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15 pages, 3039 KiB  
Article
Comparative Metabolic Analysis of Different Indica Rice Varieties Associated with Seed Storability
by Fangxi Wu, Yidong Wei, Yongsheng Zhu, Xi Luo, Wei He, Yingheng Wang, Qiuhua Cai, Huaan Xie, Guosheng Xie and Jianfu Zhang
Metabolites 2025, 15(1), 19; https://doi.org/10.3390/metabo15010019 (registering DOI) - 5 Jan 2025
Viewed by 60
Abstract
Seed storability is a crucial agronomic trait and indispensable for the safe storage of rice seeds and grains. Nevertheless, the metabolite mechanisms governing Indica rice seed storability under natural conditions are still poorly understood. Methods: Therefore, the seed storage tolerance of global rice [...] Read more.
Seed storability is a crucial agronomic trait and indispensable for the safe storage of rice seeds and grains. Nevertheless, the metabolite mechanisms governing Indica rice seed storability under natural conditions are still poorly understood. Methods: Therefore, the seed storage tolerance of global rice core germplasms stored for two years under natural aging conditions were identified, and two extreme groups with different seed storabilities from the Indica rice group were analyzed using the UPLC-MS/MS metabolomic strategy. Results: Our results proved that the different rice core accessions showed significant variability in storage tolerance, and the metabolite analysis of the two Indica rice pools exhibited different levels of storability. A total of 103 differentially accumulated metabolites (DAMs) between the two pools were obtained, of which 38 were up-regulated and 65 were down-regulated, respectively. Further analysis disclosed that the aging-resistant rice accessions had higher accumulation levels of flavonoids, terpenoids, phenolic acids, organic acids, lignans, and coumarins while exhibiting lower levels of lipids and alkaloids compared to the storage-sensitive rice accessions. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that several biosynthesis pathways were involved in the observed metabolite differences, including alpha-linolenic acid metabolism, butanoate metabolism, and propanoate metabolism. Notably, inhibition of the linolenic acid metabolic pathway could enhance seed storability. Additionally, increased accumulations of organic acids, such as succinic acid, D-malic acid, and methylmalonic acid, in the butanoate and propanoate metabolisms were identified as a beneficial factor for seed storage. Conclusions: These new findings will deepen our understanding of the underlying mechanisms governing rice storability. Full article
(This article belongs to the Special Issue Metabolic Responses of Seeds Development and Germination)
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Figure 1

Figure 1
<p>Disparity in the seed storability of the whole population of 375 rice core accessions and four groups from 47 different countries. (<b>A</b>) Distribution of and variations in seed germination percentages in 375 accessions after natural aging treatment for 24 months. (<b>B</b>) Scatter dot plot illustrating the seed germination percentages in the four rice groups (<span class="html-italic">Basmati</span>, <span class="html-italic">Indica</span>, <span class="html-italic">Aus</span>, and <span class="html-italic">Japonica</span>) with different colored means.</p>
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<p>Analysis of metabolite profiles in AR and AS pools. (<b>A</b>) Classification of 1098 identified metabolites. (<b>B</b>) Scatter plot from the PCA model representing different rice storage pools. The abscissa PC1 and ordinate PC2 represent scores of the first and second principal components, respectively. (<b>C</b>) Overall clustering heatmaps of all differentially accumulated metabolites from the two pools. Each scatter represents a sample, with the color and shape indicating different groups.</p>
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<p>Identification of differentially accumulated metabolites (DAMs) in AR and AS pools. (<b>A</b>) OPLS-DA permutation plot for two different <span class="html-italic">Indica</span> rice storage pools. (<b>B</b>) Score plot generated from OPLS-DA for two different <span class="html-italic">Indica</span> rice storage pools. (<b>C</b>) Volcano plots depicting the expression levels of DAMs for two different <span class="html-italic">Indica</span> rice storage pools. (<b>D</b>) Various types of DAMs were identified in different <span class="html-italic">Indica</span> rice storage pools. (<b>E</b>) Overall clustering heatmap displaying DAMs for two different <span class="html-italic">Indica</span> rice storage pools. Each scatter represents a sample, with color and shape indicating different <span class="html-italic">Indica</span> rice groups, respectively.</p>
Full article ">Figure 3 Cont.
<p>Identification of differentially accumulated metabolites (DAMs) in AR and AS pools. (<b>A</b>) OPLS-DA permutation plot for two different <span class="html-italic">Indica</span> rice storage pools. (<b>B</b>) Score plot generated from OPLS-DA for two different <span class="html-italic">Indica</span> rice storage pools. (<b>C</b>) Volcano plots depicting the expression levels of DAMs for two different <span class="html-italic">Indica</span> rice storage pools. (<b>D</b>) Various types of DAMs were identified in different <span class="html-italic">Indica</span> rice storage pools. (<b>E</b>) Overall clustering heatmap displaying DAMs for two different <span class="html-italic">Indica</span> rice storage pools. Each scatter represents a sample, with color and shape indicating different <span class="html-italic">Indica</span> rice groups, respectively.</p>
Full article ">Figure 3 Cont.
<p>Identification of differentially accumulated metabolites (DAMs) in AR and AS pools. (<b>A</b>) OPLS-DA permutation plot for two different <span class="html-italic">Indica</span> rice storage pools. (<b>B</b>) Score plot generated from OPLS-DA for two different <span class="html-italic">Indica</span> rice storage pools. (<b>C</b>) Volcano plots depicting the expression levels of DAMs for two different <span class="html-italic">Indica</span> rice storage pools. (<b>D</b>) Various types of DAMs were identified in different <span class="html-italic">Indica</span> rice storage pools. (<b>E</b>) Overall clustering heatmap displaying DAMs for two different <span class="html-italic">Indica</span> rice storage pools. Each scatter represents a sample, with color and shape indicating different <span class="html-italic">Indica</span> rice groups, respectively.</p>
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<p>The KEGG pathway enrichment analysis of DAMs in AR and AS pools. (<b>A</b>) Bubble chart of the KEGG pathway. (<b>B</b>) Metabolite pathway of alpha-linolenic acid metabolism. (<b>C</b>) Metabolite pathway of butanoate and propanoate metabolism. The up-regulated DAMs are highlighted in red, while the down-regulated ones are indicated in green.</p>
Full article ">Figure 4 Cont.
<p>The KEGG pathway enrichment analysis of DAMs in AR and AS pools. (<b>A</b>) Bubble chart of the KEGG pathway. (<b>B</b>) Metabolite pathway of alpha-linolenic acid metabolism. (<b>C</b>) Metabolite pathway of butanoate and propanoate metabolism. The up-regulated DAMs are highlighted in red, while the down-regulated ones are indicated in green.</p>
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18 pages, 8132 KiB  
Article
Bioinformatics and Deep Learning Approach to Discover Food-Derived Active Ingredients for Alzheimer’s Disease Therapy
by Junyu Zhou, Chen Li, Yong Kwan Kim and Sunmin Park
Foods 2025, 14(1), 127; https://doi.org/10.3390/foods14010127 (registering DOI) - 4 Jan 2025
Viewed by 404
Abstract
Alzheimer’s disease (AD) prevention is a critical challenge for aging societies, necessitating the exploration of food ingredients and whole foods as potential therapeutic agents. This study aimed to identify natural compounds (NCs) with therapeutic potential in AD using an innovative bioinformatics-integrated deep neural [...] Read more.
Alzheimer’s disease (AD) prevention is a critical challenge for aging societies, necessitating the exploration of food ingredients and whole foods as potential therapeutic agents. This study aimed to identify natural compounds (NCs) with therapeutic potential in AD using an innovative bioinformatics-integrated deep neural analysis approach, combining computational predictions with molecular docking and in vitro experiments for comprehensive evaluation. We employed the bioinformatics-integrated deep neural analysis of NCs for Disease Discovery (BioDeepNat) application in the data collected from chemical databases. Random forest regression models were utilized to predict the IC50 (pIC50) values of ligands interacting with AD-related target proteins, including acetylcholinesterase (AChE), amyloid precursor protein (APP), beta-secretase 1 (BACE1), microtubule-associated protein tau (MAPT), presenilin-1 (PSEN1), tumor necrosis factor (TNF), and valosin-containing protein (VCP). Their activities were then validated through a molecular docking analysis using Autodock Vina. Predictions by the deep neural analysis identified 166 NCs with potential effects on AD across seven proteins, demonstrating outstanding recall performance. The top five food sources of these predicted compounds were black walnut, safflower, ginger, fig, corn, and pepper. Statistical clustering methodologies segregated the NCs into six well-defined groups, each characterized by convergent structural and chemical signatures. The systematic examination of structure–activity relationships uncovered differential molecular patterns among clusters, illuminating the sophisticated correlation between molecular properties and biological activity. Notably, NCs with high activity, such as astragalin, dihydromyricetin, and coumarin, and medium activity, such as luteolin, showed promising effects in improving cell survival and reducing lipid peroxidation and TNF-α expression levels in PC12 cells treated with lipopolysaccharide. In conclusion, our findings demonstrate the efficacy of combining bioinformatics with deep neural networks to expedite the discovery of previously unidentified food-derived active ingredients (NCs) for AD intervention. Full article
(This article belongs to the Special Issue Bioactive Phenolic Compounds from Agri-Food and Its Wastes)
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Figure 1
<p>Assessment of predictive accuracy and residual analysis for the pIC<sub>50</sub> values of ligands interacting with AChE, APP, BACE1, MAPT, PSEN1, TNF-α, and VCP using a random forest regression model. (<b>A</b>) Scatter plots juxtapose the predicted and experimental pIC<sub>50</sub> values for ligands interacting with AChE, APP, BACE1, MAPT, PSEN1, TNF-α, and VCP in both the training (depicted as blue circles) and testing (illustrated as yellow triangles with red edges) datasets. Performance metrics, including mean squared error (MSE), R<sup>2</sup> value, mean absolute error (MAE), and root mean squared error (RMSE), offer insights into the model’s prediction accuracy. (<b>B</b>) The residual analysis highlights disparities between the model-predicted and experimental pIC<sub>50</sub> values. The blue circles denote discrepancies in the training dataset, and the yellow triangles with red edges signify variations in the testing dataset. The red dashed line represents the theoretical ideal of zero residuals, indicating a perfect alignment between the model’s predictions and experimental results. AChE, acetylcholinesterase; APP, amyloid precursor protein; BACE1, beta-secretase 1; MAPT, microtubule-associated protein tau; PSEN1, presenilin-1; TNF-α, tumor necrosis factor; VCP, valosin-containing protein.</p>
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<p>Applicability domain analysis of the interacting ligands via the principal component analysis (PCA). This figure utilizes PCA to assess the dataset variance of compounds interacting with AChE, APP, BACE1, MAPT, PSEN1, TNF-α, and VCP. Each point signifies an interacting ligand positioned by molecular fingerprints, with distinct markers and colors indicating the spread and concentration in the training dataset. The 95% quantile of residuals serves as a cut-off, identifying outliers. The test dataset emphasizes compounds within the applicability domain with a unique edge color. The x and y axes represent the first and second principal components, annotated with variance proportions. PCA, principal component analysis; AChE, acetylcholinesterase; APP, amyloid precursor protein; BACE1, beta-secretase 1; MAPT, microtubule-associated protein tau; PSEN1, presenilin-1; TNF-α, tumor necrosis factor; VCP, valosin-containing protein.</p>
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<p>Distribution and overlap of natural compounds (NCs) in various food sources. (<b>A</b>) A bar chart provides specific information on food sources with more than 10 NCs. (<b>B</b>) An interactive Venn diagram illustrates the distribution and overlap of NCs across the selected food sources, offering insights into their presence and shared compounds. Each Venn diagram section corresponds to a specific food source, with overlapping regions highlighting shared NCs.</p>
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<p>Model performance, optimal clustering, and structure–activity relationships in the prediction of bioactivity of natural compounds (NCs). (<b>A</b>) Scatter plots assess the model’s predictive performance on training (<b>left</b>) and testing (<b>right</b>) datasets for the pIC<sub>50</sub> values. R<sup>2</sup> and root mean squared error (RMSE) metrics quantify predictive accuracy. (<b>B</b>) Optimal cluster count determination using the Elbow and Silhouette Score methods. The Elbow method identifies the best number of clusters based on the rate of decrease in the sum of squared errors (SSE), while the Silhouette Score method gauges cluster quality. Red circle indicated the optimal number of the clusters. (<b>C</b>) Scatter plot visualizes the K-Means clustering of compounds based on molecular attributes and biological activity. (<b>D</b>) The heatmap displays fingerprint bit distribution across the K-Means clusters, revealing patterns and similarities. (<b>E</b>) Structure–activity relationship (SAR) plots illustrate various molecular descriptors against IC<sub>50</sub> values, color-coded by cluster assignment.</p>
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<p>Bioactivities and interactions of natural compounds (NCs) at different levels. (<b>A</b>) pIC<sub>50</sub> distribution across activity levels. This visualization shows pIC<sub>50</sub> distribution across ‘low’, ‘medium’, and ‘high’ activity levels. Box plots depict interquartile range (IQR) with median lines, and whiskers extend to data points within 1.5 times the IQR. Swarm plots represent individual NCs with high affinity, color-coded for medium (5 &lt; pIC<sub>50</sub> ≤ 7) and high activity (pIC<sub>50</sub> &gt; 7). (<b>B</b>) Molecular docking: The NCs’ molecular docking results with target proteins, emphasizing stronger affinity with lower negative energy. (<b>C</b>) Maximum common substructures (MCSs) across activity levels: Panels segregate NCs based on bioactivity levels, showing molecular structures with highlighted maximum common substructures (MCSs). Each grid represents a specific activity level, providing insights into the common functional groups affecting bioactivity. (<b>D</b>) Detailed visualization of the highest affinity interaction: provides a detailed view of the interaction between the target protein and the highest affinity natural compound, offering a complex visualization of specific interactions.</p>
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<p>Cell viability (<b>A</b>) and acetylcholinesterase (AChE) activity (<b>B</b>) Nerve growth factor (NGF)-differentiated PC12 cells with lipopolysaccharide-induced inflammation (LPS, 1 μg/mL) were treated with astragalin, dihydromyricetin, coumarin, quercetin, kaempferol, apigenin, and luteolin for 24 h to measure cell viability and 48 h to measure AChE activity. a–e Different letters on the bar indicated significant differences between the groups at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Lipid peroxidation (<b>A</b>) and protein (<b>B</b>) and mRNA expression (<b>C</b>) of pro-inflammatory cytokines. Nerve growth factor (NGF)-differentiated PC12 cells with lipopolysaccharide-induced inflammation (LPS, 1 μg/mL) were treated with astragalin, dihydromyricetin, coumarin, quercetin, kaempferol, apigenin, and luteolin for 48 h. a–e Different letters on the bar indicated significant differences between the groups at <span class="html-italic">p</span> &lt; 0.05.</p>
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27 pages, 5828 KiB  
Article
Τhiazolidine-4-One Derivatives with Variable Modes of Inhibitory Action Against DPP4, a Drug Target with Multiple Activities and Established Role in Diabetes Mellitus Type II
by Dionysia Amanatidou, Phaedra Eleftheriou, Anthi Petrou, Athina Geronikaki and Theodoros Lialiaris
Pharmaceuticals 2025, 18(1), 52; https://doi.org/10.3390/ph18010052 (registering DOI) - 4 Jan 2025
Viewed by 460
Abstract
Background/Objectives: DPP4 is an enzyme with multiple natural substrates and probable involvement in various mechanisms. It constitutes a drug target for the treatment of diabetes II, although, also related to other disorders. While a number of drugs with competitive inhibitory action and covalent [...] Read more.
