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12 pages, 6270 KiB  
Article
Distribution and Maturity of Medial Collagen Fibers in Thoracoabdominal Post-Dissection Aortic Aneurysms: A Comparative Study of Marfan and Non-Marfan Patients
by Panagiotis Doukas, Bernhard Hruschka, Cathryn Bassett, Eva Miriam Buhl, Florian Simon, Pepijn Saraber, Michael Johan Jacobs, Christian Uhl, Leon J. Schurgers and Alexander Gombert
Int. J. Mol. Sci. 2025, 26(1), 14; https://doi.org/10.3390/ijms26010014 - 24 Dec 2024
Abstract
Thoracoabdominal aortic aneurysms (TAAAs) are rare but serious conditions characterized by dilation of the aorta characterized by remodeling of the vessel wall, with changes in the elastin and collagen content. Individuals with Marfan syndrome have a genetic predisposition for elastic fiber fragmentation and [...] Read more.
Thoracoabdominal aortic aneurysms (TAAAs) are rare but serious conditions characterized by dilation of the aorta characterized by remodeling of the vessel wall, with changes in the elastin and collagen content. Individuals with Marfan syndrome have a genetic predisposition for elastic fiber fragmentation and elastin degradation and are prone to early aneurysm formation and progression. Our objective was to analyze the medial collagen characteristics through histological, polarized light microscopy, and electron microscopy methods across the thoracic and abdominal aorta in twenty-five patients undergoing open surgical repair, including nine with Marfan syndrome. While age at surgery differed significantly between the groups, maximum aortic diameter and aneurysm extent did not. Collagen content increased from thoracic to infrarenal segments in both cohorts, with non-Marfan patients exhibiting higher collagen percentages, notably in the infrarenal aorta (729.3 nm vs. 1068.3 nm, p = 0.02). Both groups predominantly displayed mature collagen fibers, with the suprarenal segment containing the highest proportion of less mature fibers. Electron microscopy revealed comparable collagen fibril diameters across segments irrespective of Marfan status. Our findings underscore non-uniform histological patterns in TAAAs and suggest that ECM remodeling involves mature collagen deposition, albeit with lower collagen content observed in the infrarenal aorta of Marfan patients. Full article
(This article belongs to the Special Issue Arteriogenesis, Angiogenesis and Vascular Remodeling, 2nd Edition)
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Figure 1

Figure 1
<p>Intima and media of the infrarenal aorta for non-Marfan (<b>A</b>,<b>C</b>) and Marfan (<b>B</b>,<b>D</b>) patients. (<b>A</b>,<b>B</b>): picrosirius red staining, (<b>C</b>,<b>D</b>): MOVAT pentachrome staining. Red: intima and media, Green: only intima.</p>
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<p>Total collagen percentage in the aortic media of the different aortic segments for non-Marfan and Marfan patients. <span class="html-italic">p</span>-values calculated with the Dunn-Sidàk post hoc test after Friedman ANOVA.</p>
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<p>Picrosirius red staining of the different aortic segments for Marfan and non-Marfan patients. The images on the left were taken with conventional microscopy, while those on the right were taken with polarized light microscopy. The media layer is indicated by the green line running across the diameter. Arrows point to the aortic lumen. 40× magnification.</p>
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<p>Quantification of the collagen fiber types in aortic media, according to level of maturity (red to green—mature to less mature).</p>
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<p>Collagen fibers in the different aortic segments. 60,000× magnification.</p>
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10 pages, 3149 KiB  
Article
Density Functional Theory Insight in Photocatalytic Degradation of Dichlorvos Using Covalent Triazine Frameworks Modified by Various Oxygen-Containing Acid Groups
by Shouxi Yu and Zhongliao Wang
Toxics 2024, 12(12), 928; https://doi.org/10.3390/toxics12120928 - 21 Dec 2024
Viewed by 184
Abstract
Dichlorvos (2,2-dichlorovinyl dimethyl phosphate, DDVP) is a highly toxic organophosphorus insecticide, and its persistence in air, water, and soil poses potential threats to human health and ecosystems. Covalent triazine frameworks (CTFs), with their sufficient visible-light harvesting capacity, ameliorated charge separation, and exceptional redox [...] Read more.
Dichlorvos (2,2-dichlorovinyl dimethyl phosphate, DDVP) is a highly toxic organophosphorus insecticide, and its persistence in air, water, and soil poses potential threats to human health and ecosystems. Covalent triazine frameworks (CTFs), with their sufficient visible-light harvesting capacity, ameliorated charge separation, and exceptional redox ability, have emerged as promising candidates for the photocatalytic degradation of DDVP. Nevertheless, pure CTFs lack effective oxidative active sites, resulting in elevated reaction energy barriers during the photodegradation of DDVP. In this work, density functional theory (DFT) calculations were employed to investigate the impact of various oxygen-containing acid groups (-COOH, -HSO3, -H2PO3) on DDVP photodegradation performance. First, simulations of the structure and optical properties of modified CTFs reveal that oxygen-containing acid groups induce surface distortion and result in a redshift in the absorption edge. Subsequently, analysis of the density of states, frontier molecular orbitals, surface electrostatic potential, work function, and dipole moment demonstrates that oxygen-containing acid groups enhance CTF polarization, facilitate charge separation, and ameliorate their oxidative capability. Additionally, the free-energy diagram of DDVP degradation uncovers that oxygen-containing acid groups lower the energy barrier by elevating the adsorption and activation capability of DDVP. Notably, -H2PO3 presents optimal potential for the photodegradation of DDVP by unique electronic structure and activation capability. This work offers a valuable reference for the development of oxygen-containing acid CTF-based photocatalysts applied in degrading toxic organophosphate pesticides. Full article
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Graphical abstract

Graphical abstract
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<p>Top and side perspective of crystal structures: (<b>a</b>,<b>e</b>) CTF, (<b>b</b>,<b>f</b>) CTF-COOH, (<b>c</b>,<b>g</b>) CTF-HSO<sub>3</sub>, and (<b>d</b>,<b>h</b>) CTF-H<sub>2</sub>PO<sub>3</sub>. Simulated (<b>i</b>) XRD pattern.</p>
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<p>(<b>a</b>) IR spectra, (<b>b</b>) Raman spectra, and (<b>c</b>) UV–Vis spectra of CTF, CTF-COOH, CTF-HSO<sub>3</sub>, and CTF-H<sub>2</sub>PO<sub>3</sub>. (<b>d</b>) Dipole moments on different components of CTF, CTF-COOH, CTF-HSO<sub>3</sub>, and CTF-H<sub>2</sub>PO<sub>3</sub>.</p>
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<p>Band structure of (<b>a</b>) CTF, (<b>b</b>) CTF-COOH, (<b>c</b>) CTF-HSO<sub>3</sub>, and (<b>d</b>) CTF-H<sub>2</sub>PO<sub>3</sub>. The density of states (DOS) of (<b>e</b>) CTF, (<b>f</b>) CTF-COOH, (<b>g</b>) CTF-HSO<sub>3</sub>, and (<b>h</b>) CTF-H<sub>2</sub>PO<sub>3</sub>.</p>
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<p>Electrostatic potentials curves of the (<b>a</b>) CTF, (<b>b</b>) CTF-COOH, (<b>c</b>) CTF-HSO<sub>3</sub>, and (<b>d</b>) CTF-H<sub>2</sub>PO<sub>3</sub>.</p>
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<p>Surface electrostatic potential of (<b>a</b>) CTF, (<b>b</b>) CTF-COOH, (<b>c</b>) CTF-HSO<sub>3</sub>, and (<b>d</b>) CTF-H<sub>2</sub>PO<sub>3</sub>.</p>
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<p>Interaction between DDVP and (<b>a</b>) CTF, (<b>b</b>) CTF-COOH, (<b>c</b>) CTF-HSO<sub>3</sub>, and (<b>d</b>) CTF-H<sub>2</sub>PO<sub>3</sub>. Charge density difference and charge transfer of (<b>e</b>,<b>i</b>) CTF, (<b>f</b>,<b>j</b>) CTF-COOH, (<b>g</b>,<b>k</b>) CTF-HSO<sub>3</sub>, (<b>h</b>,<b>l</b>) CTF-H<sub>2</sub>PO<sub>3</sub>.</p>
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<p>Free-energy diagrams for the photodegradation and reaction path of DDVP of (<b>a</b>) CTF, (<b>b</b>) CTF-COOH, (<b>c</b>) CTF-HSO<sub>3</sub>, and (<b>d</b>) CTF-H<sub>2</sub>PO<sub>3</sub>.</p>
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22 pages, 5635 KiB  
Article
Selection of Representative Asphaltene Molecules in an Asphalt Molecular Model Based on Quantum Chemistry and Statistical Analysis
by Jie Zhu, Ganyu Xia, Dejian Shen, Yangtao Li, Baosheng Jin and Shengxing Wu
Molecules 2024, 29(24), 6015; https://doi.org/10.3390/molecules29246015 - 20 Dec 2024
Viewed by 225
Abstract
Asphaltenes, as the most complex and strongly polar component among the four components of asphalt, have a significant impact on the macroscopic physicochemical properties of asphalt. Currently, the vast variety of molecular structures used to characterize asphaltenes increases the construction complexity of asphalt [...] Read more.
Asphaltenes, as the most complex and strongly polar component among the four components of asphalt, have a significant impact on the macroscopic physicochemical properties of asphalt. Currently, the vast variety of molecular structures used to characterize asphaltenes increases the construction complexity of asphalt molecular models. To construct a more realistic asphalt molecular model and reduce the construction difficulty, this investigation obtains the molecular morphology, molecular polarity, and infrared spectrum indicators of 21 asphaltene molecules through quantum chemical calculations. Based on statistical analysis methods, the differences among asphaltene molecules are explored, and suggestions for selecting representative asphaltene molecules are proposed. The investigation shows that AS2, AS3, AS12, AS15, and AS17 are representative molecules that are significantly different from other asphaltene molecules. Among them, AS2, AS15, and AS17 are significantly different from the other molecules in terms of polarity and functional groups, while AS3 and AS12 are significantly different from the other molecules in terms of aromatic carbon percentage. This investigation is expected to provide valuable insights into the intrinsic relationship between the nanoscale characteristics and macroscopic properties of asphalt molecules. Full article
(This article belongs to the Section Molecular Structure)
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<p>Molecular structure of asphaltenes.</p>
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<p>Correlation coefficients and scatter matrices of molecular morphology indicators of asphaltenes.</p>
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<p>Map of principal component scores of asphaltene molecular morphology.</p>
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<p>Euclidean distance based on molecular morphology indicators of asphaltenes.</p>
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<p>Hierarchical clustering based on morphology indicators of asphaltene molecules.</p>
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<p>Atomic charge and electrostatic potential coloring of asphaltene molecules.</p>
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<p>Distribution of the absolute ADCH charges of asphaltenes.</p>
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<p>Thermogram of the ADCH charges of asphaltenes.</p>
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<p>Euclidean distance based on the ADCH charges of asphaltenes.</p>
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<p>Hierarchical clustering of asphaltene molecules based on ADCH charges.</p>
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<p>Correlation coefficients and scatter matrices of molecular polarity indicators of asphaltenes.</p>
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<p>Map of principal component scores of asphaltene polarity indicators.</p>
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<p>Mean Euclidean distance and hierarchical clustering based on the molecular polarity indicator of asphaltenes.</p>
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<p>Infrared spectrum of asphaltenes.</p>
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<p>The variance percentage of the principal components of the infrared spectrum of the asphaltenes.</p>
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<p>Euclidean distance based on the infrared spectrum data of the asphaltenes.</p>
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<p>Hierarchical clustering of asphaltenes based on infrared spectrum indicators.</p>
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<p>Recommendations for representative asphaltene molecule selection.</p>
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14 pages, 11976 KiB  
Article
Tribological Characteristics of Biolubricant Obtained by Transesterification of Grape Seed Oil
by Thawan Fonseca Silva, Maria Marliete Fernandes de Melo Neta, Paulo Roberto Campos Flexa Ribeiro Filho, Francisco Murilo Tavares de Luna and Célio Loureiro Cavalcante
Lubricants 2024, 12(12), 459; https://doi.org/10.3390/lubricants12120459 - 20 Dec 2024
Viewed by 294
Abstract
Research on and the development of bio-based lubricants as alternatives to mineral-based lubricants have been encouraged worldwide owing to environmental concerns and the possible depletion of oil reserves. This study explored the use of grape seed oil (GSO), a byproduct of wine production, [...] Read more.
