Computational anti-COVID-19 drug design: progress and challenges.

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

Update on treatment and preventive interventions against COVID-19: an overview of potential pharmacological agents and vaccines.

The outbreak of coronavirus disease 2019 (COVID-19) triggered by the new member of the coronaviridae family, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created an unprecedented challenge for global health. In addition to mild to moderate clinical manifestations such as fever, cough, and fatigue, severe cases often developed lethal complications including acute respiratory distress syndrome (ARDS) and acute lung injury. Given the alarming rate of infection and increasing trend of mortality, the development of underlying therapeutic and preventive treatment, as well as the verification of its effectiveness, are the top priorities. Current research mainly referred to and evaluated the application of the empirical treatment based on two precedents, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), including antiviral drugs targeting different stages of virus replication, immunotherapy modulating the overactivated inflammation response, and other therapies such as herbal medicine and mesenchymal stem cells. Besides, the ongoing development of inventing prophylactic interventions such as various vaccines by companies and institutions worldwide is crucial to decline morbidity and mortality. This review mainly focused on promising candidates for the treatment of COVID-19 and collected recently updated evidence relevant to its feasibility in clinical practice in the near future.

virusMED: an atlas of hotspots of viral proteins.

Metal binding sites, antigen epitopes and drug binding sites are the hotspots in viral proteins that control how viruses interact with their hosts. virusMED (virus Metal binding sites, Epitopes and Drug binding sites) is a rich internet application based on a database of atomic interactions around hotspots in 7041 experimentally determined viral protein structures. 25306 hotspots from 805 virus strains from 75 virus families were characterized, including influenza, HIV-1 and SARS-CoV-2 viruses. Just as Google Maps organizes and annotates points of interest, virusMED presents the positions of individual hotspots on each viral protein and creates an atlas upon which newly characterized functional sites can be placed as they are being discovered. virusMED contains an extensive set of annotation tags about the virus species and strains, viral hosts, viral proteins, metal ions, specific antibodies and FDA-approved drugs, which permits rapid screening of hotspots on viral proteins tailored to a particular research problem. The virusMED portal (https://virusmed.biocloud.top) can serve as a window to a valuable resource for many areas of virus research and play a critical role in the rational design of new preventative and therapeutic agents targeting viral infections.

Gut, metabolism and nutritional Support for COVID-19: Experiences from China.

There is little research that focuses on the relationship between the gut, metabolism, nutritional support and COVID-19. As a group of Chinese physicians, nutritionists and scientists working on the frontline treating COVID-19 patients, we aim to integrate our experiences and the current clinical evidence to address this pressing issue in this article. Based on our clinical observations and available evidence, we recommend the following practice. Firstly, the Nutritional Risk Screening 2002 tool should be used routinely and periodically; for patients with a score ≥3, oral nutritional supplements should be given immediately. Secondly, for patients receiving the antiviral agents lopinavir/ritonavir, gastrointestinal side effects should be monitored for and timely intervention provided. Thirdly, for feeding, the enteral route should be the first choice. In patients undergoing mechanical ventilation, establishing a jejunal route as early as possible can guarantee the feeding target being achieved if gastric dilatation occurs. Fourthly, we suggest a permissive underfeeding strategy for severe/critical patients admitted to the intensive care unit during the first week of admission, with the energy target no more than 20 kcal/kg/day (for those on mechanical ventilation, this target may be lowered to 10-15 kcal/kg/day) and the protein target around 1.0-1.2 g/kg/day. If the inflammatory condition is significantly alleviated, the energy target may be gradually increased to 25-30 kcal/kg/day and the protein target to 1.2-1.5 g/kg/day. Fifthly, supplemental parenteral nutrition should be used with caution. Lastly, omega-3 fatty acids may be used as immunoregulators, intravenous administration of omega-3 fatty emulsion (10 g/day) at an early stage may help to reduce the inflammatory reaction.

Prevalence of Underlying Medical Conditions Associated With Severe COVID-19 Illness in Adult Cancer Survivors in the United States.

Cancer, and other underlying medical conditions including chronic obstructive pulmonary disease, heart diseases, diabetes, chronic kidney disease, and obesity, are associated with increased risk of severe coronavirus disease 2019 (COVID-19) illness. We identified 6411 cancer survivors and 77 748 adults without a cancer history from the 2016-2018 National Health Interview Survey and examined the prevalence and sociodemographic factors associated with these conditions in the United States. Most survivors reported having 1 or more of the conditions (56.4%, 95% confidence interval [CI] = 54.8% to 57.9%, vs 41.6%, 95% CI = 40.9% to 42.2%, in adults without a cancer history), and nearly one-quarter (22.9%, 95% CI = 21.6% to 24.3%) reported 2 or more, representing 8.7 million and 3.5 million cancer survivors, respectively. These conditions were more prevalent in survivors of kidney, liver, and uterine cancers as well as Black survivors and those with low socioeconomic status and public insurance. Findings highlight the need to protect survivors against COVID-19 transmission in health-care facilities and to prioritize cancer patients, survivors, caregivers, and their health-care providers in vaccine allocation.

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.

Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .