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Bacterial infections are re-emerging as substantial threats to global health due to the limited selection of antibiotics that are capable of overcoming antibiotic-resistant strains. By deterring such mutations whilst minimizing the need to develop new pathogen-specific antibiotics, immunotherapy offers a broad-spectrum therapeutic solution against bacterial infections. In particular, pathology resulting from excessive immune response ( i.e. fibrosis, necrosis, exudation, breath impediment) contributes significantly to negative disease outcome. Herein, we present a nanoparticle that is targeted to activated macrophages and loaded with siRNA against the Irf5 gene. This formulation is able to induce >80% gene silencing in activated macrophages in vivo , and it inhibits the excessive inflammatory response, generating a significantly improved therapeutic outcome in mouse models of bacterial infection. The versatility of the approach is demonstrated using mice with antibiotic-resistant Gram-positive (methicillin-resistant Staphylococcus aureus ) and Gram-negative ( Pseudomonas aeruginosa ) muscle and lung infections, respectively. Effective depletion of the Irf5 gene in macrophages is found to significantly improve the therapeutic outcome of infected mice, regardless of the bacteria strain and type. 

Abstract Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic. 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.