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Obesity is a multifactorial chronic metabolic disease with multiple complications. Crataegus pinnatifida (CP) and Wolfiporia extensa (WE) are traditional functional foods with improving metabolic health properties. This study demonstrated the effect of CP and WE combination on ameliorating obesity induced by a high-fat diet (HFD). Moreover, the CP-WE food pair ameliorated HFD-induced metabolic disorders, including glucose intolerance, insulin resistance, hyperlipidemia, and hepatic steatosis. 16S rRNA gene amplicon sequencing and analysis revealed that CP combined with WE reshaped the composition of gut microbiota in HFD-fed mice. Furthermore, correlation analysis revealed a substantial association between the obesity-related parameters and the shifts in predominant bacterial genera influenced by the food pair intervention. In conclusion, this study demonstrated that the CP-WE food pair ameliorated HFD-induced obesity and reshaped gut microbiota composition, providing a promising approach to combat obesity through specific food combinations.

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Machine learning (ML) has been used for many clinical decision-making processes and diagnostic procedures in bioinformatics applications. We examined eight algorithms, including linear discriminant analysis (LDA), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), Naïve Bayes classifier (NB), and artificial neural network (ANN) models, to evaluate their classification and prediction capabilities for four tissue types in Wolfiporia extensa using their monosaccharide composition profiles. All 8 ML-based models were assessed as exemplary models with AUC exceeding 0.8. Five models, namely LDA, KNN, RF, GBM, and ANN, performed excellently in the four-tissue-type classification (AUC > 0.9). Additionally, all eight models were evaluated as good predictive models with AUC value > 0.8 in the three-tissue-type classification. Notably, all 8 ML-based methods outperformed the single linear discriminant analysis (LDA) plotting method. For large sample sizes, the ML-based methods perform better than traditional regression techniques and could potentially increase the accuracy in identifying tissue samples of W. extensa.

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Osteoporosis, Greek for "porous bone," is a bone disease characterized by a decrease in bone strength, microarchitectural changes in the bone tissues, and an increased risk of fracture. An imbalance of bone resorption and bone formation may lead to chronic metabolic diseases such as osteoporosis. Wolfiporia extensa, known as "Bokryung" in Korea, is a fungus belonging to the family Polyporaceae and has been used as a therapeutic food against various diseases. Medicinal mushrooms, mycelium and fungi, possess approximately 130 medicinal functions, including antitumor, immunomodulating, antibacterial, hepatoprotective, and antidiabetic effects, and are therefore used to improve human health. In this study, we used osteoclast and osteoblast cell cultures treated with Wolfiporia extensa mycelium water extract (WEMWE) and investigated the effect of the fungus on bone homeostasis. Subsequently, we assessed its capacity to modulate both osteoblast and osteoclast differentiation by performing osteogenic and anti-osteoclastogenic activity assays. We observed that WEMWE increased BMP-2-stimulated osteogenesis by inducing Smad-Runx2 signal pathway axis. In addition, we found that WEMWE decreased RANKL-induced osteoclastogenesis by blocking c-Fos/NFATc1 via the inhibition of ERK and JNK phosphorylation. Our results show that WEMWE can prevent and treat bone metabolic diseases, including osteoporosis, by a biphasic activity that sustains bone homeostasis. Therefore, we suggest that WEMWE can be used as a preventive and therapeutic drug.

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Wolfiporia extensa (WE) is a medicinal mushroom and an excellent source of naturally occurring anti-inflammatory substances. However, the particular bioactive compound(s) and mechanism(s) of action against inflammation have yet to be determined. Here, we studied anti-inflammatory bioactive compounds and their molecular mechanisms through network pharmacology. Methanol (ME) extract of WE (MEWE) was used for GC-MS analysis to identify the bioactives, which were screened by following Lipinski's rules. Public databases were used to extract selected bioactives and inflammation-related targets, and Venn diagrams exposed the common targets. Then, STRING and Cytoscape tools were used to construct protein-protein (PPI) network and mushroom-bioactives-target (M-C-T) networks. Gene Ontology and KEGG pathway analysis were performed by accessing the DAVID database and molecular docking was conducted to validate the findings. The chemical reactivity of key compounds and standard drugs was explored by the computational quantum mechanical modelling method (DFT study). Results from GC-MS revealed 27 bioactives, and all obeyed Lipinski's rules. The public databases uncovered 284 compound-related targets and 7283 inflammation targets. A Venn diagram pointed to 42 common targets which were manifested in the PPI and M-C-T networks. KEGG analysis pointed to the HIF-1 signaling pathway and, hence, the suggested strategy for preventing the onset of inflammatory response was inhibition of downstream NFKB, MAPK, mTOR, and PI3K-Akt signaling cascades. Molecular docking revealed the strongest binding affinity for "N-(3-chlorophenyl) naphthyl carboxamide" on five target proteins associated with the HIF-1 signaling pathway. Compared to the standard drug utilized in the DFT (Density Functional Theory) analysis, the proposed bioactive showed a good electron donor component and a reduced chemical hardness energy. Our research pinpoints the therapeutic efficiency of MEWE and this work suggests a key bioactive compound and its action mechanism against inflammation.

