An arsenal of potential treatments takes aim at proteins that are key to the virus’ life cycle.  In March 2020, as the full scope of the COVID-19 pandemic was coming into view, Jen Nwankwo and colleagues turned a pair of artificial intelligence (AI) tools against SARS-CoV-2. One newly developed AI program, called SUEDE, digitally screens all known drug-like compounds for likely activity against biomolecules thought to be involved in the disease. The other, BAGEL, predicts how to build inhibitors to known targets. The two programs searched for compounds able to block human enzymes that play essential roles in enabling the virus to infect its host cells. While SUEDE sifted through 14 billion compounds in just a few hours and spat out a hit, BAGEL created equally fast new leads.

 

Read the full article at: www.sciencemag.org

COVID-19, caused by SARS-CoV-2, can involve sequelae and other medical complications that last weeks to months after initial recovery, which has come to be called Long-COVID or COVID long-haulers. This systematic review and meta-analysis aims to identify studies assessing long-term effects of COVID-19 and estimates the prevalence of each symptom, sign, or laboratory parameter of patients at a post-COVID-19 stage. LitCOVID (PubMed and Medline) and Embase were searched by two independent researchers. All articles with original data for detecting long-term COVID-19 published before 1st of January 2021 and with a minimum of 100 patients were included. For effects reported in two or more studies, meta-analyses using a random-effects model were performed using the MetaXL software to estimate the pooled prevalence with 95% CI. Heterogeneity was assessed using I2 statistics. The Preferred Reporting Items for Systematic Reviewers and Meta-analysis (PRISMA) reporting guideline was followed.

 

 

Preprint available in medRxiv (Jan. 30, 2021):

https://doi.org/10.1101/2021.01.27.21250617 

Read the full article at: www.medrxiv.org