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Search Results (9,121)

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Keywords = metabolome

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16 pages, 1777 KiB  
Article
Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence
by Fatma Hilal Yagin, Radwa El Shawi, Abdulmohsen Algarni, Cemil Colak, Fahaid Al-Hashem and Luca Paolo Ardigò
Diagnostics 2024, 14(18), 2049; https://doi.org/10.3390/diagnostics14182049 (registering DOI) - 15 Sep 2024
Abstract
Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We [...] Read more.
Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite’s individual contribution to the categorization process. Results: TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. The TPOT technique attained an AUC value of 0.81, showcasing superior accuracy, sensitivity, and specificity in comparison to the other models. Key metabolites, including L-valine, glycine, and DL-isoleucine, were identified as essential by TPOT and subsequently verified by TreeSHAP analysis. TreeSHAP provided a comprehensive explanation of the contribution of these metabolites to the model’s predictions, thereby increasing the interpretability and dependability of the results. This thorough assessment highlights the strength and reliability of the AutoML framework in the development of clinical biomarkers. Conclusions: This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC. The exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers. Furthermore, TreeSHAP boosted model transparency by highlighting the relevance of certain metabolites. This comprehensive method has the potential to enhance the identification of biomarkers and generate precise, easily understandable, AI-driven solutions for diagnosing HCC. Full article
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<p>A diagram of the proposed method in the current research.</p>
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<p>Nemenyi Test (α = 0.05) comparing the AUC of testing data for AutoML techniques and traditional machine learning techniques.</p>
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<p>Feature importance ranking based on SHAP values.</p>
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<p>SHAP waterfall plot for a representative true positive sample.</p>
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<p>SHAP waterfall plot for a representative true negative sample.</p>
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<p>Partial dependence plot of L-valine 1 showing its SHAP value and interaction with 2,3-butanediol 2.</p>
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18 pages, 2443 KiB  
Article
Biological Properties of the Mucus and Eggs of Helix aspersa Müller as a Potential Cosmetic and Pharmaceutical Raw Material: A Preliminary Study
by Anna Herman, Patrycja Wińska, Małgorzata Białek and Andrzej P. Herman
Int. J. Mol. Sci. 2024, 25(18), 9958; https://doi.org/10.3390/ijms25189958 (registering DOI) - 15 Sep 2024
Abstract
In recent years, snail mucus (SM) has become popular as an active ingredient in cosmetic and pharmaceutical products. In turn, snail eggs (SEs) also seem to be a promising active compound, but the biological activities of SEs are significantly less known. Therefore, our [...] Read more.
In recent years, snail mucus (SM) has become popular as an active ingredient in cosmetic and pharmaceutical products. In turn, snail eggs (SEs) also seem to be a promising active compound, but the biological activities of SEs are significantly less known. Therefore, our preliminary study aimed to compare the biological activities of the SEs and SM of Helix aspersa Müller. The metabolomic analysis (LC–MS technique), determination of the antimicrobial activity (agar diffusion test, broth microdilution methods), antioxidant activity (ABTS assay), cytotoxicity assay (MTT), and proapoptotic properties (flow cytometry) of the SEs and SM were evaluated. It was found that the SEs and SM contain 8005 and 7837 compounds, respectively. The SEs showed antibacterial activity against S. aureus (MIC 12.5 mg/mL) and P. aeruginosa (MIC 3.12 mg/mL). The EC50 estimation of the antioxidant activity is 89.64 mg/mL and above 100 mg/mL for the SEs and SM, respectively. The SEs also inhibited the cell proliferation of cancer cell lines (HCT-116, MCF-7, HT-29) more strongly compared to the SM. The highest proportion of apoptotic cells in HCT-116 was observed. The reach composition of the compounds in the SEs and SM may be crucial for the creation of new cosmetic and pharmaceutical raw materials with different biological activities. However, further extended studies on the biological activities of H. aspersa-delivered materials are still necessary. Full article
(This article belongs to the Special Issue New Insights in Natural Bioactive Compounds 3.0)
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<p>Radical scavenging activity of SM and SEs in ABTS methods.</p>
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<p>Viability of MCF-7, HT-29, HCT-116, and Vero cell lines after the treatment with SE (<b>A</b>) or SM (<b>B</b>). After 72 h of incubation, MTT test was performed. The data for the treated cells were analyzed by Dunnett’s multiple comparison test as follows: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 relative to control served as 100%; ns—not significant.</p>
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<p>Induction of apoptosis in HCT-116 cells. The data were determined by Accuri C6 Plus flow cytometer (BD Biosciences, San Jose, CA, USA) after 72 h of treatment with SE. Cells were stained with annexin V-FITC and PI (propidium iodide). (<b>A</b>) Mean and standard deviation (SD) of necrosis, as well as viable, early, and late apoptosis, as a percentage from three independent experiments each. (<b>B</b>) Representative cytograms for control HTC-116 cells (CTRL) and after treatment with SE. The data for early and late apoptotic cells were analyzed by Dunnett’s multiple comparison test as follows: *** <span class="html-italic">p</span> &lt; 0.001 relative to control; ns—not significant.</p>
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<p>Induction of apoptosis in MCF-7 cells. The data were determined by Accuri C6 Plus flow cytometer after 72 h of treatment with SE. Cells were stained with annexin V-FITC and PI (propidium iodide). (<b>A</b>) Mean and standard deviation (SD) of necrosis, as well as viable, early, and late apoptosis, as a percentage from three independent experiments each. (<b>B</b>) Representative cytograms for control MCF-7 cells (CTRL) and after treatment with SE. The data for early and late apoptotic cells were analyzed by Dunnett’s multiple comparison test as follows: * <span class="html-italic">p</span> &lt; 0.05, and *** <span class="html-italic">p</span> &lt; 0.001 relative to control.</p>
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14 pages, 9059 KiB  
Article
Dynamic Metabolic Responses of Resistant and Susceptible Poplar Clones Induced by Hyphantria cunea Feeding
by Zheshu Wang, Liangjian Qu, Zhibin Fan, Luxuan Hou, Jianjun Hu and Lijuan Wang
Biology 2024, 13(9), 723; https://doi.org/10.3390/biology13090723 (registering DOI) - 14 Sep 2024
Viewed by 182
Abstract
Poplar trees are significant for both economic and ecological purposes, and the fall webworm (Hyphantria cunea Drury) poses a major threat to their plantation in China. The preliminary resistance assessment in the previous research indicated that there were differences in resistance to [...] Read more.