Background/Objectives: DPP4 is an enzyme with multiple natural substrates and probable involvement in various mechanisms. It constitutes a drug target for the treatment of diabetes II, although, also related to other disorders. While a number of drugs with competitive inhibitory action and covalent binding capacity are available, undesired side effects exist partly attributed to drug kinetics, and research for finding novel, potent, and safer compounds continues. Despite the research, a low number of uncompetitive and non-competitive inhibitors, which could be of worth for pharmaceutical and mechanism studies, was mentioned. Methods: In the present study sixteen 3-(benzo[d]thiazol-2-yl)-2-aryl thiazolidin-4-ones were selected for evaluation, based on structural characteristics and docking analysis and were tested in vitro for DPP4 inhibitory action using H-Gly-Pro-amidomethyl coumarin substrate. Their mode of inhibition was also in vitro explored. Results: Twelve compounds exhibited IC50 values at the nM range with the best showing IC50 = 12 ± 0.5 nM, better than sitagliptin. Most compounds exhibited a competitive mode of inhibition. Inhibition modes of uncompetitive, non-competitive, and mixed type were also identified. Docking analysis was in accordance with the in vitro results, with a linear correlation of logIC50 with a Probability of Binding Factor(PF) derived using docking analysis to a specific target box and to the whole enzyme. According to the docking results, two probable sites of binding for uncompetitive inhibitors were highlighted in the wider area of the active site and in the propeller loop. Conclusions: Potent inhibitors with IC50 at the nM range and competitive, non-competitive, uncompetitive, and mixed modes of action, one better than sitagliptin, were found. Docking analysis was used to estimate probable sites and ways of binding. However, crystallographic or NMR studies are needed to elucidate the exact way of binding especially for uncompetitive and non-competitive inhibitors. Full article
(This article belongs to the Special Issue Enzyme Inhibitors: Potential Therapeutic Approaches)
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Graphical abstract

Graphical abstract
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<p>Structure of studied compounds.</p>
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<p>Lineweaver–Burk blots for the compounds <b>h3</b> (<b>A</b>), <b>n1</b> (<b>B</b>), <b>c4</b> (<b>C</b>), and <b>m2</b> (<b>D</b>). As shown by the curves, the modes of inhibitory action are competitive for <b>h3</b>, non-competitive for <b>n1</b>, and uncompetitive for <b>c4</b> and <b>m2</b>.</p>
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<p>Docking of the competitive inhibitors <b>h3</b> (<b>A</b>,<b>A′</b>), <b>n2</b> (<b>Β</b>,<b>Β′</b>) and <b>c2</b> (<b>C</b>,<b>C′</b>) in the active site of DPP4. The docked compound is shown in green. The initial ligand is shown in yellow.</p>
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<p>Correlation of log IC<sub>50</sub> of competitive inhibitors with the Probability Factor (PF). Only competitive inhibitors were taken into account for the estimation of linear regression. The PF is calculated if we modify the Eest exported from docking to the target site box (Eest<sub>ts</sub>), which corresponds to the active site for competitive inhibitors, by abstracting a factor (d) produced using the results of docking to the whole enzyme at all positions (x) with lower binding energy (Eest<sub>x</sub>) than that of the target site (Eest<sub>t</sub>) which is the active site for competitive inhibitors. Factor d = <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">∑</mo> <mrow> <mo>(</mo> <mi mathvariant="sans-serif">Δ</mi> <mi>E</mi> <mi>x</mi> <mo>∗</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mi>ν</mi> <mi mathvariant="normal">x</mi> </mrow> <mrow> <mn>100</mn> </mrow> </mfrac> </mstyle> </mrow> </mrow> <mo>)</mo> <mo>∗</mo> <mn>10</mn> </mrow> </semantics></math>, where ΔΕx = Est<sub>x</sub> − Eest<sub>t</sub> and v<sub>x</sub> is the frequency (%) of binding to the specific site x with the specific pose which corresponds to Estimated binding Energy Est<sub>x</sub>.</p>
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<p>Probable sites of binding of uncompetitive (sites a1, b) and non-competitive (site a2) inhibitors.</p>
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<p>Probable binding site (site a2) of the non-competitive inhibitor <b>n1</b>. (<b>A</b>) shows the orientation of n1 (in green) within the active site in relation to a competitive inhibitor (initial ligand of the structure, in yellow). The amino acids participating in interactions with <b>n1</b> are shown in (<b>B</b>,<b>C</b>).</p>
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<p>Probable site of binding (site a1) of the uncompetitive inhibitors <b>m2</b> (<b>A</b>) and <b>c4</b> (<b>B</b>) within the active site of DPP4. The docking was applied to the whole enzyme in the presence of the initial ligand.</p>
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<p>Docking of the uncompetitive inhibitors <b>m2</b> (<b>A</b>,<b>B</b>) and <b>c4</b> (<b>C</b>,<b>D</b>) at the probable <b>site b</b>, between the propeller loop (residues 234–260) and the residues around Phe713 and Thr706 near the catalytic triad (Ser630, Asp708, His740) of the enzyme.</p>
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<p>Favorable characteristics of competitive 3-(benzo[d]thiazol-2-yl)-2-aryl thiazolidin-4-one inhibitors. Green: halogens—participation in halogen bonds, Light brown spheres: groups participating in hydrophobic and pi–pi interactions. Blue: nitrogen, yellow: sulfur, red: oxygen.</p>
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<p>Characteristics of adamantane derivatives with competitive, non-competitive, and uncompetitive modes of inhibition.</p>
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<p>Verification process. Docking of the initial ligand (DLI B) to the enzyme (PDB:2OAG) from which the ligand was abstracted. Docked ligand is shown in green. The position of the initial ligand is shown in yellow. Indicative distances between the same atoms of the initial and docked ligand are shown.</p>
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27 pages, 3326 KiB  
Systematic Review
Emerging Pharmacological Interventions for Chronic Venous Insufficiency: A Comprehensive Systematic Review and Meta-Analysis of Efficacy, Safety, and Therapeutic Advances
by Camila Botelho Miguel, Ranielly de Souza Andrade, Laise Mazurek, Melissa Carvalho Martins-de-Abreu, Jamil Miguel-Neto, Aurélio de Melo Barbosa, Glicélia Pereira Silva, Aristóteles Góes-Neto, Siomar de Castro Soares, Javier Emilio Lazo-Chica and Wellington Francisco Rodrigues
Pharmaceutics 2025, 17(1), 59; https://doi.org/10.3390/pharmaceutics17010059 - 3 Jan 2025
Viewed by 260
Abstract
Background/Objectives: Chronic Venous Insufficiency (CVI) is a progressive vascular condition characterized by venous hypertension and chronic inflammation, leading to significant clinical and socioeconomic impacts. This study aimed to evaluate the efficacy and safety of emerging pharmacological interventions for CVI, focusing on clinical outcomes [...] Read more.
