Introduction. Alzheimer’s disease (AD), the most common neurodegenerative disorder, is characterized by the accumulation of amyloid-β (Aβ) peptide and hyperphosphorylated tau protein. Accumulating evidence has revealed that the slow progressive deterioration of AD is associated with oxidative stress and chronic inflammation in the brain. Nuclear factor erythroid 2- (NF-E2-) related factor 2 (Nrf2), which acts through the Nrf2/ARE pathway, is a key regulator of the antioxidant and anti-inflammatory response. Although recent data show a link between Nrf2 and AD-related cognitive decline, the mechanism is still unknown. Thus, we explored how Nrf2 protects brain cells against the oxidative stress and inflammation of AD in a mouse model of AD (APP/PS1 transgenic (AT) mice) with genetic removal of Nrf2. Methods. The spatial learning and memory abilities of 12-month-old transgenic mice were evaluated using a Morris water maze test. Hippocampal levels of Nrf2, Aβ, and p-tauS404 and of astrocytes and microglia were determined by immunostaining. Inflammatory cytokines were determined by ELISA and quantitative real-time polymerase chain reaction (qRT-PCR). Oxidative stress was measured by 8-hydroxydeoxyguanosine immunohistochemistry, and the antioxidant response was determined by qRT-PCR. Results. The spatial learning and memory abilities of AT mice were impaired after Nrf2 deletion. Aβ and p-tauS404 accumulation was increased in the hippocampus of AT/Nrf2-KO mice. Astroglial and microglial activation was exacerbated, followed by upregulation of the proinflammatory cytokines IL-1β, IL-6, and TNF-α. Conclusion. Our present results show that Nrf2 deficiency aggravates AD-like pathology in AT mice. This phenotype was associated with increased levels of oxidative and proinflammatory markers, which suggests that the Nrf2 pathway may be a promising therapeutic target for AD.

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Variational modal decomposition (VMD) has the end effect, which makes it difficult to efficiently obtain fault eigenvalues from rolling bearing fault signals. Inspired by the mirror extension, an improved VMD is proposed. This method combines VMD and mirror extension. The mirror extension is a basic algorithm to inhibit the end effect. A comparison is made with empirical mode decomposition (EMD) for fault diagnosis. Experiments show that the improved VMD outperforms EMD in extracting the fault eigenvalues. The performance of the new algorithm is proven to be effective in real-life mechanical fault diagnosis. Furthermore, in this article, combining with singular value decomposition (SVD), fault eigenvalues are extracted. In this way, fault classification is realized by K-nearest neighbor (KNN). Compared with EMD, the proposed approach has advantages in the recognition rate, which can accurately identify fault types.

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