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Microglial activation-mediated neuroinflammation is a major contributor to neuronal damage after cerebral ischemia. The Fractalkine (FKN)/CX3C chemokine receptor 1 (CX3CR1) axis plays a critical role in regulating microglial activation and neuroinflammation. The aim of this study is to ascertain the role and mechanism of FKN/CX3CR1 axis in hypoxic postconditioning (HPC)-induced anti-inflammatory and neuroprotective effects on transient global cerebral ischemia (tGCI). We found that HPC suppressed microglial activation and alleviated neuroinflammation in hippocampal CA1 after tGCI. Meanwhile, HPC upregulated the expression of FKN and CX3CR1 in neurons, but it downregulated the expression of CX3CR1 in glial cells after tGCI. In addition, the overexpression of FKN induced by the administration of FKN-carried lentivirus reduced microglial activation and inhibited neuroinflammation in CA1 after tGCI. Furthermore, silencing CX3CR1 with CX3CRi-carried lentivirus in CA1 after tGCI suppressed microglial activation and neuroinflammation and exerted neuroprotective effects. Finally, the overexpression of FKN caused a marked increase of neuronal CX3CR1 receptors, upregulated the phosphorylation of Akt, and reduced neuronal loss of rats in CA1 after tGCI. These findings demonstrated that HPC protected against neuronal damage in CA1 of tGCI rats through inhibiting microglial activation and activating Akt signaling pathway via FKN/CX3CR1 axis. [ABSTRACT FROM AUTHOR]

Summary: The distributed output‐feedback tracking control for a class of networked multiagents in nonaffine pure‐feedback form is investigated in this article. By introducing a low‐pass filter and some auxiliary variables, we first transform the nonaffine system into the affine form. Then, the finite‐time observer is designed to estimate the states of the newly derived affine system. By applying the fraction dynamic surface control approach and the neural network‐based approximation technique, the distributed output‐feedback control laws are proposed and it is proved that the tracking errors converge to an arbitrarily small bound around zero in finite time. Finally, some simulation examples are provided to confirm the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]