Poplar trees are significant for both economic and ecological purposes, and the fall webworm (Hyphantria cunea Drury) poses a major threat to their plantation in China. The preliminary resistance assessment in the previous research indicated that there were differences in resistance to the insect among these varieties, with ‘2KEN8’ being more resistant and ‘Nankang’ being more susceptible. The present study analyzed the dynamic changes in the defensive enzymes and metabolic profiles of ‘2KEN8’ and ‘Nankang’ at 24 hours post-infestation (hpi), 48 hpi, and 96 hpi. The results demonstrated that at the same time points, compared to susceptible ‘Nankang’, the leaf consumption by H. cunea in ‘2KEN8’ was smaller, and the larval weight gain was slower, exhibiting clear resistance to the insect. Biochemical analysis revealed that the increased activity of the defensive enzymes in ‘2KEN8’ triggered by the feeding of H. cunea was significantly higher than that of ‘Nankang’. Metabolomics analysis indicated that ‘2KEN8’ initiated an earlier and more intense reprogramming of the metabolic profile post-infestation. In the early stages of infestation, the differential metabolites induced in ‘2KEN8’ primarily included phenolic compounds, flavonoids, and unsaturated fatty acids, which are related to the biosynthesis pathways of phenylpropanoids, flavonoids, unsaturated fatty acids, and jasmonates. The present study is helpful for identifying the metabolic biomarkers for inductive resistance to H. cunea and lays a foundation for the further elucidation of the chemical resistance mechanism of poplar trees against this insect. Full article
(This article belongs to the Special Issue Ecological Regulation of Forest and Grassland Pests)
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<p>Evaluation of resistance to <span class="html-italic">H. cunea</span> in the two poplar clones. (<b>A</b>) Two newly mature leaves, the seventh and eighth leaves of the seedling were used for larval infestation; (<b>B</b>) Leaf consumption at 48 hpi; (<b>C</b>) Changes of larva average weight when feeding for 0 h and 96 h. The two stars indicated a significant level with a <span class="html-italic">p</span>-value less than 0.01.</p>
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<p>Changes in POD and PPO activities in leaves of the infested and the control group at 24 hpi and 48 hpi. (<b>A</b>) POD activity; (<b>B</b>) PPO activity. C: control group. T: infested group. Duncan’s multiple range tests were performed to determine significant difference among inoculated and control samples. Different letters in the figure indicated significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>PLS-DA score plot of the infested and control samples at 24 hpi, 48 hpi, and 96 hpi. The ellipses represented the Hotelling T2 with 95% confidence. t [1] and t [2] were the first and second principal component, respectively. Each square represented an individual sample. The squares with same color were 7 replicates of each material at the same time point in infested or control group. The samples on the left side of the figure were the control and infested groups of the resistant ‘2KEN8’, while those on the right side were the control and inoculated samples of the susceptible ‘Nankang’. The solid lines represented the trajectories of the inoculated samples, while the dashed lines represented the trajectories of the control samples.</p>
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<p>Number of differential metabolites between control and infested samples. (<b>A</b>–<b>C</b>) Comparison of the differential metabolites induced by feeding of <span class="html-italic">H. cunea</span> in ‘2KEN8’ and ‘Nankang’ at 24 hpi (<b>A</b>), 48 hpi (<b>B</b>), and 96 hpi (<b>C</b>), respectively. The light purple and pale-yellow circles represented the differential metabolites between the infested (T) and control (C) group for ‘2KEN8’ and ‘Nankang’, respectively. (<b>D</b>,<b>E</b>) Number of differential metabolites induced by <span class="html-italic">H. cunea</span> at the three time points in ‘2KEN8’ (<b>D</b>) and ‘Nankang’ (<b>E</b>). The pink, pistachio, and sky-blue circles represented the number of differential metabolites between the infested and control samples at 24 h, 48 h, and 96 h, respectively. T/C: Differential metabolites between infested and control group.</p>
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<p>Relative contents of differential metabolites in pathways of phenylpropanoid and flavonoid biosynthesis. R: resistant ‘2KEN8’. S: susceptible ‘Nankang’. C: control group. T: infested group. Duncan’s multiple range tests were performed to determine significant difference among infested and control samples. Different letters in the figure indicated significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Relative abundance of differential metabolites in the pathway for biosynthesis of unsaturated fatty acids. R: resistant ‘2KEN8’. S: susceptible ‘Nankang’. C: control group. T: infested group. Duncan’s multiple range tests were performed to determine significant difference among infested and control samples. Different letters in the figure indicated significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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17 pages, 9416 KiB  
Article
Impact of Mild COVID-19 History on Oral-Gut Microbiota and Serum Metabolomics in Adult Patients with Crohn’s Disease: Potential Beneficial Effects
by Bingjie Xiang, Qi Zhang, Huibo Wu, Jue Lin, Zhaoyuan Xu, Min Zhang, Lixin Zhu, Jun Hu and Min Zhi
Biomedicines 2024, 12(9), 2103; https://doi.org/10.3390/biomedicines12092103 (registering DOI) - 14 Sep 2024
Viewed by 245
Abstract
The impact of coronavirus disease 2019 (COVID-19) history on Crohn’s disease (CD) is unknown. This investigation aimed to examine the effect of COVID-19 history on the disease course, oral-gut microbiota, and serum metabolomics in patients with CD. In this study, oral-gut microbiota and [...] Read more.
The impact of coronavirus disease 2019 (COVID-19) history on Crohn’s disease (CD) is unknown. This investigation aimed to examine the effect of COVID-19 history on the disease course, oral-gut microbiota, and serum metabolomics in patients with CD. In this study, oral-gut microbiota and serum metabolomic profiles in 30 patients with CD and a history of mild COVID-19 (positive group, PG), 30 patients with CD without COVID-19 history (negative group, NG), and 60 healthy controls (HC) were assessed using 16S rDNA sequencing and targeted metabolomics. During follow-up, the CD activity index showed a stronger decrease in the PG than in the NG (p = 0.0496). PG patients demonstrated higher α-diversity and distinct β-diversity clustering in both salivary and fecal microbiota compared to NG and HC individuals. Notably, the gut microbiota composition in the PG patients showed a significantly greater similarity to that of HC than NG individuals. The interaction between oral and intestinal microbiota in the PG was reduced. Moreover, serum metabolome analysis revealed significantly increased anti-inflammatory metabolites, including short-chain fatty acids and N-Acetylserotonin, among PG patients; meanwhile, inflammation-related metabolites such as arachidonic acid were significantly reduced in this group. Our data suggest that the gut microbiota mediates a potential beneficial effect of a mild COVID-19 history in CD patients. Full article
(This article belongs to the Section Microbiology in Human Health and Disease)
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<p>Recovery of clinical activities and microbial community. (<b>A</b>) CDAI changes from the initial enrollment to the 6-month follow-up. (<b>B</b>,<b>C</b>) Differences of α-diversities. (<b>D</b>,<b>E</b>) β-diversities calculated using UniFrac-based unweighted principal coordinate analysis (PCoA). (<b>F</b>) Relative abundance of bacterial phyla. (<b>G</b>) Bray Curtis distance of gut microbiota between HC and NG or PG. CDAI, Crohn’s disease activity index; HC, healthy control; NG, negative group; PG, positive group. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Relative abundance of genera <span class="html-italic">Bifidobacterium</span> (<b>A</b>), <span class="html-italic">Akkermansia</span> (<b>B</b>), <span class="html-italic">Faecalibacterium</span> (<b>C</b>), <span class="html-italic">Klebsiella</span> (<b>D</b>)<span class="html-italic">,</span> and <span class="html-italic">Veillonella</span> (<b>E</b>) in three groups.</p>
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<p>Interaction between oral and gut microbiota. (<b>A</b>) Venn diagram illustrating ASVs of oral and gut microbiota in HC. (<b>B</b>) Spearman’s correlation network between oral and gut microbiota in HC. (<b>C</b>) Venn diagram illustrating ASVs of oral and gut microbiota in NG. (<b>D</b>) Spearman’s correlation network between oral and gut microbiota in NG. (<b>E</b>) Venn diagram illustrating ASVs of oral and gut microbiota in PG. (<b>F</b>) Spearman’s correlation network between oral and gut microbiota in PG. The red circle represents fecal microbiota, and the blue circle represents salivary microbiota. The size of the circles represents the quantity of significant correlation relationships. The red line represents positive correlation. The blue line represents negative correlation. F_, fecal microbiota; S_, salivary microbiota; HC, healthy control; NG, negative group; PG, positive group; ASV, amplicon sequence variants.</p>
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<p>Serum metabolite composition. (<b>A</b>) Relative abundance of each metabolite class in the negative and positive groups; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) Significantly different metabolites (n = 43); log2FC &gt; 0 represents an increase in the PG group, while a negative value indicates a decrease. PG, positive group.</p>
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<p>Boxplot of serum arachidonic acid (<b>A</b>), aspartic acid (<b>B</b>), serine (<b>C</b>), pyroglutamic acid (<b>D</b>), N−Acetylserotonin (<b>E</b>), and acetic acid (<b>F</b>) in the NG and PG. NG, negative group; PG, positive group. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Interaction between oral-gut microbiota and serum metabolites. (<b>A</b>) Spearman’s correlation network between gut microbiota and serum metabolites in PG. (<b>B</b>) Spearman’s correlation network between oral microbiota and serum metabolites in PG. The red circle represents fecal or salivary microbiota, and the blue circle represents serum metabolites. The size of the circles represents the number of significant correlation relationships. The red line represents positive correlation, and the blue line represents negative correlation. F_, fecal microbiota; S_, salivary microbiota; PG, positive group.</p>
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14 pages, 1696 KiB  
Article
TRPA1 Influences Staphylococcus Aureus Skin Infection in Mice and Associates with HIF-1a and MAPK Pathway Modulation
by Manoj Yadav, Prem Prashant Chaudhary, Grace Ratley, Brandon D’Souza, Mahaldeep Kaur, Sundar Ganesan, Juraj Kabat and Ian A. Myles
Int. J. Mol. Sci. 2024, 25(18), 9933; https://doi.org/10.3390/ijms25189933 (registering DOI) - 14 Sep 2024
Viewed by 192
Abstract
Infections caused by methicillin-resistant Staphylococcus aureus (MRSA) are a major public health burden. Emerging antibiotic resistance has heightened the need for new treatment approaches for MRSA infection such as developing novel antimicrobial agents and enhancing the host’s defense response. The thermo-ion channels Transient [...] Read more.