Background/Objectives: Chronic Venous Insufficiency (CVI) is a progressive vascular condition characterized by venous hypertension and chronic inflammation, leading to significant clinical and socioeconomic impacts. This study aimed to evaluate the efficacy and safety of emerging pharmacological interventions for CVI, focusing on clinical outcomes such as pain, edema, cutaneous blood flow, and quality of life. Methods: Eligible interventions comprised new vasoprotective drugs, such as hydroxyethylrutoside, Pycnogenol, aminaphthone, coumarin + troxerutin, and Venoruton, compared to the standard therapy of diosmin and hesperidin. Results: Hydroxyethylrutoside and Pycnogenol showed significant benefits in pain reduction and resting flux improvement, with mean differences of 38 (95% CI: 10.56–65.44) and 25.30 (95% CI: 18.73–31.87), respectively. Improvements in edema and quality of life were less consistent. Substantial heterogeneity was observed (I2 = 100%, p < 0.001). Conclusions: Hydroxyethylrutoside and Pycnogenol emerge as promising alternatives for managing CVI. However, limitations such as high heterogeneity, small sample sizes, and methodological inconsistencies highlight the need for more robust and standardized clinical trials. This study underscores the importance of personalized and cost-effective strategies, particularly in resource-limited settings. Full article
(This article belongs to the Special Issue Lymphatic Aspects of Drug Delivery, Formulation, and Bioavailability)
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Figure 1
<p>Flow diagram summarizing the study selection process. A total of 322 articles were identified, of which 46 duplicates were removed, 92 were excluded for ineligibility, and 178 were discarded for other reasons. Six full-text articles were reviewed, and five met the eligibility criteria. Additional searches retrieved four more records, but nine were excluded after eligibility assessment (PRISMA 2020) [<a href="#B15-pharmaceutics-17-00059" class="html-bibr">15</a>].</p>
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<p>Risk of bias assessment using ROB2 tool. (<b>A</b>) Traffic light plot illustrating the risk of bias judgments for each domain (D1 to D5) and the overall assessment (Overall) for each included study. Green indicates “Low risk”, yellow represents “Some concerns”, and red denotes “High risk”. The studies evaluated are as follows: Cesarone et al. (2006) [<a href="#B8-pharmaceutics-17-00059" class="html-bibr">8</a>], Cesarone et al. (2006) [<a href="#B33-pharmaceutics-17-00059" class="html-bibr">33</a>], Toledo et al. (2017) [<a href="#B32-pharmaceutics-17-00059" class="html-bibr">32</a>], Cesarone et al. (2005) [<a href="#B7-pharmaceutics-17-00059" class="html-bibr">7</a>], and Belczak et al. (2013) [<a href="#B34-pharmaceutics-17-00059" class="html-bibr">34</a>]. (<b>B</b>) Bar chart summarizing the proportions of risk of bias across all methodological domains (D1 to D5) and the overall assessment (Overall) for all included studies. Each bar reflects the distribution of risk levels, categorized as “Low risk”, “Some concerns”, and “High risk”. This plot highlights the areas of methodological concern, such as deviations from intended interventions (D2) and selective outcome reporting (D5), while identifying domains with greater methodological consistency, such as missing outcome data (D3).</p>
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<p>Meta-analysis results comparing alternative therapies with diosmin + hesperidin for the treatment of chronic venous insufficiency (CVI). (<b>A</b>) Pooled mean differences (MD) in symptom reduction across five studies using a random-effects model, highlighting variability in effect sizes [<a href="#B7-pharmaceutics-17-00059" class="html-bibr">7</a>,<a href="#B8-pharmaceutics-17-00059" class="html-bibr">8</a>,<a href="#B32-pharmaceutics-17-00059" class="html-bibr">32</a>,<a href="#B33-pharmaceutics-17-00059" class="html-bibr">33</a>,<a href="#B34-pharmaceutics-17-00059" class="html-bibr">34</a>]. (<b>B</b>) Stratification of biological effects, including pain, edema, quality of life, and resting flux, across 15 studies, showing significant overall benefits of alternative therapies. Error bars represent 95% confidence intervals (CI), and heterogeneity metrics (I<sup>2</sup> and tau<sup>2</sup>) indicate substantial variability across studies [<a href="#B7-pharmaceutics-17-00059" class="html-bibr">7</a>,<a href="#B8-pharmaceutics-17-00059" class="html-bibr">8</a>,<a href="#B32-pharmaceutics-17-00059" class="html-bibr">32</a>,<a href="#B33-pharmaceutics-17-00059" class="html-bibr">33</a>,<a href="#B34-pharmaceutics-17-00059" class="html-bibr">34</a>].</p>
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<p>Subgroup analysis of mean differences (MD) in symptom reduction for chronic venous insufficiency (CVI) comparing alternative therapies to diosmin + hesperidin. (<b>A</b>) displays the analysis with interventions grouped as reported in the original studies (k = 5), demonstrating significant variability across interventions [<a href="#B7-pharmaceutics-17-00059" class="html-bibr">7</a>,<a href="#B8-pharmaceutics-17-00059" class="html-bibr">8</a>,<a href="#B32-pharmaceutics-17-00059" class="html-bibr">32</a>,<a href="#B33-pharmaceutics-17-00059" class="html-bibr">33</a>,<a href="#B34-pharmaceutics-17-00059" class="html-bibr">34</a>]. (<b>B</b>) separates interventions initially grouped under coumarin + troxerutin or aminaphthone (k = 6), revealing distinct effects for each [<a href="#B7-pharmaceutics-17-00059" class="html-bibr">7</a>,<a href="#B8-pharmaceutics-17-00059" class="html-bibr">8</a>,<a href="#B32-pharmaceutics-17-00059" class="html-bibr">32</a>,<a href="#B33-pharmaceutics-17-00059" class="html-bibr">33</a>,<a href="#B34-pharmaceutics-17-00059" class="html-bibr">34</a>]. (<b>C</b>) stratifies results by biological outcomes, including pain, edema, quality of life, and resting flux (k = 15). Subgroups include hydroxyethylrutoside, Pycnogenol, aminaphthone, coumarin + troxerutin, and Venoruton. The overall pooled effects, subgroup estimates, and heterogeneity metrics (I<sup>2</sup> and tau<sup>2</sup>) are presented for each analysis [<a href="#B7-pharmaceutics-17-00059" class="html-bibr">7</a>,<a href="#B8-pharmaceutics-17-00059" class="html-bibr">8</a>,<a href="#B32-pharmaceutics-17-00059" class="html-bibr">32</a>,<a href="#B33-pharmaceutics-17-00059" class="html-bibr">33</a>,<a href="#B34-pharmaceutics-17-00059" class="html-bibr">34</a>].</p>
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<p>Subgroup analysis of mean differences (MD) in symptom reduction for chronic venous insufficiency (CVI) comparing alternative therapies to diosmin + hesperidin. (<b>A</b>) displays the analysis with interventions grouped as reported in the original studies (k = 5), demonstrating significant variability across interventions [<a href="#B7-pharmaceutics-17-00059" class="html-bibr">7</a>,<a href="#B8-pharmaceutics-17-00059" class="html-bibr">8</a>,<a href="#B32-pharmaceutics-17-00059" class="html-bibr">32</a>,<a href="#B33-pharmaceutics-17-00059" class="html-bibr">33</a>,<a href="#B34-pharmaceutics-17-00059" class="html-bibr">34</a>]. (<b>B</b>) separates interventions initially grouped under coumarin + troxerutin or aminaphthone (k = 6), revealing distinct effects for each [<a href="#B7-pharmaceutics-17-00059" class="html-bibr">7</a>,<a href="#B8-pharmaceutics-17-00059" class="html-bibr">8</a>,<a href="#B32-pharmaceutics-17-00059" class="html-bibr">32</a>,<a href="#B33-pharmaceutics-17-00059" class="html-bibr">33</a>,<a href="#B34-pharmaceutics-17-00059" class="html-bibr">34</a>]. (<b>C</b>) stratifies results by biological outcomes, including pain, edema, quality of life, and resting flux (k = 15). Subgroups include hydroxyethylrutoside, Pycnogenol, aminaphthone, coumarin + troxerutin, and Venoruton. The overall pooled effects, subgroup estimates, and heterogeneity metrics (I<sup>2</sup> and tau<sup>2</sup>) are presented for each analysis [<a href="#B7-pharmaceutics-17-00059" class="html-bibr">7</a>,<a href="#B8-pharmaceutics-17-00059" class="html-bibr">8</a>,<a href="#B32-pharmaceutics-17-00059" class="html-bibr">32</a>,<a href="#B33-pharmaceutics-17-00059" class="html-bibr">33</a>,<a href="#B34-pharmaceutics-17-00059" class="html-bibr">34</a>].</p>
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<p>Subgroup analysis of the mean differences (MD) in symptom reduction for chronic venous insufficiency (CVI), stratified by clinical manifestations: pain, edema, quality of life, and resting flux. The pooled MDs and their 95% confidence intervals (CIs) are shown for each subgroup using a random-effects model. The overall analysis included 15 assessments across five eligible studies, highlighting significant benefits for certain manifestations, particularly resting flux and pain. Heterogeneity was substantial across all subgroups, with tau<sup>2</sup> and I<sup>2</sup> values indicating variability in study-level characteristics and methodologies [<a href="#B7-pharmaceutics-17-00059" class="html-bibr">7</a>,<a href="#B8-pharmaceutics-17-00059" class="html-bibr">8</a>,<a href="#B32-pharmaceutics-17-00059" class="html-bibr">32</a>,<a href="#B33-pharmaceutics-17-00059" class="html-bibr">33</a>,<a href="#B34-pharmaceutics-17-00059" class="html-bibr">34</a>].</p>
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<p>Results of the mixed-effects meta-regression model assessing the impact of publication year on mean differences in clinical outcomes for chronic venous insufficiency (CVI) interventions across 15 assessments. The analysis revealed a significant negative temporal trend, indicating a decrease in effect sizes over time. Residual heterogeneity was substantial (tau<sup>2</sup> = 95.47; I<sup>2</sup> = 100.0%), with the year of publication accounting for 73.41% of the variability (R<sup>2</sup> = 73.41%). The regression coefficient for the year variable was −3.79 (95% CI: −4.98 to −2.60; <span class="html-italic">p</span> &lt; 0.0001), reflecting a significant decline in intervention effectiveness over the years.</p>
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<p>Funnel plot analysis of publication bias for studies evaluating alternative therapies for chronic venous insufficiency. (<b>A</b>) Funnel plot representing the primary meta-analysis results (k = 5). Egger’s regression test indicated no significant asymmetry (t = −1.48, df = 3, <span class="html-italic">p</span> = 0.2362), with a bias estimate of −37.89 (SE = 25.65) and tau<sup>2</sup> = 1563.94. (<b>B</b>) Funnel plot encompassing all stratified biological effects, including pain, edema, quality of life, and resting flux (k = 15). Egger’s regression test showed no significant asymmetry (t = 0.69, df = 13, <span class="html-italic">p</span> = 0.5044), with a bias estimate of 17.46 (SE = 25.43) and tau<sup>2</sup> = 7219.35. Both analyses suggest the absence of strong publication bias, although high heterogeneity reflects variability in study methodologies, populations, and the different types of interventions categorized as “Other therapies”, including hydroxyethylrutoside, Pycnogenol, aminaphthone, coumarin + troxerutin, and Venoruton, compared to diosmin + hesperidin.</p>
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<p>Bubble plot summarizing the GRADE assessment of mean differences in effect sizes across clinical outcomes in chronic venous insufficiency (CVI). The x-axis represents the outcomes (pain, edema, quality of life, and resting flux), while the y-axis indicates the mean differences (effect sizes). Bubble sizes reflect the quality of evidence: “Low” (small), “Moderate” (medium), and “High” (large). Bubble colors indicate the strength of recommendations: blue for “Strong for Other Therapies”, red for “Weak for Other Therapies”, green for “Strong for Diosmin + Hesperidin”, and orange for “Weak for Diosmin + Hesperidin”. Numeric values within the plot denote the mean differences for each outcome, and error bars represent 95% confidence intervals. Overlapping intervals highlight uncertainties in specific outcomes.</p>
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17 pages, 7718 KiB  
Article
Effect of Geographic Regions on the Flavor Quality and Non-Volatile Compounds of Chinese Matcha
by Hongchun Cui, Yun Zhao, Hongli Li, Min Ye, Jizhong Yu and Jianyong Zhang
Foods 2025, 14(1), 97; https://doi.org/10.3390/foods14010097 - 2 Jan 2025
Viewed by 394
Abstract
Matcha is a very popular tea food around the world, being widely used in the food, beverage, health food, and cosmetic industries, among others. At present, matcha shade covering methods, matcha superfine powder processing technology, and digital evaluations of matcha flavor quality are [...] Read more.
