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16 pages, 4145 KiB  
Article
Antagonistic Strain Bacillus velezensis JZ Mediates the Biocontrol of Bacillus altitudinis m-1, a Cause of Leaf Spot Disease in Strawberry
by Li Zhang, Zirui Liu, Yilei Pu, Boyuan Zhang, Boshen Wang, Linman Xing, Yuting Li, Yingjun Zhang, Rong Gu, Feng Jia, Chengwei Li and Na Liu
Int. J. Mol. Sci. 2024, 25(16), 8872; https://doi.org/10.3390/ijms25168872 (registering DOI) - 15 Aug 2024
Abstract
Biofertilizers are environmentally friendly compounds that can enhance plant growth and substitute for chemically synthesized products. In this research, a new strain of the bacterium Bacillus velezensis, designated JZ, was isolated from the roots of strawberry plants and exhibited potent antagonistic properties [...] Read more.
Biofertilizers are environmentally friendly compounds that can enhance plant growth and substitute for chemically synthesized products. In this research, a new strain of the bacterium Bacillus velezensis, designated JZ, was isolated from the roots of strawberry plants and exhibited potent antagonistic properties against Bacillus altitudinis m-1, a pathogen responsible for leaf spot disease in strawberry. The fermentation broth of JZ exerted an inhibition rate of 47.43% against this pathogen. Using an optimized acid precipitation method, crude extracts of lipopeptides from the JZ fermentation broth were obtained. The crude extract of B. velezensis JZ fermentation broth did not significantly disrupt the cell permeability of B. altitudinis m-1, whereas it notably reduced the Ca2+-ATPase activity on the cell membrane and markedly elevated the intracellular reactive oxygen species (ROS) concentration. To identify the active compounds within the crude extract, QTOF-MS/MS was employed, revealing four antimicrobial compounds: fengycin, iturin, surfactin, and a polyene antibiotic known as bacillaene. The strain JZ also produced various plant-growth-promoting substances, such as protease, IAA, and siderophore, which assists plants to survive under pathogen infection. These findings suggest that the JZ strain holds significant potential as a biological control agent against B. altitudinis, providing a promising avenue for the management of plant bacterial disease. Full article
(This article belongs to the Section Molecular Microbiology)
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<p>Colony morphology and genetic similarity of strain JZ. (<b>A</b>) Growth of JZ colony in PDA medium. (<b>B</b>) Dendrogram based on 16S rRNA gene sequences constructed using the neighbor-joining method.</p>
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<p>Colony morphology and genetic similarity of strain m-1. (<b>A</b>) Strain m-1 colonies grown on PDA medium. (<b>B</b>) Dendrogram based on 16S rRNA gene sequences constructed using the neighbor-joining method. <span class="html-italic">Flavobacterium lacisediminis</span> TH16-21<sup>T</sup> (OP422636.1) was used as the outgroup.</p>
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<p>Interactions between the bacterial strains used in this study. (<b>A</b>) Streaking method. (<b>B</b>,<b>C</b>) K-B test: (a,b): a transparent ring is visible around the filter paper soaked with JZ fermentation broth; (c,d): no transparent ring is visible around the filter paper soaked with sterile water; (e,f): transparent ring is conspicuous around filter paper soaked with the extract from JZ fermentation broth.</p>
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<p>Growth curves of m-1, antibacterial activity, and optimization of the extraction. (<b>A</b>) Growth curves of m-1 cultures treated with different crude extract concentrations. The crude extract was diluted 1, 5, 10, and 20 times to generate the 0.5 C, 0.2 C, 0.1 C, and 0.05 C extracts, respectively; CK: no crude extract treatment. (<b>B</b>) Antibacterial activity of lipopeptide crude extracts. (<b>C</b>) Optimization of the pH for acid precipitation. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Conductivity and Ca<sup>2+</sup>-ATPase activity of m-1 treated with JZ crude extract. (<b>A</b>) Relative conductivity. Control: 9 mL PDB + 1 mL sterile water; treatment: 9 mL PDB + 1 mL 0.5 C crude extract. Significance represents the difference between the current and preceding time points. (<b>B</b>) Ca<sup>2+</sup>-ATPase activity. ** <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.</p>
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<p>Reactive oxygen species (ROS) concentration and superoxide dismutase (SOD) activity of m-1 treated with JZ crude extract. (<b>A</b>) Intracellular ROS concentration. (<b>B</b>) SOD activity. *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Semi-preparative HPLC chromatogram of the crude extract.</p>
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<p>QTOF-MS/MS analysis of JZ crude extract. Spectra of (<b>A</b>) fengycin, (<b>B</b>) iturin, (<b>C</b>) bacillaene, (<b>D</b>) surfactin.</p>
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13 pages, 3846 KiB  
Article
3D-STARNET: Spatial–Temporal Attention Residual Network for Robust Action Recognition
by Jun Yang, Shulong Sun, Jiayue Chen, Haizhen Xie, Yan Wang and Zenglong Yang
Appl. Sci. 2024, 14(16), 7154; https://doi.org/10.3390/app14167154 (registering DOI) - 15 Aug 2024
Abstract
Existing skeleton-based action recognition methods face the challenges of insufficient spatiotemporal feature mining and a low efficiency of information transmission. To solve these problems, this paper proposes a model called the Spatial–Temporal Attention Residual Network for 3D human action recognition (3D-STARNET). This model [...] Read more.
Existing skeleton-based action recognition methods face the challenges of insufficient spatiotemporal feature mining and a low efficiency of information transmission. To solve these problems, this paper proposes a model called the Spatial–Temporal Attention Residual Network for 3D human action recognition (3D-STARNET). This model significantly improves the performance of action recognition through the following three main innovations: (1) the conversion from skeleton points to heat maps. Using Gaussian transform to convert skeleton point data into heat maps effectively reduces the model’s strong dependence on the original skeleton point data and enhances the stability and robustness of the data; (2) a spatiotemporal attention mechanism (STA). A novel spatiotemporal attention mechanism is proposed, focusing on the extraction of key frames and key areas within frames, which significantly enhances the model’s ability to identify behavioral patterns; (3) a multi-stage residual structure (MS-Residual). The introduction of a multi-stage residual structure improves the efficiency of data transmission in the network, solves the gradient vanishing problem in deep networks, and helps to improve the recognition efficiency of the model. Experimental results on the NTU-RGBD120 dataset show that 3D-STARNET has significantly improved the accuracy of action recognition, and the top1 accuracy of the overall network reached 96.74%. This method not only solves the robustness shortcomings of existing methods, but also improves the ability to capture spatiotemporal features, providing an efficient and widely applicable solution for action recognition based on skeletal data. Full article
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<p>Visualization of 3D-STARNET input data processing and overall network architecture.</p>
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<p>Data acquisition process.</p>
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<p>STA module architecture embedded in 3D CNN model.</p>
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<p>STA module architecture.</p>
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<p>The original residual structure.</p>
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<p>Multi-stage residual structure.</p>
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<p>Comparison before and after shortcut improvement.</p>
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<p>Confusion matrix.</p>
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17 pages, 2327 KiB  
Article
Evaluating Source Complexity in Blended Milk Cheese: Integrated Strontium Isotope and Multi-Elemental Approach to PDO Graviera Naxos
by Majda Nikezić, Paraskevi Chantzi, Johanna Irrgeher and Tea Zuliani
Foods 2024, 13(16), 2540; https://doi.org/10.3390/foods13162540 (registering DOI) - 14 Aug 2024
Abstract
Graviera Naxos, a renowned cheese with Protected Designation of Origin status, is crafted from a blend of cow, goat, and sheep milk. This study focused on assessing the Sr isotopic and multi-elemental composition of both the processed cheese and its ingredients, as well [...] Read more.