Research on and the development of bio-based lubricants as alternatives to mineral-based lubricants have been encouraged worldwide owing to environmental concerns and the possible depletion of oil reserves. This study explored the use of grape seed oil (GSO), a byproduct of wine production, as a raw material for biolubricant synthesis. GSO contains a triglyceride molecule rich in unsaturated fatty acids, which is ideal for obtaining biolubricants. This study addresses the technical challenges of converting GSO into a lubricant by synthesizing methyl esters (FAME) via transesterification with 2-ethylhexanol to produce a biolubricant (BL) sample. The obtained products were characterized using Fourier-transform infrared spectroscopy and nuclear magnetic resonance spectroscopy to confirm the conversion of the molecules. The density, kinematic viscosity, and viscosity index were determined using the parameters established by ASTM. The tribological characteristics of BL were evaluated using a four-ball tribometer configuration. BL exhibited physicochemical characteristics comparable with those of an ISO VG 10 lubricant, a friction coefficient (FC) 40.82% lower than that of a hydrotreated mineral oil sample, and a smoother wear surface. These results indicate that the polarity of the ester functional group was efficient in producing a protective film on metal surfaces. Full article
(This article belongs to the Special Issue Tribological Properties of Biolubricants)
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Figure 1
<p>Synthetic route used to obtain methyl esters (FAME) from grape seed oil (GSO).</p>
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<p>Experimental setup for synthesis of bio-based lubricants.</p>
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<p>Synthetic route to obtain biolubricants (BL) from transesterification of FAME.</p>
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<p>Four-ball test configuration.</p>
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<p>FTIR spectra of GSO, FAME, and BL samples.</p>
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<p><sup>1</sup>H NMR spectra of (<b>a</b>) GSO, (<b>b</b>) FAME, and (<b>c</b>) BL samples.</p>
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<p>FC and WSD values of FAME, BL, and HMO after tribological test. <span class="html-italic">p</span> &lt; 0.05. Different letters indicate significant differences between WSDs.</p>
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<p>Sketches of the lubrication mechanisms of FAME, BL, and HMO at the friction interface. (<b>a</b>) The initial formation of the lubricating film of the bio-based samples (FAME and BL). (<b>b</b>) The rupture of unsaturated bonds of the bio-based samples (FAME and BL) and (<b>c</b>) HMO lubricating film.</p>
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27 pages, 4435 KiB  
Article
Remote Ischemic Post-Conditioning (RIC) Mediates Anti-Inflammatory Signaling via Myeloid AMPKα1 in Murine Traumatic Optic Neuropathy (TON)
by Naseem Akhter, Jessica Contreras, Mairaj A. Ansari, Andrew F. Ducruet, Md Nasrul Hoda, Abdullah S. Ahmad, Laxman D. Gangwani, Kanchan Bhatia and Saif Ahmad
Int. J. Mol. Sci. 2024, 25(24), 13626; https://doi.org/10.3390/ijms252413626 - 19 Dec 2024
Viewed by 382
Abstract
Traumatic optic neuropathy (TON) has been regarded a vision-threatening condition caused by either ocular or blunt/penetrating head trauma, which is characterized by direct or indirect TON. Injury happens during sports, vehicle accidents and mainly in military war and combat exposure. Earlier, we have [...] Read more.
Traumatic optic neuropathy (TON) has been regarded a vision-threatening condition caused by either ocular or blunt/penetrating head trauma, which is characterized by direct or indirect TON. Injury happens during sports, vehicle accidents and mainly in military war and combat exposure. Earlier, we have demonstrated that remote ischemic post-conditioning (RIC) therapy is protective in TON, and here we report that AMPKα1 activation is crucial. AMPKα1 is the catalytic subunit of the heterotrimeric enzyme AMPK, the master regulator of cellular energetics and metabolism. The α1 isoform predominates in immune cells including macrophages (Mφs). Myeloid-specific AMPKα1 KO mice were generated by crossing AMPKα1Flox/Flox and LysMcre to carry out the study. We induced TON in mice by using a controlled impact system. Mice (mixed sex) were randomized in six experimental groups for Sham (mock); Sham (RIC); AMPKα1F/F (TON); AMPKα1F/F (TON+RIC); AMPKα1F/F LysMCre (TON); AMPKα1F/F LysMCre (TON+RIC). RIC therapy was given every day (5–7 days following TON). Data were generated by using Western blotting (pAMPKα1, ICAM1, Brn3 and GAP43), immunofluorescence (pAMPKα1, cd11b, TMEM119 and ICAM1), flow cytometry (CD11b, F4/80, CD68, CD206, IL-10 and LY6G), ELISA (TNF-α and IL-10) and transmission electron microscopy (TEM, for demyelination and axonal degeneration), and retinal oxygenation was measured by a Unisense sensor system. First, we observed retinal morphology with funduscopic images and found TON has vascular inflammation. H&E staining data suggested that TON increased retinal inflammation and RIC attenuates retinal ganglion cell death. Immunofluorescence and Western blot data showed increased microglial activation and decreased retinal ganglion cell (RGCs) marker Brn3 and axonal regeneration marker GAP43 expression in the TON [AMPKα1F/F] vs. Sham group, but TON+RIC [AMPKα1F/F] attenuated the expression level of these markers. Interestingly, higher microglia activation was observed in the myeloid AMPKα1F/F KO group following TON, and RIC therapy did not attenuate microglial expression. Flow cytometry, ELISA and retinal tissue oxygen data revealed that RIC therapy significantly reduced the pro-inflammatory signaling markers, increased anti-inflammatory macrophage polarization and improved oxygen level in the TON+RIC [AMPKα1F/F] group; however, RIC therapy did not reduce inflammatory signaling activation in the myeloid AMPKα1 KO mice. The transmission electron microscopy (TEM) data of the optic nerve showed increased demyelination and axonal degeneration in the TON [AMPKα1F/F] group, and RIC improved the myelination process in TON [AMPKα1F/F], but RIC had no significant effect in the AMPKα1 KO mice. The myeloid AMPKα1c deletion attenuated RIC induced anti-inflammatory macrophage polarization, and that suggests a molecular link between RIC and immune activation. Overall, these data suggest that RIC therapy provided protection against inflammation and neurodegeneration via myeloid AMPKα1 activation, but the deletion of myeloid AMPKα1 is not protective in TON. Further investigation of RIC and AMPKα1 signaling is warranted in TON. Full article
(This article belongs to the Special Issue New Therapeutic Targets for Neuroinflammation and Neurodegeneration)
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Figure 1

Figure 1
<p>(<b>A</b>) Representative in vivo funduscopic fluorescein image from C56BL/6 mice showing inflammation in blood vessels in TON as compared with control eye. Intravenous fluorescein angiography of the mouse retina shows poor perfusion through attenuated vasculature (due to progression of the retinal degeneration) following TON. (<b>B</b>) H&amp;E data showed increased neuronal cell death in ganglion cell layer in TON compared with control. However, the neuronal cell death is prevented with RIC treatment. Fluorescein angiography imaging (<b>A</b>) was captured within 5 mins of fluorescein dye injection through tail vein.</p>
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<p>(<b>A</b>,<b>B</b>) Immunofluorescence staining showed microglial marker TMEM119 expression in mouse retina. TON (with AMPK) increases microglial activation, and RIC downregulated significantly. Myeloid pAMPKα1 KO group showed heightened microglial activation; notably, RIC demonstrated no significant effects. Florescence color intensity was measured by Image J software (NIH, <a href="https://imagej.net/ij/" target="_blank">https://imagej.net/ij/</a>). White boxes show the TMEM119 expression in inner nuclear layer (INL) and GCL (ganglion cell layer) region of mouse eye. For Sham (mock) and Sham (RIC), both groups are regarded as AMPKα1<sup>F/F</sup>. (<b>C</b>–<b>I</b>) Representative pseudocolor and histograms of flow cytometry show the gating strategy for microglia/macrophages (CD11b+_F4/80+) and CD68+ and CD206+ expressing microglia in blood. Bar graph summarizing the cell counts of microglia (M1/M2) in the blood after 5 days of TON. Red, TMEM119 (activated microglial marker); Blue, DAPI. We used 6 experimental groups, Sham (mock); Sham (RIC); AMPKα1<sup>F/F</sup> (TON); AMPKα1<sup>F/F</sup> (TON+RIC); AMPKα1<sup>F/F</sup> LysMCre (TON); AMPKα1<sup>F/F</sup> LysMCre (TON+RIC). Differences among experimental groups were determined by analysis of variance (one-way ANOVA) followed by Newman–Keuls multiple comparison tests. The results represent the means ± SEM of fold changes (<span class="html-italic">n</span> = 5). * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. ns, non-significant. For Sham (mock) and Sham (RIC), both groups are regarded as AMPKα1<sup>F/F</sup>.</p>
Full article ">Figure 2 Cont.
