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Background and objectives Because of its beneficial off‐target effects against non‐mycobacterial infectious diseases, bacillus Calmette–Guérin (BCG) vaccination might be an accessible early intervention to boost protection against novel pathogens. Multiple epidemiological studies and randomised controlled trials (RCTs) are investigating the protective effect of BCG against coronavirus disease 2019 (COVID‐19). Using samples from participants in a placebo‐controlled RCT aiming to determine whether BCG vaccination reduces the incidence and severity of COVID‐19, we investigated the immunomodulatory effects of BCG on in vitro immune responses to SARS‐CoV‐2. Methods This study used peripheral blood taken from participants in the multicentre RCT and BCG vaccination to reduce the impact of COVID‐19 on healthcare workers (BRACE trial). The whole blood taken from BRACE trial participants was stimulated with γ‐irradiated SARS‐CoV‐2‐infected or mock‐infected Vero cell supernatant. Cytokine responses were measured by multiplex cytokine analysis, and single‐cell immunophenotyping was made by flow cytometry. Results BCG vaccination, but not placebo vaccination, reduced SARS‐CoV‐2‐induced secretion of cytokines known to be associated with severe COVID‐19, including IL‐6, TNF‐α and IL‐10. In addition, BCG vaccination promoted an effector memory phenotype in both CD4+ and CD8+ T cells, and an activation of eosinophils in response to SARS‐CoV‐2. Conclusions The immunomodulatory signature of BCG’s off‐target effects on SARS‐CoV‐2 is consistent with a protective immune response against severe COVID‐19.

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For several years cloud computing has been generating considerable debate and interest within IT corporations. Since cloud computing environments provide storage and processing systems that are adaptable, efficient, and straightforward, thereby enabling rapid infrastructure modifications to be made according to constantly varying workloads, organizations of every size and type are migrating to web-based cloud supported solutions. Due to the advantages of the pay-per-use model and scalability factors, current video on demand (VoD) streaming services rely heavily on cloud infrastructures to offer a large variety of multimedia content. Recent well documented failure events in commercial VoD services have demonstrated the fundamental importance of maintaining high availability in cloud computing infrastructures, and hierarchical modeling has proved to be a useful tool for evaluating the availability of complex systems and services. This paper presents an availability model for a video streaming service deployed in a private cloud environment which includes redundancy mechanisms in the infrastructure. Differential sensitivity analysis was applied to identify and rank the critical components of the system with respect to service availability. The results demonstrate that such a modeling strategy combined with differential sensitivity analysis can be an attractive methodology for identifying which components should be supported with redundancy in order to consciously increase system dependability.

Architectures and services that are provided by cloud computing system must have high availability, scalability, security and furthermore be fault tolerant. Plan these environments is not an easy task, it is necessary to ensure that undesirable situations or errors do not occur or can be minimized. The use of Sensitivity Analysis combined with the use of modeling hierarchy and the study of models for representing redundancy mechanisms arise in order to investigate the possible changes that these systems suffer and identify their shortcomings and propose improvements solutions for the planning of these systems. In this work the redundancy mechanisms is a solution to improve the performance of environments in cloud computing.