Dexamethasone (Dexa), frequently used as an anti-inflammatory agent, paradoxically leads to muscle inflammation and muscle atrophy. Receptor for advanced glycation end products (RAGE) and Toll-like receptor 4 (TLR4) lead to nucleotide-binding oligomerization domain-like receptor pyrin domain containing 3 (NLRP3) inflammasome formation through nuclear factor-κB (NF-κB) upregulation. NLRP3 inflammasome results in pyroptosis and is associated with the Murf-1 and atrogin-1 upregulation involved in protein degradation and muscle atrophy. The effects of Ecklonia cava extract (ECE) and dieckol (DK) on attenuating Dexa-induced muscle atrophy were evaluated by decreasing NLRP3 inflammasome formation in the muscles of Dexa-treated animals. The binding of AGE or high mobility group protein 1 to RAGE or TLR4 was increased by Dexa but significantly decreased by ECE or DK. The downstream signaling pathways of RAGE (c-Jun N-terminal kinase or p38) were increased by Dexa but decreased by ECE or DK. NF-κB, downstream of RAGE or TLR4, was increased by Dexa but decreased by ECE or DK. The NLRP3 inflammasome component (NLRP3 and apoptosis-associated speck-like), cleaved caspase -1, and cleaved gasdermin D, markers of pyroptosis, were increased by Dexa but decreased by ECE and DK. Interleukin-1β/Murf-1/atrogin-1 expression was increased by Dexa but restored by ECE or DK. The mean muscle fiber cross-sectional area and grip strength were decreased by Dexa but restored by ECE or DK. In conclusion, ECE or DK attenuated Dexa-induced muscle atrophy by decreasing NLRP3 inflammasome formation and pyroptosis.
Outliers in wireless sensor networks (WSNs) are sensor nodes that issue attacks by abnormal behaviours and fake message dissemination. However, existing cryptographic techniques are hard to detect these inside attacks, which cause outlier recognition a critical and challenging issue for reliable and secure data dissemination in WSNs. To efficiently identify and isolate outliers, this study presents a novel outlier detection and countermeasure scheme (ODCS), which consists of three mechanisms: (i) abnormal event observation mechanism for network surveillance; (ii) exceptional message supervision mechanism for distinguishing fake messages by exploiting spatiotemporal correlation and consistency and (iii) abnormal behaviour supervision mechanism for the evaluation of node behaviour. The ODCS provides a heuristic methodology and does not need the knowledge about normal or malicious sensors in advance. This property makes the ODCS not only to distinguish and deal with various dynamic attacks automatically without advance learning, but also to reduce the requirement of capability for constrained nodes. In the ODCS, the communication is limited in a local range, such as one-hop or a cluster, which can reduce the communication frequency and circumscribe the session range further. Moreover, the ODCS provides countermeasures for different types of attacks, such as the rerouting scheme and the rekey security scheme, which can separate outliers from normal sensors and enhance the robustness of network, even when some nodes are compromised by adversary. Simulation results indicate that our approach can effectively detect and defend the outlier attack.
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