<p>(<b>A</b>,<b>B</b>) Immunofluorescence staining showed microglial marker TMEM119 expression in mouse retina. TON (with AMPK) increases microglial activation, and RIC downregulated significantly. Myeloid pAMPKα1 KO group showed heightened microglial activation; notably, RIC demonstrated no significant effects. Florescence color intensity was measured by Image J software (NIH, <a href="https://imagej.net/ij/" target="_blank">https://imagej.net/ij/</a>). White boxes show the TMEM119 expression in inner nuclear layer (INL) and GCL (ganglion cell layer) region of mouse eye. For Sham (mock) and Sham (RIC), both groups are regarded as AMPKα1<sup>F/F</sup>. (<b>C</b>–<b>I</b>) Representative pseudocolor and histograms of flow cytometry show the gating strategy for microglia/macrophages (CD11b+_F4/80+) and CD68+ and CD206+ expressing microglia in blood. Bar graph summarizing the cell counts of microglia (M1/M2) in the blood after 5 days of TON. Red, TMEM119 (activated microglial marker); Blue, DAPI. We used 6 experimental groups, Sham (mock); Sham (RIC); AMPKα1<sup>F/F</sup> (TON); AMPKα1<sup>F/F</sup> (TON+RIC); AMPKα1<sup>F/F</sup> LysMCre (TON); AMPKα1<sup>F/F</sup> LysMCre (TON+RIC). Differences among experimental groups were determined by analysis of variance (one-way ANOVA) followed by Newman–Keuls multiple comparison tests. The results represent the means ± SEM of fold changes (<span class="html-italic">n</span> = 5). * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. ns, non-significant. For Sham (mock) and Sham (RIC), both groups are regarded as AMPKα1<sup>F/F</sup>.</p>
Full article ">Figure 3
<p>Effect of RIC on IL10 and neutrophil expression following TON. (<b>A</b>,<b>B</b>,<b>D</b>,<b>F</b>) Representative pseudocolor and histograms of flow cytometry show the gating strategy for microglia/macrophages (CD11b+_IL10+, F4/80+_IL10+ and CD68+_IL10+) and CD68+_LY6G+-expressing neutrophils in blood. (<b>C</b>,<b>E</b>,<b>G</b>,<b>H</b>) Representative bar graph summarizing the cell counts of IL10+ and Ly6G+ in the blood after 5 days of TON. Six experimental groups included Sham (mock); Sham (RIC); AMPKα1<sup>F/F</sup> (TON); AMPKα1<sup>F/F</sup> (TON+RIC); AMPKα1<sup>F/F</sup> LysMCre (TON); AMPKα1<sup>F/F</sup> LysMCre (TON+RIC). Differences among experimental groups were determined by analysis of variance (one-way ANOVA) followed by Newman–Keuls multiple comparison tests. The results represent the means ± SEM of fold changes (<span class="html-italic">n</span> = 5). ** <span class="html-italic">p</span> &lt; 0.01.*** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. ns, non-significant. For Sham (mock) and Sham (RIC), both groups are regarded as AMPKα1<sup>F/F</sup>.</p>
Full article ">Figure 3 Cont.
<p>Effect of RIC on IL10 and neutrophil expression following TON. (<b>A</b>,<b>B</b>,<b>D</b>,<b>F</b>) Representative pseudocolor and histograms of flow cytometry show the gating strategy for microglia/macrophages (CD11b+_IL10+, F4/80+_IL10+ and CD68+_IL10+) and CD68+_LY6G+-expressing neutrophils in blood. (<b>C</b>,<b>E</b>,<b>G</b>,<b>H</b>) Representative bar graph summarizing the cell counts of IL10+ and Ly6G+ in the blood after 5 days of TON. Six experimental groups included Sham (mock); Sham (RIC); AMPKα1<sup>F/F</sup> (TON); AMPKα1<sup>F/F</sup> (TON+RIC); AMPKα1<sup>F/F</sup> LysMCre (TON); AMPKα1<sup>F/F</sup> LysMCre (TON+RIC). Differences among experimental groups were determined by analysis of variance (one-way ANOVA) followed by Newman–Keuls multiple comparison tests. The results represent the means ± SEM of fold changes (<span class="html-italic">n</span> = 5). ** <span class="html-italic">p</span> &lt; 0.01.*** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. ns, non-significant. For Sham (mock) and Sham (RIC), both groups are regarded as AMPKα1<sup>F/F</sup>.</p>
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<p>The effect of RIC on TON induced pro-inflammatory signaling. (<b>A</b>,<b>B</b>) ELISA results in blood plasma showing TNF and IL10 expression. Fluorescence color intensity was measured by Image J software. We used 6 experimental group, Sham (mock); Sham (RIC); AMPKα1<sup>F/F</sup> (TON); AMPKα1<sup>F/F</sup> (TON+RIC); AMPKα1<sup>F/F</sup> LysM<sup>Cre</sup> (TON); AMPKα1<sup>F/F</sup> LysM<sup>Cre</sup> (TON+RIC). Differences among experimental groups were determined by analysis of variance (one-way ANOVA) followed by Newman–Keuls multiple comparison tests. The results represent the means ± SEM of fold changes (<span class="html-italic">n</span> = 5). * <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, **** <span class="html-italic">p</span> &lt; 0.0001. ns, non-significant. For Sham (mock) and Sham (RIC), both groups are regarded as AMPKα1<sup>F/F</sup>.</p>
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<p>Effect of RIC on TON induced pro-inflammatory signaling. (<b>A</b>,<b>B</b>) The effect of RIC on ICAM-1 expression assessed by immunofluorescence and (<b>C</b>,<b>D</b>) ICAM1 Protein expression was checked by Western blot. Fluorescence color intensity as well as western blot band intensity was measured by Image J software (NIH, <a href="https://imagej.net/ij/" target="_blank">https://imagej.net/ij/</a>). We used 6 experimental groups, Sham (mock); Sham (RIC); AMPKα1<sup>F/F</sup> (TON); AMPKα1<sup>F/F</sup> (TON+RIC); AMPKα1<sup>F/F</sup> LysM<sup>Cre</sup> (TON); AMPKα1<sup>F/F</sup> LysM<sup>Cre</sup> (TON+RIC). Differences among experimental groups were determined by analysis of variance (one-way ANOVA) followed by Newman–Keuls multiple comparison tests. The results represent the means ± SEM of fold changes (<span class="html-italic">n</span> = 5). * <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, **** <span class="html-italic">p</span> &lt; 0.0001. ns, non-significant. Scale bar 50 μm. For Sham (mock) and Sham (RIC), both groups are regarded as AMPKα1<sup>F/F</sup>.</p>
Full article ">Figure 5 Cont.
<p>Effect of RIC on TON induced pro-inflammatory signaling. (<b>A</b>,<b>B</b>) The effect of RIC on ICAM-1 expression assessed by immunofluorescence and (<b>C</b>,<b>D</b>) ICAM1 Protein expression was checked by Western blot. Fluorescence color intensity as well as western blot band intensity was measured by Image J software (NIH, <a href="https://imagej.net/ij/" target="_blank">https://imagej.net/ij/</a>). We used 6 experimental groups, Sham (mock); Sham (RIC); AMPKα1<sup>F/F</sup> (TON); AMPKα1<sup>F/F</sup> (TON+RIC); AMPKα1<sup>F/F</sup> LysM<sup>Cre</sup> (TON); AMPKα1<sup>F/F</sup> LysM<sup>Cre</sup> (TON+RIC). Differences among experimental groups were determined by analysis of variance (one-way ANOVA) followed by Newman–Keuls multiple comparison tests. The results represent the means ± SEM of fold changes (<span class="html-italic">n</span> = 5). * <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, **** <span class="html-italic">p</span> &lt; 0.0001. ns, non-significant. Scale bar 50 μm. For Sham (mock) and Sham (RIC), both groups are regarded as AMPKα1<sup>F/F</sup>.</p>
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<p>(<b>A</b>–<b>D</b>) Effect of RIC therapy on retinal oxygenation in TON. Oxygen levels were analyzed with UniSense sensor system (Sweden). We used 6 experimental groups, Sham (mock); Sham (RIC); AMPKα1F/F (TON); AMPKα1F/F (TON+RIC); AMPKα1F/F LysM<sup>Cre</sup> (TON); AMPKα1F/F LysM<sup>Cre</sup> (TON+RIC). Differences among experimental groups were determined by analysis of variance (one-way ANOVA) followed by Newman–Keuls multiple comparison tests. The results represent the means ± SEM of fold changes (<span class="html-italic">n</span> = 5). * <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, **** <span class="html-italic">p</span> &lt; 0.0001. ns, non-significant. For Sham (mock) and Sham (RIC), both groups are regarded as AMPKα1<sup>F/F</sup>.</p>
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<p>(<b>A</b>–<b>D</b>) Effect of RIC therapy on TON retina. Western blot analysis demonstrated significant changes in protein expression level of Brn3a and GAP43 between TON+RIC and TON group. Densitometry analysis was carried out by Image J software (NIH, <a href="https://imagej.net/ij/" target="_blank">https://imagej.net/ij/</a>). We used 4 experimental groups, AMPKα1F/F (TON); AMPKα1F/F (TON+RIC); AMPKα1F/F LysM<sup>Cre</sup> (TON); AMPKα1F/F LysM<sup>Cre</sup> (TON+RIC). Differences among experimental groups were determined by analysis of variance (one-way ANOVA) followed by Newman–Keuls multiple comparison tests. The results represent the means ± SEM of fold changes (<span class="html-italic">n</span> = 5). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. ns, non-significant.</p>
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<p>Representative ultrastructural features of axonal injury in traumatic optic neuropathy. Electron micrographs are taken across the longitudinal plane through the injury front and show a range of axoplasmic, axolemmal and myelin sheath abnormalities. RIC therapy attenuated this degenerating process in TON. We used 6 experimental groups, Sham (mock); Sham (RIC); AMPKα1F/F (TON); AMPKα1F/F (TON+RIC); AMPKα1F/F LysM<sup>Cre</sup> (TON); AMPKα1F/F LysM<sup>Cre</sup> (TON+RIC). Scale bar 4 μm. For Sham (mock) and Sham (RIC), both groups are regarded as AMPKα1<sup>F/F</sup>.</p>
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<p>Schematic representation demonstrating increased M1-type macrophages causing inflammation and demyelination of optic nerve (ON) in TON. Our hypothesis demonstrates that RIC therapy activates AMPKα1 to modulate macrophage polarization toward M2-type anti-inflammatory macrophages that protect demyelination of downregulated ON.</p>
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33 pages, 1784 KiB  
Article
Dynamic Evaluation of Adaptive Product Design Concepts Using m-Polar Linguistic Z-Numbers
by Zhifeng Zhao and Qinghua Liu
Symmetry 2024, 16(12), 1686; https://doi.org/10.3390/sym16121686 - 19 Dec 2024
Viewed by 284
Abstract
Adaptive design focuses on creating flexible products that meet evolving demands and enhance sustainability. However, evaluating adaptive design concepts poses significant challenges due to the dynamic nature of product features over time and the inherent uncertainty in decision-makers’ (DMs’) evaluations. Most traditional frameworks [...] Read more.
Adaptive design focuses on creating flexible products that meet evolving demands and enhance sustainability. However, evaluating adaptive design concepts poses significant challenges due to the dynamic nature of product features over time and the inherent uncertainty in decision-makers’ (DMs’) evaluations. Most traditional frameworks rely on static models that fail to capture the temporal evolution of attributes and often overlook decision-makers’ (DMs’) confidence levels, resulting in incomplete or unreliable evaluations. To bridge these gaps, we propose the m-polar linguistic Z-number (mLZN) to address these issues. This framework uses the dynamic representation capabilities of m-polar fuzzy sets (mFSs) and the symmetrical structure of linguistic Z-numbers (LZNs), which effectively integrate linguistic evaluations with corresponding confidence levels, providing a balanced and robust approach to handling uncertainty. This approach models design characteristics across multiple periods while accounting for DMs’ confidence levels. Based on this framework, we develop mLZN weighted and geometric aggregation operators, computation rules, and ranking methods to support dynamic multi-attribute group decision-making (MAGDM). The proposed framework’s effectiveness is demonstrated through a case study on adaptive furniture design for children, which showcases its ability to dynamically evaluate key attributes, including safety, ease of use, fun, and comfort. Furthermore, we validate its robustness and feasibility through comprehensive sensitivity and comparative analyses. Full article
(This article belongs to the Section Mathematics)
26 pages, 2868 KiB  
Article
Group Polarization and Echo Chambers in #GaijinTwitter Community
by Seval Yurtcicek Ozaydin, Vasily Lubashevskiy and Fatih Ozaydin
Soc. Sci. 2024, 13(12), 692; https://doi.org/10.3390/socsci13120692 - 19 Dec 2024
Viewed by 295
Abstract
This study explores the phenomena of group polarization and echo chambers within the context of online discussions among immigrants in Japan, also known as gaijins, specifically within the #GaijinTwitter community. By analyzing the key topics discussed by divergent groups of Twitter users [...] Read more.