Infections caused by methicillin-resistant Staphylococcus aureus (MRSA) are a major public health burden. Emerging antibiotic resistance has heightened the need for new treatment approaches for MRSA infection such as developing novel antimicrobial agents and enhancing the host’s defense response. The thermo-ion channels Transient Receptor Potential (TRP-) A1 and V1 have been identified as modulators of S. aureus quorum sensing in cell culture models. However, their effects on in vivo infection control are unknown. In this study, we investigated the therapeutic effect of natural TRP ion channel inhibitors on MRSA skin infection in mice. While deletion of TRPV1 did not affect lesion size or inflammatory markers, TRPA1−/− mice demonstrated significantly reduced infection severity and abscess size. Treatment with natural inhibitors of TRPA1 with or without blockade of TRPV1 also reduced abscess size. Tissue transcriptomic data coupled with immunohistochemistry revealed that TRPA1 inhibition impacted heat shock protein expression (HSP), modulated the HIF-1a and MAPK pathways, and reduced IL4 expression. Additionally, metabolomics data showed an impact on purine and glycosaminoglycan pathways. Multi-omic integration of transcriptomic and metabolic data revealed that diacylglycerol metabolism was the likely bridge between metabolic and immunological impacts. Our findings suggest that TRPA1 antagonism could provide a promising and cost-effective therapeutic approach for reducing the severity of MRSA infection, and presents a novel underlying molecular mechanism. Full article
21 pages, 6380 KiB  
Article
Combined Metabolome and Transcriptome Analyses of Maize Leaves Reveal Global Effect of Biochar on Mechanisms Involved in Anti-Herbivory to Spodoptera frugiperda
by Tianjun He, Lin Chen, Yingjun Wu, Jinchao Wang, Quancong Wu, Jiahao Sun, Chaohong Ding, Tianxing Zhou, Limin Chen, Aiwu Jin, Yang Li and Qianggen Zhu
Metabolites 2024, 14(9), 498; https://doi.org/10.3390/metabo14090498 (registering DOI) - 14 Sep 2024
Viewed by 145
Abstract
Fall armyworm (FAW, Spodoptera frugiperda) has now spread to more than 26 Chinese provinces. The government is working with farmers and researchers to find ways to prevent and control this pest. The use of biochar is one of the economic and environmentally [...] Read more.
Fall armyworm (FAW, Spodoptera frugiperda) has now spread to more than 26 Chinese provinces. The government is working with farmers and researchers to find ways to prevent and control this pest. The use of biochar is one of the economic and environmentally friendly strategies to increase plant growth and improve pest resistance. We tested four v/v combinations of bamboo charcoal with coconut bran [BC1 (10:1), BC2(30:1), BC3(50:1)] against a control (CK) in maize. We found that plant height, stem thickness, fresh weight and chlorophyll content were significantly higher in BC2, in addition to the lowest FAW survival %. We then compared the metabolome and transcriptome profiles of BC2 and CK maize plants under FAW herbivory. Our results show that the levels of flavonoids, amino acids and derivatives, nucleotides and derivatives and most phenolic acids decreased, while terpenoids, organic acids, lipids and defense-related hormones increased in BC-grown maize leaves. Transcriptome sequencing revealed consistent expression profiles of genes enriched in these pathways. We also observed the increased expression of genes related to abscisic acid, jasmonic acid, auxin and MAPK signaling. Based on these observations, we discussed the possible pathways involved in maize against FAW herbivory. We conclude that bamboo charcoal induces anti-herbivory responses in maize leaves. Full article
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<p>(<b>a</b>) Growth performance of maize in different biochar treatments 10 and 20 days after sowing. The bars show mean ± SEM (n = 27). (<b>b</b>) Probability of survival (%) of <span class="html-italic">S. frugiperda,</span> larval survival (%) and pupal survival (%). CK = control; and BC1, BC2 and BC3 are BCcoal to pure coconut bran (<span class="html-italic">v</span>/<span class="html-italic">v</span>) ratios, respectively. Bars on the plots show ± standard deviation (n = 60). The different letters on the bars indicate that the treatments differ significantly at <span class="html-italic">p</span> &lt; 0.05. The bars show mean ± SEM (n = 12).</p>
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<p>Global metabolome profile of maize leaves grown in BC under FAW herbivory. (<b>a</b>) Heatmap of metabolites detected in BC and CK. (<b>b</b>) The % of compounds in each class detected in BC and CK. (<b>c</b>) Principal component analysis and (<b>d</b>) Pearson’s correlation coefficient analysis of BC and CK based on relative metabolite intensities. BC = 30:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>) bamboo charcoal and coconut bran supplementation, and CK is without BC. Numbers (1–3) with BC and CK represent replicates.</p>
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<p>Differential metabolome profile of maize leaves grown in BC under FAW herbivory. (<b>a</b>) Sum of metabolite intensities of different compound classes in BC and CK. (<b>b</b>) Top up- and down-accumulated metabolites accumulated in BC vs. CK. (<b>c</b>) Scatter plot of KEGG pathway enrichment of differentially accumulated metabolites. (<b>d</b>) Heatmap of differentially accumulated metabolites in BC vs. CK. BC = 30:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>) bamboo charcoal and coconut bran supplementation, and CK is without BC. Numbers (1–3) with BC and CK represent replicates.</p>
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<p>Global transcriptome profile of maize leaves grown in BC under FAW herbivory. (<b>a</b>) Overall distribution of gene expression, (<b>b</b>) principal component analysis and (<b>c</b>) Pearson’s correlation coefficient analysis based on gene expression. (<b>d</b>) Number of differentially expressed genes and (<b>e</b>) KEGG pathway enrichment scatter plot between BC and CK. BC = 30:1 (<span class="html-italic">v</span>/<span class="html-italic">v</span>) bamboo charcoal and coconut bran supplementation, and CK is without BC.</p>
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<p>Differential regulation of plant–pathogen interaction and signaling pathways. Top panel shows plant–pathogen interaction KEGG pathway (04626), and bottom left panel shows MAPK signaling—plant KEGG pathway (04016). Heatmaps show log 2-foldchange values of genes enriched in plant–pathogen interaction and signaling pathways (MAPK and phytohormone). Heatmaps were prepared in TBtools [<a href="#B29-metabolites-14-00498" class="html-bibr">29</a>].</p>
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<p>Quantitative real-time PCR analysis of maize genes in CK and BC2 leaves infested with <span class="html-italic">S. frugiperda.</span> The bars represent relative gene expression values (mean of <span class="html-italic">n</span> = 3). The error bars represent ± standard deviation.</p>
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14 pages, 1514 KiB  
Article
Features of Metabolites and Biomarkers in Inflammatory and Infectious Complications of Childhood Cancers
by Maria Getsina, Ekaterina Chernevskaya, Natalia Beloborodova, Evgeniy Golovnya, Petr Polyakov and Nicolai Kushlinskii
Biomedicines 2024, 12(9), 2101; https://doi.org/10.3390/biomedicines12092101 (registering DOI) - 14 Sep 2024
Viewed by 173
Abstract
Background: In the treatment of oncological diseases in children, the search for opportunities for the earlier detection of complications to improve treatment results is very important. Metabolomic studies are actively conducted to stratify different groups of patients in order to identify the [...] Read more.