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Dried sclerotia of Wolfiporia extensa have been used as medicine in Asia from Eastern Han Dynasty, and also used as traditional snack called "fulingjiabing" in Beijing, China. In this paper, 18 macro and trace elements (Ag, As, Ba, Cd, Co, Cr, Cs, Cu, Fe, Li, Mn, Ni, Pb, Rb, Se, Sr, V, and Zn) in both flesh and peel of Wolfiporia extensa from seven sites of Yunnan province in China were determined by inductively coupled plasma mass spectrometer. The average recovery rates of certified reference materials for GBW10015 (spinach leaves) ranged from 90.5 to 113%, for GBW10028 (citrus leaves) from 92.8 to 106%, and for GBW07603 (bush branch and leaves) from 83.3 to 114.6%. Generally speaking, the concentration of all elements determined was at common level. The results of this survey indicate that mineral compositions in peel were higher than in flesh. In peel, the contents of investigated trace metals in mushroom samples were found to be in the range of 1,660-13,400 µg·g-1 dry matter (dm) for Fe and 29.6-710 µg·g-1 dm for Mn. The mean contents of Cr, Cu, Rb, V, and Zn in peel were between 10 and 20 µg·g-1 dm, followed by As, Co, Li, Ni, Pb, Se, and Sr with mean contents between 1 and 10 µg·g-1 dm, while Ag, Cd, and Cs had mean contents of <1 µg·g-1 dm. In flesh, the concentration of Fe was in the range of 54-900 µg·g-1 dm, and it was 1.5-49 µg·g-1 dm for Mn, followed by Ba, Cu, Rb, and Zn in the range of 1 to 10 µg·g-1 dm, while for Ag, As, Cd, Co, Cr, Cs, Li, Ni, Pb, Se, Sr, and V it was <1 µg·g-1 dm. The concentration of toxic elements, such as As, Cd, and Pb, in both flesh and peel was below the permissible limits of World Health Organization. However, As and Pb contents in peel were higher than the limits permitted in the Chinese Pharmacopoeia. The results of principal component analysis showed that the flesh of Wolfiporia extensa from all the seven sites of the Yunnan province tend to cluster together, most probably because the origin of mineral elements in both flesh and peel is wood substrate (old and dead pine trees).

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A quality assessment system comprised of a tandem technique of ultraviolet (UV) spectroscopy and ultra-fast liquid chromatography (UFLC) aided by multivariate analysis was presented for the determination of geographic origin of Wolfiporia extensa collected from five regions in Yunnan Province of China. Characteristic UV spectroscopic fingerprints of samples were determined based on its methanol extract. UFLC was applied for the determination of pachymic acid (a biomarker) presented in individual test samples. The spectrum data matrix and the content of pachymic acid were integrated and analyzed by partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA). The results showed that chemical properties of samples were clearly dominated by the epidermis and inner part as well as geographical origins. The relationships among samples obtained from these five regions have been also presented. Moreover, an interesting finding implied that geographical origins had much greater influence on the chemical properties of epidermis compared with that of the inner part. This study demonstrated that a rapid tool for accurate discrimination of W. extensa by UV spectroscopy and UFLC could be available for quality control of complicated medicinal mushrooms.

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Considering the environmental pollution, edible mushroom safety is of great concern to consumers. This study aimed at providing and evaluating data on As, Cd and Pb content of sclerotia of Wolfiporia extensa collected across Yunnan in China. For the presented survey As, Cd, and Pb concentration in sclerotia of wild and cultivated W. extensa was determined by inductively coupled plasma mass spectrometry. The results showed that As, Cd, and Pb content were below the limit promulgated by WHO, with the ranges 5.27-161, 1.51-42.1 and < 1-634 ng g(-1) dry matter. Calculated hazard quotient (HQ) and hazard index (HI) were used to evaluate the non-carcinogenic health risk from individual and combined metals via daily consumption of 50 g sclerotia. Both HQ and HI through consumption of sclerotia were below 1, indicating that weekly consumption of sclerotia at the indicated doses poses no significant health risk to an adult consumer.

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