This study explores the phenomena of group polarization and echo chambers within the context of online discussions among immigrants in Japan, also known as gaijins, specifically within the #GaijinTwitter community. By analyzing the key topics discussed by divergent groups of Twitter users and examining their interactions through qualitative and quantitative approaches, we provide evidence of group polarization. Additionally, we investigate how blocking and sharing screenshots of tweets instead of reacting to them in the standard ways contribute to the formation and perpetuation of online echo chambers. Full article
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<p>Screenshot of tweet <span class="html-italic">TW19</span> of the user <span class="html-italic">B1</span> on the <span class="html-italic">T1</span> topic.</p>
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<p>Screenshot of the tweet of <span class="html-italic">Unseen Japan</span> media account on the <span class="html-italic">T2</span> topic. The text in Japanese reads Miss Japan Association: “The Gramd Prix will be vacant”. Carolina’s request to resign was “accepted”—a first in history for both the title and the vacancy.</p>
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<p>Screenshot of the tweet <span class="html-italic">TW34</span> on the <span class="html-italic">T4</span> topic, which can be translated into English as follows: <span class="html-italic">Yesterday, when a grandpa asked me, “Where are you from?”, I answered, “Fukuoka.” And the grandpa said, “No, what country were you originally from?” Well, of course no harm was meant, but it made me think that in Japan that is becoming increasingly global, we need to be careful about making these kinds of statements</span>.</p>
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<p>Users <span class="html-italic">A1</span>, <span class="html-italic">A2</span>, <span class="html-italic">A3</span>, <span class="html-italic">A4</span>, and <span class="html-italic">A6</span> in <span class="html-italic">Group A</span> were shown in a meme in a tweet of <span class="html-italic">A4</span> to be tweeting against the users in <span class="html-italic">Group B</span>.</p>
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<p>Interaction map from <span class="html-italic">Group A</span> and <span class="html-italic">Group B</span> to the set of selected tweets from <span class="html-italic">Group A</span> or <span class="html-italic">Group B</span>. Some lines are displayed as solid, dashed, or dotted-dashed only for the sake of clarity and do not have an additional meaning.</p>
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<p>Percentages of interactions from <span class="html-italic">Group A</span> and <span class="html-italic">Group B</span> towards tweets from <span class="html-italic">Group A</span> or <span class="html-italic">Group B</span>.</p>
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<p>The screenshot of a tweet of user <span class="html-italic">B5</span> showing the mass blocking of other Twitter users.</p>
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<p>Screenshot of a tweet of user <span class="html-italic">A1</span> claiming to be blocked by 90% of the users in the <span class="html-italic">#GaijinTwitter</span> community.</p>
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<p>Screenshot of a tweet of user <span class="html-italic">A1</span> showing that they are blocked by the <span class="html-italic">Unseen Japan</span> media account.</p>
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<p>Screenshot of the tweet of user <span class="html-italic">A2</span> on being blocked by user <span class="html-italic">B2</span>. In the picture, “23 (Chinese character)” means 23 hours in English.</p>
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<p>Screenshot of the tweet of user <span class="html-italic">A1</span> showing that they are blocked by user <span class="html-italic">B3</span>.</p>
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<p>An example tweet showing that instead of reacting to the tweet of user <span class="html-italic">B3</span> in a standard way, user <span class="html-italic">A3</span> chose the capture the screenshot of that tweet and posted it in a new tweet.</p>
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<p>Entity relationship diagram of the database.</p>
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24 pages, 8130 KiB  
Article
Structural Characterization and In Vitro and In Silico Studies on the Anti-α-Glucosidase Activity of Anacardic Acids from Anacardium occidentale
by Ana Priscila Monteiro da Silva, Gisele Silvestre da Silva, Francisco Oiram Filho, Maria Francilene Souza Silva, Guilherme Julião Zocolo and Edy Sousa de Brito
Foods 2024, 13(24), 4107; https://doi.org/10.3390/foods13244107 - 19 Dec 2024
Viewed by 596
Abstract
The growing focus on sustainable use of natural resources has brought attention to cashew nut shell liquid (CNSL), a by-product rich in anacardic acids (AAs) with potential applications in diabetes treatment. In this study, three different AAs from CNSL, monoene (15:1, AAn1), diene [...] Read more.
The growing focus on sustainable use of natural resources has brought attention to cashew nut shell liquid (CNSL), a by-product rich in anacardic acids (AAs) with potential applications in diabetes treatment. In this study, three different AAs from CNSL, monoene (15:1, AAn1), diene (15:2, AAn2), and triene (15:3, AAn3), and a mixture of the three (mix) were evaluated as α-glucosidase inhibitors. The samples were characterized by combining 1D and 2D NMR spectroscopy, along with ESI-MS. In vitro assays revealed that AAn1 had the strongest inhibitory effect (IC50 = 1.78 ± 0.08 μg mL−1), followed by AAn2 (1.99 ± 0.76 μg mL−1), AAn3 (3.31 ± 0.03 μg mL−1), and the mixture (3.72 ± 2.11 μg mL−1). All AAs significantly outperformed acarbose (IC50 = 169.3 μg mL−1). In silico docking suggested that polar groups on the aromatic ring are key for enzyme–ligand binding. The double bond at C15, while not essential, enhanced the inhibitory effects. Toxicity predictions classified AAs as category IV, and pharmacokinetic analysis suggested moderately favorable drug-like properties. These findings highlight AAs as a promising option in the search for new hypoglycemic compounds. Full article
(This article belongs to the Section Nutraceuticals, Functional Foods, and Novel Foods)
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<p>Analysis of anacardic acids via NMR: (<b>a</b>) 2D <sup>1</sup>H-<sup>1</sup>H COSY contour map of AAn2, (<b>b</b>) 2D <sup>1</sup>H-<sup>13</sup>C HSQC contour map of AAn2, and (<b>c</b>) 2D <sup>1</sup>H-<sup>13</sup>C HSQC contour map of the anacardic acid mixture (mix: AAn1 + AAn2 + AAn3), presents the antiphase relationship between the =C<b>H</b><sub>2</sub> and –C<b>H</b><sub>2</sub>– groups (blue) and the -C<b>H</b>-/-C<b>H</b><sub>3</sub> groups (red).</p>
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<p>Inhibition <span class="html-italic">of α</span>-GLU by anacardic acids (0.75 to 24 µg mL<sup>−1</sup>) compared with acarbose (62.5 to 2000 µg mL<sup>−1</sup>). The values are presented as the means ± SDs.</p>
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<p>3D and 2D binding modes of anacardic acids in the active site of <span class="html-italic">α</span>-glucosidase: AAn1-8<span class="html-italic">E</span> (<b>a</b>), AAn1-8<span class="html-italic">Z</span> (<b>b</b>), and AAn0 (<b>c</b>). Atom colors: oxygen (O: red), nitrogen (N: blue), hydrogen (H: white), and carbon from AAn1 (gray), AAn2 (orange), and AAn0 (green). In the 2D representation, only the most favorable hydrogen bonds (distances up to 3.7 Å) are shown, whereas the 3D representation includes all hydrogen bonds (distances up to 4.7 Å).</p>
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<p>2D and 3D representations of the binding modes of AAn2-8<span class="html-italic">E</span>,11<span class="html-italic">E</span> (n = 2, diene, −10.2 kcal mol<sup>−1</sup>) and AAn2-8<span class="html-italic">Z</span>,11<span class="html-italic">Z</span> (n = 2, diene, 8.9 kcal mol<sup>−1</sup>) (<b>a</b>,<b>c</b>) at the active site of <span class="html-italic">α</span>-glucosidase (<b>b</b>,<b>d</b>).</p>
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<p>3D representation of the superimposed AAn2-8<span class="html-italic">Z</span>, 11<span class="html-italic">E</span> (n = 2, dieno, −9.2 kcal mol<sup>−1</sup>) and AAn3-8<span class="html-italic">Z</span>, 11<span class="html-italic">E</span> (n = 3, triene, −9.2 kcal mol<sup>−1</sup>) ligands docked at the catalytic site of <span class="html-italic">α</span>-glucosidase.</p>
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15 pages, 2325 KiB  
Review
Wnt Signaling Inhibitors as Therapeutic Approach in Ischemic Heart Disease
by Barbora Boťanská Svetláková, Viktória Pecníková Líšková and Miroslav Barančík
Molecules 2024, 29(24), 5958; https://doi.org/10.3390/molecules29245958 - 17 Dec 2024
Viewed by 274
Abstract
Wnt (wingless-type MMTV integration site family) signaling is an evolutionary conserved system highly active during embryogenesis, but in adult hearts has low activities under normal conditions. It is essential for a variety of physiological processes including stem cell regeneration, proliferation, migration, cell polarity, [...] Read more.
