A multi-layer model for the early detection of COVID-19.

Current COVID-19 screening efforts mainly rely on reported symptoms and the potential exposure to infected individuals. Here, we developed a machine-learning model for COVID-19 detection that uses four layers of information: (i) sociodemographic characteristics of the individual, (ii) spatio-temporal patterns of the disease, (iii) medical condition and general health consumption of the individual and (iv) information reported by the individual during the testing episode. We evaluated our model on 140 682 members of Maccabi Health Services who were tested for COVID-19 at least once between February and October 2020.

Native human autoantibodies targeting GIPC1 identify differential expression in malignant tumors of the breast and ovary.

Background: We have been studying the native humoral immune response to cancer and have isolated a library of fully human autoantibodies to a variety of malignancies. We previously described the isolation and characterization of two fully human monoclonal antibodies, 27.F7 and 27.