The clinical impact of anti-spike monoclonal antibodies (mAb) in Coronavirus Disease 2019 (COVID-19) breakthrough infections is unclear. We present the results of an observational prospective cohort study assessing and comparing COVID-19 progression in high-risk outpatients receiving mAb according to primary or breakthrough infection. Clinical, serological and virological predictors associated with 28-day COVID-19-related hospitalization were identified using multivariate logistic regression and summarized with odds ratio (aOR) and 95% confidence interval (CI). A total of 847 COVID-19 outpatients were included: 414 with primary and 433 with breakthrough infection. Hospitalization was observed in 42/414 (10.1%) patients with primary and 8/433 (1.8%) patients with breakthrough infection (p < 0.001). aOR for hospitalization was significantly lower for breakthrough infection (aOR 0.12, 95%CI: 0.05–0.27, p < 0.001) and higher for immunocompromised status (aOR:2.35, 95%CI:1.08–5.08, p = 0.003), advanced age (aOR:1.06, 95%CI: 1.03–1.08, p < 0.001), and male gender (aOR:1.97, 95%CI: 1.04–3.73, p = 0.037). Among the breakthrough infection group, the median SARS-CoV-2 anti-spike IgGs was lower (p < 0.001) in immunocompromised and elderly patients >75 years compared with that in the immunocompetent patients. Our findings suggest that, among mAb patients, those with breakthrough infection have significantly lower hospitalization risk compared with patients with primary infection. Prognostic algorithms combining clinical and immune-virological characteristics are needed to ensure appropriate and up-to-date clinical protocols targeting high-risk categories.
Federated Learning approaches are becoming increasingly relevant in various fields. These approaches promise to facilitate an integrative data analysis without sharing the data, which is highly beneficial for applications with sensitive data such as healthcare. Yet, the risk of data leakage caused by malicious attacks needs to be assessed carefully. In this study, we consider a new attack route and present an algorithm that depends on being able to compute sample means, sample covariances, and construct known linearly independent vectors on the data owner side. We show that these basic functionalities, available in several established Federated Learning frameworks, suffice to reconstruct privacy-protected data.
Moreover, the attack algorithm is robust to defence strategies that build on random noise. We demonstrate this limitation of existing frameworks and discuss possible defence strategies. The novel insights will facilitate the improvement of Federated Learning frameworks.
We describe a theoretical and experimental study of an axisymmetric viscous gravity current with a constant flux, confined to the space between two horizontal parallel plates. The effect of confinement results in two regions of flow: an inner region where the fluid is in contact with both plates and an outer annular region where the fluid forms a gravity current along the lower plate. We present a simple theoretical model that describes the flow dynamics by a single dimensionless parameter
$J$
, which is the ratio of the characteristic height of an unconfined gravity current to the height of the confined space. Theoretical height profiles display the same characteristics as unconfined gravity currents until
$J \approx 0.48$
, where a rapid change in behaviour occurs as confinement comes into effect. For larger values of
$J$
, the confined viscous gravity current gradually tends to Hele-Shaw flow, with the transition essentially complete by
$J \approx 2$
. We compare the findings from our theoretical model with the results of a series of experiments using golden syrup with various fluxes and gap spacings. Although the data aligns with the major aspects of the model, it is clear that other physics is at play and a single non-dimensional parameter is not sufficient to capture the flow behaviour fully. We speculate on the factors absent in our model that may be responsible for this mismatch.
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