Matcha is a very popular tea food around the world, being widely used in the food, beverage, health food, and cosmetic industries, among others. At present, matcha shade covering methods, matcha superfine powder processing technology, and digital evaluations of matcha flavor quality are receiving research attention. However, research on the differences in flavor and quality characteristics of matcha from the same tea tree variety from different typical regions in China is relatively weak and urgently required. Taking Japan Shizuoka matcha (R) as a reference, the differences in sensory quality characteristics and non-volatile substances of matcha processed with the same tea variety from different regions in China were analyzed. The samples were China Hangzhou matcha (Z1), China Wuyi matcha (Z2), China Enshi matcha (H), and China Tongren matcha (G), which represent the typical matcha of eastern, central, and western China. A total of 1131 differential metabolites were identified in the matcha samples, comprising 118 flavonoids, 14 tannins, 365 organic acids, 42 phenolic acids, 22 alkaloids, 39 saccharides, 208 amino acids and derivatives, 17 lignans and coumarins, seven quinones, 44 nucleotides and derivatives, 14 glycerophospholipids, two glycolipids, 15 alcohols and amines, 140 benzenes and substituted derivatives, 38 terpenoids, 30 heterocyclic compounds, and 15 lipids. Kaempferol-7-O-rhamnoside, 3,7-Di-O-methylquercetin, epigallocatechin gallate, epicatechin gallate, and epigallocatechin were detected in Z1, Z2, H, and G. A total of 1243 metabolites differed among Z1, Z2, and R. A total of 1617 metabolites differed among G, H, and R. The content of non-volatile difference metabolites of Z2 was higher than that of Z1. The content of non-volatile difference metabolites of G was higher than that of H. The 20 key differential non-volatile metabolites of Z1, Z2, G, and H were screened out separately. The types of non-volatile flavor differential metabolites of G and H were more numerous than those of Z1 and Z2. The metabolic pathways, biosynthesis of secondary metabolites, biosynthesis of co-factors, flavonoid biosynthesis, biosynthesis of amino acids, biosynthesis of various plant secondary metabolites, and purine metabolism of metabolic pathways were the main KEGG pathways. This study provides new insights into the differences in metabolite profiles among typical Chinese matcha geographic regions with the same tea variety. Full article
(This article belongs to the Special Issue Tea: Processing Techniques, Flavor Chemistry and Health Benefits)
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Figure 1
<p>Heat map of non-volatile substances of matcha in different geographic regions. (<b>a</b>) is a heat map referring to the geographical cluster analysis of matcha samples, (<b>b</b>) is a heat map of the composition and content difference of the non-volatile substances in the matcha samples from different geographic regions. (Note: R refers to Japan matcha, G refers to China Tongren matcha, H refers to China Enshi matcha, Z1 refers to China Hangzhou matcha, Z2 refers to China Wuyi matcha).</p>
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<p>Proportional analysis of non-volatile substances in matcha from different geographic regions.</p>
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<p>PCA analysis of non-volatile substances in matcha tea from different geographic regions. (Note: 1. R refers to Japan matcha, G refers to China Tongren matcha, H refers to China Enshi matcha, Z1 refers to China Hangzhou matcha, Z2 refers to China Wuyi matcha. 2. (<b>a</b>) Metabolite TIC multi-peak plot, (<b>b</b>) PCA analysis).</p>
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<p>PCA analysis of non-volatile substances in matcha tea from different geographic regions. (Note: 1. R refers to Japan matcha, G refers to China Tongren matcha, H refers to China Enshi matcha, Z1 refers to China Hangzhou matcha, Z2 refers to China Wuyi matcha. 2. (<b>a</b>) Metabolite TIC multi-peak plot, (<b>b</b>) PCA analysis).</p>
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<p>Analysis of differential non-volatile substances of matcha (R vs. Z1 vs. _Z2). (Note: (<b>a</b>) is all metabolites differed from R, Z1 and Z2. (<b>b</b>) is the internal structure of the data with a network diagram of R vs. Z1 vs. _Z2. (<b>c</b>) is the scores OPLS-DA plot of non-volatile metabolites of R vs. Z1 vs. _Z2. (<b>d</b>) is the VIP score plot of R vs. Z1 vs. _Z2. (<b>e</b>) is KEGG classification of R vs. Z1 vs. _Z2. (<b>f</b>) the <span class="html-italic">p</span>-value of main metabolic pathways of R vs. Z1 vs. _Z2. R refers to Japan matcha, Z1 refers to China Hangzhou matcha, Z2 refers to China Wuyi matcha).</p>
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<p>Analysis of differential non-volatile substances of matcha (R vs. Z1 vs. _Z2). (Note: (<b>a</b>) is all metabolites differed from R, Z1 and Z2. (<b>b</b>) is the internal structure of the data with a network diagram of R vs. Z1 vs. _Z2. (<b>c</b>) is the scores OPLS-DA plot of non-volatile metabolites of R vs. Z1 vs. _Z2. (<b>d</b>) is the VIP score plot of R vs. Z1 vs. _Z2. (<b>e</b>) is KEGG classification of R vs. Z1 vs. _Z2. (<b>f</b>) the <span class="html-italic">p</span>-value of main metabolic pathways of R vs. Z1 vs. _Z2. R refers to Japan matcha, Z1 refers to China Hangzhou matcha, Z2 refers to China Wuyi matcha).</p>
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<p>Analysis of differential non-volatile substances of matcha (R vs. H vs. G). (Note: (<b>a</b>) is all metabolites differed among R, H, G. (<b>b</b>) is the internal structure of the data with a network diagram of R vs. H vs. G. (<b>c</b>) is the scores OPLS-DA plot of non-volatile metabolites of R vs. H vs. G. (<b>d</b>) is the VIP score plot of R vs. H vs. G. (<b>e</b>) is KEGG classification of R vs. H vs. G. (<b>f</b>) the P-value of main metabolic pathways of R vs. H vs. G. R refers to Japan matcha, G refers to China Tongren matcha, H refers to China Enshi matcha).</p>
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19 pages, 8204 KiB  
Article
Rapid Detection of Aluminium and Iron Impurities in Lithium Carbonate Using Water-Soluble Fluorescent Probes
by Hong-Mei Wu, Huai-Gang Cheng, Zi-Wen Zhu and Li Cui
Molecules 2025, 30(1), 135; https://doi.org/10.3390/molecules30010135 - 31 Dec 2024
Viewed by 273
Abstract
The real-time measurement of the content of impurities such as iron and aluminium ions is one of the keys to quality evaluation in the production process of high-purity lithium carbonate; however, impurity detection has been a time-consuming process for many years, which limits [...] Read more.
The real-time measurement of the content of impurities such as iron and aluminium ions is one of the keys to quality evaluation in the production process of high-purity lithium carbonate; however, impurity detection has been a time-consuming process for many years, which limits the optimisation of the production of high-purity lithium carbonate. In this context, this work explores the possibility of using water-soluble fluorescent probes for the rapid detection of impurity ions. Salicylaldehyde was modified with the hydrophilic group dl-alanine to synthesise a water-soluble Al3+ fluorescent probe (Probe A). Moreover, a water-soluble Fe3+ fluorescent probe (Probe B) was synthesised from coumarin-3-carboxylic acid and 3-hydroxyaminomethane. Probe A and Probe B exhibited good stability in the pH range of 4–9 in aqueous solutions, high sensitivity, as well as high selectivity for Al3+ and Fe3+; the detection limits for Al3+ and Fe3+ were 1.180 and 1.683 μmol/L, whereas the response times for Al3+ and Fe3+ were as low as 10 and 30 s, respectively. Electrostatic potential (ESP) analysis and density functional theory calculations identified the binding sites and fluorescence recognition mechanism; theoretical calculations showed that the enhanced fluorescence emission of Probe A when detecting Al3+ was due to the excited intramolecular proton transfer (ESIPT) effect, whereas the fluorescence quenching of Probe B when detecting Fe3+ was due to the electrons turning off fluorescence when binding through the photoelectron transfer (PET) mechanism. Full article
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Figure 1
<p>Structure of probe A. (In the figure, red, white, gray and blue correspond to oxygen, hydrogen, carbon and nitrogen, respectively.)</p>
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<p>Structure of Probe B. (In the figure, red, white, gray and blue correspond to oxygen, hydrogen, carbon and nitrogen, respectively.)</p>
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<p>(<b>a</b>,<b>b</b>) Effect of different metal ions on the fluorescence spectrum of the probe; (<b>c</b>,<b>f</b>) photographs of probes after the addition of different metal ions under a 365 nm UV lamp; (<b>d</b>,<b>g</b>) three-dimensional fluorescence spectra of Probe A and Probe B aqueous solutions; (<b>e</b>,<b>h</b>) three-dimensional fluorescence spectra of Probe A/Al<sup>3+</sup> = 1:1 and Probe B/Fe<sup>3+</sup> = 1:1 aqueous solutions.</p>
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<p>(<b>a</b>,<b>b</b>) Fluorescence spectra of Probe A and Probe B in the presence of different concentrations of Al<sup>3+</sup> and Fe<sup>3+</sup>; linear relationships between (<b>c</b>) Al<sup>3+</sup> concentration and Probe A fluorescence intensity and (<b>d</b>) Fe<sup>3+</sup> concentration and Probe B fluorescence intensity.</p>
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<p>(<b>a</b>,<b>b</b>) Fluorescence spectra of Probe A and Probe B in the presence of different concentrations of Al<sup>3+</sup> and Fe<sup>3+</sup>; linear relationships between (<b>c</b>) Al<sup>3+</sup> concentration and Probe A fluorescence intensity and (<b>d</b>) Fe<sup>3+</sup> concentration and Probe B fluorescence intensity.</p>
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<p>(<b>a</b>) Effect of reaction time on the fluorescence intensity of the Probe A+Al<sup>3+</sup> system; (<b>b</b>) effect of reaction time on the fluorescence intensity of the Probe B+Fe<sup>3+</sup> system.</p>
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<p>(<b>a</b>) Effect of pH on the fluorescence intensity of Probe A and Probe A+Al<sup>3+</sup> systems; (<b>b</b>) effect of pH on the fluorescence intensity of Probe B and Probe B+Fe<sup>3+</sup> systems.</p>
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<p>Fluorescence spectrum (<b>a</b>) and fluorescence intensity change (<b>b</b>) after successive additions of Al<sup>3+</sup> and EDTA in Probe A solution.</p>
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<p>Fluorescence spectrum (<b>a</b>) and fluorescence intensity changes (<b>b</b>) of Fe<sup>3+</sup> and EDTA added to Probe B solution.</p>
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<p>(<b>a</b>) Fluorescence spectra of Probe A and Al<sup>3+</sup> in different ratios in H<sub>2</sub>O solution; (<b>b</b>) job diagram of Probe A combined with Al<sup>3+</sup>; (<b>c</b>) fluorescence spectra of Probe B and Fe<sup>3+</sup> mixed in different ratios in H<sub>2</sub>O solution; (<b>d</b>) Job plot of Probe B binding to Fe<sup>3+</sup>.</p>
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<p>(<b>a</b>) Infrared spectra of Probe A and the Probe A+Al<sup>3+</sup> complex; (<b>b</b>) infrared spectra of Probe B and the Probe B+Fe<sup>3+</sup> complex.</p>
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<p>(<b>a</b>) Electrostatic potential of Probe A; (<b>b</b>) electrostatic potential of Probe B.(In the molecular structure formula, red, white, gray and blue correspond to oxygen, hydrogen, carbon and nitrogen, respectively.)</p>
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<p>(<b>a1</b>,<b>a2</b>) Optimised structures of Probe A and Probe A+Al<sup>3+</sup>; (<b>b1</b>,<b>b2</b>) FMOs of Probe A and Probe A+Al<sup>3+</sup> in different electronic states.</p>
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<p>(<b>a1</b>,<b>a2</b>) Optimised structures of Probe B and Probe B+Fe<sup>3+</sup>; (<b>b1</b>,<b>b2</b>) FMOs of Probe B and Probe B+Fe<sup>3+</sup> in different electronic states.</p>
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<p>Results of Al<sup>3+</sup> and Fe<sup>3+</sup> content determination in the sample using two different methods.</p>
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17 pages, 6383 KiB  
Article
Optimization of the Heterogeneous Synthesis Conditions for Cellulose Tosylation and Synthesis of a Propargylamine Cellulosic Derivative
by Marcos V. Ferreira, Poliana Ricci, Henrique A. Sobreira, Anizio M. Faria, Rodrigo B. Panatieri, Brent S. Sumerlin and Rosana M. N. Assunção
Polymers 2025, 17(1), 58; https://doi.org/10.3390/polym17010058 - 29 Dec 2024
Viewed by 503
Abstract
Cellulose tosylate (MCC-Tos) is a key derivative for surface modification and a crucial precursor for cellulose compatibilization in click reactions, enabling its functionalization for advanced applications. Replacing tosyl groups with alkyne groups broadens cellulose’s potential in biocompatible reactions, such as thiol-yne click chemistry [...] Read more.