Background: In the treatment of oncological diseases in children, the search for opportunities for the earlier detection of complications to improve treatment results is very important. Metabolomic studies are actively conducted to stratify different groups of patients in order to identify the most promising markers. Methods: Three groups of patients participated in this study: healthy children as a control group (n = 18), children with various malignant oncological diseases (leukemia, lymphoma, nephroblastoma, ependymoma, etc.) as patients (n = 40) without complications, and patients (n = 31) with complications (inflammatory and infectious). The mitochondrial metabolites (succinic and fumaric acids); biomarkers related to inflammation such as C-reactive protein (CRP), procalcitonin (PCT), and presepsin (PSP); and sepsis-associated aromatic metabolites, such as phenyllactic (PhLA), hydroxyphenyllactic (p-HPhLA), and hydroxyphenylacetic acids (p-HPhAA), were identified. Results: It was found that children with malignant oncological diseases had profound metabolic dysfunction compared to healthy children, regardless of the presence of systemic inflammatory response syndrome (SIRS) or sepsis. The prognostic ability of procalcitonin and presepsin for detecting sepsis was high: AUROC = 0.875, cut-off value (Youden index) = 0.913 ng/mL, and AUROC = 0.774, with cut-off value (Youden index) of 526 pg/mL, respectively. Conclusions: A significant increase in aromatic microbial metabolites and biomarkers in non-survivor patients that is registered already in the first days of the development of complications indicates the appropriateness of assessing metabolic dysfunction for its timely targeted correction. Full article
(This article belongs to the Special Issue Sepsis and Septic Shock: From Molecular Mechanism to Novel Therapies)
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Graphical abstract
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<p>Patient groups. This study included 31 pediatric cancer patients with complications. Blood serum was collected on the first day of detection of complications—point 1, on days 2–4 of observation—point 2, and on days 5–9 of observation—point 3; a total of 93 blood serum samples were collected. The group of practically healthy children without cancer and/or infectious complications included 18 healthy children; the other group included 40 primary patients without complications.</p>
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<p>AUROC curve for procalcitonin (<b>A</b>) and presepsin (<b>B</b>) levels at time point 1.</p>
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18 pages, 2447 KiB  
Article
Profiling Metabolites with Antifungal Activities from Endophytic Plant-Beneficial Strains of Pseudomonas chlororaphis Isolated from Chamaecytisus albus (Hack.) Rothm.
by Wojciech Sokołowski, Monika Marek-Kozaczuk, Piotr Sosnowski, Ewa Sajnaga, Monika Elżbieta Jach and Magdalena Anna Karaś
Molecules 2024, 29(18), 4370; https://doi.org/10.3390/molecules29184370 (registering DOI) - 14 Sep 2024
Viewed by 172
Abstract
Fungal phytopathogens represent a large and economically significant challenge to food production worldwide. Thus, the application of biocontrol agents can be an alternative. In the present study, we carried out biological, metabolomic, and genetic analyses of three endophytic isolates from nodules of Chamaecytisus [...] Read more.
Fungal phytopathogens represent a large and economically significant challenge to food production worldwide. Thus, the application of biocontrol agents can be an alternative. In the present study, we carried out biological, metabolomic, and genetic analyses of three endophytic isolates from nodules of Chamaecytisus albus, classified as Pseudomonas chlororaphis acting as antifungal agents. The efficiency of production of their diffusible and volatile antifungal compounds (VOCs) was verified in antagonistic assays with the use of soil-borne phytopathogens: B. cinerea, F. oxysporum, and S. sclerotiorum. Diffusible metabolites were identified using chromatographic and spectrometric analyses (HPTLC, GC-MS, and LC-MS/MS). The phzF, phzO, and prnC genes in the genomes of bacterial strains were confirmed by PCR. In turn, the plant growth promotion (PGP) properties (production of HCN, auxins, siderophores, and hydrolytic enzymes, phosphate solubilization) of pseudomonads were bioassayed. The data analysis showed that all tested strains have broad-range antifungal activity with varying degrees of antagonism. The most abundant bioactive compounds were phenazine derivatives: phenazine-1-carboxylic acid (PCA), 2-hydroxy-phenazine, and diketopiperazine derivatives as well as ortho-dialkyl-aromatic acids, pyrrolnitrin, siderophores, and HCN. The results indicate that the tested P. chlororaphis isolates exhibit characteristics of biocontrol organisms; therefore, they have potential to be used in sustainable agriculture and as commercial postharvest fungicides to be used in fruits and vegetables. Full article
(This article belongs to the Topic Natural Products in Crop Pest Management)
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<p>Antifungal activity of nodule endophytic <span class="html-italic">Pseudomonas</span> strains. (<b>A</b>) Mean inhibition of mycelial growth (%) in relation to control non-treated phytopathogen cultures. In (<b>A</b>), values are shown as mean ± SD from three independent experiments with a 95% confidence level. * means <span class="html-italic">p</span> &lt; 0.05. (<b>B</b>–<b>D</b>) Representative plates for individual fungi with the largest fungistasis zones in the antagonism assays.</p>
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<p>Heat map showing the relative abundance (%) of secondary metabolites identified in extracts from cell-free supernatants of 16A, 16B1, and 23aP cultures with LC-MS/MS. The color code ranging from blue to red indicates low to high relative content. Explanations of the abbreviations can be found in the text.</p>
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<p>Production of siderophores by <span class="html-italic">Pseudomonas</span> strains: (<b>A</b>) catechol-type compared to the standard SA (<span class="html-italic">p</span> &gt; 0.05, ns—not significant), (<b>B</b>) catechol-type in relation to 2,3-DHBA (*; <span class="html-italic">p</span> &lt; 0.05), (<b>C</b>) pyoverdine (<span class="html-italic">p</span> &gt; 0.05, ns), and (<b>D</b>) CAS assay after four days of incubation (<span class="html-italic">p</span> &gt; 0.05, ns). Student’s <span class="html-italic">t</span>-test with a 95% confidence level was applied for statistical analysis.</p>
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<p>Fungistatic effects of VOCs produced by endophytic strains of <span class="html-italic">Pseudomonas</span>. The activity of isolated 16A1, 16B1, and 23aP was tested against three fungal strains: <span class="html-italic">B. cinerea</span>, <span class="html-italic">S. sclerotiorum</span>, and <span class="html-italic">F. oxysporum</span>. K—control dual-plates with fungi non-inoculated with bacteria. The error bars indicate the standard error of the mean (SEM) with a 95% confidence level, * means <span class="html-italic">p</span> &lt; 0.05.</p>
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20 pages, 3457 KiB  
Article
Non-Invasive Endometrial Cancer Screening through Urinary Fluorescent Metabolome Profile Monitoring and Machine Learning Algorithms
by Monika Švecová, Katarína Dubayová, Anna Birková, Peter Urdzík and Mária Mareková
Cancers 2024, 16(18), 3155; https://doi.org/10.3390/cancers16183155 (registering DOI) - 14 Sep 2024
Viewed by 199
Abstract
Endometrial cancer is becoming increasingly common, highlighting the need for improved diagnostic methods that are both effective and non-invasive. This study investigates the use of urinary fluorescence spectroscopy as a potential diagnostic tool for endometrial cancer. Urine samples were collected from endometrial cancer [...] Read more.
