Continuous training, regulation of working conditions, providing supportive resources, coordinating a diversity of actors, and reviewing and updating regulations were roles identified  at the local health system level. CONCLUSIONS: The experiences of frontline healthcare workers have been the focus of attention of the majority of primary qualitative studies as of October 2020. However, organisational and wider system level studies indicate that some responses to COVID-19 have been characterised by increased emphasis on coordination activities by local health system actors, making service adaptations at pace, and reliance on expanded roles of front-line workers. The need for theory-informed qualitative studies was identified at the organisational level. TRIAL REGISTRATION: CRD42020202875.

Quantifying the publication preferences of leading research universities.

Research universities have a strong devotion and advocacy for research in their core academic mission. This is why they are widely recognized for their excellence in research which make them take the most renowned positions in the different worldwide university leagues. In order to examine the uniqueness of this group of universities we analyze the scientific production of a sample of them in a 5 year period of time. On the one hand, we analyze their preferences in research measured with the relative percentage of publications in the different subject areas, and on the other hand, we calculate the similarity between them in research preferences. In order to select a set of research universities, we studied the leading university rankings of Shanghai, QS, Leiden, and Times Higher Education (THE). Although the four rankings own well established and developed methodologies and hold great prestige, we choose to use THE because data were readily available for doing the study we had in mind. Having done that, we selected the twenty academic institutions ranked with the highest score in the last edition of THE World University Rankings 2020 and to contrast their impact, we also, we compared them with the twenty institutions with the lowest score in this ranking. At the same time, we extracted publication data from Scopus database for each university and we applied bibliometrics indicators from Elsevier's SciVal. We applied the statistical techniques cosine similarity and agglomerative hierarchical clustering analysis to examine and compare affinities in research preferences among them. Moreover, a cluster analysis through VOSviewer was done to classify the total scientific production in the four major fields (health sciences, physical sciences, life sciences and social sciences). As expected, the results showed that top universities have strong research profiles, becoming the leaders in the world in those areas and cosine similarity pointed out that some are more affine among them than others. The results provide clues for enhancing existing collaboration, defining and re-directing lines of research, and seeking for new partnerships to face the current pandemic to find was to tackle down the covid-19 outbreak.