Graviera Naxos, a renowned cheese with Protected Designation of Origin status, is crafted from a blend of cow, goat, and sheep milk. This study focused on assessing the Sr isotopic and multi-elemental composition of both the processed cheese and its ingredients, as well as the environmental context of Naxos Island, including samples of milk, water, soil, and feed. The objective was to delineate the geochemical signature of Graviera Naxos cheese and to explore the utility of Sr isotopes as indicators of geographic origin. The 87Sr/86Sr values for Graviera Naxos samples ranged from 0.70891 to 0.70952, indicating a relatively narrow range. However, the Sr isotopic signature of milk, heavily influenced by the feed, which originates from geologically distinct areas, does not always accurately reflect the local breeding environment. Multi-elemental analysis revealed variations in milk composition based on type and season; yet, no notable differences were found between raw and pasteurized milk. A mixing model evaluating the contributions of milk, sea salt, and rennet to the cheese’s Sr isotopic signature suggested a significant average contribution of 73.1% from sea salt. This study highlights the complexities of linking dairy products with their geographical origins and emphasizes the need for sophisticated geochemical authentication methods. Full article
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Graphical abstract

Graphical abstract
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<p>Naxos island and the sampling stations.</p>
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<p>Variations in Sr and essential elements’ levels across (<b>a</b>) sampling seasons (January 2021 and June 2021), (<b>b</b>) milk types, (<b>c</b>) pasteurized and raw milk, and (<b>d</b>) milk from milk tanks and Graviera Naxos cheese. The relative mass fraction is normalized to 100% for all elements. Elements are displayed based on their total mass fraction, from lowest to highest.</p>
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<p>Ranges and distribution of <sup>87</sup>Sr/<sup>86</sup>Sr values across matrices (KDE bw = 0.0005641063).</p>
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<p>Boxplots representing ranges of <sup>87</sup>Sr/<sup>86</sup>Sr values for (<b>a</b>) milk and (<b>b</b>) feed across different sampling campaigns. The median of the data is the line; the points outside the whiskers are points beyond 1.5 times the IQR from the quartiles.</p>
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<p>Correlation between <sup>87</sup>Sr/<sup>86</sup>Sr values of (<b>a</b>) milk and feed samples from January 2021 and (<b>b</b>) milk (June 2021) and water samples. The line of best fit is represented by a dashed blue line; the shaded area surrounding the line depicts the 95% confidence region.</p>
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<p>Isospace plot, ⁸⁷Sr/⁸⁶Sr ratios of sources (milk, sea salt, rennet) and Graviera Naxos cheese samples plotted against their respective inverse Sr mass fractions.</p>
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15 pages, 13689 KiB  
Article
Impact of Changing Inlet Modes in Ski Face Masks on Adolescent Skiing: A Finite Element Analysis Based on Head Models
by Minxin Huang, Ruiqiu Zhang and Xiaocheng Zhang
Modelling 2024, 5(3), 936-950; https://doi.org/10.3390/modelling5030049 - 14 Aug 2024
Abstract
Due to the material properties of current ski face masks for adolescents, moisture in exhaled air can become trapped within the material fibers and freeze, leading to potential issues such as breathing difficulties and increased risk of facial frostbite after prolonged skiing. This [...] Read more.
Due to the material properties of current ski face masks for adolescents, moisture in exhaled air can become trapped within the material fibers and freeze, leading to potential issues such as breathing difficulties and increased risk of facial frostbite after prolonged skiing. This paper proposes a research approach combining computational fluid dynamics (CFD) and ergonomics to address these issues and enhance the comfort of adolescent skiers. We developed head and face mask models based on the head dimensions of 15–17-year-old males. For enclosed cavities, ensuring the smooth expulsion of exhaled air to prevent re-inhalation is the primary challenge. Through fluid simulation of airflow characteristics within the cavity, we evaluated three different inlet configurations. The results indicate that the location of the air inlets significantly affects the airflow characteristics within the cavity. The side inlet design (type II) showed an average face temperature of 35.35 °C, a 38.5% reduction in average CO2 concentration within the cavity, and a smaller vortex area compared to the other two inlet configurations. Although the difference in airflow velocity within the cavity among the three configurations was minimal, the average exit velocity differed by up to 0.11 m/s. Thus, we conclude that the side inlet configuration offers minimal obstruction to airflow circulation and better thermal insulation when used in the design of fully enclosed helmets. This enhances the safety and comfort of adolescent wearers during physical activities in cold environments. Through this study, we aim to further promote the development of skiing education, enhance the overall quality of adolescents’ skiing, and thus provide them with more opportunities for the future. Full article
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<p>RUROC RG1-DX adult ski helmet (airflow mechanism).</p>
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<p>Ski face protection design, ventilation mode, and size.</p>
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<p>(<b>a</b>) Fine modeling size of underage man’s nose (nasal length/depth/height); (<b>b</b>) nasal breadth; (<b>c</b>) anterior nostril opening angle/diffusion angle; (<b>d</b>) nose opening angle/diffusion angle.</p>
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<p>CO<sub>2</sub> distribution in different intake modes.</p>
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<p>(<b>a</b>) Vorticity research slice position; (<b>b</b>) velocity and temperature research section position.</p>
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<p>Total vorticity of 5 cross-slices.</p>
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<p>(<b>a</b>) The point of velocity and temperature section; (<b>b</b>) the analysis area division of ski face protection.</p>
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<p>(<b>a</b>) Temperatures of different air intake modes in the cavity; (<b>b</b>) velocities of different air intake modes in the cavity; (<b>c</b>) average velocity and temperature at the outlet.</p>
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15 pages, 8804 KiB  
Article
Prognostic Significance of Elevated UCHL1, SNRNP200, and PAK4 Expression in High-Grade Clear Cell Renal Cell Carcinoma: Insights from LC-MS/MS Analysis and Immunohistochemical Validation
by Michał Kasperczak, Gabriel Bromiński, Iga Kołodziejczak-Guglas, Andrzej Antczak and Maciej Wiznerowicz
Cancers 2024, 16(16), 2844; https://doi.org/10.3390/cancers16162844 - 14 Aug 2024
Abstract
Recent advancements in proteomics have enhanced our understanding of clear cell renal cell carcinoma (CCRCC). Utilizing a combination of liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by immunohistochemical validation, we investigated the expression levels of UCHL1, PAK4, and SNRNP200 in high-grade CCRCC samples. Our [...] Read more.
Recent advancements in proteomics have enhanced our understanding of clear cell renal cell carcinoma (CCRCC). Utilizing a combination of liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by immunohistochemical validation, we investigated the expression levels of UCHL1, PAK4, and SNRNP200 in high-grade CCRCC samples. Our analysis also integrated Reactome pathway enrichment to elucidate the roles of these proteins in cancer-related pathways. Our results revealed significant upregulation of UCHL1 and SNRNP200 and downregulation of PAK4 in high-grade CCRCC tissues compared to non-cancerous tissues. UCHL1, a member of the ubiquitin carboxy-terminal hydrolase family, showed variable expression across different tissues and was notably involved in the Akt signaling pathway, which plays a critical role in cellular survival in various cancers. SNRNP200, a key component of the RNA splicing machinery, was found to be essential for proper cell cycle progression and possibly linked to autosomal dominant retinitis pigmentosa. PAK4’s role was noted as critical in RCC cell proliferation and invasion and its expression correlated significantly with poor progression-free survival in CCRCC. Additionally, the expression patterns of these proteins suggested potential as prognostic markers for aggressive disease phenotypes. This study confirms the upregulation of UCHL1, SNRNP200, and PAK4 as significant factors in the progression of high-grade CCRCC, linking their enhanced expression to poor clinical outcomes. These findings propose these proteins as potential prognostic markers and therapeutic targets in CCRCC, offering novel insights into the molecular landscape of this malignancy and highlighting the importance of targeted therapeutic interventions. Full article
(This article belongs to the Special Issue Clear Cell Renal Cell Carcinoma 2024–2025)
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<p>Study design: key steps of the study. Acquisition of protein data from the CPTAC discovery CCRCC cohort dataset and subsequent analysis of protein expression of tumor (T) and normal adjacent tissue (N) to select candidate protein markers. The selected protein markers were next subjected to immunohistochemical analysis. Assessment of the clinical value of the selected proteins for prognosis, diagnosis, and targeted therapy for CCRCC patients.</p>
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<p>Protein abundance value distribution between normal adjacent tissue and tumor samples for PAK4, SNRNP200, and UCHL1 as measured by LC-MS/MS. Boxes outlined in green represent normal adjacent tissue samples and boxes outlined in red represent tumor samples (<span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Immunohistochemical analysis showing immunostaining patterns in representative tumor sections for PAK4, SNRNP200, and UCHL1 high and low expression in the CCRCC validation cohort. Brown color indicates positive immunoreaction and blue indicates negative immunoreaction.</p>
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<p>Distribution of IHC scores obtained for PAK4, SNRNP200, and UCHL1. The scatter plots demonstrate the variation in scores between “high score” and “low score” groups across the different proteins. All plots show statistically significant differences (<span class="html-italic">p</span> &lt; 0.0001), indicating pronounced disparities in IHC scores between the two groups. Notably, for UCHL1, the high score group exhibits a wider range of scores, indicating varying expression levels among individuals, unlike PAK4 and SNRNP200, where the high score groups are more tightly clustered without significant outliers.</p>
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<p>IHC scores for PAK4 (<b>A</b>), SNRNP200 (<b>B</b>), and UCHL1 (<b>C</b>) were analyzed to determine their correlation with patients’ clinical outcomes, specifically PFS and OS. Patient outcomes were compared by categorizing them according to the highest and lowest IHC scores, providing a clear comparison between these two groups. Both OS and PFS were meticulously tracked over a five-year follow-up period.</p>
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<p>Significant Reactome Enrichment Pathways for PAK4, SNRNP200, and UCHL1. Visual representation of statistically significant (<span class="html-italic">p</span>-value &lt; 0.05) enriched terms associated with each protein.</p>
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18 pages, 10080 KiB  
Article
Analysis of Moisture Migration and Microstructural Characteristic of Green Sichuan Pepper (Zanthoxylum armatum) during the Hot-Air Drying Process Based on LF-NMR
by Bin Li, Chuandong Liu, Hang Luo, Chongyang Han, Xuefeng Zhang, Qiaofei Li, Lian Gong, Pan Wang and Zhiheng Zeng
Agriculture 2024, 14(8), 1361; https://doi.org/10.3390/agriculture14081361 - 14 Aug 2024
Abstract
To have a deeper understanding on the moisture migration patterns and microstructural changes of Green Sichuan Pepper during the hot-air drying process, the low-field nuclear magnetic resonance (LF-NMR) technology and scanning electron microscopy (SEM) methodology were adopted to analyze the moisture distribution, migration [...] Read more.