Endometrial cancer is becoming increasingly common, highlighting the need for improved diagnostic methods that are both effective and non-invasive. This study investigates the use of urinary fluorescence spectroscopy as a potential diagnostic tool for endometrial cancer. Urine samples were collected from endometrial cancer patients (n = 77), patients with benign uterine tumors (n = 23), and control gynecological patients attending regular checkups or follow-ups (n = 96). These samples were analyzed using synchronous fluorescence spectroscopy to measure the total fluorescent metabolome profile, and specific fluorescence ratios were created to differentiate between control, benign, and malignant samples. These spectral markers demonstrated potential clinical applicability with AUC as high as 80%. Partial Least Squares Discriminant Analysis (PLS-DA) was employed to reduce data dimensionality and enhance class separation. Additionally, machine learning models, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), were utilized to distinguish between controls and endometrial cancer patients. PLS-DA achieved an overall accuracy of 79% and an AUC of 90%. These promising results indicate that urinary fluorescence spectroscopy, combined with advanced machine learning models, has the potential to revolutionize endometrial cancer diagnostics, offering a rapid, accurate, and non-invasive alternative to current methods. Full article
(This article belongs to the Special Issue Image Analysis and Machine Learning in Cancers)
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<p>Semiquantitive strip analysis comparison of positive urine parameters.</p>
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<p>Urinary total fluorescent metabolome profiles (uTFMP) divided into fluorescent zones.</p>
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<p>Fluorescent urinary zones. Values are expressed as median ± interquartile range. **** indicates <span class="html-italic">p</span> &lt; 0.0001, *** indicates <span class="html-italic">p</span> &lt; 0.001, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Fluorescent ratios (<b>A</b>) Ratio Z4a/Z5. (<b>B</b>) Ratio Z6/Z7. Values are expressed as median ± interquartile range. **** indicates <span class="html-italic">p</span> &lt; 0.0001, *** indicates <span class="html-italic">p</span> &lt; 0.001, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Receiver operating characteristic curves (<b>A</b>) Ratio Z4a/Z5 (<b>B</b>) Ratio Z6/Z7.</p>
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<p>Partial Least Squares Discriminant Analysis (PLS-DA) (<b>A</b>) Train set between controls and malignant samples; (<b>B</b>) Test set between controls and malignant samples; (<b>C</b>) Train set between controls and benign samples; (<b>D</b>) Test set between controls and malignant samples; (<b>E</b>) ROC curve between controls and malignant samples; (<b>F</b>) ROC curve between controls and benign samples.</p>
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<p>ROC curves of built machine learning models (<b>A</b>) ML based on fluorescent zones and spectral ratios (<b>B</b>) ML based overall urinary total fluorescent metabolome profile.</p>
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<p>Confusion matrices for machine learning models: (<b>A</b>) fluorescent zones and spectral ratios. (<b>B</b>) overall urine total fluorescent metabolome profiles.</p>
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14 pages, 1486 KiB  
Article
Comparative Effects of Efavirenz and Dolutegravir on Metabolomic and Inflammatory Profiles, and Platelet Activation of People Living with HIV: A Pilot Study
by Crystal G. Roux, Shayne Mason, Louise D. V. du Toit, Jan-Gert Nel, Theresa M. Rossouw and Helen C. Steel
Viruses 2024, 16(9), 1462; https://doi.org/10.3390/v16091462 (registering DOI) - 14 Sep 2024
Viewed by 183
Abstract
Antiretroviral therapy (ART) has reduced the mortality and morbidity associated with HIV. However, irrespective of treatment, people living with HIV remain at a higher risk of developing non-AIDS-associated diseases. In 2019, the World Health Organization recommended the transition from efavirenz (EFV)- to dolutegravir [...] Read more.
Antiretroviral therapy (ART) has reduced the mortality and morbidity associated with HIV. However, irrespective of treatment, people living with HIV remain at a higher risk of developing non-AIDS-associated diseases. In 2019, the World Health Organization recommended the transition from efavirenz (EFV)- to dolutegravir (DTG)-based ART. Data on the impact of this transition are still limited. The current study therefore investigated the metabolic profiles, cytokine inflammatory responses, and platelet activation before and after the treatment transition. Plasma samples from nine virally suppressed adults living with HIV and sixteen healthy, HIV-uninfected individuals residing in Gauteng, South Africa were compared. Metabolite and cytokine profiles, and markers associated with platelet activation, were investigated with untargeted proton magnetic resonance metabolomics, multiplex suspension bead array immunoassays, and sandwich enzyme-linked immunosorbent assays, respectively. In those individuals with normal C-reactive protein levels, the transition to a DTG-based ART regimen resulted in decreased concentrations of acetoacetic acid, creatinine, adenosine monophosphate, 1,7-dimethylxanthine, glycolic acid, 3-hydroxybutyric acid, urea, and lysine. Moreover, increased levels of formic acid, glucose, lactic acid, myo-inositol, valine, glycolic acid, and 3-hydroxybutyric acid were observed. Notably, levels of interleukin-6, platelet-derived growth factor-BB, granulocyte-macrophage colony-stimulating factor, tumor necrosis factor–alpha, soluble cluster of differentiation 40 ligand, as well as regulated on activation, normal T-cell expressed and secreted (RANTES) reached levels close to those observed in the healthy control participants. The elevated concentration of macrophage inflammatory protein-1 alpha was the only marker indicative of elevated levels of inflammation associated with DTG-based treatment. The transition from EFV- to DTG-based regimens therefore appears to be of potential benefit with metabolic and inflammatory markers, as well as those associated with cardiovascular disease and other chronic non-AIDS-related diseases, reaching levels similar to those observed in individuals not living with HIV. Full article
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<p>Violin and box plots depicting metabolite peak intensities of interest of five people living with HIV, before and after transitioning from an efavirenz- to a dolutegravir-based regimen, compared to sixteen healthy, HIV-uninfected control individuals. The results presented exclude participants with CRP concentrations above 5 mg/L. Levels of significance (<span class="html-italic">p</span>-values) between the treatment groups and control cohort are unpaired; <span class="html-italic">p</span>-values indicated between the two treatment groups are paired analyses. Abbreviations: Con: control; DTG: dolutegravir; EFV: efavirenz; NS: not significant.</p>
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<p>Violin and box plots depicting inflammatory markers of interest. Concentrations are depicted in pg/mL in five people living with HIV before and after transitioning from an efavirenz- to a dolutegravir-based regimen, compared to sixteen healthy, HIV-uninfected control individuals. Results exclude participants with concentrations of CRP above 5 mg/L. Levels of significance (<span class="html-italic">p</span>-values) between the treatment groups and control cohorts are unpaired; <span class="html-italic">p</span>-values between the two treatment groups are paired analyses. Abbreviations: α: alpha; Con: control; DTG: dolutegravir; EFV: efavirenz; IL: interleukin; MIP: macrophage inflammatory protein; NS: not significant; G-CSF: granulocyte colony-stimulating factor; GM-CSF: granulocyte–macrophage colony-stimulating factor; PDGF: platelet-derived growth factor.</p>
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<p>Violin and box plots of the platelet activation markers in five people living with HIV receiving efavirenz- or dolutegravir-based regimens, as well as the concentrations of sixteen healthy, HIV-uninfected controls. Results exclude participants with CRP concentrations above 5 mg/L. Levels of significance (<span class="html-italic">p</span>-values) between the treatment groups and control cohort are unpaired; <span class="html-italic">p</span>-values between the two treatment groups are paired analyses. Abbreviations: Con: control, DTG: dolutegravir; EFV: efavirenz; NS: not significant; RANTES: regulated on activation, normal T-cell expressed and secreted; sCD40L: soluble cluster of differentiation 40 ligand.</p>
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15 pages, 5144 KiB  
Article
Insights into the Flavor Profile of Yak Jerky from Different Muscles Based on Electronic Nose, Electronic Tongue, Gas Chromatography–Mass Spectrometry and Gas Chromatography–Ion Mobility Spectrometry
by Bingde Zhou, Xin Zhao, Luca Laghi, Xiaole Jiang, Junni Tang, Xin Du, Chenglin Zhu and Gianfranco Picone
Foods 2024, 13(18), 2911; https://doi.org/10.3390/foods13182911 (registering DOI) - 14 Sep 2024
Viewed by 214
Abstract
It is well known that different muscles of yak exhibit distinctive characteristics, such as muscle fibers and metabolomic profiles. We hypothesized that different muscles could alter the flavor profile of yak jerky. Therefore, the objective of this study was to investigate the differences [...] Read more.
