Exposure to Leishmania braziliensis triggers neutrophil activation and apoptosis.

BACKGROUND: Neutrophils are the first line of defense against invading pathogens and are rapidly recruited to the sites of Leishmania inoculation. During Leishmania braziliensis infection, depletion of inflammatory cells significantly increases the parasite load whereas co-inoculation of neutrophils plus L. braziliensis had an opposite effect. Moreover, the co-culture of infected macrophages and neutrophils also induced parasite killing leading us to ask how neutrophils alone respond to an L. braziliensis exposure. Herein we focused on understanding the interaction between neutrophils and L. braziliensis, exploring cell activation and apoptotic fate. METHODS AND FINDINGS: Inoculation of serum-opsonized L. braziliensis promastigotes in mice induced neutrophil accumulation in vivo, peaking at 24 h. In vitro, exposure of thyoglycollate-elicited inflammatory or bone marrow neutrophils to L. braziliensis modulated the expression of surface molecules such as CD18 and CD62L, and induced the oxidative burst. Using mCherry-expressing L. braziliensis, we determined that such effects were mainly observed in infected and not in bystander cells. Neutrophil activation following contact with L. braziliensis was also confirmed by the release of TNF-α and neutrophil elastase. Lastly, neutrophils infected with L. braziliensis but not with L. major displayed markers of early apoptosis. CONCLUSIONS: We show that L. braziliensis induces neutrophil recruitment in vivo and that neutrophils exposed to the parasite in vitro respond through activation and release of inflammatory mediators. This outcome may impact on parasite elimination, particularly at the early stages of infection

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided