A new machine learning tool developed by a team of researchers led by Justin Reese of Berkeley Lab and Peter Robinson of Jackson Lab analyzes electronic health records to find symptoms in common between people who have been diagnosed with long COVID and to define subtypes of the condition.
An international team led by Justin Reese, a research scientist in the Environmental Genomics and Systems Biology (EGSB) Division, analyzed electronic health record data aggregated in the National COVID Cohort Collaborative (N3C) Data Enclave to assess whether metformin is associated with reduced COVID-19 severity in people with prediabetes or polycystic ovary syndrome (PCOS), two common conditions that increase the risk of severe COVID-19 presentation.
An antibody therapy that appears to neutralize all known SARS-CoV-2 strains, and other coronaviruses, was developed with a little help from structural biologist Jay Nix.
An imaging technique pioneered by Berkeley Lab is helping reveal the best antibodies to test for in rapid and reliable COVID-19 detection. Although current tests such as polymerase chain reaction (PCR) are highly accurate, these samples must be sent to an accredited lab for testing, causing a longer wait time for results. Michal Hammel, a research scientist in the Molecular Biophysics and Integrated Bioimaging Division, and Curtis D. Hodge led a study that could help get reliable, self-administered tests with instant results on the market.
Scientists at the Berkeley Center for Structural Biology contributed resources and data to a recently-published study revealing a new site on the coronavirus spike protein used by antibodies to block the invasion of the virus into healthy cells. The discovery of this new antibody binding site will help scientists as they work to continue improving treatment and vaccine formulations for COVID-19 and its variants.