Wnt (wingless-type MMTV integration site family) signaling is an evolutionary conserved system highly active during embryogenesis, but in adult hearts has low activities under normal conditions. It is essential for a variety of physiological processes including stem cell regeneration, proliferation, migration, cell polarity, and morphogenesis, thereby ensuring homeostasis and regeneration of cardiac tissue. Its dysregulation and excessive activation during pathological conditions leads to morphological and functional changes in the heart resulting in impaired myocardial regeneration under pathological conditions such as myocardial infarction, heart failure, and coronary artery disease. Several groups of Wnt inhibitors have demonstrated the ability to modulate the Wnt pathway and thereby significantly reduce fibrosis and improve cardiac function after myocardial ischemia. Their inhibitory effect can be realized at multiple levels, which include the inhibition of Wnt ligands, the inhibition of Frizzled receptors, the stabilization of the β-catenin destruction complex, and the disruption of nuclear β-catenin interactions. In this review, we overview the function of Wnt signaling in responses of cardiac cells to pathological conditions, especially ischemic heart disease, with an emphasis on the use of inhibitors of this signaling as a therapeutic approach. Finally, we summarize the current knowledge about the potential of the targeting of Wnt signaling in therapeutic applications. Full article
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<p>Wnt signaling pathways and the targets of their inhibitors. Abbreviations: Wnt—Wnt protein; Sfrp—secreted Frizzled-related protein; Dkk—Dickkopf family of secreted proteins; Wif—Wnt inhibitory factor; ROR2—receptor tyrosine kinase-like orphan receptor 2; PORCN—porcupine; Dvl—Disheveled; Rac—Rac protein kinase; JNK—c-Jun N-terminal kinase; Jun—Jun Proto-Oncogene; AP-1 —Transcription Factor Subunit; Daam1—Disheveled-associated activator of morphogenesis 1; RhoA—Ras homolog family member A; ROCK2—Rho-associated coiled-coil–containing protein kinase 2; PLC—phospholipase C; PIP2—phosphatidylinositol 4,5-bisphosphate; DAG—1,2-diacylglycerol; IP3—inositol 1,4,5-triphosphate; PKC—protein kinase-C; CAMKII—calmodulin-dependent protein kinase II; NFAT—nuclear factor of activated T cells; TNKS—tankyrase; Ck1—casein kinase 1; APC—adenomatous polyposis coli protein; GSK3 β—glycogen synthase kinase 3β; CBP—CREB binding protein; TCF/LEF—TCF/LEF transcription factors. Yellow color represents components of Planar cell polarity pathway, red color represents components of Ca<sup>2+</sup>-dependent Wnt signaling, blue color represents components of canonical Wnt signaling pathway, and gray arrows represent inhibitors of Wnt signaling and their site of action. More details are provided in the text.</p>
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<p>The role of the Wnt signaling pathway in the development of cardiac fibrosis and potential targets of their inhibitors. Abbreviations: Wnt—Wnt protein; Ang-II—angiotensin II; TGF-ß—transforming growth factor ß; Smad—Smad proteins; JNK—c-Jun N-terminal kinase; CBP—CREB-binding protein; MMPs—matrix metalloproteinases; TIMPs— tissue inhibitors of matrix metalloproteinases; Cx43—connexin 43. Red Xs represent sites for potential inhibition of Wnt signaling. More details are provided in the text.</p>
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17 pages, 6290 KiB  
Article
Accuracy of the Apple Watch Series 9 for Measures of Energy Expenditure and Heart Rate at Rest and During Exercise: Impact of Skin Pigmentation
by Sydney E. Chase, Rebecca G. Liddell, Chloe L. McGonagle and Stephen J. Ives
J. Funct. Morphol. Kinesiol. 2024, 9(4), 275; https://doi.org/10.3390/jfmk9040275 - 17 Dec 2024
Viewed by 290
Abstract
Background: The Apple Watch provides promising health data that could aid in increasing exercise adherence; regular exercise can help individuals manage and prevent diseases such as obesity and cardiovascular disease. However, the impact of skin pigmentation on the accuracy of the Apple Watch [...] Read more.
Background: The Apple Watch provides promising health data that could aid in increasing exercise adherence; regular exercise can help individuals manage and prevent diseases such as obesity and cardiovascular disease. However, the impact of skin pigmentation on the accuracy of the Apple Watch Series 9 for measures of energy expenditure (EE) and heart rate (HR) is unknown. Purpose: The purpose of this study was to determine the accuracy of the Apple Watch Series 9 on various skin pigmentations for measures of EE and HR. Methods: Thirty young, healthy individuals were assigned to one of three groups based on their scores on the Fitzpatrick skin survey. Participants completed a 10 min treadmill protocol with varying speeds and inclines while wearing an Apple Watch Series 9, a two-way non-rebreathing mouthpiece connected to a Parvo Medics metabolic cart, and a Polar H7 chest strap to measure EE and HR. Results: Overall, EE was found to be inconsistent for all skin pigmentation groups. However, for HR, the Apple Watch Series 9 was more variable (i.e., less accurate) for darker skin pigmentations compared to lighter skin pigmentations. Conclusions: The Apple Watch Series 9 was found to vary in both EE and HR measures from criterion across intensity and skin pigmentation, with greater discrepancies for individuals in Group 3 for measures of HR. Further investigation might aim to study the impact of skin pigmentations and wrist subcutaneous fat on the accuracy of the latest Apple Watch Series 9 for measures of EE and HR. Full article
(This article belongs to the Special Issue Understanding Sports-Related Health Issues, 2nd Edition)
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<p>Overview of the experimental design.</p>
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<p>Energy expenditure response in calories per min (kcal/min) of the college-aged participants (N = 30) from the Parvo Medics (panel <b>A</b>) or the Apple Watch Series 9 (panel <b>B</b>) during each stage of the 10-min treadmill protocol. Data presented as means ± standard deviation. * Significant difference from 0 min (<span class="html-italic">p</span> &lt; 0.05). # Significant difference from 2 min (<span class="html-italic">p</span> &lt; 0.05). <span>$</span> Significant difference from 4 min (<span class="html-italic">p</span> &lt; 0.05). % Significant difference from 6 min (<span class="html-italic">p</span> &lt; 0.05). &amp; Significant difference from 8 min (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 2 Cont.
<p>Energy expenditure response in calories per min (kcal/min) of the college-aged participants (N = 30) from the Parvo Medics (panel <b>A</b>) or the Apple Watch Series 9 (panel <b>B</b>) during each stage of the 10-min treadmill protocol. Data presented as means ± standard deviation. * Significant difference from 0 min (<span class="html-italic">p</span> &lt; 0.05). # Significant difference from 2 min (<span class="html-italic">p</span> &lt; 0.05). <span>$</span> Significant difference from 4 min (<span class="html-italic">p</span> &lt; 0.05). % Significant difference from 6 min (<span class="html-italic">p</span> &lt; 0.05). &amp; Significant difference from 8 min (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Heart rate response in beats per min (bpm) of the college-aged participants (N = 30) from the Polar H7 (panel <b>A</b>) or the Apple Watch Series 9 (panel <b>B</b>) during each stage of the 10-min treadmill protocol. Data presented as means ± standard deviation. * Significant difference from 0 min (<span class="html-italic">p</span> &lt; 0.05). # Significant difference from 2 min (<span class="html-italic">p</span> &lt; 0.05). <span>$</span> Significant difference from 4 min (<span class="html-italic">p</span> &lt; 0.05). % Significant difference from 6 min (<span class="html-italic">p</span> &lt; 0.05). &amp; Significant difference from 8 min (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3 Cont.
<p>Heart rate response in beats per min (bpm) of the college-aged participants (N = 30) from the Polar H7 (panel <b>A</b>) or the Apple Watch Series 9 (panel <b>B</b>) during each stage of the 10-min treadmill protocol. Data presented as means ± standard deviation. * Significant difference from 0 min (<span class="html-italic">p</span> &lt; 0.05). # Significant difference from 2 min (<span class="html-italic">p</span> &lt; 0.05). <span>$</span> Significant difference from 4 min (<span class="html-italic">p</span> &lt; 0.05). % Significant difference from 6 min (<span class="html-italic">p</span> &lt; 0.05). &amp; Significant difference from 8 min (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The agreement between Apple Watch Series 9 and Parvo Medics EE for all timepoints for Group 1 (panel <b>A</b>; M = −0.884), Group 2 (panel <b>B</b>; M = −1.020), and Group 3 (panel <b>C</b>; M = −8.529).</p>
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<p>The agreement between Apple Watch Series 9 and Polar H7 HR for all time points for Group 1 (panel <b>A</b>; M = −0.875), Group 2 (panel <b>B</b>; M = −3.169), and Group 3 (panel <b>C</b>; M = −8.354).</p>
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<p>The relationship between the Apple Watch Series 9 and Parvo Medics on measures of energy expenditure (EE) for rest (panel <b>A</b>; r = 0.495; <span class="html-italic">p</span> &lt; 0.001), 0 min (panel <b>B</b>; r = 0.095; <span class="html-italic">p</span> = 0.430), 2 min (panel <b>C</b>; r = 0.261; <span class="html-italic">p</span> = 0.220), 4 min (panel <b>D</b>; r = 0.535; <span class="html-italic">p</span> = 0.017), 6 min (panel <b>E</b>; r = 0.585; <span class="html-italic">p</span> = 0.002), 8 min (panel <b>F</b>; r = 0.554; <span class="html-italic">p</span> = 0.048), 10 min (panel <b>G</b>; r = 0.520; <span class="html-italic">p</span> = 0.003), and post-rest (panel <b>H</b>; r = 0.641; <span class="html-italic">p</span> &lt; 0.001) in the participants in the study (N = 30). Data presented as values. * Statistical significance <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The relationship between the Apple Watch Series 9 and Polar H7 on measures of heart rate (HR) at rest (panel <b>A</b>; r = 0.365; <span class="html-italic">p</span> &lt; 0.001), 0 min (panel <b>B</b>; r = 0.635; <span class="html-italic">p</span> &lt; 0.001), 2 min (panel <b>C</b>; r = 0.431; <span class="html-italic">p</span> &lt; 0.001), 4 min (panel <b>D</b>; r = 0.819; <span class="html-italic">p</span> &lt; 0.001), 6 min (panel <b>E</b>; r = 0.732; <span class="html-italic">p</span> &lt; 0.001), 8 min (panel <b>F</b>; r = 0.690; <span class="html-italic">p</span> &lt; 0.001), 10 min (panel <b>G</b>; r = 0.732; <span class="html-italic">p</span> &lt; 0.001), and post-rest (panel <b>H</b>; r = 0.438; <span class="html-italic">p</span> &lt; 0.001) in the participants in the study (N = 30). Data presented as values. * Statistical significance <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The relationship between the Apple Watch Series 9 and Polar H7 on measures of heart rate (HR) at rest (panel <b>A</b>; r = 0.899; <span class="html-italic">p</span> = 0.006), 0 min (panel <b>B</b>; r = 0.902; <span class="html-italic">p</span> = 0.005), 2 min (panel <b>C</b>; r = 0.965; <span class="html-italic">p</span> &lt; 0.001), 4 min (panel <b>D</b>; r = 0.985; <span class="html-italic">p</span> &lt; 0.001), 6 min (panel <b>E</b>; r = 0.955; <span class="html-italic">p</span> &lt; 0.001), 8 min (panel <b>F</b>; r = 0.964; <span class="html-italic">p</span> &lt; 0.001), 10 min (panel <b>G</b>; r = 0.993; <span class="html-italic">p</span> &lt; 0.001), and post-rest (panel <b>H</b>; r = 0.986; <span class="html-italic">p</span> &lt; 0.001) in the Group 1 participants in the study (n = 7). Data presented as values. * Statistical significance <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The relationship between the Apple Watch Series 9 and Polar H7 on measures of heart rate (HR) for rest (panel <b>A</b>; r = 0.187; <span class="html-italic">p</span> = 0.093), 0 min (panel <b>B</b>; r = 0.531; <span class="html-italic">p</span> = 0.006), 2 min (panel <b>C</b>; r = 0.297; <span class="html-italic">p</span> = 0.048), 4 min (panel <b>D</b>; r = 0.881; <span class="html-italic">p</span> &lt; 0.001), 6 min (panel <b>E</b>; r = 0.810; <span class="html-italic">p</span> &lt; 0.001), 8 min (panel <b>F</b>; r = 0.959; <span class="html-italic">p</span> &lt; 0.001), 10 min (panel <b>G</b>; r = 0.995; <span class="html-italic">p</span> &lt; 0.001), and post-rest (panel <b>H</b>; r = 0.554; <span class="html-italic">p</span> = 0.002) in the Group 2 participants in the study (n = 17). Data presented as values. * Statistical significance <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The relationship between the Apple Watch Series 9 and Polar H7 on measures of heart rate (HR) for rest (panel <b>A</b>; r = 0.936; <span class="html-italic">p</span> = 0.006), 0 min (panel <b>B</b>; r = 0.964; <span class="html-italic">p</span> = 0.002), 2 min (panel <b>C</b>; r = 0.841; <span class="html-italic">p</span> = 0.050), 4 min (panel <b>D</b>; r = 0.786; <span class="html-italic">p</span> = 0.064), 6 min (panel <b>E</b>; r = −0.159; <span class="html-italic">p</span> = 0.763), 8 min (panel <b>F</b>; r = −0.336; <span class="html-italic">p</span> = 0.658), 10 min (panel <b>G</b>; r = −0.079; <span class="html-italic">p</span> = 1.000), and post-rest (panel <b>H</b>; r = −0.306; <span class="html-italic">p</span> = 0.827) in the Group 3 participants in the study (n = 6). Data presented as values. * Statistical significance <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The relationship between the Apple Watch Series 9 and Polar H7 on measures of heart rate (HR) for rest (panel <b>A</b>; r = 0.936; <span class="html-italic">p</span> = 0.006), 0 min (panel <b>B</b>; r = 0.964; <span class="html-italic">p</span> = 0.002), 2 min (panel <b>C</b>; r = 0.841; <span class="html-italic">p</span> = 0.050), 4 min (panel <b>D</b>; r = 0.786; <span class="html-italic">p</span> = 0.064), 6 min (panel <b>E</b>; r = −0.159; <span class="html-italic">p</span> = 0.763), 8 min (panel <b>F</b>; r = −0.336; <span class="html-italic">p</span> = 0.658), 10 min (panel <b>G</b>; r = −0.079; <span class="html-italic">p</span> = 1.000), and post-rest (panel <b>H</b>; r = −0.306; <span class="html-italic">p</span> = 0.827) in the Group 3 participants in the study (n = 6). Data presented as values. * Statistical significance <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The relationship between the Apple Watch Series 9 and Parvo Medics on measures of energy expenditure (EE) for all time points for Group 1 (<b>A</b>; r = 0.674; <span class="html-italic">p</span> = &lt; 0.001), Group 2 (<b>B</b>; r = 0.706; <span class="html-italic">p</span> = &lt; 0.001), and Group 3 (<b>C</b>; r = 0.694; <span class="html-italic">p</span> &lt; 0.001). Data presented as values. * Statistical significance <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The relationship between the Apple Watch Series 9 and Polar H7 on measures of heart rate (HR) for all timepoints for Group 1 (<b>A</b>; r = 0.982; <span class="html-italic">p</span> &lt; 0.001), Group 2 (<b>B</b>; r = 0.802; <span class="html-italic">p</span> &lt; 0.001), and Group 3 (<b>C</b>; r = 0.675; <span class="html-italic">p</span> &lt; 0.001). Data presented as values. * Statistical significance <span class="html-italic">p</span> &lt; 0.05.</p>
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15 pages, 5422 KiB  
Article
Four-Channel Polarimetric-Spectral Intensity Modulation Imager
by Jian Bo, Xueping Ju and Changxiang Yan
Appl. Sci. 2024, 14(24), 11759; https://doi.org/10.3390/app142411759 - 17 Dec 2024
Viewed by 262
Abstract
To solve the problems of channel crosstalk and edge jitter caused by the Fourier transform demodulation of polarimetric-spectral intensity modulation in polarization spectral data, this paper proposes a Four-Channel Polarimetric Spectrometer (FCPS) with two groups of polarimetric-spectral intensity modulation (PSIM). FCPS can demodulate [...] Read more.
To solve the problems of channel crosstalk and edge jitter caused by the Fourier transform demodulation of polarimetric-spectral intensity modulation in polarization spectral data, this paper proposes a Four-Channel Polarimetric Spectrometer (FCPS) with two groups of polarimetric-spectral intensity modulation (PSIM). FCPS can demodulate the full Stokes spectra information by system matrix calibration in the spatial domain. The traditional channel filtering method and the FCPS data demodulation method are simulated, and their results are compared. The simulated results show that the FCPS does not have the problem of the edge jitter, and the demodulation accuracy is higher. It is confirmed that the angle error of phase retarders has little influence on the data reconstruction, and the maximum allowable angle error of the calibration light linear polarizer cannot exceed 0.4°. Full article
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Figure 1

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<p>The optical layout of FCPS.</p>
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<p>The rank of the system matrix.</p>
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<p>The rank of the system matrix with first phase retarder angle error.</p>
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<p>The rank of the system matrix with second phase retarder angle error.</p>
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<p>The rank of the system matrix with both phase retarder angle error. (<b>a</b>) First phase retarder angle error is −3° and second phase retarder angle error ranges from −3° to 3°. (<b>b</b>) First phase retarder angle error is −2° and second phase retarder angle error ranges from −3° to 3°. (<b>c</b>) First phase retarder angle error is −1° and second phase retarder angle error ranges from −3° to 3°. (<b>d</b>) First phase retarder angle error is 1° and second phase retarder angle error ranges from −3° to 3°. (<b>e</b>) First phase retarder angle error is 2° and second phase retarder angle error ranges from −3° to 3°. (<b>f</b>) First phase retarder angle error is 3° and second phase retarder angle error ranges from −3° to 3°.</p>
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<p>The calibrated system matrix. (<b>a</b>) The first channel’s system matrix calibration parameters. (<b>b</b>) The second channel’s system matrix calibration parameters. (<b>c</b>) The third channel’s system matrix calibration parameters. (<b>d</b>) The fourth channel’s system matrix calibration parameters.</p>
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<p>The input Stokes spectra.</p>
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<p>The simulated modulated spectra. (<b>a</b>) R3, R4, and P1 modulation spectra; (<b>b</b>–<b>d</b>) the modulation spectra through R1, R2, P2, P3, and P4, respectively.</p>
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<p>Reconstructed full Stokes spectra. (<b>a</b>) The red line shows the reconstructed <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and the green line shows the input simulation <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>0</mn> </msub> </mrow> </semantics></math>; (<b>b</b>) the red line shows the reconstructed <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and the green line shows the input simulation <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </semantics></math>; (<b>c</b>) the red line shows the reconstructed <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>2</mn> </msub> </mrow> </semantics></math> and the green line shows the input simulation <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>2</mn> </msub> </mrow> </semantics></math>; (<b>d</b>) the red line shows the reconstructed <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>3</mn> </msub> </mrow> </semantics></math> and the green line shows the input simulation <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>3</mn> </msub> </mrow> </semantics></math>.</p>
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<p>The simulated modulated spectra.</p>
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<p>The reconstructed full Stokes spectra obtained using the traditional channel filter Fourier transform. (<b>a</b>) The red line shows the reconstructed <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and the blue line shows the input simulation <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>0</mn> </msub> </mrow> </semantics></math>; (<b>b</b>) the red line shows the reconstructed <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and the blue line shows the input simulation <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </semantics></math>; (<b>c</b>) the red line shows the reconstructed <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>2</mn> </msub> </mrow> </semantics></math> and the blue line shows the input simulation <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>2</mn> </msub> </mrow> </semantics></math>; (<b>d</b>) the red line shows the reconstructed <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>3</mn> </msub> </mrow> </semantics></math> and the blue line shows the input simulation <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mn>3</mn> </msub> </mrow> </semantics></math>.</p>
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<p>Phase retarder angle error.</p>
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<p>The rank of the system matrix.</p>
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<p>The demodulated DOLP.</p>
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<p>The RMS of DOLP.</p>
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<p>The linear polarization angle error. (<b>a</b>) Linear polarization angle error is 0.2°. (<b>b</b>) Linear polarization angle error is 0.3°. (<b>c</b>) Linear polarization angle error is 0.4°. (<b>d</b>) Linear polarization angle error is 0.5°.</p>
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17 pages, 2235 KiB  
Article
A Study of the Structure of an Anion Exchange Resin with a Quaternary Ammonium Functional Group by Using Infrared Spectroscopy and DFT Calculations
by Katarzyna Chruszcz-Lipska and Elżbieta Szostak
Materials 2024, 17(24), 6132; https://doi.org/10.3390/ma17246132 - 15 Dec 2024
Viewed by 399
Abstract
The large numbers of ion exchange resins used in various industries (food, pharmaceutitics, mining, hydrometallurgy), and especially in water treatment, are based on cross-linked polystyrene and divinylbenzene copolymers with functional groups capable of ion exchange. Their advantage, which makes them environmentally friendly, is [...] Read more.
The large numbers of ion exchange resins used in various industries (food, pharmaceutitics, mining, hydrometallurgy), and especially in water treatment, are based on cross-linked polystyrene and divinylbenzene copolymers with functional groups capable of ion exchange. Their advantage, which makes them environmentally friendly, is the possibility of their regeneration and reuse. Taking into account the wide application of these materials, styrene–divinylbenzene resin with a quaternary ammonium functional group, Amberlite®IRA402, was characterized using a well-known and widely used method, FT-IR spectroscopy. As the infrared spectrum of the tested ion exchange resin was rich in bands, its detailed assignment was supported by quantum chemical calculations (DFT/B3LYP/6-31g** and DFT/PCM/B3LYP/6-31g**). Using appropriate 3D models of the resin structure, the optimization of geometry, the infrared spectrum and atomic charges from an atomic polar tensor (APT) were calculated. A detailed description of the infrared spectrum of Amberlite®IRA402 resin (Cl form) in the spectral range of 4000–700 cm−1 was performed for the first time. The charge distribution on individual fragments of the resin structure in aqueous solution was also calculated for the first time. These studies will certainly allow for a better understanding of the styrene–divinylbenzene resin interaction in various processes with other substances, particularly in sorption processes. Full article
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Figure 1

Figure 1
<p>Fragment of the structure of the styrene–divinylbenzene anion exchange resin with a trimethylammonium functional group. Names of models reflecting the structure of the resin: 1, 2, 3a and 3b.</p>
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<p>Optimized geometry for the models (1, 2, 3a and 3b) of the structure of the styrene–divinylbenzene anion exchange resin with a quaternary ammonium functional group (Amberlite<sup>®</sup>IRA402) (DFT/PCM/B3LYP/6-31g**).</p>
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<p>Comparison of the experimental IR spectrum of IRA402 exchange resin with theoretical IR spectra (B3LYP/6-31g**) obtained for optimized model structures 1–3 (<a href="#materials-17-06132-f002" class="html-fig">Figure 2</a>) in two spectral ranges: 1800–1150 cm<sup>−1</sup> (<b>A</b>) and 1150–400 cm<sup>−1</sup> (<b>B</b>). Calculations for the isolated structures—models 1, 2, 3a and 3b; calculations for structures in water (PCM solvation model)—PCM models 1, 2, 3a and 3b.</p>
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<p>Comparison of the experimental IR spectrum of Amberlite<sup>®</sup>IRA402 exchange resin with the theoretical IR spectra (B3LYP/6-31g**) obtained for model 3b (<a href="#materials-17-06132-f002" class="html-fig">Figure 2</a>) in two spectral ranges: 4000–1800 cm<sup>−1</sup> (<b>A</b>) and 1800–700 cm<sup>−1</sup> (<b>B</b>). Calculations for the isolated structure 3b—blue line; calculations for structure 3b in water (PCM solvation model)—orange line.</p>
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<p>Distribution of atomic charges (APT) on individual atoms for the functional group of the resin and its surroundings (<a href="#materials-17-06132-f002" class="html-fig">Figure 2</a>, model 3b, DFT/PCM/B3LYP/6-31g**).</p>
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14 pages, 2845 KiB  
Article
Detection and Quantification of DNA by Fluorophore-Induced Plasmonic Current: A Novel Sensing Approach
by Daniel R. Pierce, Zach Nichols, Clifton Cunningham, Sean Avryl Villaver, Abdullah Bajwah, Samuel Oluwarotimi, Herbert Halaa and Chris D. Geddes
Sensors 2024, 24(24), 7985; https://doi.org/10.3390/s24247985 - 14 Dec 2024
Viewed by 364
Abstract
We report on the detection and quantification of aqueous DNA by a fluorophore-induced plasmonic current (FIPC) sensing method. FIPC is a mechanism described by our group in the literature where a fluorophore in close proximity to a plasmonically active metal nanoparticle film (MNF) [...] Read more.