Cellulose tosylate (MCC-Tos) is a key derivative for surface modification and a crucial precursor for cellulose compatibilization in click reactions, enabling its functionalization for advanced applications. Replacing tosyl groups with alkyne groups broadens cellulose’s potential in biocompatible reactions, such as thiol-yne click chemistry and protein/enzyme immobilization. To achieve this, we optimized the heterogeneous synthesis of MCC-Tos using a Doehlert matrix statistical design, evaluating the influence and interaction of the reaction conditions. The optimized conditions—144 h reaction time, 10:1 molar ratio, and 30 °C—yielded a degree of substitution for tosyl groups (DStos) of 1.80, determined via elemental analysis and FTIR-ATR spectroscopy. The reaction kinetics followed a first-order model. A subsequent reaction with propargylamine produced aminopropargyl cellulose (MCC-PNH), reducing DStos by 65%, which was confirmed via FTIR, and improving thermal stability by a margin of 30 °C (TGA/DTG). 13C CP/MAS NMR confirmed the alkyne group attachment, further validated via coupling an azide-functionalized coumarin through copper(I)-catalyzed alkyne-azide cycloaddition (CuAAC). Fluorescence microscopy and UV spectroscopy were used to estimate a substitution degree of 0.21. This study establishes a feasible route for synthesizing alkyne-functionalized cellulose, paving the way for eco-friendly materials, including protein/enzyme bioconjugates, composites, and advanced materials via thiol-yne and CuAAC reactions. Full article
(This article belongs to the Section Polymer Chemistry)
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Graphical abstract

Graphical abstract
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<p>Three-dimensional response surfaces and contour plots for the Doehlert matrix at encoded values of (0,0,0): (<b>A</b>) molar ratio (x<sub>3</sub>) = 5 equiv (TosCl); (<b>B</b>) time (x<sub>2</sub>) = 38 h; (<b>C</b>) temperature (x<sub>1</sub>) = 60 °C.</p>
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<p>(<b>A</b>) MCC, MCC-Tos, and MCC-P<sub>NH</sub> 24 to 168 h FTIR spectra in the 1650 to 650 cm<sup>−1</sup> range. (<b>B</b>) Plot of DS<sub>tos</sub> as a function of propargyl reaction time.</p>
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<p>(<b>A</b>) TGA curves; and (<b>B</b>) DTG curves for MCC, MCC-Tos, and MCC-P<sub>NH</sub> 168 h.</p>
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<p>CP/MAS <sup>13</sup>C-NMR spectra of (<b>A</b>) cellulose (MCC); (<b>B</b>) tosyl-cellulose (MCC-Tos); and (<b>C</b>) amino propargyl-cellulose (MCC-P<sub>NH</sub>) after 168 h of amino propargylation reaction.</p>
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<p>Fluorescence microscopy images of MCC-P<sub>NH</sub> after azide-coumarin attachment: (<b>A</b>) wide-field image; and (<b>B</b>–<b>D</b>) images for different regions of the same sample, excited at 315 nm with emission at around 430 nm.</p>
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<p>(<b>A</b>) Synthetic approach toward the obtention of tosyl-cellulose (MCC-Tos); (<b>B</b>) synthetic route for the obtention of aminopropargyl-cellulose (MCC-PNH) from MCC-Tos.</p>
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<p>Synthetic route to obtention of the azide-coumarin attached to MCC-P<sub>NH</sub> via CuAAC reaction.</p>
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34 pages, 2356 KiB  
Article
Application of Supercritical CO2 Extraction and Identification of Polyphenolic Compounds in Three Species of Wild Rose from Kamchatka: Rosa acicularis, Rosa amblyotis, and Rosa rugosa
by Mayya P. Razgonova, Muhammad A. Nawaz, Elena A. Rusakova and Kirill S. Golokhvast
Plants 2025, 14(1), 59; https://doi.org/10.3390/plants14010059 - 27 Dec 2024
Viewed by 262
Abstract
A comparative metabolomic study of three varieties of wild Rosa (Rosa acicularis, Rosa amblyotis, and Rosa rugosa) from a Kamchatka expedition (2024) was conducted via extraction with supercritical carbon dioxide modified with ethanol (EtOH), and detection of bioactive compounds [...] Read more.
A comparative metabolomic study of three varieties of wild Rosa (Rosa acicularis, Rosa amblyotis, and Rosa rugosa) from a Kamchatka expedition (2024) was conducted via extraction with supercritical carbon dioxide modified with ethanol (EtOH), and detection of bioactive compounds was realized via tandem mass spectrometry. Several experimental conditions were investigated in the pressure range 50–350 bar, with the used volume of co-solvent ethanol in the amount of 2% in the liquid phase at a temperature in the range of 31–70 °C. The most effective extraction conditions are the following: pressure 200 Bar and temperature 55 °C for Rosa acicularis; pressure 250 Bar and temperature 60 °C for Rosa amblyotis; pressure 200 Bar and temperature 60 °C for Rosa rugosa. Three varieties of wild Rosa contain various phenolic compounds and compounds of other chemical groups with valuable biological activity. Tandem mass spectrometry (HPLC-ESI–ion trap) was applied to detect the target analytes. A total of 283 bioactive compounds (two hundred seventeen compounds from the polyphenol group and sixty-six compounds from other chemical groups) were tentatively identified in extracts from berries of wild Rosa. For the first time, forty-eight chemical constituents from the polyphenol group (15 flavones, 14 flavonols, 4 flavan-3-ols, 3 flavanones, 1 phenylpropanoid, 2 gallotannins, 1 ellagitannin, 4 phenolic acids, 1 dihydrochalcone, and 3 coumarins) were identified in supercritical extracts of R. acicularis, R. amblyotis, and R. rugosa. Full article
(This article belongs to the Special Issue Phytochemical Analysis and Metabolic Profiling in Plants)
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<p>Pictures of the studied three Rosa species.</p>
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<p>Global metabolome profile of three Rosa species. (<b>A</b>) Bar plot showing compound classes (and subclasses) and respective number of compounds detected in each Rosa species. (<b>B</b>) Venn diagram showing number of common and specific compounds detected in three Rosa species. (<b>C</b>) Venn diagram showing number of common and specific polyphenolic compounds detected in three Rosa species. (<b>D</b>) Scatter plot showing number of anthocyanins detected in each Rosa species.</p>
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<p>CID spectrum of formononetin from SC-CO<sub>2</sub> extracts of <span class="html-italic">R. rugosa</span>, <span class="html-italic">m</span>/<span class="html-italic">z</span> 269.24. Above is an MS scan in the range 100–1700 <span class="html-italic">m</span>/<span class="html-italic">z</span> and below is fragmentation spectra (top to bottom): MS2 of protonated formononetin ion (269.24 <span class="html-italic">m</span>/<span class="html-italic">z</span>, red diamond), MS3 fragment 269.24 → 251.28 <span class="html-italic">m</span>/<span class="html-italic">z</span>, and MS4 fragment 269.24 → 251.28 → 235.25 <span class="html-italic">m</span>/<span class="html-italic">z</span>.</p>
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<p>CID spectrum of nevadensin from SC-CO<sub>2</sub> extracts of <span class="html-italic">R. acicularis</span>, <span class="html-italic">m</span>/<span class="html-italic">z</span> 345.27. Above is an MS scan in the range 100–1700 <span class="html-italic">m</span>/<span class="html-italic">z</span> and below is fragmentation spectra (top to bottom): MS2 of protonated nevadensin ion (345.27 <span class="html-italic">m</span>/<span class="html-italic">z</span>, red diamond), MS3 fragment 345.27 → 312.19 <span class="html-italic">m</span>/<span class="html-italic">z</span>, and MS4 fragment 345.27 → 312.19 → 284.19 <span class="html-italic">m</span>/<span class="html-italic">z</span>.</p>
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<p>CID spectrum of luteolin-7-<span class="html-italic">O</span>-glucoside from SC-CO<sub>2</sub> extracts of <span class="html-italic">R. amblyotis</span>, <span class="html-italic">m</span>/<span class="html-italic">z</span> 449.36. Above is an MS scan in the range 100–1700 <span class="html-italic">m</span>/<span class="html-italic">z</span> and below is fragmentation spectra (top to bottom): MS2 of protonated luteolin-7-<span class="html-italic">O</span>-glucoside ion (449.36 <span class="html-italic">m</span>/<span class="html-italic">z</span>, red diamond), MS3 fragment 449.36 → 287.19 <span class="html-italic">m</span>/<span class="html-italic">z</span>, and MS4 fragment 449.36 → 287.19 → 287.19 <span class="html-italic">m</span>/<span class="html-italic">z</span>.</p>
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<p>CID spectrum of delphinidin <span class="html-italic">O</span>-pentoside from SC-CO<sub>2</sub> extracts of <span class="html-italic">R. rugosas</span>, <span class="html-italic">m</span>/<span class="html-italic">z</span> 435.24. Above is an MS scan in the range 100–1700 <span class="html-italic">m</span>/<span class="html-italic">z</span> and below is fragmentation spectra (top to bottom): MS2 of protonated delphinidin <span class="html-italic">O</span>-pentoside ion (435.24 <span class="html-italic">m</span>/<span class="html-italic">z</span>, red diamond), MS3 fragment 435.24 → 303.17 <span class="html-italic">m</span>/<span class="html-italic">z</span>, and MS4 fragment 435.24 → 303.17 → 285.17 <span class="html-italic">m</span>/<span class="html-italic">z</span>.</p>
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21 pages, 6841 KiB  
Article
Marine Origin vs. Synthesized Compounds: In Silico Screening for a Potential Drug Against SARS-CoV-2
by Amar Osmanović, Mirsada Salihović, Elma Veljović, Lamija Hindija, Mirha Pazalja, Maja Malenica, Aida Selmanagić and Selma Špirtović-Halilović
Sci. Pharm. 2025, 93(1), 2; https://doi.org/10.3390/scipharm93010002 - 26 Dec 2024
Viewed by 530
Abstract
Although COVID-19 is not a pandemic anymore, the virus frequently mutates, resulting in new strains and presenting global public health challenges. The lack of oral antiviral drugs makes it difficult to treat him, which makes the creation of broadly acting antivirals necessary to [...] Read more.
Although COVID-19 is not a pandemic anymore, the virus frequently mutates, resulting in new strains and presenting global public health challenges. The lack of oral antiviral drugs makes it difficult to treat him, which makes the creation of broadly acting antivirals necessary to fight current and next epidemics of viruses. Using the molecular docking approach, 118 compounds derived from marine organisms and 92 previously synthesized compounds were screened to assess their binding affinity for the main protease and papain-like protease enzymes of SARS-CoV-2. The best candidates from the xanthene, benzoxazole, and coumarin classes were identified. Marine-derived compounds showed slightly better potential as enzyme inhibitors, though the binding affinities of synthesized compounds were similar, with the best candidates displaying affinity values between 0.2 and 0.4 mM. Xanthenes, among both marine origin and synthesized compounds, emerged as the most promising scaffolds for further research as inhibitors. The papain-like protease was found to be more druggable than the main protease. Additionally, all top candidates met the criteria for various drug-likeness properties, indicating good oral bioavailability and low risk of adverse effects. This research provides valuable insights into the comparative affinities of marine origin and synthesized compounds from the xanthene, coumarin, and benzoxazole classes, highlighting promising candidates for further in vitro and in vivo studies. Full article
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<p>The general structure of the best marine origin compounds (<a href="#scipharm-93-00002-t001" class="html-table">Table 1</a>) screened against the main protease.</p>
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<p>The general structure of the best synthesized compounds (<a href="#scipharm-93-00002-t002" class="html-table">Table 2</a>) screened against the main protease.</p>
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<p>Structure of nirmatrelvir.</p>
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<p>Structures of the best marine origin compounds (<a href="#scipharm-93-00002-t003" class="html-table">Table 3</a>) screened against papain-like protease.</p>
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<p>The general structure of the best synthesized compounds (<a href="#scipharm-93-00002-t004" class="html-table">Table 4</a>) screened against papain-like protease.</p>
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<p>Structure of Jun12682.</p>
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<p>Binding mode and contacting amino acid residues of the compounds <b>1</b> (<b>a</b>) and <b>2</b> (<b>b</b>) within the main protease active site shown with HYDE contributions of individual atoms to the total affinity (green circled atoms).</p>
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<p>Binding modes and contacting amino acid residues of the marine compounds <b>3</b> (<b>a</b>), <b>4</b> (<b>b</b>), and <b>5</b> (<b>c</b>) within the main protease active site, with HYDE contributions of individual atoms to the total affinity (green circled atoms).</p>
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<p>Binding mode and contacting amino acid residues of the synthesized compounds <b>6</b> (<b>a</b>) and <b>7</b> (<b>b</b>) within the main protease active site shown with HYDE contributions of individual atoms to the total affinity (green circled atoms).</p>
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<p>Binding modes and contacting amino acid residues of the synthesized compounds <b>8</b> (<b>a</b>), <b>9</b> (<b>b</b>), and <b>10</b> (<b>c</b>) within the main protease active site, with HYDE contributions of individual atoms to the total affinity (green circled atoms).</p>
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<p>Binding mode and contacting amino acid residues of the marine compounds <b>11</b> (<b>a</b>) and <b>12</b> (<b>b</b>) within papain-like protease active site shown with HYDE contributions of individual atoms to the total affinity (green circled atoms).</p>
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<p>Binding modes and contacting amino acid residues of the marine compounds <b>13</b> (<b>a</b>), <b>14</b> (<b>b</b>), and <b>15</b> (<b>c</b>) within papain-like protease active site, with HYDE contributions of individual atoms to the total affinity (green circled atoms).</p>
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<p>Binding mode and contacting amino acid residues of the best synthesized compounds <b>16</b> (<b>a</b>) and <b>17</b> (<b>b</b>) within papain-like protease active site shown with HYDE contributions of individual atoms to the total affinity (green circled atoms).</p>
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<p>Binding modes and contacting amino acid residues of the best synthesized compounds <b>18</b> (<b>a</b>), <b>19</b> (<b>b</b>), and <b>20</b> (<b>c</b>) within papain-like protease active site, with HYDE contributions of individual atoms to the total affinity (green circled atoms).</p>
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<p>Overlay of the top five marine compounds within the active site of Mpro.</p>
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<p>Overlay of the top five synthesized compounds within the active site of Mpro (<b>6</b>—green, <b>7</b>—orange, <b>8</b>—magenta, <b>9</b>—cyan, <b>10</b>—yellow).</p>
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<p>Overlay of the top five marine compounds within the active site of PLpro (<b>11</b>—green, <b>12</b>—orange, <b>13</b>—magenta, <b>14</b>—cyan, <b>15</b>—yellow).</p>
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<p>Overlay of the top five synthesized compounds within the active site of PLpro (<b>16</b>—green, <b>17</b>—orange, <b>18</b>—magenta, <b>19</b>—cyan, <b>20</b>—yellow).</p>
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14 pages, 2808 KiB  
Article
Human Tyrosinase Displayed on the Surface of Chinese Hamster Ovary Cells for Ligand Fishing of Tyrosinase Inhibitors from Medicinal Plants
by Xiao-Rui Zhai, Ming-Jie Li, Xiang Yin, Ayzohra Ablat, Yuan Wang, Peng Shu and Xun Liao
Molecules 2025, 30(1), 30; https://doi.org/10.3390/molecules30010030 - 25 Dec 2024
Viewed by 254
Abstract
Ligand fishing is a promising strategy for the screening of active ingredients from complex natural products. In this work, human tyrosinase (hTYR) was displayed on the surface of Chinese hamster ovary (CHO) cells for the first time; it was then used as bait [...] Read more.
