journal article·2021-03

Porous selenium nanozymes targeted scavenging ROS synchronize therapy local inflammation and sepsis injury

Xu Chen, Xufeng Zhu, Youcong Gong, Guanglong Yuan, Jieqiong Cen, Qiaoshan Lie, Yida Hou, Gang Ye, Shengming Liu, Jie Liu

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Abstract

Abstract Excessive reactive oxygen species (ROS) is the main factor leading to high mortality in local inflammation and sepsis. Therefore, developing efficient ROS scavenger in pro-inflammatory immune cells is an urgent strategy for therapy local inflammatory and sepsis injury. Herein, we constructed a novel mesoporous selenium-hyaluronic acid nanoenzyme therapeutic system (MSe-HA NPs) for therapy local inflammatory and sepsis injury by targeting eliminate ROS in inflammatory macrophage. In detail manners, the high specific surface area of mesoporous selenium nanoenzyme exhibited ultra-high activity like glutathione peroxidase (GPx), which catalytic speed of H2O2 scavenging was increasing to 2.02 times compare with solid Se NPs. Moreover, the broad spectrum of ROS eliminating capacity further promoted ability of MSe NPs in eliminating various ROS. More interestingly, we found that choose HA as the modification not only endowed the MSe NPs targeting to inflammatory macrophage, but the ROS degradable property of HA could enhance the MSe NPs to eliminating ROS. In vivo, MSe-HA NPs after injection showed that simultaneously cured the local inflammation and sepsis induced with LPS by quickly eliminating excessive ROS. Especially in sepsis mouse models, the survival rate and the organic dysfunction significantly improved after treated with MSe-HA NPs. Thus, this novel mesoporous nanozyme provides an effective strategy for the design of nanomedicine and the treatment of inflammation-related diseases.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Nanomaterials in Catalysis · Nanoplatforms for cancer theranostics · Graphene and Nanomaterials Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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