We report on the detection and quantification of aqueous DNA by a fluorophore-induced plasmonic current (FIPC) sensing method. FIPC is a mechanism described by our group in the literature where a fluorophore in close proximity to a plasmonically active metal nanoparticle film (MNF) is able to couple with it, when in an excited state. This coupling produces enhanced fluorescent intensity from the fluorophore–MNF complex, and if conditions are met, a current is generated in the film that is intrinsically linked to the properties of the fluorophore in the complex. The magnitude of this induced current is related to the spectral properties of the film, the overlap between these film properties and those of the fluorophore, the spacing between the nanoparticles in the film, the excitation wavelength, and the polarization of the excitation source. Recent literature has shown that the FIPC system is ideal for aqueous ion sensing using turn-on fluorescent probes, and in this paper, we subsequently examine if it is possible to detect aqueous DNA also via a turn-on fluorescent probe, as well as other commercially available DNA detection strategies. We report the effects of DNA concentration, probe concentration, and probe characteristics on the development of an FIPC assay for the detection of non-specific DNA in aqueous solutions. Full article
(This article belongs to the Special Issue Optical Sensing for Environmental Monitoring—2nd Edition)
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Figure 1
<p>Absorption spectra of various solutions of ethidium bromide, ranging from 20 µM to 100 µM, both with and without the addition of 1 mg/mL DNA from a salmon sperm DNA stock solution. The solutions were allowed to mix for 30 min prior to analysis.</p>
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<p>Fluorescence emission spectra of various solutions of ethidium bromide, ranging from 20 µM to 100 µM, both with and without the addition of 1 mg/mL DNA from a salmon sperm DNA stock solution, excited at 365 nm. The solutions were allowed to mix for 30 min prior to analysis.</p>
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<p>Peak fluorescence emission spectra values for various solutions of ethidium bromide, ranging from 20 µM to 100 µM with the addition of 1 mg/mL DNA from a salmon sperm DNA stock solution, excited at 365 nm. The solutions were allowed to mix for 30 min prior to analysis. Data organized as the peak fluorescence intensity at 600 nm vs. concentration.</p>
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<p>Plasmonic current response of various solutions of ethidium bromide, ranging from 20 µM to 100 µM, both with and without the addition of 1 mg/mL DNA from a salmon sperm DNA stock solution, excited at 266 nm at 100 µW excitation power. The solutions were allowed to mix for 30 min prior to analysis.</p>
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<p>Plasmonic current response of various solutions of ethidium bromide, ranging from 20 µM to 100 µM with the addition of 1 mg/mL DNA from a salmon sperm DNA stock solution, excited at 266 nm at 100 µW excitation power. The solutions were allowed to mix for 30 min prior to analysis.</p>
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<p>Absorbance spectra of a 50µM solution of ethidium bromide mixed with varying concentrations of DNA from a salmon sperm DNA stock solution, ranging from 1 µg/mL to 10,000 µg/mL. The solutions were allowed to mix for 30 min prior to analysis.</p>
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<p>Fluorescence emission spectra of a 50 µM of ethidium bromide mixed with varying concentrations of DNA from a salmon sperm DNA stock solution, ranging from 1 µg/mL to 10,000 µg/mL, excited at 266 nm. The solutions were allowed to mix for 30 min prior to analysis.</p>
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<p>Peak fluorescence emission spectra values of a 50 µM of ethidium bromide solution mixed with varying concentrations of DNA from a salmon sperm DNA stock solution, ranging from 1 µg/mL to 10,000 µg/mL, excited at 266 nm. The solutions were allowed to mix for 30 min prior to analysis. Data shown as peak fluorescence intensity at 600 nm vs. DNA concentration.</p>
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<p>Plasmonic current response of a 50 µM of ethidium bromide solution mixed with varying concentrations of DNA from a salmon sperm DNA stock solution, ranging from 1 µg/mL to 10,000 µg/mL, excited at 266 nm at 100 µW power. The solutions were allowed to mix for 30 min prior to analysis.</p>
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<p>Absorbance spectra of various solutions of SYBR Green with varying concentrations of added DNA. Concentrations in μg/mL and mg/mL are related to DNA concentration added; 100×, 10× and 1× are related to the concentrations of SYBR Green.</p>
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<p>Fluorescence spectra of various solutions of SYBR Green with different concentrations of DNA added, excited at 473 nm. (<b>A</b>) 1× Concentration, (<b>B</b>) 10× Concentration and, (<b>C</b>) 100× Concentration of SYBR Green.</p>
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<p>FIPC responses for various solutions of SYBR Green with various amounts of DNA added, excited via a 473 nm (CW) laser, 10 mW power.</p>
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<p>FIPC response for 1× SYBR Green with added DNA, excited via 473 nm laser. Comparison between P and S polarized light for excitation.</p>
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<p>Standard curve obtained using 1× SYBR Green and various DNA concentrations (Left). In a blinded study, the sample values were found to be within 15% of their true value. Table detailing the raw responses and their calculated values (on the right). Each current response shown is the average of 10 values, n = 10.</p>
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25 pages, 31841 KiB  
Article
From Tea to Functional Foods: Exploring Caryopteris mongolica Bunge for Anti-Rheumatoid Arthritis and Unraveling Its Potential Mechanisms
by Xin Dong, Zhi Wang, Yao Fu, Yuxin Tian, Peifeng Xue, Yuewu Wang, Feiyun Yang, Guojing Li and Ruigang Wang
Nutrients 2024, 16(24), 4311; https://doi.org/10.3390/nu16244311 - 13 Dec 2024
Viewed by 417
Abstract
Background: Caryopteris mongolica Bunge (CM) shows promising potential for managing rheumatoid arthritis (RA) and digestive disorders, attributed to its rich content of bioactive compounds such as polyphenols and flavonoids. Despite its common use in herbal tea, the specific mechanisms underlying CM’s anti-inflammatory and [...] Read more.
Background: Caryopteris mongolica Bunge (CM) shows promising potential for managing rheumatoid arthritis (RA) and digestive disorders, attributed to its rich content of bioactive compounds such as polyphenols and flavonoids. Despite its common use in herbal tea, the specific mechanisms underlying CM’s anti-inflammatory and joint-protective effects remain unclear, limiting its development as a functional food. This study investigated the effects of aqueous CM extract on RA in collagen-induced arthritis (CIA) rats and explored the underlying mechanisms. Methods: Forty-eight female Sprague-Dawley rats were randomly assigned to six groups (n = 8): normal control, CIA model, methotrexate (MTX), and CM high-, middle-, and low-dose groups. Anti-inflammatory and joint-protective effects were evaluated using biochemical and histological analyses. To elucidate the mechanisms, we applied metabolomics, network pharmacology, and transcriptomics approaches. Results: The results demonstrated that CM extract effectively suppressed synovial inflammation in CIA rats, reducing joint degradation. CM’s anti-inflammatory effects were mediated through the TNF signaling pathway, modulating glycerophospholipid and amino acid metabolism, including reduced levels of tryptophan, LysoPC, and asparagine. Molecular docking identified scutellarin and apigenin as key bioactive compounds. Additionally, immunofluorescence analysis revealed CM’s therapeutic effects via TNF signaling inhibition and suppression of M1 macrophage polarization. Conclusions: These findings highlight the therapeutic potential of CM for RA and support its development as a functional food or pharmaceutical product. Full article
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Graphical abstract
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<p>Induction protocol for collagen-induced arthritis (CIA) in SD rats and the drug treatment schedule employed during the experiments.</p>
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<p>Network analysis of CM in treating rheumatoid arthritis. (<b>a</b>) Venn diagram showing 244 common targets between CM and rheumatoid arthritis. (<b>b</b>) STRING network visualization of the 244 common targets with topological analysis. (<b>c</b>) The top 10 significantly enriched terms in KEGG pathways. (<b>d</b>) Top 15 significantly enriched terms in biological processes. (<b>e</b>) Top 15 significantly enriched terms in cellular components (<b>e</b>). (<b>f</b>) Top 15 significantly enriched terms in molecular functions.</p>
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<p>Network analysis of CM in treating rheumatoid arthritis. (<b>a</b>) Venn diagram showing 244 common targets between CM and rheumatoid arthritis. (<b>b</b>) STRING network visualization of the 244 common targets with topological analysis. (<b>c</b>) The top 10 significantly enriched terms in KEGG pathways. (<b>d</b>) Top 15 significantly enriched terms in biological processes. (<b>e</b>) Top 15 significantly enriched terms in cellular components (<b>e</b>). (<b>f</b>) Top 15 significantly enriched terms in molecular functions.</p>
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<p>(<b>a</b>) Derived different MCODE clusters: Light yellow nodes were kernel genes in MCODE cluster1; Light orange nodes were kernel genes in MCODE cluster2; Pink nodes were kernel genes in MCODE cluster3; Light purple nodes were kernel genes in MCODE cluster4; Light blue nodes were kernel genes in MCODE cluster5. (<b>b</b>) The top 15 significantly enriched terms of MCODE cluster5 in KEGG pathways. (<b>c</b>) The top 5 enriched terms of MCODE cluster5 in biological processes (BP parts), cellular components (CC parts), and molecular functions (MF parts). (<b>d</b>) Illustration of the relevance among components of CM, the key targets, and diseases. Yellow nodes refer to RA; Pink nodes refer to the components contained in CM; Blue nodes refer to the key targets.</p>
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<p>(<b>a</b>) Derived different MCODE clusters: Light yellow nodes were kernel genes in MCODE cluster1; Light orange nodes were kernel genes in MCODE cluster2; Pink nodes were kernel genes in MCODE cluster3; Light purple nodes were kernel genes in MCODE cluster4; Light blue nodes were kernel genes in MCODE cluster5. (<b>b</b>) The top 15 significantly enriched terms of MCODE cluster5 in KEGG pathways. (<b>c</b>) The top 5 enriched terms of MCODE cluster5 in biological processes (BP parts), cellular components (CC parts), and molecular functions (MF parts). (<b>d</b>) Illustration of the relevance among components of CM, the key targets, and diseases. Yellow nodes refer to RA; Pink nodes refer to the components contained in CM; Blue nodes refer to the key targets.</p>