Ligand fishing is a promising strategy for the screening of active ingredients from complex natural products. In this work, human tyrosinase (hTYR) was displayed on the surface of Chinese hamster ovary (CHO) cells for the first time; it was then used as bait to develop a new method for ligand fishing. The localization of hTYR on the CHO cell surface was verified by an enzyme activity test and fluorescence microscopy. The displayed tyrosinase (CHO@hTYR) maintained relatively stable enzymatic activity (82.59 ± 2.70%) within 7 days. Furthermore, it can be reused for fishing five times. Guided by the proposed ligand fishing method, four tyrosinase inhibitors, including 4-methoxy-5-methyl coumarin (1), cupressuflavone (2), amentoflavone (3), and 3,4-dimethoxy-5-methyl coumarin (4), were isolated from Alhagi sparsifolia, and the active fraction with low polarity was isolated from Coffea arabica; these two medicinal plants possess skin-lightening potential. All the isolated tyrosinase inhibitors significantly reduced the intracellular tyrosinase activity and melanin level in B16 cells enhanced by α-MSH. Meanwhile, the active fraction (100 μg/mL) from C. arabica exhibited stronger inhibitory effects than the positive controls (α-arbutin and kojic acid) by recovering them to the normal levels. This work demonstrated the promising application of the cell surface display in the field of ligand fishing and is helpful in unveiling the chemical basis of the skin-lightening effect of A. sparsifolia and C. arabica. Full article
(This article belongs to the Special Issue Study on the Bioactive Compounds from Plant Extraction)
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<p>Fluorescence microscope images of CHO@hTYR and control cells. (<b>A</b>) CHO@hTYR; (<b>B</b>) control cells; (<b>left</b>): fluorescence; (<b>right</b>): light; scale bars: 100 μm.</p>
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<p>Storage stability of CHO@hTYR cells.</p>
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<p>Optimization of the conditions for ligand fishing: (<b>A</b>) cell quantity; (<b>B</b>) incubation time; (<b>C</b>) incubation buffer; (<b>D</b>) desorption solvent; (<b>E</b>) desorption time. Data are presented as mean ± SD. The experiments were performed in triplicate.</p>
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<p>HPLC chromatograms of (a) mS0, (b) mS5, and (c) mS5−blank. Peak identities: <b>1</b>, α−arbutin; <b>2</b>, quercitrin; <b>3,</b> dihydrotanshinone I.</p>
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<p>HPLC chromatograms of ligand fishing of <span class="html-italic">A. sparsifolia</span>: (a) S0−A, (b) S5−A, and (c) S5−A−blank. Chemical structures of 4−methoxy−5−methyl coumarin (<b>1</b>), cupressuflavone (<b>2</b>), amentoflavone (<b>3</b>), and 3, 4−dimethoxy−5−methyl coumarin (<b>4</b>).</p>
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<p>HPLC chromatograms of ligand fishing of <span class="html-italic">C. arabica</span>: (a) S0−C, (b) S5−C, and (c) S5−C−blank.</p>
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<p>Effect of compounds from <span class="html-italic">A. sparsifolia</span> on B16 melanoma cells: (<b>A</b>) cell cytotoxicity; (<b>B</b>) tyrosinase inhibitory activity; (<b>C</b>) melanin content reduction ability. α−Arbutin and kojic acid serve as positive controls. (<sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 and <sup>####</sup> <span class="html-italic">p</span> &lt; 0.0001, compared with the control 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 and **** <span class="html-italic">p</span> &lt; 0.0001, compared with the model group).</p>
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<p>Effects of the active fraction from <span class="html-italic">C. arabica</span> (S5) on B16 melanoma cells: (<b>A</b>) cell cytotoxicity of compounds on B16 melanoma cells, (<b>B</b>) tyrosinase inhibitory activity, (<b>C</b>) melanin content reduction ability. α−Arbutin and kojic acid serve (25 μM) as positive controls. (<sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 and <sup>####</sup> <span class="html-italic">p</span> &lt; 0.0001, compared with the control group; *** <span class="html-italic">p</span> &lt; 0.001 and **** <span class="html-italic">p</span> &lt; 0.0001, compared with the model group).</p>
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19 pages, 3620 KiB  
Article
Phytochemical Profiling, Bioactivity, and Insecticidal Effectiveness of Mammea americana L. Leaf Extracts Against Ferrisia sp.
by Mike Vázquez-Torres, Nilka Rivera-Portalatín and Irma Cabrera-Asencio
Plants 2025, 14(1), 21; https://doi.org/10.3390/plants14010021 - 25 Dec 2024
Viewed by 297
Abstract
Plant botanical extracts are recognized for being a source of biologically active phytochemicals that potentially have diverse applications. The phytochemical composition, potential cytotoxicity, and insecticidal effectiveness of three leaf extracts from the folkloric medicinal plant Mammea americana L. (Calophyllaceae) were investigated. Micro-Soxhlet extraction [...] Read more.
Plant botanical extracts are recognized for being a source of biologically active phytochemicals that potentially have diverse applications. The phytochemical composition, potential cytotoxicity, and insecticidal effectiveness of three leaf extracts from the folkloric medicinal plant Mammea americana L. (Calophyllaceae) were investigated. Micro-Soxhlet extraction with chloroform, dichloromethane, and methanol was used, and key phytochemicals were identified via Gas Chromatography-Mass Spectrometry (GC-MS). The extracts were mainly composed of sesquiterpenes, carboxylic acids, coumarins, esters, diterpenes, and other bioactive compounds. Potential cytotoxicity was assessed using brine shrimp lethality tests, where all extracts displayed high toxicity to Artemia salina. The dichloromethane extract (MAD) had the lowest LC50 value (8.39 μg/mL), followed by methanol extract (MAM, 11.66 μg/mL) and chloroform extract (MAC, 12.67 μg/mL). Insecticidal activity was tested against Ferrisia sp. (Hemiptera:Pseudococcidae), demonstrating the highest efficacy with the methanolic extract (LC50 = 5.90 mg/mL after 48 h). These findings provide a basis for further research into the bioactive components of Mammea americana leaves, particularly their antibacterial, anti-inflammatory, and anticancer properties. It also highlights the potential of Mammea americana L. leaf extracts as botanical insecticides due to their high bioactivity against agricultural pests of economic significance. This is the first study that evaluates the insecticidal activity of Mammea americana leaf extracts against Ferrisia sp. insects, offering valuable insights into using plant-based natural products in pest control. Full article
(This article belongs to the Special Issue Chemical Analysis, Bioactivity, and Application of Essential Oils)
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<p>Extraction Yields (%<span class="html-italic">v</span>/<span class="html-italic">w</span>) with different solvents for <span class="html-italic">Mammea americana</span> L. leaf extracts by micro-Soxhlet Extraction. Data in the bar charts is expressed as means ± standard deviation (n = 3).</p>
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<p>Total Ion Chromatogram for <span class="html-italic">Mammea americana</span> chloroform (MAC) leaf extract by GC-MS analysis. Peak identities are listed in <a href="#plants-14-00021-t001" class="html-table">Table 1</a>.</p>
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<p>Total Ion Chromatogram for <span class="html-italic">Mammea americana</span> dichloromethane (MAD) leaf extract by GC-MS analysis. Peak identities are listed in <a href="#plants-14-00021-t002" class="html-table">Table 2</a>.</p>
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<p>Total Ion Chromatogram for <span class="html-italic">Mammea americana</span> methanolic (MAM) leaf extract by GC-MS analysis. Peak identities are listed in <a href="#plants-14-00021-t003" class="html-table">Table 3</a>.</p>
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<p>Molecular structures for shared phytochemical constituents in the three analyzed leaf extracts from <span class="html-italic">Mammea americana</span> L. (Calophyllaceae) were identified by GC-MS analysis [<a href="#B24-plants-14-00021" class="html-bibr">24</a>].</p>
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<p>Relative amount (%) for the different classes of phytochemicals present in the three leaf extracts from <span class="html-italic">Mammea americana</span> L. (Calophyllaceae).</p>
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<p>Effect of <span class="html-italic">Mammea americana</span> leaf extracts on <span class="html-italic">Artemia salina</span> larvae zoomed to 0–100 μg/mL concentration range. Logistic regression was used for curve fitting. Percentages represent the means ± standard error of triplicates. Significant change at <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Effect of <span class="html-italic">Mammea americana</span> L. leaf extracts on <span class="html-italic">Ferrisia</span> sp. insects after (<b>a</b>) 24 and (<b>b</b>) 48 h of treatment. Logistic regression was used for curve fitting. Percentages represent the means ± standard error of triplicates. Significant change at <span class="html-italic">p</span> ≤ 0.05.</p>
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<p><span class="html-italic">Mammea americana</span> L. (Calophyllaceae) fresh leaves collected at Mayagüez, Puerto Rico.</p>
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18 pages, 9730 KiB  
Article
Influence of Sulfur Fumigation on Angelicae Dahuricae Radix: Insights from Chemical Profiles, MALDI-MSI and Anti-Inflammatory Activities
by Changshun Wang, Yongli Liu, Xiaolei Wang, Zhenhe Chen, Zhenxia Zhao, Huizhu Sun, Jian Su and Ding Zhao
Molecules 2025, 30(1), 22; https://doi.org/10.3390/molecules30010022 - 25 Dec 2024
Viewed by 303
Abstract
Background: Angelicae Dahuricae Radix (ADR) is used as both a traditional Chinese medicine and a food ingredient in China and East Asian countries. ADR is generally sun-dried post-harvest but is sometimes sulfur-fumigated to prevent decay and rot. Although there are some studies on [...] Read more.