It is well known that different muscles of yak exhibit distinctive characteristics, such as muscle fibers and metabolomic profiles. We hypothesized that different muscles could alter the flavor profile of yak jerky. Therefore, the objective of this study was to investigate the differences in flavor profiles of yak jerky produced by longissimus thoracis (LT), triceps brachii (TB) and biceps femoris (BF) through electronic nose (E-nose), electronic tongue (E-tongue), gas chromatography–mass spectrometry (GC-MS) and gas chromatography–ion mobility spectrometry (GC-IMS). The results indicated that different muscles played an important role on the flavor profile of yak jerky. And E-nose and E-tongue could effectively discriminate between yak jerky produced by LT, TB and BF from aroma and taste points of view, respectively. In particular, the LT group exhibited significantly higher response values for ANS (sweetness) and NMS (umami) compared to the BF and TB groups. A total of 65 and 47 volatile compounds were characterized in yak jerky by GC-MS and GC-IMS, respectively. A principal component analysis (PCA) model and robust principal component analysis (rPCA) model could effectively discriminate between the aroma profiles of the LT, TB and BF groups. Ten molecules could be considered potential markers for yak jerky produced by different muscles, filtered based on the criteria of relative odor activity values (ROAV) > 1, p < 0.05, and VIP > 1, namely 1-octen-3-ol, eucalyptol, isovaleraldehyde, 3-carene, D-limonene, γ-terpinene, hexanal-D, hexanal-M, 3-hydroxy-2-butanone-M and ethyl formate. Sensory evaluation demonstrated that the yak jerky produced by LT exhibited superior quality in comparison to that produced by BF and TB, mainly pertaining to lower levels of tenderness and higher color, taste and aroma levels. This study could help to understand the specific contribution of different muscles to the aroma profile of yak jerky and provide a scientific basis for improving the quality of yak jerky. Full article
(This article belongs to the Special Issue Quantitative NMR and MRI Methods Applied for Foodstuffs)
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<p>Sample preparation flowchart. <span class="html-italic">Longissimus thoracis</span> (LT), <span class="html-italic">triceps brachii</span> (TB) and <span class="html-italic">biceps femoris</span> (BF).</p>
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<p>Score plot and loading plot of robust principal component analysis (rPCA) models based on electronic nose (E-nose) (<b>a</b>,<b>b</b>) and electronic tongue (E-tongue) (<b>c</b>,<b>d</b>) response data.</p>
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<p>(<b>a</b>) Venn diagram plot of the number of volatile compounds in yak jerky from different muscles. (<b>b</b>) Bar plot of the percentage of volatile compound species in yak jerky from different muscles. (<b>c</b>) Principal component analysis (PCA) model based on volatile compounds in yak jerky from different muscles. (<b>d</b>) VIP score plots for the partial least-squares discriminant analysis (PLS-DA) model on volatile compounds in yak jerky from different muscles.</p>
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<p>Gas chromatography–ion mobility spectrometry (GC-IMS) observation results of yak jerky from different muscles. (<b>a</b>) 3D topographic plot. (<b>b</b>) Subtraction plot, with spectra from TB group as a reference and the corresponding spectra from LT and BF groups represented as differences from TB group. (<b>c</b>) Gallery plots indicating the variations in volatile compounds’ concentrations among the four groups. Red and blue colors highlight over- and underexpressed components, respectively.</p>
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<p>(<b>a</b>) Bar plot of the relative content of volatile compound species in yak jerky from different muscles characterized by GC-IMS. (<b>b</b>) Venn diagram plot of the number of volatile compounds characterized by GC-MS and GC-IMS.</p>
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<p>The rPCA model was established based on the relative content of GC-IMS differential volatile compounds. (<b>a</b>) The score plot shows the samples from the three groups as follows: squares (LT), circles (TB) and triangles (BF). The median of each group is represented by a wide and empty circle. (<b>b</b>) The loading plot illustrates the significant correlation between the molecule concentration and their importance along PC 1.</p>
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<p>Radar chart for sensory evaluation of yak jerky from different muscles.</p>
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<p>Correlation analysis of E-nose, E-tongue, sensory evaluation and volatile compounds quantified by GC-IMS (<b>a</b>) and GC-MS (<b>b</b>). The size of node is indicative of the number of substances that are significantly correlated with the substance in question. The blue circles represent the E-nose and E-tongue probes; the pink squares represent volatile compounds; and the yellow triangles represent sensory evaluation. In addition, the larger the node, the greater the number of substances with which it is significantly correlated. The thickness of line is representative of the size of the absolute value of the correlation between two substances. In this context, the thicker the line, the greater the absolute value of the correlation between the two substances.</p>
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10 pages, 390 KiB  
Article
Serum Erythritol and Risk of Overall and Cause-Specific Mortality in a Cohort of Men
by Jungeun Lim, Hyokyoung G. Hong, Jiaqi Huang, Rachael Stolzenberg-Solomon, Alison M. Mondul, Stephanie J. Weinstein and Demetrius Albanes
Nutrients 2024, 16(18), 3099; https://doi.org/10.3390/nu16183099 (registering DOI) - 14 Sep 2024
Viewed by 351
Abstract
Erythritol occurs naturally in some fruits and fermented foods, and has also been used as an artificial sweetener since the 1990s. Although there have been questions and some studies regarding its potential adverse health effects, the association between serum erythritol and long-term mortality [...] Read more.
Erythritol occurs naturally in some fruits and fermented foods, and has also been used as an artificial sweetener since the 1990s. Although there have been questions and some studies regarding its potential adverse health effects, the association between serum erythritol and long-term mortality has not been evaluated. To examine the association between serum erythritol’s biochemical status and risk of overall and cause-specific mortality, a prospective cohort analysis was conducted using participants in the ATBC Study (1985–1993) previously selected for metabolomic sub-studies. The analysis included 4468 participants, among whom 3377 deaths occurred during an average of 19.1 years of follow-up. Serum erythritol was assayed using an untargeted, global, high-resolution, accurate-mass platform of ultra-high-performance liquid and gas chromatography. Cause-specific deaths were identified through Statistics Finland and defined by the International Classification of Diseases. After adjustment for potential confounders, serum erythritol was associated with increased risk of overall mortality (HR = 1.50 [95% CI = 1.17–1.92]). We found a positive association between serum erythritol and cardiovascular disease mortality risk (HR = 1.86 [95% CI = 1.18–2.94]), which was stronger for heart disease mortality than for stroke mortality risk (HR = 3.03 [95% CI = 1.00–9.17] and HR = 2.06 [95% CI = 0.72–5.90], respectively). Cancer mortality risk was also positively associated with erythritol (HR = 1.54 [95% CI = 1.09–2.19]). The serum erythritol–overall mortality risk association was stronger in men ≥ 55 years of age and those with diastolic blood pressure ≥ 88 mm Hg (p for interactions 0.045 and 0.01, respectively). Our study suggests that elevated serum erythritol is associated with increased risk of overall, cardiovascular disease, and cancer mortality. Additional studies clarifying the role of endogenous production and dietary/beverage intake of erythritol in human health and mortality are warranted. Full article
(This article belongs to the Section Nutrition and Public Health)
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<p>Kaplan–Meier plots of overall and cause-specific mortality according to serum erythritol quartiles.</p>
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22 pages, 1251 KiB  
Article
Characterising the Metabolomic Diversity and Biological Potentials of Extracts from Different Parts of Two Cistus Species Using UHPLC-MS/MS and In Vitro Techniques
by Shakeel Ahmed, Gokhan Zengin, Selami Selvi, Gunes Ak, Zoltán Cziáky, József Jekő, Maria J. Rodrigues, Luisa Custodio, Roberto Venanzoni, Giancarlo Angeles Flores, Gaia Cusumano and Paola Angelini
Pathogens 2024, 13(9), 795; https://doi.org/10.3390/pathogens13090795 - 13 Sep 2024
Viewed by 276
Abstract
This study investigates the biochemical composition and biological properties of different parts (leaves, roots, and twigs) of two Cistus species (Cistus monspeliasis and Cistus parviflorus). The extracts were analysed using UHPLC-MS/MS to determine their chemical profiling. A range of antioxidant assays [...] Read more.
This study investigates the biochemical composition and biological properties of different parts (leaves, roots, and twigs) of two Cistus species (Cistus monspeliasis and Cistus parviflorus). The extracts were analysed using UHPLC-MS/MS to determine their chemical profiling. A range of antioxidant assays were performed to evaluate the extract’s antioxidant capabilities. The enzyme inhibition studies focused on acetylcholinesterase (AChE), butyrylcholinesterase (BChE), α-amylase, and α-glucosidase and tyrosinase. In addition, the study examined the antimicrobial effects on different bacteria and yeasts and evaluated the toxicity using the MTT assay. Quinic acid, citric acid, gallic acid, catechin, quercetin derivatives, kaempferol, myricetin, ellagic acid, prodelphinidins, procyanidins, scopoletin, and flavogallonic acid dilactone are the main bioactive compounds found in both species. In enzyme inhibition assays, C. monspeliasis roots exhibited significant activity against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), with the values of 2.58 ± 0.02 mg GALAE/g and 11.37 ± 1.93 mg GALAE/g, respectively. Cytotoxicity studies showed mostly weak toxicity, with some samples moderately reducing viability in RAW and HepG2 cells. These findings underscore the diverse biochemical profiles and bioactive potential of Cistus species, suggesting their utility as natural sources of antioxidants and enzyme inhibitors for pharmaceutical and nutraceutical development. Full article
(This article belongs to the Section Fungal Pathogens)
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<p>Venn diagrams based on the numbers of identified compounds in the tested extracts. (<b>a</b>): The parts of <span class="html-italic">Cistus monspeliasis</span>; (<b>b</b>): The parts of <span class="html-italic">Cistus parviflorus</span>; (<b>c</b>): Leaves extracts of both <span class="html-italic">Cistus</span> species; (<b>d</b>): twigs extracts of both <span class="html-italic">Cistus</span> species; (<b>e</b>): root extracts of both <span class="html-italic">Cistuss</span> species.</p>
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<p>Pearson correlation between phenolic components and antioxidant and enzyme inhibitory effects (<span class="html-italic">p</span> &lt; 0.05). ABTS, 2,2-azino-bis(3-ethylbenzothiazoline) 6-sulfonic acid; AChE, acetylcholinesterase; BChE, butyrylcholinesterase; CUPRAC, cupric ion-reducing antioxidant capacity; DPPH, 1,1-diphenyl-2-picrylhydrazyl; FRAP, ferric ion-reducing antioxidant power; MCA, metal chelating activity; PBD, Phosphomolybdenum. TPC, Total phenolic content; TFC, Total flavonoid content. (R &gt; 0.7 indicates strong correlation between phenolic components and biolocal activities).</p>
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15 pages, 3636 KiB  
Article
Effects of Kimchi Intake on the Gut Microbiota and Metabolite Profiles of High-Fat-Induced Obese Rats
by Dong-Wook Kim, Quynh-An Nguyen, Saoraya Chanmuang, Sang-Bong Lee, Bo-Min Kim, Hyeon-Jeong Lee, Gwang-Ju Jang and Hyun-Jin Kim
Nutrients 2024, 16(18), 3095; https://doi.org/10.3390/nu16183095 - 13 Sep 2024
Viewed by 290
Abstract
With rising global obesity rates, the demand for effective dietary strategies for obesity management has intensified. This study evaluated the potential of kimchi with various probiotics and bioactive compounds as a dietary intervention for high-fat diet (HFD)-induced obesity in rats. Through a comprehensive [...] Read more.
