Differential effects of antiseptic mouth rinses on SARS-CoV-2 infectivity in vitro (preprint)

SARS-CoV-2 is detectable in saliva from asymptomatic individuals, suggesting the potential necessity for the use of mouth rinses to suppress viral load to reduce virus spread. Published studies on anti-SARS-CoV-2 activities of antiseptics determined by virus-induced cytotoxic effects cannot exclude antiseptic-associated cytotoxicity. Here, we determined the effect of commercially available mouth rinses and antiseptic povidone-iodine on the infectivity of pseudotyped SARS-CoV-2 virus. We first determined the effect of mouth rinses on cell viability to ensure that antiviral activity was not a consequence of mouth rinse-induced cytotoxicity. Colgate Peroxyl (hydrogen peroxide) exhibited the most cytotoxicity, followed by povidone-iodine-10% solution, chlorhexidine gluconate-0.12% (CHG), and Listerine (essential oils and alcohol). Analysis of the anti-viral activity of mouth rinses at non-cytotoxic concentrations showed that 1.5% (v/v) diluted CHG was a potent inhibitor when present in cells during infection, but the potency was reduced when CHG was removed after viral attachment, suggesting that the prolonged effect of mouth rinses on cells impacts the anti-viral activity. To minimalize mouth rinse-associated cytotoxicity, we pelleted treated-viruses to remove most of the mouth rinse prior to infection of cells. Colgate Peroxyl or povidone-iodine at 5% (v/v) completely blocked the viral infectivity. Listerine or CHG at 5% (v/v) had a moderate suppressive effect on the virus, and 50% (v/v) Listerine or CHG blocked the viral infectivity completely. Prolonged incubation of virus with mouth rinses was not required to block viral infectivity. Our results indicate that mouth rinses can significantly reduce virus infectivity, suggesting their potential use to reduce SARS-CoV-2 spread.

Neutrophil and monocyte dysfunctional effector response towards bacterial challenge in critically-ill COVID-19 patients (preprint)

COVID-19 displays diverse disease severities and symptoms. Elevated inflammation mediated by hypercytokinemia induces a detrimental dysregulation of immune cells. However, there is limited understanding of how SARS-CoV-2 pathogenesis impedes innate immune signaling and function against secondary bacterial infections. We assessed the influence of COVID-19 hypercytokinemia on the functional responses of neutrophils and monocytes upon bacterial challenges from acute and corresponding recovery COVID-19 ICU patients. We show that severe hypercytokinemia in COVID-19 patients correlated with bacterial superinfections. Neutrophils and monocytes from acute COVID-19 patients showed severely impaired microbicidal capacity, reflected by abrogated ROS and MPO production as well as reduced NETs upon bacterial challenges. We observed a distinct pattern of cell surface receptor expression on both neutrophils and monocytes leading to a suppressive autocrine and paracrine signaling during bacterial challenges. Our data provide insights into the innate immune status of COVID-19 patients mediated by their hypercytokinemia and its transient effect on immune dysregulation upon subsequent bacterial infections

Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units (preprint)

RationaleThe COVID-19 pandemic induces considerable strain on intensive care unit resources. ObjectivesWe aim to provide early predictions of individual patients intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic. MethodsWe developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry. MeasurementsThe RISC-19-ICU Investigators for Switzerland collected data of 557 critically ill patients with COVID-19. Main ResultsThe model gives probabilistically and marginally calibrated predictions which are more informative than the empirical length of stay distribution of the training data. However, marginal calibration was worse after approximately 20 days in the whole cohort and in different subgroups. Long staying COVID-19 patients have shorter length of stay than regular acute respiratory distress syndrome patients. We found differences in LoS with respect to age categories and gender but not in regions of Switzerland with different stress of intensive care unit resources. ConclusionA new probabilistic model permits calibrated and informative probabilistic prediction of LoS of individual patients with COVID-19. Long staying patients could be discovered early. The model may be the basis to simulate stochastic models for bed occupation in intensive care units under different casemix scenarios.

Timing COVID-19 - Synchronization of longitudinal patient data to the underlying disease progression using CRP as a temporal marker (preprint)

Advances in medical technology and IT infrastructure have led to increased availability of continuous patient data that allows to investigate the longitudinal progression of novel and known diseases in unprecedented detail. However, to accurately describe any underlying pathophysiology with longitudinal data, the individual patient trajectories have to be synchronized based on temporal markers. In this study, we use longitudinal data from 28 critically ill ICU COVID-19 patients to compare the commonly used alignment markers "onset of symptoms", "hospital admission" and "ICU admission" with a novel objective method based on the peak value of inflammatory marker C-reactive protein (CRP). By applying our CRP-based method to align the progression of neutrophils and lymphocytes, we were able to define a pathophysiological window that allowed further mortality risk stratification in our COVID-19 patient cohort. Our data highlights that proper synchronization of patient data to the underlying pathophysiology is crucial to differentiate severity subgroups and to allow reliable interpatient comparisons.