To have a deeper understanding on the moisture migration patterns and microstructural changes of Green Sichuan Pepper during the hot-air drying process, the low-field nuclear magnetic resonance (LF-NMR) technology and scanning electron microscopy (SEM) methodology were adopted to analyze the moisture distribution, migration patterns and microscopic structural changes under different drying temperatures (45, 55 and 65 °C). The LF-NMR scanning results showed that the internal moisture of the Green Sichuan Pepper mainly includes bound water, immobilized water and free water, which can be respectively symbolized by the relaxation time ranges of T21 (0.1–10 ms), T22 (10–500 ms) and T23 (500–10,000 ms). The immobilized water accounts for 83.72% of the internal water, resulting in the significant drying difficulty of Green Sichuan Pepper. During the drying process, the content of immobilized water and free water exhibited a decreasing trend, while the bound moisture content initially increased and then decreased. In addition, the LF-NMR analysis showed that the parameters peak area A2 demonstrated a high correlation with the moisture content of Green Sichuan Pepper, enabling the prediction of moisture content changes during the drying process. Additionally, the SEM results showed that the pore degree and pore density on the pericarp surface of Green Sichuan Pepper perform significant changes during the drying process, which might be a good explanation for revealing some commonly recognized drying phenomena on Green Sichuan Pepper hot-air drying. In summary, the findings presented in the present work provide some new insights into the moisture distribution, migration patterns and microstructural changes of Green Sichuan Pepper, which can offer theoretical guidance for optimizing the drying process of Green Sichuan Pepper. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Scene graph of the LF-NMR analyzer (<b>a</b>) and the Green Sichuan Peppers sample (<b>b</b>).</p>
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<p>The scene graph of the SEM analyzer (<b>a</b>) and the prepared samples (<b>b</b>).</p>
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<p>The variations of transverse relaxation time with signal amplitude. (Note: T<sub>21</sub> refers to relaxation peak of bound water, T<sub>22</sub> refers to relaxation peak of immobile water, T<sub>23</sub> refers to relaxation peak of free water).</p>
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<p>Variation of T<sub>2</sub> with amplitude signals during the drying process under 45 °C.</p>
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<p>LF-NMR spectrum of different water states in Green Sichuan Pepper at drying temperatures of 45 °C (<b>a</b>), 55 °C (<b>b</b>) and 65 °C (<b>c</b>).</p>
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<p>The variations of the water activity and the moisture content with the drying time at drying temperatures of 45, 55 and 65 °C.</p>
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<p>Fitting curves of A<sub>2</sub> and MC at drying temperatures of 45 °C (<b>a</b>), 55 °C (<b>b</b>) and 65 °C (<b>c</b>).</p>
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<p>The microstructure of the surface (<b>a</b>) and a section (<b>b</b>) of the fresh Green Sichuan Pepper pericarp.</p>
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<p>Microstructure of Green Sichuan Pepper pericarp at 60 min (<b>a</b>) and 360 min (<b>b</b>) with drying temperature of 45 °C.</p>
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<p>Microstructure of Green Sichuan Pepper pericarp at 60 min (<b>a</b>) and 360 min (<b>b</b>) with drying temperature of 55 °C.</p>
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<p>Microstructure of Green Sichuan Pepper pericarp at 60 min (<b>a</b>) and 360 min (<b>b</b>) with drying temperature of 65 °C.</p>
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<p>The local positioning (<b>a</b>), comprehensive EDS (<b>b</b>), and energy spectrum elemental distribution (<b>c</b>) images of Green Sichuan Pepper epidermal samples at 60 min under 55 °C.</p>
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<p>The local positioning (<b>a</b>), comprehensive EDS (<b>b</b>), and energy spectrum elemental distribution (<b>c</b>) images of Green Sichuan Pepper epidermal samples at 360 min under 55 °C.</p>
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24 pages, 10977 KiB  
Article
Examining the Controls on the Spatial Distribution of Landslides Triggered by the 2008 Wenchuan Ms 8.0 Earthquake, China, Using Methods of Spatial Point Pattern Analysis
by Guangshun Bai, Xuemei Yang, Guangxin Bai, Zhigang Kong, Jieyong Zhu and Shitao Zhang
Sustainability 2024, 16(16), 6974; https://doi.org/10.3390/su16166974 - 14 Aug 2024
Abstract
Landslide risk management contributes to the sustainable development of the region. Understanding the spatial controls on the distribution of landslides triggered by earthquakes (EqTLs) is difficult in terms of the prediction and risk assessment of EqTLs. In this study, landslides are regarded as [...] Read more.
