A new multiplex SARS-CoV-2 antigen microarray showed correlation of IgG, IgA, and IgM antibodies from patients with COVID-19 disease severity and maintenance of relative IgA and IgM antigen binding over time.

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 different from the same isotype in whole serum, probably due to competition from the other isotypes present. Using purified antibody isotypes from naïve Irish COVID-19 patients, we correlated antibody isotype binding to different panels of antigens with disease severity, with binding to the S region S1 expressed in insect cells (S1 Sf21) significant for IgG, IgA, and IgM. Assessing longitudinal response for constant concentrations of purified antibody isotypes for a patient subset demonstrated that the relative proportion of antigen-specific IgGs decreased over time for severe disease, but 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, important 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.

Dissociating COVID-19 from other respiratory infections based on acoustic, motor coordination, and phonemic patterns.

In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based and standard acoustic features extracted from recordings of simple speech tasks could aid in detecting the presence of COVID-19. We further hypothesized that these features would aid in characterizing the effect of COVID-19 on speech production systems. A protocol, consisting of a variety of speech tasks, was administered to 12 individuals with COVID-19 and 15 individuals with other viral infections at University Hospital Galway. From these recordings, we extracted a set of acoustic time series representative of speech production subsystems, as well as their univariate statistics. The time series were further utilized to derive correlation-based features, a proxy for speech production motor coordination. We additionally extracted phoneme-based features. These features were used to create machine learning models to distinguish between the COVID-19 positive and other viral infection groups, with respiratory- and laryngeal-based features resulting in the highest performance. Coordination-based features derived from harmonic-to-noise ratio time series from read speech discriminated between the two groups with an area under the ROC curve (AUC) of 0.94. A longitudinal case study of two subjects, one from each group, revealed differences in laryngeal based acoustic features, consistent with observed physiological differences between the two groups. The results from this analysis highlight the promise of using nonintrusive sensing through simple speech recordings for early warning and tracking of COVID-19.

Epidemiology and outcomes of early-onset AKI in COVID-19-related ARDS in comparison with non-COVID-19-related ARDS: insights from two prospective global cohort studies.

BACKGROUND: Acute kidney injury (AKI) is a frequent and severe complication of both COVID-19-related acute respiratory distress syndrome (ARDS) and non-COVID-19-related ARDS. The COVID-19 Critical Care Consortium (CCCC) has generated a global data set on the demographics, management and outcomes of critically ill COVID-19 patients. The LUNG-SAFE study was an international prospective cohort study of patients with severe respiratory failure, including ARDS, which pre-dated the pandemic. METHODS: The incidence, demographic profile, management and outcomes of early AKI in patients undergoing invasive mechanical ventilation for COVID-19-related ARDS were described and compared with AKI in a non-COVID-19-related ARDS cohort. RESULTS: Of 18,964 patients in the CCCC data set, 1699 patients with COVID-19-related ARDS required invasive ventilation and had relevant outcome data. Of these, 110 (6.5%) had stage 1, 94 (5.5%) had stage 2, 151 (8.9%) had stage 3 AKI, while 1214 (79.1%) had no AKI within 48 h of initiating invasive mechanical ventilation. Patients developing AKI were older and more likely to have hypertension or chronic cardiac disease. There were geo-economic differences in the incidence of AKI, with lower incidence of stage 3 AKI in European high-income countries and a higher incidence in patients from middle-income countries. Both 28-day and 90-day mortality risk was increased for patients with stage 2 (HR 2.00, p < 0.001) and stage 3 AKI (HR 1.95, p < 0.001). Compared to non-COVID-19 ARDS, the incidence of shock was reduced with lower cardiovascular SOFA score across all patient groups, while hospital mortality was worse in all groups [no AKI (30 vs 50%), Stage 1 (38 vs 58%), Stage 2 (56 vs 74%), and Stage 3 (52 vs 72%), p < 0.001]. The time profile of onset of AKI also differed, with 56% of all AKI occurring in the first 48 h in patients with COVID-19 ARDS compared to 89% in the non-COVID-19 ARDS population. CONCLUSION: AKI is a common and serious complication of COVID-19, with a high mortality rate, which differs by geo-economic location. Important differences exist in the profile of AKI in COVID-19 versus non-COVID-19 ARDS in terms of their haemodynamic profile, time of onset and clinical outcomes.

An analysis of patient self-reported COVID-19 symptoms during the first wave of the pandemic in Ireland.

BACKGROUND: Since the outbreak of COVID-19 in December 2019, there have been more than 115 million cases worldwide (1). Symptoms of COVID-19 vary widely and the spectrum of clinical presentation has yet to be fully characterised (2). Many countries have detailed their early experience with COVID-19, with a focus on the clinical characteristics of the disease. However, to our knowledge, there has been no such study detailing symptoms in the Irish population. AIM: Our aim is to describe COVID-19 symptoms in the Irish population at the beginning of the COVID-19 pandemic and compare symptoms between those reporting positive and negative test results. METHOD: A Web page MyCovidSymptoms.ie was created by researchers at the National University of Ireland, Galway, in April 2020 to investigate COVID-19 symptoms in Ireland. The Web page invited participants to self-report RT-PCR test outcome data (positive, negative, untested), temperature and a range of symptoms (cough, shortness of breath, fatigue, loss of taste, loss of smell). RESULTS: One hundred and twenty-three Irish participants who had a RT-PCR test for COVID-19 logged their symptoms. Eighty-four patients reported that they tested positive for COVID-19, and 39 patients reported a negative COVID-19 test. In our cohort of respondents with a positive COVID-19 test, 49/84 (58%) respondents reported a cough. Of the 39 respondents with a negative COVID-19 test, 17 (44%) reported having a cough. The distribution of temperature was similar in both those with and without COVID-19. Levels of self-reported fatigue were high in both groups with 65/84 (77%) of COVID-19-positive patients reporting fatigue and 30/39 (77%) of those who were COVID-19-negative reporting fatigue. New symptoms emerging at the time of data collection included loss of taste and smell. We demonstrated a higher proportion of loss of smell (p = 0.02) and taste (p = 0.01) in those reporting a positive result, compared to those reporting a negative result. CONCLUSION: These data represents an early picture of the clinical characteristics of COVID-19 in an Irish population. It also highlights the potential use of self-reported data globally as a powerful tool in helping with the pandemic.