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<p>Alleviating effects of CM on CIA rats. (<b>a</b>) paw thickness, (<b>b</b>) AI scores (<b>c</b>) spleen index, (<b>d</b>) thymus index, (<b>e</b>) IL-17, (<b>f</b>) TNF-α, (<b>g</b>) IL-10, (<b>h</b>) pathological scores of H&amp;E and safranin O/fast green staining, (<b>i</b>) H&amp;E staining and safranin O/fast green staining. multiple comparisons test (The red arrow shows cartilage erosion, the black arrow shows inflammatory infiltration, the green arrow shows synovial proliferation, and the orange arrow shows pannus formation). All values were presented as mean ± SD, <span class="html-italic">n</span> = 8 per group. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. NC group, <sup>#</sup><span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup><span class="html-italic">p</span> &lt; 0.01 vs. CIA group, <sup>∆</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>∆∆</sup> <span class="html-italic">p</span> &lt; 0.01 vs. MTX group.</p>
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<p>Alleviating effects of CM on CIA rats. (<b>a</b>) paw thickness, (<b>b</b>) AI scores (<b>c</b>) spleen index, (<b>d</b>) thymus index, (<b>e</b>) IL-17, (<b>f</b>) TNF-α, (<b>g</b>) IL-10, (<b>h</b>) pathological scores of H&amp;E and safranin O/fast green staining, (<b>i</b>) H&amp;E staining and safranin O/fast green staining. multiple comparisons test (The red arrow shows cartilage erosion, the black arrow shows inflammatory infiltration, the green arrow shows synovial proliferation, and the orange arrow shows pannus formation). All values were presented as mean ± SD, <span class="html-italic">n</span> = 8 per group. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. NC group, <sup>#</sup><span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup><span class="html-italic">p</span> &lt; 0.01 vs. CIA group, <sup>∆</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>∆∆</sup> <span class="html-italic">p</span> &lt; 0.01 vs. MTX group.</p>
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<p>Alleviating effects of CM on CIA rats. (<b>a</b>) paw thickness, (<b>b</b>) AI scores (<b>c</b>) spleen index, (<b>d</b>) thymus index, (<b>e</b>) IL-17, (<b>f</b>) TNF-α, (<b>g</b>) IL-10, (<b>h</b>) pathological scores of H&amp;E and safranin O/fast green staining, (<b>i</b>) H&amp;E staining and safranin O/fast green staining. multiple comparisons test (The red arrow shows cartilage erosion, the black arrow shows inflammatory infiltration, the green arrow shows synovial proliferation, and the orange arrow shows pannus formation). All values were presented as mean ± SD, <span class="html-italic">n</span> = 8 per group. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. NC group, <sup>#</sup><span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup><span class="html-italic">p</span> &lt; 0.01 vs. CIA group, <sup>∆</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>∆∆</sup> <span class="html-italic">p</span> &lt; 0.01 vs. MTX group.</p>
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<p>Multivariate analysis based on the UHPLC-Q-Exactive MS/MS profiling data: (<b>a</b>) Score scatter plot of PCA model for three different group; (<b>b</b>) OPLS-DA score plot for group NC vs. CIA; (<b>c</b>) OPLS-DA score plot for group CIA vs. CM; (<b>d</b>) Volcano plot for group NC vs. CIA; (<b>e</b>) Volcano plot for group CIA vs. CM; (<b>f</b>) Venn diagram of 27 common metabolites of CIA vs. CM and NC vs. CIA. (<b>g</b>) Heatmap of hierarchical clustering analysis for three different group. (<b>h</b>) Summary of ingenuity pathway analysis with MetaboAnalyst. (<b>i</b>) Illustration of the relevance among components of CM, the key targets, and metabolites. Pink nodes refer to the components contained in CM; Yellow nodes refer to the key targets; Blue nodes refer to metabolites.</p>
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<p>Multivariate analysis based on the UHPLC-Q-Exactive MS/MS profiling data: (<b>a</b>) Score scatter plot of PCA model for three different group; (<b>b</b>) OPLS-DA score plot for group NC vs. CIA; (<b>c</b>) OPLS-DA score plot for group CIA vs. CM; (<b>d</b>) Volcano plot for group NC vs. CIA; (<b>e</b>) Volcano plot for group CIA vs. CM; (<b>f</b>) Venn diagram of 27 common metabolites of CIA vs. CM and NC vs. CIA. (<b>g</b>) Heatmap of hierarchical clustering analysis for three different group. (<b>h</b>) Summary of ingenuity pathway analysis with MetaboAnalyst. (<b>i</b>) Illustration of the relevance among components of CM, the key targets, and metabolites. Pink nodes refer to the components contained in CM; Yellow nodes refer to the key targets; Blue nodes refer to metabolites.</p>
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<p>Multivariate analysis based on the UHPLC-Q-Exactive MS/MS profiling data: (<b>a</b>) Score scatter plot of PCA model for three different group; (<b>b</b>) OPLS-DA score plot for group NC vs. CIA; (<b>c</b>) OPLS-DA score plot for group CIA vs. CM; (<b>d</b>) Volcano plot for group NC vs. CIA; (<b>e</b>) Volcano plot for group CIA vs. CM; (<b>f</b>) Venn diagram of 27 common metabolites of CIA vs. CM and NC vs. CIA. (<b>g</b>) Heatmap of hierarchical clustering analysis for three different group. (<b>h</b>) Summary of ingenuity pathway analysis with MetaboAnalyst. (<b>i</b>) Illustration of the relevance among components of CM, the key targets, and metabolites. Pink nodes refer to the components contained in CM; Yellow nodes refer to the key targets; Blue nodes refer to metabolites.</p>
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<p>Transcriptomic landscape of CM treating rheumatoid arthritis: (<b>a</b>) DEGs volcano plot; (<b>b</b>) DEGs heatmap. (<b>c</b>) The top 15 significantly enriched terms in KEGG pathways. (<b>d</b>) The top 5 enriched terms in biological processes (blue parts), cellular components (yellow parts), and molecular functions (pink parts).</p>
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<p>Transcriptomic landscape of CM treating rheumatoid arthritis: (<b>a</b>) DEGs volcano plot; (<b>b</b>) DEGs heatmap. (<b>c</b>) The top 15 significantly enriched terms in KEGG pathways. (<b>d</b>) The top 5 enriched terms in biological processes (blue parts), cellular components (yellow parts), and molecular functions (pink parts).</p>
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<p>(<b>a</b>) Venn diagram of common targets between CM, rheumatoid arthritis and DEGs. (<b>b</b>) 13 common targets via STRING network topological analysis. (<b>c</b>) Fluorescence quantitative PCR verification of core differential expressed gene. Molecular docking results, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. NC group, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. CIA group: (<b>d</b>) The heatmap of docking scores of key targets combining to 5 active compounds in CM. (<b>e</b>–<b>j</b>) The representative docking complex of key targets and compounds.</p>
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<p>(<b>a</b>) Venn diagram of common targets between CM, rheumatoid arthritis and DEGs. (<b>b</b>) 13 common targets via STRING network topological analysis. (<b>c</b>) Fluorescence quantitative PCR verification of core differential expressed gene. Molecular docking results, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. NC group, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 vs. CIA group: (<b>d</b>) The heatmap of docking scores of key targets combining to 5 active compounds in CM. (<b>e</b>–<b>j</b>) The representative docking complex of key targets and compounds.</p>
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<p>Immunofluorescence double staining of M1 and M2 macrophages in rats’ joint tissues of each group (iNOS as the positive marker for M1 macrophages with green color, CD206 as the positive marker for M2 macrophages with red color, DAPI as the positive marker for cell nuclei with blue color, and Merge as the combined marker of all three).</p>
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32 pages, 6769 KiB  
Review
Strategies and Methodologies for Improving Toughness of Starch Films
by Yiwen Yang, Jun Fu, Qingfei Duan, Huifang Xie, Xinyi Dong and Long Yu
Foods 2024, 13(24), 4036; https://doi.org/10.3390/foods13244036 - 13 Dec 2024
Viewed by 385
Abstract
Starch films have attracted increasing attention due to their biodegradability, edibility, and potential use as animal feed from post-products. Applications of starch-based films include food packaging, coating, and medicine capsules. However, a major drawback of starch-based films is their brittleness, particularly under dry [...] Read more.
Starch films have attracted increasing attention due to their biodegradability, edibility, and potential use as animal feed from post-products. Applications of starch-based films include food packaging, coating, and medicine capsules. However, a major drawback of starch-based films is their brittleness, particularly under dry conditions, caused by starch retrogradation and the instability of plasticizers. To address this challenge, various strategies and methodologies have been developed, including plasticization, chemical modification, and physical reinforcement. This review covers fundamental aspects, such as the microstructures, phase transitions, and compatibility of starch, as well as application-oriented techniques, including processing methods, plasticizer selection, and chemical modifications. Plasticizers play a crucial role in developing starch-based materials, as they mitigate brittleness and improve processability. Given the abundance of hydroxyl groups in starch, the plasticizers used must also contain hydroxyl or polar groups for compatibility. Chemical modification, such as esterification and etherification, effectively prevents starch recrystallization. Reinforcements, particularly with nanocellulose, significantly improved the mechanical properties of starch film. Drawing upon both the literature and our expertise, this review not only summarizes the advancements in this field but also identifies the limitations of current technologies and outlines promising research directions for future development. Full article
(This article belongs to the Special Issue Natural Polymer-Based Films and Coatings for Food Packaging)
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Figure 1

Figure 1
<p>Schematic representation of hierarchical starch structures [<a href="#B12-foods-13-04036" class="html-bibr">12</a>].</p>
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<p>Scheme of starch granule structures [<a href="#B27-foods-13-04036" class="html-bibr">27</a>].</p>
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<p>Schematic representation of the phase transitions of starch during thermal processing and aging [<a href="#B94-foods-13-04036" class="html-bibr">94</a>]. (<b>A</b>) native starch structure; (<b>B</b>) gelatinization; (<b>C</b>) cooling and gel formation and (<b>D</b>) retrogradation.</p>
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<p>Chemical modification on starch. Cross-linking modification with (<b>1</b>) CS<sub>2</sub>, (<b>2</b>) POCl<sub>3</sub>, Na<sub>3</sub>P<sub>3</sub>O<sub>9</sub>, (<b>3</b>) epichlorohydrin, and (<b>4</b>) C<sub>6</sub>H<sub>5</sub>O(COOH)<sub>3</sub>; oxidation modification with (<b>5</b>) O<sub>3</sub> and HIO<sub>4</sub>; grafting modification with (<b>6</b>) acrylamide with possible initiators; etherification modification with (<b>7</b>) C<sub>3</sub>H<sub>6</sub>O, (<b>8</b>) CH<sub>2</sub>ClCOONa, and (<b>9</b>) C<sub>2</sub>H<sub>5</sub>Cl; acid hydrolysis with (<b>10</b>) HCl, TFA, and HNO<sub>3</sub>; esterification modification with (<b>11</b>) Na<sub>2</sub>HPO<sub>4</sub> and (<b>12</b>) CH<sub>3</sub>COOH [<a href="#B139-foods-13-04036" class="html-bibr">139</a>].</p>
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<p>(<b>a</b>,<b>b</b>) Schematic summary of condensing reaction [<a href="#B142-foods-13-04036" class="html-bibr">142</a>].</p>
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