Landslide risk management contributes to the sustainable development of the region. Understanding the spatial controls on the distribution of landslides triggered by earthquakes (EqTLs) is difficult in terms of the prediction and risk assessment of EqTLs. In this study, landslides are regarded as a spatial point pattern to test the controls on the spatial distribution of landslides and model the landslide density prediction. Taking more than 190,000 landslides triggered by the 2008 Wenchuan Ms 8.0 earthquake (WcEqTLs) as the research object, the relative density estimation, Kolmogorov–Smirnov testing based on cumulative distribution, receiver operating characteristic curve (ROC) analysis, and Poisson density modeling are comprehensively applied to quantitatively determine and discuss the different control effects of seven factors representing earthquakes, geology, and topography. The distance to the surface ruptures (dSR) and the distance to the epicenter (dEp) show significant and strong control effects, which are far stronger than the other five factors. Using only the dSR, dEp, engineering geological rock group (Eg), and the range, a particularly effective Poisson model of landslide density is constructed, whose area under the ROC (AUC) reaches 0.9244 and whose very high-density (VHD) zones can contain 50% of landslides and only comprise 3.9% of the study areas. This research not only deepens our understanding of the spatial distribution of WcEqTLs but also provides new technical methods for such investigation and analysis. Full article
(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction, 2nd Volume)
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<p>Landslide point locations [<a href="#B33-sustainability-16-06974" class="html-bibr">33</a>], WcEq epicenter location, and surface ruptures [<a href="#B43-sustainability-16-06974" class="html-bibr">43</a>,<a href="#B44-sustainability-16-06974" class="html-bibr">44</a>]. The gray grid lines in the study area display a custom coordinate system, with the epicenter as (0,0), along the surface rupture zone as the X-axis, the main propagation direction of the earthquake as the positive X-axis, and the vertical surface rupture as the Y-axis, with an interval of 10 km. The inset map shows major tectonic features in Longmenshan vicinity [<a href="#B43-sustainability-16-06974" class="html-bibr">43</a>]: The red box in the map indicates the location of the study area. LTB—Longmenshan thrust belt (southwestern China, eastern edge of the Qinghai–Tibet Plateau); ATF—Altyn Tagh fault; HF—Haiyuan fault; JLF—Jiali fault; NCB—North China block; RRF—Red River fault; SCB—South China block; XF—Xianshuihe fault; XJF—Xiaojiang fault; I—Qaidam–Qilian block; II—Bayan Har block; III—Sichuan–Yunnan block. The white arrow indicates the block motion direction.</p>
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<p>Maps of seven covariates.</p>
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<p>The relative distribution estimation of WcEqTLs density on the dSR. A is the curve of the estimation. B is the map of the estimation. The red horizontal dashed line in the (<b>A</b>) and the red circular dashed line in the (<b>B</b>) indicate that the average landslide density in the whole study area is 2.6 landslides/km<sup>2</sup>. The solid black line is the estimation result of this method. The blue dots are results of the discrete method.</p>
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<p>The relative distribution estimation of WcEqTLs density on the dEp. A is the curve of the estimation. B is the map of the estimation. The red horizontal dashed line in (<b>A</b>) and the red circular dashed line in (<b>B</b>) indicate that the average landslide density in the whole study area is 2.6 landslides/km<sup>2</sup>. The blue dots are results of the discrete method.</p>
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<p>The relative distribution estimation of the WcEqTLs density on the Elv, the range, the Slp, and the Asp. The red horizontal dashed lines in (<b>A</b>–<b>D</b>) indicate the average landslide density. The red dots are results of the discrete method.</p>
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<p>The relative distribution estimation of the WcEqTLs density on the Eg. (<b>A</b>) is the density histogram of classified landslides. The blue horizontal dashed line indicates that the average landslide density in the whole study area is 2.6 landslides/km<sup>2</sup>. (<b>B</b>) is the spatial distribution map of landslide density in different engineering geological rock groups.</p>
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<p>The statistical curve of the landslide cumulative probability relative to the dSR (<b>A</b>) and the dEp (<b>B</b>). The solid black line is the observation statistical curve, and the red dashed line is the CSR hypothetical statistical curve.</p>
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<p>The landslide density depends on the topographical and geomorphological factors significance test result chart. (<b>A</b>,<b>B</b>,<b>C</b>,<b>D</b>) are the range, the Slp, the Elv, and the Asp, respectively.</p>
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<p>The ROC chart of WcEqTLs dependents on covariates.</p>
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<p>ROC charts of models. (<b>B</b>) is a partial enlargement of (<b>A</b>), whose range of the X axis is 0.10~0.25 and the range of the Y axis is 0.80~0.95. The red dashed line in the figure (<b>A</b>) is for the CSR.</p>
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<p>A landslide density prediction map and classification map of each model.</p>
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12 pages, 947 KiB  
Article
Establishing the Locomotor Performance Profile of Elite Ladies Gaelic Football across Position and Quarters
by Shane Malone, Aideen McGuinness, John David Duggan, Paul Francis Talty, Cliodhna O’Connor and Kieran Collins
Appl. Sci. 2024, 14(16), 7145; https://doi.org/10.3390/app14167145 - 14 Aug 2024
Abstract
The aim of the current investigation was to examine the locomotor performance profile of elite ladies Gaelic football (LGF) players in relation to playing position and quarters of play. Thirty (n = 30) elite LGF players (age: 24 ± 4 years, height: [...] Read more.
The aim of the current investigation was to examine the locomotor performance profile of elite ladies Gaelic football (LGF) players in relation to playing position and quarters of play. Thirty (n = 30) elite LGF players (age: 24 ± 4 years, height: 169 ± 5 cm, body mass: 61 ± 4 kg) were monitored using 10 Hz GPS technology (Playertek; Catapult Sports; Australia) resulting in 145 individual samples collected over 18 competitive matches across the 2021 and 2022 LGF seasons. Locomotor performance was determined across the following variables: total distance covered (TD, m), relative distance (m·min−1), high-speed running (HSR, ≥ 4.4 m·s−1), very high-speed running (VHSR, ≥ 5.5 m·s−1), relative HSR (RHSR; m·min−1), peak velocity (m·s−1), percentage peak velocity (%PeakV), accelerations (n; ≥ 3 m·s−2), and decelerations (n; ≤ −3 m·s−2). Data were classified based on playing position and quarter of play. The greatest TD was covered by half-backs, midfielders, and half-forwards, with these positions covering significantly greater distances than full-backs (p < 0.05). Similarly, half-backs, midfielders, and half-forwards covered the greatest high-speed distance (HSR). When running performance was analysed across quarters, a significant position by quarter interaction was observed for all running performance variables, except peak velocity and percentage peak velocity. A consistent trend for reduced locomotor performance was evident in the second and fourth quarters across all positional lines. The current data provide coaches with the position-specific locomotor requirements of LGF match-play, which can inform the design of training content for LGF players, along with match-day strategies. Given the reduction in locomotor performance observed across the match, performance staff may consider the use of nutritional interventions, rewarm-up strategies, or specific substitution policies to mitigate the decrement in locomotor performance observed across match-play. Full article
(This article belongs to the Special Issue Exercise Physiology and Biomechanics in Human Health)
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<p>Changes in running performance across quarters irrespective of playing position. Error bars represent the lower and upper bounds of the 90% confidence interval of the mean estimate. (<b>A</b>) Total distance (m). (<b>B</b>) High-speed running (m). (<b>C</b>) Relative total distance (m·min<sup>−1</sup>). (<b>D</b>) Very high-speed running (m). (<b>E</b>) Maximal velocity (m·s<sup>−1</sup>). (<b>F</b>) Relative high-speed running (m·min<sup>−1</sup>). * Represents a significant difference from the first quarter * (<span class="html-italic">p</span> &lt; 0.05), ** (<span class="html-italic">p</span> &lt; 0.01). † Represents a significant difference from the third playing quarter † (<span class="html-italic">p</span> &lt; 0.05), †† (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Changes in running performance across quarter of play. (<b>a</b>) Total distance (m), (<b>b</b>) relative distance (m·min<sup>−1</sup>), (<b>c</b>) high-speed running distance (m; ≥ 4.4 m·s<sup>−1</sup>), and (<b>d</b>) peak velocity (m·s<sup>−1</sup>). Data presented as mean ± SD.</p>
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14 pages, 469 KiB  
Article
Clinical Implications of Metabolic Syndrome in Psoriasis Management
by Maria-Lorena Mustata, Carmen-Daniela Neagoe, Mihaela Ionescu, Maria-Cristina Predoi, Ana-Maria Mitran and Simona-Laura Ianosi
Diagnostics 2024, 14(16), 1774; https://doi.org/10.3390/diagnostics14161774 - 14 Aug 2024
Abstract
Psoriasis is an increasingly common chronic immune-mediated skin disease recognized for its systemic effects that extend beyond the skin and include various cardiovascular diseases, neurological diseases, type 2 diabetes, and metabolic syndrome. This study aimed to explore the complex relationship between psoriasis and [...] Read more.
Psoriasis is an increasingly common chronic immune-mediated skin disease recognized for its systemic effects that extend beyond the skin and include various cardiovascular diseases, neurological diseases, type 2 diabetes, and metabolic syndrome. This study aimed to explore the complex relationship between psoriasis and metabolic syndrome by analyzing clinical, biochemical, and immunological parameters in patients with psoriasis alone and in patients combining psoriasis and metabolic syndrome. A total of 150 patients were enrolled, 76 with psoriasis only (PSO) and 74 with psoriasis and metabolic syndrome (PSO–MS). Data collected included anthropometric measurements, blood tests, and inflammatory markers. Statistical analysis was performed using the independent t-test, Mann–Whitney U test, Kruskal–Wallis test, and chi-square test to compare the two groups. Patients in the PSO–MS group had a significantly higher body weight, abdominal circumference, BMI, and inflammatory markers compared to patients with PSO. In addition, increased levels of IL-17A, cholesterol, triglycerides, and glucose were observed in the PSO–MS group. This study highlights the increased metabolic risk and exacerbated systemic inflammation associated with the coexistence of psoriasis and metabolic syndrome. These findings demonstrate the need for a comprehensive therapeutic approach and early intervention to manage metabolic complications in patients with psoriasis and metabolic syndrome. Full article
17 pages, 1961 KiB  
Article
Knockdown of Gonadotropin-Releasing Hormone II Receptor Impairs Ovulation Rate, Corpus Luteum Development, and Progesterone Production in Gilts
by Amy T. Desaulniers, Rebecca A. Cederberg, Clay A. Lents and Brett R. White
Animals 2024, 14(16), 2350; https://doi.org/10.3390/ani14162350 - 14 Aug 2024
Abstract
Reproduction is classically controlled by gonadotropin-releasing hormone (GnRH-I) and its receptor (GnRHR-I) within the brain. In pigs, a second form (GnRH-II) and its specific receptor (GnRHR-II) are also produced, with greater abundance in peripheral vs. central reproductive tissues. The binding of GnRH-II to [...] Read more.