With rising global obesity rates, the demand for effective dietary strategies for obesity management has intensified. This study evaluated the potential of kimchi with various probiotics and bioactive compounds as a dietary intervention for high-fat diet (HFD)-induced obesity in rats. Through a comprehensive analysis incorporating global and targeted metabolomics, gut microbiota profiling, and biochemical markers, we investigated the effects of the 12-week kimchi intake on HFD-induced obesity. Kimchi intake modestly mitigated HFD-induced weight gain and remarkably altered gut microbiota composition, steroid hormones, bile acids, and metabolic profiles, but did not reduce adipose tissue accumulation. It also caused significant shifts in metabolomic pathways, including steroid hormone metabolism, and we found substantial interactions between dietary interventions and gut microbiota composition. Although more research is required to fully understand the anti-obesity effects of kimchi, our findings support the beneficial role of kimchi in managing obesity and related metabolic disorders. Full article
(This article belongs to the Section Prebiotics and Probiotics)
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<p>Comparison of gut microbiota’s relative abundance after 12 weeks of kimchi intervention. (<b>A</b>) Bar charts showing the overall microbial composition at genus levels in feces from rats fed ND, HFD, and KHD diets, with the average relative abundance. (<b>B</b>) Chao1 and Shannon indices calculated after rarefying to an equal number of sequence reads. (<b>C</b>) Principal-coordinate analysis plots of weighted UniFrac distance dissimilarities (PC1 and PC2). (<b>D</b>) Relative abundances of bacteria at the genus level. ND, control; HFD; high-fat diet; KHD, high-fat-kimchi diet. * <span class="html-italic">p</span>-value &lt; 0.05, ** <span class="html-italic">p</span>-value &lt; 0.01, *** <span class="html-italic">p</span>-value &lt;0.001, # <span class="html-italic">p</span>-value &lt; 0.0001. Data represent the relative abundance of microbes analyzed from six samples. Different letters on the bar and box plot indicate significant differences in the <span class="html-italic">t</span>-test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>(<b>A</b>) Metabolite analysis of rats fed HFD and KHD. Partial least-squares discriminant analysis (PLS-DA) score plot obtained from UPLC-Q-TOF MS data of plasma, urine, large intestine, liver, and kidney (<span class="html-italic">n</span> = 10). (<b>B</b>) Fold change of identified metabolites. Metabolites were analyzed using UPLC-Q-TOF MS via an Acquity BEH C18 column (2.1 mm × 100 mm, 1.7 μm) with a positive ESI mode. The qualification of PLS-DA models was evaluated by R2X, R2Y, Q2, and <span class="html-italic">p</span>-values. R2X and R2Y show the fitting quality of the models, while Q2 shows their prediction quality. ND, control; HFD; high-fat diet; KHD, high-fat-kimchi diet.</p>
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<p>Proposed metabolomic pathway associated with HFD and kimchi intake and the relative abundance of metabolites. Box plots present the relative abundance of metabolites analyzed by UPLC-Q-TOF MS, with significant differences determined by <span class="html-italic">t</span>-tests at <span class="html-italic">p</span>-values &lt; 0.05 (*), &lt;0.01 (**), &lt;0.001 (***), and &lt;0.0001 (#). N, normal-diet group; H, high-fat-diet group; K, kimchi-high-fat-diet group; P, plasma; L, liver; K, kidney; LI, large intestinal residues.</p>
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<p>Proposed bile acid and steroid hormone pathway associated with HFD and kimchi intake and their relative abundances. Box plots present the relative abundance of bile acids and steroid hormones analyzed by UPLC-Q-TOF MS, with significant differences determined using <span class="html-italic">t</span>-tests at <span class="html-italic">p</span>-values of &lt;0.05 (*), &lt; 0.01 (**), &lt;0.001 (***), and &lt;0.0001 (#). N, normal-diet group; H, high-fat-diet group; K, kimchi-high-fat-diet group.</p>
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<p>Analysis of correlations of gut microbiota with bile acid and steroid hormones (<b>A</b>), and gut microbiota with identified metabolites’ data (<b>B</b>). The correlation matrix was analyzed and visualized with a heat map. Positive correlations are shown in blue, and negative correlations are shown in red. A dark color means a stronger correlation.</p>
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27 pages, 7404 KiB  
Article
Metabolomics-Based Study of the Protective Effect of 4-Hydroxybenzyl Alcohol on Ischemic Astrocytes
by Tian Xiao, Xingzhi Yu, Jie Tao, Liping Yang and Xiaohua Duan
Int. J. Mol. Sci. 2024, 25(18), 9907; https://doi.org/10.3390/ijms25189907 - 13 Sep 2024
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Abstract
Ischemic stroke is a common and dangerous disease in clinical practice. Astrocytes (ASs) are essential for maintaining the metabolic balance of the affected regions during the disease process. 4-Hydroxybenzyl alcohol (4HBA) from Gastrodia elata Bl. has potential neuroprotective properties due to its ability [...] Read more.
