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https://www.selleckchem.com/products/nu7026.html The high proportion of transmission events derived from asymptomatic or presymptomatic infections make SARS-CoV-2, the causative agent in COVID-19, difficult to control through the traditional non-pharmaceutical interventions (NPIs) of symptom-based isolation and contact tracing. As a consequence, many US universities developed asymptomatic surveillance testing labs, to augment NPIs and control outbreaks on campus throughout the 2020-2021 academic year (AY); several of those labs continue to support asymptomatic surveillance efforts on campus in AY2021-2022. At the height of the pandemic, we built a stochastic branching process model of COVID-19 dynamics at UC Berkeley to advise optimal control strategies in a university environment. Our model combines behavioral interventions in the form of group size limits to deter superspreading, symptom-based isolation, and contact tracing, with asymptomatic surveillance testing. We found that behavioral interventions offer a cost-effective means of epidemic control grouthrough infections, halting onward transmission, and reducing total caseload. We offer this blueprint and easy-to-implement modeling tool to other academic or professional communities navigating optimal return-to-work strategies.Lasting immunity will be critical for overcoming the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, factors that drive the development of high titers of anti-SARS-CoV-2 antibodies and how long those antibodies persist remain unclear. Our objective was to comprehensively evaluate anti-SARS-CoV-2 antibodies in a clinically diverse COVID-19 convalescent cohort at defined time points to determine if anti-SARS-CoV-2 antibodies persist and to identify clinical and demographic factors that correlate with high titers. Using a novel multiplex assay to quantify IgG against four SARS-CoV-2 antigens, a receptor binding doma

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