Reproduction is classically controlled by gonadotropin-releasing hormone (GnRH-I) and its receptor (GnRHR-I) within the brain. In pigs, a second form (GnRH-II) and its specific receptor (GnRHR-II) are also produced, with greater abundance in peripheral vs. central reproductive tissues. The binding of GnRH-II to GnRHR-II has been implicated in the autocrine/paracrine regulation of gonadal steroidogenesis rather than gonadotropin secretion. Blood samples were collected from transgenic gilts, with the ubiquitous knockdown of GnRHR-II (GnRHR-II KD; n = 8) and littermate controls (n = 7) at the onset of estrus (follicular) and 10 days later (luteal); serum concentrations of 16 steroid hormones were quantified by high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS). Upon euthanasia, ovarian weight (OWT), ovulation rate (OR), and the weight of each excised Corpus luteum (CLWT) were recorded; HPLC-MS/MS was performed on CL homogenates. During the luteal phase, serum progesterone concentration was reduced by 18% in GnRHR-II KD versus control gilts (p = 0.0329). Age and weight at puberty, estrous cycle length, and OWT were similar between lines (p > 0.05). Interestingly, OR was reduced (p = 0.0123), and total CLWT tended to be reduced (p = 0.0958) in GnRHR-II KD compared with control females. Luteal cells in CL sections from GnRHR-II KD gilts were hypotrophic (p < 0.0001). Therefore, GnRH-II and its receptor may help regulate OR, CL development, and progesterone production in gilts. Full article
(This article belongs to the Special Issue Endocrinology of the Female Reproductive System)
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<p>Experimental design. The once daily detection of estrus began at approximately 170 d of age. Puberty was considered the first display of behavioral estrus. The detection of estrus continued for a total of five consecutive estrous cycles. At the onset of the third estrous cycle (d 0), blood was collected via jugular venipuncture (follicular sample) and 10 d later (luteal sample). Animals were euthanized and reproductive tissues were collected on approximately day 7 of the fifth estrous cycle.</p>
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<p>Body weights were not different between GnRHR-II KD (<span class="html-italic">n</span> = 8) and littermate control (<span class="html-italic">n</span> = 7) gilts over time. Body weight was recorded at birth, weaning, and during pre-pubertal development (40, 60, 80, 100, 125, 145 and 165 d of age). Results are presented as least squares means (LSMEANS) ± the standard error of the mean (SEM). Line, <span class="html-italic">p</span> = 0.3677; Age, <span class="html-italic">p</span> &lt; 0.0001; Line × Age, <span class="html-italic">p</span> = 0.7960.</p>
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<p>Age at puberty (<b>a</b>), weight at puberty (<b>b</b>), and estrous cycle length (<b>c</b>) in GnRHR-II KD (<span class="html-italic">n</span> = 8) and littermate control (<span class="html-italic">n</span> = 7) gilts. No line effects were detected (<span class="html-italic">p</span> &gt; 0.10). Results are presented as least squares means (LSMEANS) ± the standard error of the mean (SEM).</p>
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<p>Concentrations of 11-deoxycortisol in blood serum samples from GnRHR-II KD (<span class="html-italic">n</span> = 8) and littermate control (<span class="html-italic">n</span> = 7) gilts during the follicular and luteal phases of the estrous cycle. There was no effect of phase or line by phase interaction (<span class="html-italic">p</span> &gt; 0.05). However, there was an overall effect (<span class="html-italic">p</span> = 0.0320) of line; GnRHR-II KD gilts had reduced circulating concentrations. Results are presented as least squares means (LSMEANS) ± the standard error of the mean (SEM). * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Progestogen concentrations in blood serum samples from GnRHR-II KD (<span class="html-italic">n</span> = 8) and littermate control (<span class="html-italic">n</span> = 7) gilts during the follicular and luteal phase of the estrous cycle. Neither an effect of line (GnRHR-II KD versus control) nor a line by phase interaction was detected for 17α-hydroxyprogesterone, so these data are not reported. However, a phase effect (<span class="html-italic">p</span> = 0.0006) was detected for 17α-hydroxyprogesterone, with the concentration greater during the luteal phase (<b>a</b>). A line by phase interaction (<span class="html-italic">p</span> = 0.0341) was detected for progesterone; GnRHR-II KD gilts produced less progesterone during the luteal phase (<b>b</b>). Results are presented as least squares means (LSMEANS) ± the standard error of the mean (SEM). <sup>a,b,c</sup> Divergent letters differ significantly (<span class="html-italic">p</span> &lt; 0.05); * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Androgen concentrations during the follicular phase and luteal phase in blood serum samples from GnRHR-II KD (<span class="html-italic">n</span> = 8) and littermate control (<span class="html-italic">n</span> = 7) gilts. No effect of line (GnRHR-II KD versus control) nor line by phase interaction was detected for any androgen examined (<span class="html-italic">p</span> &gt; 0.05); therefore, these data are not reported. A phase effect (<span class="html-italic">p</span> &lt; 0.05) was detected for testosterone (<b>a</b>), androsterone (<b>b</b>) and androstenedione (<b>c</b>) with concentrations greater during the follicular phase. Results are presented as least squares means (LSMEANS) ± the standard error of the mean (SEM). * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Estrogen concentrations during the follicular and luteal phase in blood serum samples from GnRHR-II KD (<span class="html-italic">n</span> = 8) and littermate control (<span class="html-italic">n</span> = 7) gilts. No effect of line (GnRHR-II KD versus control) nor line by phase interaction was detected for estrogens (<span class="html-italic">p</span> &gt; 0.05); therefore, these data are not reported. A phase effect (<span class="html-italic">p</span> &lt; 0.05) was detected for both 17β-estradiol (<b>a</b>) and estrone (<b>b</b>) with concentrations greater during the follicular phase compared with luteal phase. Results are presented as least squares means (LSMEANS) ± the standard error of the mean (SEM). * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Ovarian characteristics in GnRHR-II KD (<span class="html-italic">n</span> = 8) and littermate control (<span class="html-italic">n</span> = 7) gilts. Paired ovary weight (<b>a</b>) was similar (<span class="html-italic">p</span> &gt; 0.10) between lines, but ovulation rate (number of <span class="html-italic">Corpora lutea</span>) (<b>b</b>) was reduced (<span class="html-italic">p</span> = 0.0123) in GnRHR-II KD gilts compared with littermate controls. Results are presented as least squares means (LSMEANS) ± the standard error of the mean (SEM). * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p><span class="html-italic">Corpus luteum</span> (CL) metrics and GnRHR-II expression in CL samples from GnRHR-II KD and littermate control gilts. Average individual CL weight was greater (<span class="html-italic">p</span> &lt; 0.0001) in GnRHR-II KD (<span class="html-italic">n</span> = 4) versus control (<span class="html-italic">n</span> = 4) gilts (<b>a</b>). Total CL weight per ovary tended to be reduced (<span class="html-italic">p</span> = 0.0958) in GnRHR-II KD (<span class="html-italic">n</span> = 4) versus control (<span class="html-italic">n</span> = 4) gilts (<b>b</b>). Luteal cell area was reduced (<span class="html-italic">p</span> &lt; 0.0001) in GnRHR-II KD (<span class="html-italic">n</span> = 7) versus littermate control (<span class="html-italic">n</span> = 6) gilts (<b>c</b>). Expression of GnRHR-II tended to be reduced (<span class="html-italic">p</span> = 0.0774) by 21% in CL from GnRHR-II KD (<span class="html-italic">n</span> = 8) versus control (<span class="html-italic">n</span> = 7) gilts (<b>d</b>). Results are presented as least squares means (LSMEANS) ± the standard error of the mean (SEM). * <span class="html-italic">p</span> &lt; 0.05; † <span class="html-italic">p</span> &lt; 0.10.</p>
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19 pages, 1864 KiB  
Article
Effects of Pulsed Electromagnetic Field Treatment on Skeletal Muscle Tissue Recovery in a Rat Model of Collagenase-Induced Tendinopathy: Results from a Proteome Analysis
by Enrica Torretta, Manuela Moriggi, Daniele Capitanio, Carlotta Perucca Orfei, Vincenzo Raffo, Stefania Setti, Ruggero Cadossi, Laura de Girolamo and Cecilia Gelfi
Int. J. Mol. Sci. 2024, 25(16), 8852; https://doi.org/10.3390/ijms25168852 - 14 Aug 2024
Abstract
Tendon disorders often result in decreased muscle function and atrophy. Pulsed Electromagnetic Fields (PEMFs) have shown potential in improving tendon fiber structure and muscle recovery. However, the molecular effects of PEMF therapy on skeletal muscle, beyond conventional metrics like MRI or markers of [...] Read more.