Background: Angelicae Dahuricae Radix (ADR) is used as both a traditional Chinese medicine and a food ingredient in China and East Asian countries. ADR is generally sun-dried post-harvest but is sometimes sulfur-fumigated to prevent decay and rot. Although there are some studies on the effect of sulfur fumigation on ADR, they are not comprehensive. Methods: This study used HPLC fingerprinting, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), in vitro anti-inflammatory assays, and metabolite analysis in blood based on UPLC-MS/MS to assess the impact of sulfur fumigation on the active ingredients of ADR. Results: There were significant decreases in specific coumarins and amino acids, particularly byakangelicol, oxypeucedanin, L-proline, and L-arginine, following sulfur fumigation. Among the 185 metabolites in blood, there were 30 different compounds, and oxypeucedanin was the most obvious component to decrease after sulfur fumigation. ADR showed anti-inflammatory activity regardless of sulfur fumigation. However, the effects on the production of cytokines in LPS-induced RAW264.7 cells were different. Conclusions: Chemometric analysis and in vitro anti-inflammatory studies suggested that byakangelicol and oxypeucedanin could serve as potential quality markers for identifying sulfur-fumigated ADR. These findings provide a chemical basis for comprehensive safety and functional evaluations of sulfur-fumigated ADR, supporting further research in this field. Full article
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<p>The fingerprints of reference compounds (<b>A</b>), non-fumigated ADR (20 batches) (<b>B</b>), and fumigated ADR (15 batches) (<b>C</b>).</p>
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<p>Similarity analysis diagram of ADR. (<b>A</b>) Individual Value Plot of similarity of ADR. (<b>B</b>) Matrix graph of similarity and chromatographic peak number. (<b>C</b>) HCA of ADR.</p>
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<p>Content of coumarins in ADR. (<b>A</b>) Accumulation chart of 10 coumarin components in ADR; (<b>B</b>) box plot of 10 coumarins in non-sulfur-fumigated ADR; (<b>C</b>) box plot of 10 coumarins in sulfur-fumigated ADR; (<b>D</b>) effects of sulfur fumigation on coumarins in ADR.</p>
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<p>PCA and OPLS-DA analysis of ADR. (<b>A</b>) Score scatter plot of PCA; (<b>B</b>) loading scatter plot of PCA; (<b>C</b>) score scatter plot of OPLS-DA; (<b>D</b>) VIP value.</p>
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<p>Comparison between natural drying and sulfur fumigation of ADR. (<b>A</b>) Comparison of chromatographic peaks between naturally dried and sulfur-fumigated ADR; (<b>B</b>) comparison of chromatographic peak areas of six markers between sulfur-fumigated and non-sulfur-fumigated ADR.</p>
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<p>Experimental procedure of mass spectrometry imaging for ADR.</p>
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<p>Spatial distribution of typical ingredients in fumigated and non-fumigated ADR. (<b>A</b>) Optical image of ADR slices; (<b>B</b>) distribution of ingredients identified; (<b>C</b>) distribution of some unknown components.</p>
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<p>Signal strength of identified compounds in ADR before and after sulfur fumigation by MALDI-MSI.</p>
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<p>In vitro validation of byakangelicol and oxypeucedanin. (<b>A</b>) Effects of byakangelicol and oxypeucedanin on the viability of RAW264.7 cells; effect of byakangelicol and oxypeucedanin on the production of NO (<b>B</b>), TNF-α (<b>C</b>), IL-6 (<b>D</b>), IL-1β (<b>E</b>), and IL-10 (<b>F</b>) in LPS-induced RAW264.7 cells. The results represent the mean ± SEM (n = 3), * <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 (vs. LPS), ### <span class="html-italic">p</span> &lt; 0.001 (vs. Control), ns: not significant.</p>
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<p>In vitro validation of ADR before and after sulfur fumigation. Effect of ADR before and after sulfur fumigation on the production of TNF-α (<b>A</b>), IL-6 (<b>B</b>), IL-1β (<b>C</b>), and IL-10 (<b>D</b>) in LPS-induced RAW264.7 cells. The results represent the mean ± SEM (n = 3), * <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 (vs. LPS), <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 (vs. Control), <sup>+</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>+++</sup> <span class="html-italic">p</span> &lt; 0.001 (non-sulfur-fumigated ADR vs. sulfur-fumigated ADR).</p>
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<p>Difference in metabolites in blood in sulfur-fumigated and non-sulfur-fumigated ADR. (<b>A</b>) PCA; (<b>B</b>) OPLS-DA analysis; (<b>C</b>) heatmap cluster of ADR; (<b>D</b>) fold change bar chart; (<b>E</b>) radar chart.</p>
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15 pages, 8986 KiB  
Article
Self-Anchoring Coumarin Oxime Ester Photoinitiators with Low Migration for UV-LED Curable Coatings
by Zhihong Chen, Pin Yang, Huaqiao Lu, Shiyun Xiong, Gaole Dai and Zhiquan Li
Coatings 2025, 15(1), 6; https://doi.org/10.3390/coatings15010006 - 24 Dec 2024
Viewed by 400
Abstract
Reducing photoinitiator migration from photocured coatings remains a critical challenge, particularly for applications in food packaging and healthcare products. Here, we report a series of novel UV-LED sensitive oxime ester photoinitiators that possess self-anchoring ability through incorporating polymerizable double bonds into the coumarin [...] Read more.
Reducing photoinitiator migration from photocured coatings remains a critical challenge, particularly for applications in food packaging and healthcare products. Here, we report a series of novel UV-LED sensitive oxime ester photoinitiators that possess self-anchoring ability through incorporating polymerizable double bonds into the coumarin chromophore. All photoinitiators exhibit strong absorption around 340 nm and efficient photolysis under 365 nm LED irradiation, showing good initiating efficiency in acrylates and thiol-ene formulations. Migration studies show that the incorporation of polymerizable groups at the oxime ester terminus reduces the migration rate of the residual photoinitiator from 81% to 16.3%, while introducing an allyl group into the coumarin structure further decreases it to 5% and potentially suppresses the migration of low-molecular-weight photolysis products. The dual-functionalized derivative achieves the lowest migration rate of 3%. This molecular design strategy provides an effective approach toward safe UV-LED curable coatings with minimal photoinitiator migration. Full article
(This article belongs to the Section Functional Polymer Coatings and Films)
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<p>Chemical structures of the photoinitiators and the monomers.</p>
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<p>HOMO and LUMO orbit of the oxime ester photoinitiators.</p>
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<p>UV–vis absorption spectra of the oxime ester photoinitiators.</p>
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<p>Steady-state photolysis of the oxime ester photoinitiators in acetonitrile solution under 365 nm LED irradiation. (<b>a</b>): OEC-C1, (<b>b</b>): OEC-C2, (<b>c</b>): OEC-C3, (<b>d</b>): OEC-C4.</p>
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<p>(<b>a</b>) ESR spectra of the radicals generated from OEC-C2 and trapped by PBN in toluene under the irradiation of 3655 nm LED lamp: (up) experimental and (down) simulated spectrum; (<b>b</b>) proposed photolysis mechanism of the oxime ester photoinitiator.</p>
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<p>Functional group conversion of (<b>a</b>) TMPTA and (<b>b</b>) PETMP/TMPTA polymerization initiated by OEC-Cs under 365 nm LED lamp.</p>
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<p>(<b>a</b>) UV–vis absorption spectra of the photoinitiators after immersion for 30 h; (<b>b</b>) migration rate of the photoinitiators in the thiol-ene formulation.</p>
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<p>Synthetic routes of the coumarin oxime ester photoinitiators.</p>
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<p>Migration scheme of the photoinitiators: (<b>a</b>) OEC-C2; (<b>b</b>) OEC-C3.</p>
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25 pages, 17721 KiB  
Article
The Ameliorative Effect of Coumarin on Copper Toxicity in Citrus sinensis: Insights from Growth, Nutrient Uptake, Oxidative Damage, and Photosynthetic Performance
by Wei-Lin Huang, Hui Yang, Xu-Feng Chen, Fei Lu, Rong-Rong Xie, Lin-Tong Yang, Xin Ye, Zeng-Rong Huang and Li-Song Chen
Plants 2024, 13(24), 3584; https://doi.org/10.3390/plants13243584 - 22 Dec 2024
Viewed by 513
Abstract
Excessive copper (Cu) has become a common physiological disorder restricting the sustainable production of citrus. Coumarin (COU) is a hydroxycinnamic acid that can protect plants from heavy metal toxicity. No data to date are available on the ameliorative effect of COU on plant [...] Read more.
Excessive copper (Cu) has become a common physiological disorder restricting the sustainable production of citrus. Coumarin (COU) is a hydroxycinnamic acid that can protect plants from heavy metal toxicity. No data to date are available on the ameliorative effect of COU on plant Cu toxicity. ‘Xuegan’ (Citrus sinensis (L.) Osbeck) seedlings were treated for 24 weeks with nutrient solution containing two Cu levels (0.5 (Cu0.5) and 400 (Cu400) μM CuCl2) × four COU levels (0 (COU0), 10 (COU10), 50 (COU50), and 100 (COU100) μM COU). There were eight treatments in total. COU supply alleviated Cu400-induced increase in Cu absorption and oxidative injury in roots and leaves, decrease in growth, nutrient uptake, and leaf pigment concentrations and CO2 assimilation (ACO2), and photo-inhibitory impairment to the whole photosynthetic electron transport chain (PETC) in leaves, as revealed by chlorophyll a fluorescence (OJIP) transient. Further analysis suggested that the COU-mediated improvement of nutrient status (decreased competition of Cu2+ with Mg2+ and Fe2+, increased uptake of nutrients, and elevated ability to maintain nutrient balance) and mitigation of oxidative damage (decreased formation of reactive oxygen species and efficient detoxification system in leaves and roots) might lower the damage of Cu400 to roots and leaves (chloroplast ultrastructure and PETC), thereby improving the leaf pigment levels, ACO2, and growth of Cu400-treated seedlings. Full article
(This article belongs to the Special Issue Molecular Regulation of Plant Stress Responses)
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Figure 1
<p>Effects of Cu-COU interactions on the mean (±SE, <span class="html-italic">n</span> = 10) root (<b>A</b>), stem (<b>B</b>), leaf (<b>C</b>), shoot (<b>D</b>), and whole plant (<b>E</b>) DW, root DW/shoot DW ratio (<b>F</b>), and shoot (<b>G</b>) and root (<b>H</b>) growth of <span class="html-italic">Citrus sinensis</span> seedlings. Significant differences were analyzed by two ANOVA and followed by the least significant difference (LSD) at <span class="html-italic">p</span> &lt; 0.05. Error bars with different letters are significant different at <span class="html-italic">p</span> &lt; 0.05. *, significant difference at <span class="html-italic">p</span> &lt; 0.05; NS, non-significant difference. COU, coumarin; DW, dry weight; 1, 0.5 μM Cu + 0 μM COU; 2, 0.5 μM Cu + 10 μM COU; 3, 0.5 μM Cu + 50 μM COU; 4, 0.5 μM Cu + 100 μM COU; 5, 400 μM Cu + 0 μM COU; 6, 400 μM Cu + 10 μM COU; 7, 400 μM Cu + 50 μM COU; and 8, 400 μM Cu + 100 μM COU.</p>
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<p>Effects of Cu-COU interactions on the mean (±SE, <span class="html-italic">n</span> = 4) concentrations of micronutrients in leaves (<b>A</b>–<b>E</b>), stems (<b>F</b>–<b>J</b>), and roots (<b>K</b>–<b>O</b>). Significant differences were analyzed by two ANOVA and followed by the LSD at <span class="html-italic">p</span> &lt; 0.05. Error bars with different letters are significant different at <span class="html-italic">p</span> &lt; 0.05. *, significant difference at <span class="html-italic">p</span> &lt; 0.05; NS, non-significant difference.</p>
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<p>Effects of Cu-COU interactions on the mean (±SE, <span class="html-italic">n</span> = 4) concentrations of macronutrients in leaves (<b>A</b>–<b>F</b>), stems (<b>G</b>–<b>L</b>), and roots (<b>M</b>–<b>R</b>). Significant differences were analyzed by two ANOVA and followed by the LSD at <span class="html-italic">p</span> &lt; 0.05. Error bars with different letters are significant different at <span class="html-italic">p</span> &lt; 0.05. *, significant difference at <span class="html-italic">p</span> &lt; 0.05; NS, non-significant difference.</p>
