Hepatoprotective potential of four fruit extracts rich in different structural flavonoids against alcohol-induced liver injury via gut microbiota-liver axis.

Alcoholic liver injury (ALI) accounts for a major share of the global burden of non-viral liver disease. In the absence of specialized medications, research on using fruit flavonoids as a treatment is gaining momentum. This study investigated the hepatoprotective effects of four fruits rich in structurally diverse flavonoids: ougan (Citrus reticulata cv. Suavissima, OG), mulberry (Morus alba L., MB), apple (Malus × domestica Borkh., AP), and turnjujube (Hovenia dulcis Thunnb., TJ). A total of one flavanone glycoside, three polymethoxyflavones, two anthocyanins, one flavonol glycoside, and one dihydroflavonol were identified through UPLC analysis. In an acute ethanol-induced ALI mouse model, C57BL/6J mice were supplemented with 200 mg/kg·BW/day of different fruit extracts for three weeks. Our results showed that the four extracts exhibited promising benefits in improving lipid metabolism disorders, iron overload, and oxidative stress. RT-PCR and Western blot tests suggested that the potential mechanism may partially be attributed to the activation of the NRF2-mediated antioxidant response and the inhibition of ferroptosis pathways. Furthermore, fruit extracts administration demonstrated a specific regulatory role in intestinal microecology, with increases in beneficial bacteria such as Dubosiella, Lactobacillus, and Bifidobacterium. Spearman correlation analysis revealed strong links between intestinal flora, lipid metabolism, and iron homeostasis, implying that the fruit extracts mitigated ALI via the gut microbiota-liver axis. In vitro experiments reaffirmed the activity against ethanol-induced oxidative damage and highlighted the positive effects of flavonoid components. These findings endorse the prospective application of OG, MB, AP, and TJ as dietary supplements or novel treatments for ALI.

Feature Interaction Learning Network for Cross-Spectral Image Patch Matching.

Recently, feature relation learning has attracted extensive attention in cross-spectral image patch matching. However, most feature relation learning methods can only extract shallow feature relations and are accompanied by the loss of useful discriminative features or the introduction of disturbing features. Although the latest multi-branch feature difference learning network can relatively sufficiently extract useful discriminative features, the multi-branch network structure it adopts has a large number of parameters. Therefore, we propose a novel two-branch feature interaction learning network (FIL-Net). Specifically, a novel feature interaction learning idea for cross-spectral image patch matching is proposed, and a new feature interaction learning module is constructed, which can effectively mine common and private features between cross-spectral image patches, and extract richer and deeper feature relations with invariance and discriminability. At the same time, we re-explore the feature extraction network for the cross-spectral image patch matching task, and a new two-branch residual feature extraction network with stronger feature extraction capabilities is constructed. In addition, we propose a new multi-loss strong-constrained optimization strategy, which can facilitate reasonable network optimization and efficient extraction of invariant and discriminative features. Furthermore, a public VIS-LWIR patch dataset and a public SEN1-2 patch dataset are constructed. At the same time, the corresponding experimental benchmarks are established, which are convenient for future research while solving few existing cross-spectral image patch matching datasets. Extensive experiments show that the proposed FIL-Net achieves state-of-the-art performance in three different cross-spectral image patch matching scenarios.