Tendon disorders often result in decreased muscle function and atrophy. Pulsed Electromagnetic Fields (PEMFs) have shown potential in improving tendon fiber structure and muscle recovery. However, the molecular effects of PEMF therapy on skeletal muscle, beyond conventional metrics like MRI or markers of muscle decline, remain largely unexplored. This study investigates the metabolic and structural changes in PEMF-treated muscle tissue using proteomics in a rat model of Achilles tendinopathy induced by collagenase. Sprague Dawley rats were unilaterally induced for tendinopathy with type I collagenase injection and exposed to PEMFs for 8 h/day. Gastrocnemius extracts from untreated or PEMF-treated rats were analyzed with LC-MS/MS, and proteomics differential analysis was conducted through label-free quantitation. PEMF-treated animals exhibited decreased glycolysis and increased LDHB expression, enhancing NAD signaling and ATP production, which boosted respiratory chain activity and fatty acid beta-oxidation. Antioxidant protein levels increased, controlling ROS production. PEMF therapy restored PGC1alpha and YAP levels, decreased by tendinopathy. Additionally, myosins regulating slow-twitch fibers and proteins involved in fiber alignment and force transmission increased, supporting muscle recovery and contractile function. Our findings show that PEMF treatment modulates NAD signaling and oxidative phosphorylation, aiding muscle recovery through the upregulation of YAP and PGC1alpha and increasing slow myosin isoforms, thus speeding up physiological recovery. Full article
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<p>Experimental design and label-free LC–ESI–MS/MS results. (<b>A</b>) Untreated rats. Type I collagenase was injected into the right limbs (COL), while PBS was injected into their left counterparts (PBS). Animals were sacrificed on day 21, 30, and 45 after the injection. Based on this, right muscles (COL) were named COL21, COL30, and COL45, while left muscles (PBS) were named PBS21, PBS30, and PBS45. (<b>B</b>) PEMF-treated rats. Animals were treated with PEMFs (1.5 mT SD 0.2; 75 Hz) for eight hours/day. Right muscles (COL + PEMF) were named COL + PEMF21, COL + PEMF30, and COL + PEMF45. (<b>C</b>) Venn diagram. Comparisons of shared and distinct significantly changed proteins in COL vs. PBS (dusty blue circles) and in COL + PEMF and COL (pink circles). Data resulted from label-free quantitation after LC–ESI–MS/MS analysis (ANOVA followed by Tukey’s multiple comparison test, <span class="html-italic">p</span>-value &lt; 0.05). The graphical illustration was generated using BioRender (version 4).</p>
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<p>Glucose and stress metabolism. (<b>A</b>) Summary of changed metabolic enzymes in glucose metabolism in muscles attached to inflamed tendons, both without (COL) and with PEMF treatment (PEMF). (<b>B</b>) Heatmap illustrating the expression profile of significantly increased (in red) or decreased (in green) enzymes (ANOVA and Tukey’s test, <span class="html-italic">p</span>-value &lt; 0.05) involved in glucose metabolism and stress response pathways. The graphical illustration was generated using BioRender (version 4).</p>
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<p>TCA cycle, fatty acid oxidation, and oxidative phosphorylation pathways. (<b>A</b>,<b>B</b>) Heatmaps illustrating the expression profile of significantly increased (in red) or decreased (in green) enzymes (ANOVA and Tukey’s test, <span class="html-italic">p</span>-value &lt; 0.05) involved in TCA cycle, fatty acid oxidation (<b>A</b>), and oxidative phosphorylation (<b>B</b>) pathways. (<b>C</b>) Graphical representation of metabolic canonical pathways: activated (z-score &gt; 2; orange arrows) or inhibited (z-blue &lt; 2; blue arrows) in muscles attached to inflamed tendons, without (COL) and with PEMF treatment (PEMF). (<b>D</b>) Heatmap displaying the expression profile of enzymes involved in the NAD signaling pathway. (<b>E</b>) Heatmap presenting the most significant upstream regulators. Orange- and blue-colored rectangles indicate predicted regulator activation or inhibition, respectively, via the z-score statistic. (<b>F</b>,<b>G</b>) Bar graphs depicting the expression of Yes-Associated Protein (YAP) (<b>F</b>) and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1alpha) (<b>G</b>) in the <span class="html-italic">gastrocnemius</span> muscle from PBS, COL, and COL + PEMF groups (mean ± SD; * = significant difference, ANOVA and Tukey’s test, <span class="html-italic">n</span> = 2, * <span class="html-italic">p</span>-value &lt; 0.05; ** <span class="html-italic">p</span>-value &lt; 0.01; *** <span class="html-italic">p</span>-value &lt; 0.001). Full-length images are available in <a href="#app1-ijms-25-08852" class="html-app">Supplementary Figure S1</a>. The graphical illustration was generated using BioRender (version 4).</p>
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<p>Muscle fiber characterization and contractile proteins. (<b>A</b>–<b>C</b>) Bar graphs showing the distribution of myosin heavy chain (MyHC) isoforms in untreated and PEMF-treated animals sacrificed at 21 days (<b>A</b>), 30 days (<b>B</b>), and 45 days (<b>C</b>) after collagenase injection. ANOVA and Tukey’s test, <span class="html-italic">n</span> = 3, * <span class="html-italic">p</span>-value &lt; 0.05; ** <span class="html-italic">p</span>-value &lt; 0.01; *** <span class="html-italic">p</span>-value &lt; 0.001. Representative gel images are displayed. (<b>D</b>–<b>G</b>) Heatmaps illustrating the expression profile and % fold changes in significantly increased (in red) or decreased (in green) proteins (ANOVA and Tukey’s test, <span class="html-italic">p</span>-value &lt; 0.05) in sarcomere structures: thin filaments (<b>D</b>), thick filaments (<b>E</b>), M line (<b>F</b>), and Z disk (<b>G</b>). Full-length images are available in <a href="#app1-ijms-25-08852" class="html-app">Supplementary Figure S2</a>. The graphical illustration was generated using BioRender (version 4).</p>
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23 pages, 1856 KiB  
Review
Exploring Microbial Dynamics: The Interaction between Yeasts and Acetic Acid Bacteria in Port Wine Vinegar and Its Implications on Chemical Composition and Sensory Acceptance
by João Mota and Alice Vilela
Fermentation 2024, 10(8), 421; https://doi.org/10.3390/fermentation10080421 - 14 Aug 2024
Abstract
Port wine vinegar, a product of the esteemed Port wine, is renowned for its intricate blend of flavors and aromas, a result of complex microbial interactions. This study delves into the fascinating world of yeast and acetic acid bacteria (AAB) interactions during fermentation, [...] Read more.
Port wine vinegar, a product of the esteemed Port wine, is renowned for its intricate blend of flavors and aromas, a result of complex microbial interactions. This study delves into the fascinating world of yeast and acetic acid bacteria (AAB) interactions during fermentation, which significantly influence the vinegar’s chemical composition and sensory properties. We specifically investigate the role of yeasts in fermenting sugars into ethanol, a process that AAB then converts into acetic acid. The impact of these interactions on the production of secondary metabolites, such as gluconic acid, ketones, aldehydes, and esters, which contribute to the vinegar’s unique sensory profile, is thoroughly examined. Advanced analytical techniques, including GC-MS and e-nose technology, alongside sensory evaluation, are employed to assess these effects. The research underscores the significance of ethanol tolerance in AAB and other production challenges in determining vinegar quality and underscores the importance of optimizing fermentation conditions and sustainable practices. The findings of this study underscore the importance of strain interactions and production techniques, which can significantly enhance the quality and market appeal of Port wine vinegar, providing valuable insights for the industry. This review also identifies exciting and critical areas for future research, inspiring further exploration and proposing strategies for advancing production and application in culinary, health, and industrial contexts. Full article
(This article belongs to the Section Microbial Metabolism, Physiology & Genetics)
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<p>Alcoholic fermentation pathway. The most abundant fermentable sugars in <span class="html-italic">Vitis vinifera’s</span> leaves, bark, roots, and berries are glucose and fructose, with sucrose in lower levels [<a href="#B28-fermentation-10-00421" class="html-bibr">28</a>,<a href="#B29-fermentation-10-00421" class="html-bibr">29</a>].</p>
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<p>Conversion of ethanol into acetic acid by acetic acid bacteria.</p>
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<p>Comparative case study of Port wine vinegar, balsamic vinegar, cider vinegar, and Sherry vinegar. Data obtained from [<a href="#B7-fermentation-10-00421" class="html-bibr">7</a>,<a href="#B12-fermentation-10-00421" class="html-bibr">12</a>,<a href="#B75-fermentation-10-00421" class="html-bibr">75</a>,<a href="#B94-fermentation-10-00421" class="html-bibr">94</a>,<a href="#B95-fermentation-10-00421" class="html-bibr">95</a>].</p>
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<p>Por wine vinegar applications.</p>
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24 pages, 16101 KiB  
Article
Differential Expression of PACAP/VIP Receptors in the Post-Mortem CNS White Matter of Multiple Sclerosis Donors
by Margo Iris Jansen, Giuseppe Musumeci and Alessandro Castorina
Int. J. Mol. Sci. 2024, 25(16), 8850; https://doi.org/10.3390/ijms25168850 - 14 Aug 2024
Abstract
Pituitary adenylate cyclase-activating polypeptide (PACAP) and vasoactive intestinal peptide (VIP) are two neuroprotective and anti-inflammatory molecules of the central nervous system (CNS). Both bind to three G protein-coupled receptors, namely PAC1, VPAC1 and VPAC2, to elicit their beneficial effects in various CNS diseases, [...] Read more.