Ischemic stroke is a common and dangerous disease in clinical practice. Astrocytes (ASs) are essential for maintaining the metabolic balance of the affected regions during the disease process. 4-Hydroxybenzyl alcohol (4HBA) from Gastrodia elata Bl. has potential neuroprotective properties due to its ability to cross the blood–brain barrier. In an in vitro experiment, we replicated the oxygen–glucose deprivation/reoxygenation model, and used methyl thiazoly tertrazolium, flow cytometry, kits, and other technical means to clarify the protective effect of 4HBA on primary ASs. In in vivo experiments, the 2VO model was replicated, and immunofluorescence and immunohistochemistry techniques were used to clarify the protective effect of 4HBA on ASs and the maintenance of the blood-brain barrier. Differential metabolites and related pathways were screened and verified using metabolomics analysis and western blot. 4HBA noticeably amplified AS cell survival, reduced mitochondrial dysfunction, and mitigated oxidative stress. It demonstrated a protective effect on ASs in both environments and was instrumental in stabilizing the blood–brain barrier. Metabolomic data indicated that 4HBA regulated nucleic acid and glutathione metabolism, influencing purines, pyrimidines, and amino acids, and it activated the N-methyl-D-aspartate/p-cAMP-response element binding protein/brain-derived neurotrophic factor signaling pathway via N-methyl-D-aspartate R1/N-methyl-D-aspartate 2C receptors. Our findings suggest that 4HBA is a potent neuroprotective agent against ischemic stroke, enhancing AS cell survival and function while stabilizing the blood–brain barrier. The N-methyl-D-aspartate/p-cAMP-response element binding protein/brain-derived neurotrophic factor signaling pathway is activated by 4HBA. Full article
(This article belongs to the Section Molecular Pharmacology)
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Graphical abstract

Graphical abstract
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<p>Chemical structure of 4HBA (<b>A</b>). Immunofluorescence staining plots of GFAP (bar = 50 μm) (<b>B</b>). Effect of 4HBA at different concentrations on the viability of normal ASs (<b>C</b>). Effect of 4HBA on the viability of OGD/R ASs (<b>D</b>). Data are presented as the mean ± standard deviation (SD). ### <span class="html-italic">p</span> &lt; 0.001, ## <span class="html-italic">p</span> &lt; 0.01, # <span class="html-italic">p</span> &lt; 0.05 vs. the control group. *** <span class="html-italic">p</span> &lt; 0.001, * <span class="html-italic">p</span> &lt; 0.05 vs. the OGD/R group. Abbreviations: OGD/R, oxygen–glucose deprivation/reoxygenation; 4HBA, 4-hydroxybenzyl alcohol.</p>
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<p>Effect of 4HBA on CAT activity (<b>A</b>), MPTP opening (<b>B</b>), and Ca<sup>2+</sup> levels within cells (<b>C</b>) and mitochondria (<b>D</b>). Data are presented as the mean ± standard deviation (SD). ### <span class="html-italic">p</span> &lt; 0.001, ## <span class="html-italic">p</span> &lt; 0.01, # <span class="html-italic">p</span> &lt; 0.05 vs. the control group. *** <span class="html-italic">p</span> &lt; 0.001 vs. the OGD/R group. Abbreviations: OGD/R, oxygen–glucose deprivation/reoxygenation; 4HBA, 4-hydroxybenzyl alcohol; CAT, catalase; MPTP, mitochondrial membrane transition pore.</p>
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<p>Immunofluorescence staining plots of ROS (bar = 50 μm) (<b>A</b>). Fluorescence intensity of ROS (<b>B</b>). Data are presented as the mean ± standard deviation (SD). ### <span class="html-italic">p</span> &lt; 0.001 vs. the control group. *** <span class="html-italic">p</span> &lt; 0.001 vs. the OGD/R group. Abbreviations: OGD/R, oxygen–glucose deprivation/reoxygenation; 4HBA, 4-hydroxybenzyl alcohol; ROS, reactive oxygen species.</p>
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<p>Immunofluorescence staining plots of MitoTracker (bar = 50 μm) (<b>A</b>). A representative picture of mitochondria in ASs by transmission electron microscopy (bar = 500 nm) (<b>B</b>). Abbreviations: OGD/R, oxygen–glucose deprivation/reoxygenation; 4HBA, 4-hydroxybenzyl alcohol.</p>
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<p>Typical chromatogram of negative ions (<b>A</b>). Typical chromatogram of positive ions (<b>B</b>). Abbreviations: NEG, negative; POS, positive.</p>
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<p>Enrichment analysis of differentially abundant metabolites between the control and OGD/R groups and 4HBA group PLSDA score map in positive mode (<b>A</b>). PLS-DA score diagram in negative mode (<b>B</b>). Abbreviations: NEG, negative; POS, positive; PLSDA, partial least squares discriminant analysis; 4HBA, 4-hydroxybenzyl alcohol.</p>
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<p>Volcano plot of the control and OGD/R groups in the negative ion model (<b>A</b>). Volcano plot of OGD/R and 4HBA in the negative ion model (<b>B</b>). Volcano plot of the control and OGD/R groups in the positive ion model (<b>C</b>). Volcano plot of OGD/R and 4HBA in the positive ion model (<b>D</b>). Abbreviations: NEG, negative; POS, positive; OGD/R, oxygen–glucose deprivation/reoxygenation; 4HBA, 4-hydroxybenzyl alcohol.</p>
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<p>Pathway analysis of negative ion mode (<b>A</b>). Pathway analysis of positive ion mode (<b>B</b>).</p>
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<p>Fluorescence intensity of BDNF, NR1, NR2C, CREB, and p-CREB protein bands from the the control, agonist, A + 4, OGD/R, and O + 4 groups (<b>A</b>). Graphs showing the expression levels of NR1 (<b>B</b>), NR2C (<b>C</b>), CREB (<b>D</b>), p-CREB (<b>E</b>), and BDNF (<b>F</b>) in the five groups. The content of BDNF in the medium (<b>G</b>). Data are presented as the mean ± standard deviation (SD). *** <span class="html-italic">p</span> &lt; 0.001 vs. the control group. ▲▲ <span class="html-italic">p</span> &lt; 0.01, ▲ <span class="html-italic">p</span> &lt; 0.05 vs. the agonist group, ### <span class="html-italic">p</span> &lt; 0.001, ## <span class="html-italic">p</span> &lt; 0.01, # <span class="html-italic">p</span> &lt; 0.05 vs. the OGD/R group. Abbreviations: A + 4, agonist + 4HBA group; OGD/R, oxygen–glucose deprivation/reoxygenation; O + 4, OGD/R + 4HBA group.</p>
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<p>EB staining diagram (bar = 5 mm) (<b>A</b>) and HE staining diagram (bar = 100 μm) (<b>B</b>) of each group of brain tissues. Electron microscopy images of mitochondria (bar = 500 nm) (<b>C</b>) and EB content (<b>D</b>) of brain in each group. Data are presented as the mean ± standard deviation (SD). *** <span class="html-italic">p</span> &lt; 0.001 vs. the sham group. ### <span class="html-italic">p</span> &lt; 0.001 vs. the model group. Abbreviations: EB, Evans blue.</p>
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<p>Claudin-5 (<b>A</b>) and Occludin (<b>B</b>) fluorescence expression patterns, and Claudin-5 (<b>C</b>) and Occludin (<b>D</b>) fluorescence average optical density in each group (bar = 100 μm). Data are presented as the mean ± standard deviation (SD). *** <span class="html-italic">p</span> &lt; 0.001 vs. the sham group. ### <span class="html-italic">p</span> &lt; 0.001, ## <span class="html-italic">p</span> &lt; 0.01 vs. the model group.</p>
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<p>Claudin-5 (<b>A</b>) and Occludin (<b>B</b>) fluorescence expression patterns, and Claudin-5 (<b>C</b>) and Occludin (<b>D</b>) fluorescence average optical density in each group (bar = 100 μm). Data are presented as the mean ± standard deviation (SD). *** <span class="html-italic">p</span> &lt; 0.001 vs. the sham group. ### <span class="html-italic">p</span> &lt; 0.001, ## <span class="html-italic">p</span> &lt; 0.01 vs. the model group.</p>
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<p>ZO-1 fluorescence expression patterns (bar = 100 μm) (<b>A</b>), GFAP immunohistochemistry images (bar = 100 μm) (<b>B</b>), ZO-1 fluorescence average optical density (<b>C</b>), and GFAP positive area percentage (<b>D</b>) in each group. Data are presented as the mean ± standard deviation (SD). *** <span class="html-italic">p</span> &lt; 0.001 vs. the sham group. ### <span class="html-italic">p</span> &lt; 0.001, ## <span class="html-italic">p</span> &lt; 0.01, # <span class="html-italic">p</span> &lt; 0.05 vs. the model group.</p>
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<p>AQP4 (<b>A</b>) and BDNF (<b>B</b>) immunohistochemistry images (bar = 100 μm), and AQP4 (<b>C</b>) and BDNF (<b>D</b>) positive area percentage in each group. Data are presented as the mean ± standard deviation (SD). *** <span class="html-italic">p</span> &lt; 0.001 vs. the sham group. ### <span class="html-italic">p</span> &lt; 0.001, ## <span class="html-italic">p</span> &lt; 0.01 vs. the model group.</p>
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<p>Fluorescence intensity of BDNF, NR1, NR2C, CREB, and p-CREB protein bands from each group (<b>A</b>). Graphs showing the expression levels of BDNF (<b>B</b>), NR1 (<b>C</b>), NR2C (<b>D</b>), CREB (<b>E</b>), and p-CREB (<b>F</b>) in the four groups. Data are presented as the mean ± standard deviation (SD). *** <span class="html-italic">p</span> &lt; 0.001 vs. the sham group. ### <span class="html-italic">p</span> &lt; 0.001, ## <span class="html-italic">p</span> &lt; 0.01, # <span class="html-italic">p</span> &lt; 0.05 vs. the model group.</p>
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