ABSTRACT
Zoonotic spillover of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to humans in December 2019 caused the coronavirus disease 2019 (COVID-19) pandemic. Serological monitoring is critical for detailed understanding of individual immune responses to infection and protection to guide clinical therapeutic and vaccine strategies. We developed a high throughput multiplexed SARS-CoV-2 antigen microarray incorporating spike (S) and nucleocapsid protein (NP) and fragments expressed in various hosts which allowed simultaneous assessment of serum IgG, IgA, and IgM responses. Antigen glycosylation influenced antibody binding, with S glycosylation generally increasing and NP glycosylation decreasing binding. Purified antibody isotypes demonstrated a binding pattern and intensity that differed from the same isotype in the presence of other isotypes in whole serum, probably due to competition. Using purified antibody isotypes from naive Irish COVID-19 patients, we correlated antibody isotype binding to different panels of antigens with disease severity, with significance for binding to the S region S1 expressed in insect cells (S1 Sf21) for all three antibody isotypes. Assessing longitudinal response for constant concentrations of antibody isotypes for a subset of patients demonstrated that while the relative proportion of antigen-specific IgGs decreased over time for severe disease, the relative proportion of antigen-specific IgA binding remained at the same magnitude at 5 and 9 months post-first symptom onset. Further, the relative proportion of IgM binding decreased for S antigens but remained the same for NP antigens. This may support antigen specific serum IgA and IgM playing a role in maintaining longer-term protection, of importance for developing and assessing vaccine strategies. Overall, these data demonstrate the multiplexed platform as a sensitive and useful platform for expanded humoral immunity studies, allowing detailed elucidation of antibody isotypes response against multiple antigens. This approach will be useful for monoclonal antibody therapeutic studies and screening of donor polyclonal antibodies for patient infusions.
ABSTRACT
Clinicians and patients often confuse drug names that sound alike. We conducted auditory perception experiments in the United States to assess the impact of similarity, familiarity, background noise and other factors on clinicians' (physicians, family pharmacists, nurses) and laypersons' ability to identify spoken drug names. We found that accuracy increased significantly as the signal-to-noise (S/N) ratio increased, as subjective familiarity with the name increased and as the national prescribing frequency of the name increased. For clinicians only, similarity to other drug names reduced identification accuracy, especially when the neighboring names were frequently prescribed. When one name was substituted for another, the substituted name was almost always a more frequently prescribed drug. Objectively measurable properties of drug names can be used to predict confusability. The magnitude of the noise and familiarity effects suggests that they may be important targets for intervention. We conclude that the ability of clinicians and lay people to identify spoken drug names is influenced by signal-to-noise ratio, subjective familiarity, prescribing frequency, and the similarity neighborhoods of drug names.