Pituitary adenylate cyclase-activating polypeptide (PACAP) and vasoactive intestinal peptide (VIP) are two neuroprotective and anti-inflammatory molecules of the central nervous system (CNS). Both bind to three G protein-coupled receptors, namely PAC1, VPAC1 and VPAC2, to elicit their beneficial effects in various CNS diseases, including multiple sclerosis (MS). In this study, we assessed the expression and distribution of PACAP/VIP receptors in the normal-appearing white matter (NAWM) of MS donors with a clinical history of either relapsing–remitting MS (RRMS), primary MS (PPMS), secondary progressive MS (SPMS) or in aged-matched non-MS controls. Gene expression studies revealed MS-subtype specific changes in PACAP and VIP and in the receptors’ levels in the NAWM, which were partly corroborated by immunohistochemical analyses. Most PAC1 immunoreactivity was restricted to myelin-producing cells, whereas VPAC1 reactivity was diffused within the neuropil and in axonal bundles, and VPAC2 in small vessel walls. Within and around lesioned areas, glial cells were the predominant populations showing reactivity for the different PACAP/VIP receptors, with distinctive patterns across MS subtypes. Together, these data identify the differential expression patterns of PACAP/VIP receptors among the different MS clinical entities. These results may offer opportunities for the development of personalized therapeutic approaches to treating MS and/or other demyelinating disorders. Full article
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<p>Representative lesions and normal-appearing white matter in human brain sections from donors with different MS subtypes. Luxol Fast Blue (LFB) staining shows the intense blue staining of myelinated fibers in the white matter (WM) of (<b>A</b>) non-MS donors, differentiating it from the less myelinated grey matter (GM). Evident discoloring of lesioned areas (indicated by black arrowheads) can be appreciated in sections from (<b>B</b>) RRMS, (<b>C</b>) PPMS and (<b>D</b>) SPMS cases. Myelin is stained blue, resulting in a clear distinction between GM and WM. Scale bar in (<b>A</b>) 200 µm, (<b>B</b>,<b>C</b>) 500 µm, (<b>D</b>) 2000 µm and NAWM (panels on the right) 25 µm. MS = multiple sclerosis, RRMS = relapsing–remitting MS, PPMS = primary-progressive MS, SPMS = secondary-progressive MS, GM = grey matter, WM = white matter.</p>
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<p>Differential expression of PACAP and VIP neuropeptide genes in the normal-appearing white matter of MS donors. (<b>A</b>) PACAP (gene name = ADCYAP1) expression was measured using RT-qPCR, comparing non-MS and MS cases. Further stratification of cases by clinical course, showing the expression levels of ADCYAP1 in (<b>A′</b>) non-MS vs. RRMS, (<b>A″</b>) non-MS vs. PPMS and (<b>A‴</b>) non-MS vs. SPMS. (<b>B</b>) VIP gene expression in non-MS vs. MS cases. A stratification similar to that in A demonstrates relative changes in the transcript levels between non-MS and (<b>B′</b>) RRMS, (<b>B″</b>) PPMS and (<b>B‴</b>) SPMS cases. The data shown are the mean fold change ± SEM, obtained from <span class="html-italic">n</span> = 6 (non-MS), <span class="html-italic">n</span> = 5 (RRMS), <span class="html-italic">n</span> = 6 (SPMS) and <span class="html-italic">n</span> = 4 (PPMS) cases. <span class="html-italic">p</span>-values &gt; 0.05 are also shown. * <span class="html-italic">p</span> &lt; 0.05 or *** <span class="html-italic">p</span> &lt; 0.001 vs. non-MS, as determined by unpaired <span class="html-italic">t</span>-test. VIP = vasoactive intestinal peptide, PACAP = pituitary adenylate cyclase activating polypeptide, MS = multiple sclerosis, NAWM = normal-appearing white matter, RRMS = relapsing–remitting MS, PPMS = primary progressive MS, SPMS = secondary progressive MS.</p>
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<p>Differential expression of PAC1, VPAC1 and VPAC2 genes in the normal-appearing white matter of MS donors. Gene expression of (<b>A</b>) ADCYAP1R1 (aka PAC1), (<b>B</b>) VIPR1 (VPAC1) and (<b>C</b>) VIPR2 (VPAC2) in the NAWM of non-MS vs. MS donors. Upon stratification based on the clinical MS course, the gene expression levels of ADCYAP1R1, VIPR1 and VIPR2 were determined for (<b>A′</b>–<b>C′</b>) RRMS, (<b>A″</b>–<b>C″</b>) PPMS and (<b>A‴</b>–<b>C‴</b>) SPMS cases. The data shown are the mean fold change ± SEM, obtained from <span class="html-italic">n</span> = 6 (non-MS), <span class="html-italic">n</span> = 5 (RRMS), <span class="html-italic">n</span> = 6 (SPMS) and <span class="html-italic">n</span> = 4 (PPMS) cases. <span class="html-italic">p</span>-values &gt; 0.05 are also shown. * <span class="html-italic">p</span> &lt; 0.05 or *** <span class="html-italic">p</span> &lt; 0.001 vs. non-MS, as determined by unpaired <span class="html-italic">t</span>-test. ADCYAP1R1 = Pituitary adenylate cyclase-activating polypeptide type I receptor, VIPR1 = Vasoactive intestinal polypeptide receptor 1, VIPR2 = Vasoactive intestinal polypeptide receptor 2, MS = multiple sclerosis, NAWM = normal-appearing white matter, RRMS = relapsing–remitting MS, PPMS = primary progressive MS, SPMS = secondary progressive MS.</p>
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<p>PAC1 immunoreactivity in the normal-appearing white matter of RRMS, PPMS and SPMS cases. (<b>A</b>) Representative images showing PAC1 immunoreactive cells in the NAWM of MS donors with a clinical history of RRMS, PPMS or SPMS and non-MS control cases. White arrows in each panel point to PAC1<sup>+</sup> cells, which exhibit chromatin-dense and rounded/oval shaped nuclei, consistent with the oligodendrocyte/OPC morphology. (<b>B</b>) The average cell density (total # of cells per region of interest (ROI); ROI area = 1.23 mm<sup>2</sup>) was calculated using 2–4 ROIs from <span class="html-italic">n</span> = 5 (non-MS), <span class="html-italic">n</span> = 4 (PPMS), <span class="html-italic">n</span> = 6 (RRMS) and <span class="html-italic">n = 6</span> (SPMS) cases. (<b>C</b>) The PAC1 immunoreactivity in cells was determined by normalizing the mean PAC1 staining intensity/average # of cells counted within the same ROIs/cases as in (<b>B</b>). *** <span class="html-italic">p</span> &lt; 0.001 or **** <span class="html-italic">p</span> &lt; 0.0001 vs. non-MS cases, as determined by one-way ANOVA followed by Sidak’s post hoc test. Scale bar = 30 µm. OPC = Oligodendrocyte progenitor cell, PAC1 = Pituitary adenylate cyclase-activating polypeptide type I receptor, MS = multiple sclerosis, NAWM = normal-appearing white matter, RRMS = relapsing–remitting MS, PPMS = primary progressive MS, SPMS = secondary progressive MS.</p>
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<p>PAC1 co-localizes to OLIG2<sup>+</sup> cells in the normal-appearing white matter of RRMS, PPMS and SPMS cases. Representative images showing PAC1 (green)/OLIG2 (red) colocalization in the NAWM of (<b>A</b>) non-MS, (<b>B</b>) RRMS, (<b>C</b>) PPMS or (<b>D</b>) SPMS donors. Nuclei were counterstained with DAPI (blue). Scale bar = 50 µm.</p>
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<p>PAC1 immunoreactivity in representative white matter lesions from selected MS clinical cases. (<b>A</b>–<b>C</b>, left panels) Low-magnification images showing PAC1 immunoreactivity in a lesion taken from one RRMS, PPMS or SPMS-exemplary case. Lesion borders are demarcated by the black dashed lines. Scale bar = 1000 µm. (Insets in <b>A</b>–<b>C</b>) High-power images of ROIs in the left panels (orange and red squares) demonstrating PAC1<sup>+</sup> staining around the lesion edge (top inset) and within the lesion (bottom inset) of the selected RRMS, PPMS and SPMS cases. Scale bar = 30 µm. WM = white matter.</p>