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<p>Effects of Cu-COU interactions on the mean (±SE, <span class="html-italic">n</span> = 4) nutrient UPP (<b>A</b>–<b>K</b>) and UPR (<b>L</b>–<b>V</b>). UPP, uptake per plant; UPR, uptake per root DW. Significant differences were analyzed by two ANOVA and followed by the LSD at <span class="html-italic">p</span> &lt; 0.05. Error bars with different letters are significant different at <span class="html-italic">p</span> &lt; 0.05. *, significant difference at <span class="html-italic">p</span> &lt; 0.05; NS, non-significant difference.</p>
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<p>Effects of Cu-COU interactions on the mean (±SE, <span class="html-italic">n</span> = 4) ratios of N, K, Ca, Mg, and S (Mg and Fe) concentrations to P (Cu) concentration in leaves (<b>A</b>–<b>G</b>) and ratios of N, K, Ca, Mg, and S (Mg and Fe) UPP to P (Cu) UPP (<b>H</b>–<b>N</b>) in <span class="html-italic">C. sinensis</span> seedlings. Significant differences were analyzed by two ANOVA and followed by the LSD at <span class="html-italic">p</span> &lt; 0.05. Error bars with different letters are significant different at <span class="html-italic">p</span> &lt; 0.05. *, significant difference at <span class="html-italic">p</span> &lt; 0.05; NS, non-significant difference.</p>
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<p>Effects of Cu-COU interactions on the mean (±SE, <span class="html-italic">n</span> = 4) Chl <span class="html-italic">a</span> (<b>A</b>), Chl <span class="html-italic">b</span> (<b>B</b>), Chl <span class="html-italic">a+b</span> (<b>C</b>), Chl <span class="html-italic">a/b</span> (<b>D</b>), Car (<b>E</b>), Car/Chl <span class="html-italic">a+b</span> (<b>F</b>), A<sub>CO2</sub> (<b>G</b>), g<sub>s</sub> (<b>H</b>), and C<sub>i</sub> (<b>I</b>) in leaves. Significant differences were analyzed by two ANOVA and followed by the LSD at <span class="html-italic">p</span> &lt; 0.05. Error bars with different letters are significant different at <span class="html-italic">p</span> &lt; 0.05. *, significant difference at <span class="html-italic">p</span> &lt; 0.05; NS, non-significant difference. A<sub>CO2</sub>, CO<sub>2</sub> assimilation; Car, carotenoids; Chl, cholorophyll; C<sub>i</sub>, intercellular CO<sub>2</sub> concentration; g<sub>s</sub>, stomatal conductance.</p>
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<p>Effects of Cu-COU interactions on the mean OJIP transients of ten measured samples normalized between O-P (V<sub>O-P</sub>), O-K (V<sub>O-K</sub>), and O-J (V<sub>O-J</sub>) (<b>A</b>–<b>C</b>) and the differences in the eight samples to the reference sample treated with Cu0.5COU0 (<b>D</b>–<b>F</b>). V<sub>O-P</sub> = (F<sub>t</sub> − F<sub>o</sub>)/(F<sub>m</sub> − F<sub>o</sub>); V<sub>O-K</sub> = (F<sub>t</sub> − F<sub>o</sub>)/(F<sub>300μs</sub> − F<sub>o</sub>); V<sub>O-J</sub> = (F<sub>t</sub> − F<sub>o</sub>)/(F<sub>J</sub> − F<sub>o</sub>); F<sub>m</sub>, maximum fluorescence; F<sub>o</sub>, minimum fluorescence; F<sub>t</sub>, fluorescence intensity at time t after onset of actinic illumination; F<sub>300μs</sub>, fluorescence intensity at 300 μs; F<sub>J</sub>, fluorescence intensity at the J-step (2 ms).</p>
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<p>Effects of Cu-COU interactions on the mean (±SE, <span class="html-italic">n</span> = 10) F<sub>o</sub> (<b>A</b>), F<sub>m</sub> (<b>B</b>), F<sub>v</sub>/F<sub>m</sub> (<b>C</b>), F<sub>v</sub>/F<sub>o</sub> (<b>D</b>), V<sub>J</sub> (<b>E</b>), V<sub>I</sub> (<b>F</b>), M<sub>o</sub> (<b>G</b>), ET<sub>o</sub>/ABS (<b>H</b>), RE<sub>o</sub>/ABS (<b>I</b>), TR<sub>o</sub>/RC (<b>J</b>), ET<sub>o</sub>/TR<sub>o</sub> (<b>K</b>), DI<sub>o</sub>/RC (<b>L</b>), RE<sub>o</sub>/TR<sub>o</sub> (<b>M</b>), MAIP (<b>N</b>), and PI<sub>abs,total</sub> (<b>O</b>) in leaves. Significant differences were analyzed by two ANOVA and followed by the LSD at <span class="html-italic">p</span> &lt; 0.05. Error bars with different letters are significantly different at <span class="html-italic">p</span> &lt; 0.05. *, significant difference at <span class="html-italic">p</span> &lt; 0.05. F<sub>o</sub>, minimum fluorescence; F<sub>m</sub>, maximum fluorescence; F<sub>v</sub>/F<sub>m</sub>, maximum quantum yield of primary photochemistry; F<sub>v</sub>/F<sub>o</sub>, maximum primary yield of photochemistry of photosystem II (PSII); V<sub>J</sub>, relative variable fluorescence at the J-step (2 ms); V<sub>I</sub>, relative variable fluorescence at the I-step (30 ms); M<sub>o</sub>, approximated initial slope (in ms<sup>−1</sup>) of the fluorescence transient <span class="html-italic">V</span> = <span class="html-italic">f(t)</span>; ET<sub>o</sub>/ABS (φ<sub>Eo</sub>), quantum yield for electron transport; RE<sub>o</sub>/ABS (φ<sub>Ro</sub>), quantum yield for the reduction in end acceptors of photosystem I per photon absorbed; TR<sub>o</sub>/RC, trapped energy flux per reaction center; ET<sub>o</sub>/TR<sub>o</sub> (ψ<sub>Eo</sub>), probability that a trapped exciton moves an electron into the electron transport chain beyond Q<sub>A</sub><sup>−</sup>; DI<sub>o</sub>/RC, specific energy fluxes per reaction center for energy dissipation; RE<sub>o</sub>/TR<sub>o</sub> (ρ<sub>Ro</sub>), efficiency with which a trapped exciton can move an electron into the electron transport chain from Q<sub>A</sub><sup>−</sup> to the photosystem I end electron acceptors; MAIP, maximum amplitude of IP phase; PI<sub>abs,total</sub>, total performance index.</p>
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<p>Effects of Cu-COU interactions on the mean (±SE, <span class="html-italic">n</span> = 4) concentrations of MDA (<b>A</b>), HPR (<b>B</b>), and activities of SOD (<b>C</b>), APX (<b>D</b>), CAT (<b>E</b>), and GuPX (<b>F</b>) in leaves (above column) and roots (below column). Significant differences were analyzed by two ANOVA and followed by the LSD at <span class="html-italic">p</span> &lt; 0.05. Error bars with different letters are significant different at <span class="html-italic">p</span> &lt; 0.05. *, significant difference at <span class="html-italic">p</span> &lt; 0.05. APX, ascorbate peroxidase; CAT, catalase; COU, coumarin; GuPX, guaiacol peroxidase; HPR, H<sub>2</sub>O<sub>2</sub> production rate; MDA, malondialdehyde; SOD, superoxide dismutase.</p>
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<p>PCoA plots of 21 parameters for growth (6) and fluorescence (15) (<b>A</b>) and 123 parameters for nutrients (33 nutrient concentrations, 33 nutrient fractions, 11 nutrient UPR, 11 nutrient UPP, and 14 ratio), pigments (6), gas exchange (3), antioxidant enzymes (8), MDA (2), and HPR (2) (<b>B</b>) from <span class="html-italic">C. sinensis</span> seedlings submitted to different Cu and COU levels. PCoA, principal coordinate analysis; Cu0.5COU0, 0.5 μM Cu + 0 μM COU; Cu0.5COU10, 0.5 μM Cu + 10 μM COU; Cu0.5COU50, 0.5 μM Cu + 50 μM COU; Cu0.5COU100, 0.5 μM Cu + 100 μM COU; Cu400COU0, 400 μM Cu + 0 μM COU; Cu400COU10, 400 μM Cu + 10 μM COU; Cu400COU50, 400 μM Cu + 50 μM COU; Cu400COU100, 400 μM Cu + 100 μM COU.</p>
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<p>Matrices of Pearson correlation coefficients (PCCs) for the mean values of 63 physiological parameters in <span class="html-italic">C. sinensis</span> leaves and roots. Data came from <a href="#plants-13-03584-f001" class="html-fig">Figure 1</a>, <a href="#plants-13-03584-f002" class="html-fig">Figure 2</a>, <a href="#plants-13-03584-f003" class="html-fig">Figure 3</a>, <a href="#plants-13-03584-f004" class="html-fig">Figure 4</a>, <a href="#plants-13-03584-f005" class="html-fig">Figure 5</a> and <a href="#plants-13-03584-f006" class="html-fig">Figure 6</a>, <a href="#plants-13-03584-f008" class="html-fig">Figure 8</a>, and <a href="#plants-13-03584-f009" class="html-fig">Figure 9</a>. *, significant difference at <span class="html-italic">p</span> &lt; 0.05; leaf element (pigment), leaf (pigment) concentration; root element, root element concentration; leaf (root) enzyme, leaf (root) enzyme activity.</p>
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<p>A proposed model for the underlying mechanisms by which COU mitigated copper toxicity in <span class="html-italic">Citrus sinensis</span> seedlings. Red, upregulation. Blue, downregulation.</p>
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12 pages, 4448 KiB  
Article
Stretchable Thermochromic Fluorescent Fibers Based on Self-Crystallinity Phase Change for Smart Wearable Displays
by Yongmei Guo, Zixi Hu, Luyao Zhan, Yongkun Liu, Luping Sun and Ying Ma
Polymers 2024, 16(24), 3575; https://doi.org/10.3390/polym16243575 - 21 Dec 2024
Viewed by 345
Abstract
Smart fibers with tunable luminescence properties, as a new form of visual output, present the potential to revolutionize personal living habits in the future and are receiving more and more attention. However, a huge challenge of smart fibers as wearable materials is their [...] Read more.
Smart fibers with tunable luminescence properties, as a new form of visual output, present the potential to revolutionize personal living habits in the future and are receiving more and more attention. However, a huge challenge of smart fibers as wearable materials is their stretching capability for seamless integration with the human body. Herein, stretchable thermochromic fluorescent fibers are prepared based on self-crystallinity phase change, using elastic polyurethane (PU) as the fiber matrix, to meet the dynamic requirements of the human body. The switching fluorescence-emitting characteristic of the fibers is derived from the reversible conversion of the dispersion/aggregation state of the fluorophore coumarin 6 (C6) and the quencher methylene blue (MB) in the phase-change material hexadecanoic acid (HcA) during heating/cooling processes. Considering the important role of phase-change materials, thermochromic fluorescent dye is encapsuled in the solid state via the piercing–solidifying method to avoid the dissolution of HcA by the organic solvent of the PU spinning solution and maintain excellent thermochromic behavior in the fibers. The fibers obtained by wet spinning exhibit good fluorescent emission contrast and reversibility, as well as high elasticity of 800% elongation. This work presents a strategy for constructing stretchable smart luminescence fibers for human–machine interaction and communications. Full article
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Figure 1
<p>Schematic illustration of the preparation process of thermochromic fluorescent capsules via the piercing–solidifying method.</p>
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<p>(<b>a</b>) Treatment process of the thermochromic fluorescent capsules to be used for wet spinning; (<b>b</b>) photograph of the thermochromic fluorescent capsules; (<b>c</b>) SEM image of a thermochromic fluorescent capsule; (<b>d</b>) size distribution of the thermochromic fluorescent capsules.</p>
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<p>Fluorescent photographs of the thermochromic fluorescent capsules at 25 °C and 75 °C, in (<b>a</b>) air and (<b>b</b>) DMAc solvent; (<b>c</b>) fluorescence switching cycles of the thermochromic capsules at 25 °C and 75 °C.</p>
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<p>(<b>a</b>) Schematic illustration of the wet-spinning process for preparing stretchable thermochromic fluorescent fibers. (<b>b</b>) Optical photograph and (<b>c</b>) SEM images of the prepared thermochromic fluorescent PU fiber.</p>
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<p>Cyclic loading–unloading tensile curves of the thermochromic fluorescent PU fiber at increasing strain.</p>
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<p>Cyclic loading–unloading tensile curves of the thermochromic fluorescent PU fiber at (<b>a</b>) 100% strain, (<b>b</b>) 300% strain, and (<b>c</b>) 500% strain.</p>
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<p>(<b>a</b>) Schematic illustration of the thermochromic fluorescent mechanisms of the fibers, based on self-crystallinity phase change. (<b>b</b>) Fluorescence switching cycles of the stretchable thermochromic fiber at 25 °C and 75 °C.</p>
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<p>Fluorescent photographs of the thermochromic fluorescent fiber under different stretching levels.</p>
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