Sirtuin 1 (SIRT1) is a class III histone deacetylase that exerts an anti-inflammatory effect in airway diseases. Activated macrophages play an important role in asthma. However, the roles of SIRT1 on allergic airway inflammation in macrophages remain largely unexplored. In this study, we aimed to determine the roles of SIRT1 on allergic airway inflammation in macrophages. The effect of myeloid-specific SIRT1 deletion (Sirt1 fl/fl -LysMcre) on airway inflammation was assessed by using in vivo models of asthma following allergen exposure and in vitro culture of primary bone marrow-derived macrophages (BMDMs) exposed to house dust mite (HDM). We observed that Sirt1 fl/fl -LysMcre mice substantially enhanced airway inflammation and mucus production in response to allergen exposure. Expression of chemokine ligand (CXCL) 2, interleukin (IL)-1β, and tumor necrosis factor (TNF)-α were reduced in BMDMs with myeloid-specific deletion of Sirt1 after stimulation of HDM. Moreover, SIRT1 suppressed the inflammatory cytokines expression in BMDMs partially via the ERK/p38 MAPK pathways. Our study demonstrated that SIRT1 suppresses the allergic airway inflammation in macrophages, and suggested that activation of SIRT1 in macrophages may represent therapeutic strategy for asthma.

In the inter-domain network, a route leak occurs when a routing announcement is propagated outside of its intended scope, which is a violation of the agreed routing policy. The route leaks can disrupt the internet traffic and cause large outages. The accurately detection of route leaks requires the share of AS business relationship information of ASes. However, the business relationship information between ASes is confidential due to economic issues. Thus, ASes are usually unwilling to revealing this information to the other ASes, especially their competitors. Recent advancements in federated learning make it possible to share data while maintaining privacy. Motivated by this, in this paper we study the route leak problem by considering the privacy of business relationships between ASes, and propose a method for route leak detection with privacy guarantee by using blockchain-based federated learning framework, in which ASes can train a global detection model without revealing their business relationships directly. Moreover, the proposed method provides a self-validation scheme by labeling AS triples with local routing policies, which mitigates route leaks' lack of ground truth. We evaluate the proposed method under a variety of datasets including unbalanced and balanced datasets. The different deployment strategies of the proposed method under different topologies are also examined. The results show that the proposed method has a better performance in detecting route leaks than a single AS detection regardless of whether using balanced or unbalanced datasets. In the analysis of the deployment, the results show that ASes with more peers have more possible route leaks and can contribute more on the detection of route leaks with the proposed method.