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<p>VPAC1 immunoreactivity in the normal-appearing white matter of RRMS, PPMS and SPMS cases. (<b>A</b>–<b>D</b>) Representative images depicting VPAC1 immunoreactive sites in the NAWM of MS donors with a clinical history of RRMS, PPMS or SPMS and non-MS control cases. Scale bar = 1000 µm. (<b>A′</b>–<b>D′</b>) Insets of the NAWM taken at a higher magnification. Black arrowheads point to VPAC1<sup>+</sup> axonal fibers. Scale bar (NAWM) = 30 µm (<b>B″</b>–<b>D″</b>). Insets showing VPAC1<sup>+</sup> in the grey matter of the selected cases. Black arrowheads indicate VPAC1<sup>+</sup> neurons. Scale bar (GM) = 50 µm. (<b>E</b>) Bar graph showing the average VPAC1 immunoreactivity (IR) in the NAWM. The data shown are the mean grey intensity ± SEM and were calculated by averaging the grey intensity of 2–4 ROIs from <span class="html-italic">n</span> = 5 (non-MS), <span class="html-italic">n</span> = 4 (PPMS), <span class="html-italic">n</span> = 6 (RRMS) and <span class="html-italic">n</span> = 6 (SPMS) cases. Each ROI area = 1.23 mm<sup>2</sup>. No statistical significance was found using one-way ANOVA. Ns = Not significant. VPAC1 = Vasoactive Intestinal Peptide/Pituitary Adenylate Cyclase Activating Polypeptide Receptor 1, MS = multiple sclerosis, NAWM = normal-appearing white matter, WM = white matter, GM = grey matter, RRMS = relapsing–remitting MS, PPMS = primary progressive MS, SPMS = secondary progressive MS.</p>
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<p>VPAC1 immunoreactivity in white matter lesions from selected MS clinical cases. (<b>A</b>–<b>C</b>, left panels) Low-magnification images showing VPAC1 immunoreactivity in a lesion taken from one RRMS, PPMS or SPMS-exemplary case. Lesion borders are demarcated by the black dashed lines. Scale bar = 1000 µm. (Insets in <b>A</b>–<b>C</b>) High-power images of ROIs in left panels (orange and red squares) demonstrating VPAC1<sup>+</sup> staining around the lesion edge (top inset) and within the lesion (bottom inset) of the selected RRMS, PPMS and SPMS cases. Scale bar = 30 µm. WM = white matter.</p>
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<p>VPAC2 immunoreactivity in the normal-appearing white matter of RRMS, PPMS and SPMS cases. (<b>A</b>–<b>D</b>) Representative images depicting VPAC2 immunoreactive (IR) cells in the NAWM of MS donors with a clinical history of RRMS, PPMS or SPMS and non-MS controls. Scale bar = 1000 µm. (<b>A′</b>–<b>D′</b>) Insets of the NAWM taken at a higher magnification. White arrowheads in C′ show VPAC2<sup>+</sup> vessel walls. Scale bar (NAWM) = 30 µm. (<b>B″</b>–<b>D″</b>) Insets showing VPAC2<sup>+</sup> in the grey matter of the selected cases. Black arrowheads indicate VPAC2<sup>+</sup> neurons, whereas white arrowheads show VPAC2-IR in axons. Scale bar (GM) = 50 µm. (<b>E</b>) Bar graph showing the average VPAC1 immunoreactivity (IR) in the NAWM. The data shown are the mean grey intensity ± SEM and were calculated by averaging the grey intensity of 2–4 ROIs from <span class="html-italic">n = 5</span> (non-MS), <span class="html-italic">n</span> = 4 (PPMS), <span class="html-italic">n</span> = 6 (RRMS) and <span class="html-italic">n</span> = 6 (SPMS) cases. Each ROI area = 1.23 mm<sup>2</sup>. **** <span class="html-italic">p</span> &lt; 0.0001 vs. non-MS (control) cases, as determined by one-way ANOVA and Sidak’s post hoc test. VPAC2 = Vasoactive Intestinal Peptide/Pituitary Adenylate Cyclase Activating Polypeptide Receptor 1, MS = multiple sclerosis, NAWM = normal-appearing white matter, GM = grey matter, RRMS = relapsing–remitting MS, PPMS = primary progressive MS, SPMS = secondary progressive MS.</p>
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<p>VPAC2 immunoreactivity in white matter lesions from selected MS clinical cases. (<b>A</b>–<b>C</b>, left panels) Low-magnification images showing VPAC2 immunoreactivity in a lesion taken from one RRMS, PPMS or SPMS-exemplary case. Lesion borders are demarcated by the black dashed lines. Scale bar = 1000 µm. (Insets in <b>A</b>–<b>C</b>) High-power images of ROIs in left panels (orange and red squared) demonstrating VPAC2 staining around the lesion edge (top inset) and within the lesion (bottom inset) of the selected RRMS, PPMS and SPMS cases. Scale bar = 30 µm. GM = grey matter; WM = white matter.</p>
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21 pages, 7226 KiB  
Article
Analysis of Maize Planting Mode and Simulation and Optimization of Ridging and Fertilization Components in Arid Area of Northwest China
by Fei Dai, Haifu Pan, Wenqi Zhou, Han Tang, Qi Wang, Wenglong Li and Jinwu Wang
Agriculture 2024, 14(8), 1360; https://doi.org/10.3390/agriculture14081360 - 14 Aug 2024
Abstract
The arid area of Northwest China belongs to the rain-fed agricultural area of the Loess Plateau, and water resources have become one of the important factors limiting agricultural development in this area. This study employed the AquaCrop model to predict the yield advantages [...] Read more.
The arid area of Northwest China belongs to the rain-fed agricultural area of the Loess Plateau, and water resources have become one of the important factors limiting agricultural development in this area. This study employed the AquaCrop model to predict the yield advantages and environmental adaptability of maize in Dingxi City from 2016 to 2020 under two cultivation practices: ridge tillage (100% film coverage with double ridge-furrow planting) and flat planting (81.8% film coverage with wide-film planting). The numerical simulation of the tillage and fertilization process of the double-ridge seedbed was carried out by EDEM, and the key components were tested by the Box–Behnken center combination test design principle to obtain the optimal parameter combination. The results showed that ridge planting was more suitable for agricultural planting in rain-fed arid areas in Northwest China. The simulation analysis of ridging and fertilization showed that the forward speed of the combined machine was 0.50 m/s, the rotation speed of the trough wheel of the fertilizer discharger was 39 rmp, and the rotary tillage depth was 150 mm. The qualified rate of seedbed tillage was 93.6%, and the qualified rate of fertilization was 92.1%. The research shows that the whole-film double ridge-furrow sowing technology of maize is more suitable for the rain-fed agricultural area in the arid area of Northwest China. The simulation results of the ridging fertilization device are consistent with the field experiment results. The research results provide a certain technical reference for the optimization of the whole-film double ridge-furrow sowing technology. Full article
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<p>Schematic diagram of two planting patterns: (<b>a</b>) flat planting mode and (<b>b</b>) ridge planting mode.</p>
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<p>Meteorological data of maize growth period.</p>
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<p>Meteorological data of maize growth period.</p>
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<p>Relationship between soil total water content and rainfall under different planting patterns.</p>
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<p>The whole structure. 1. Mainframe; 2. suspension device; 3. fertilizer box; 4. sprayer device; 5. compression device; 6. hole planter; 7. mulch hanging frame; 8. ridging device; 9. ground wheel; 10. fertilizer shovel; 11. soil-covering device; 12. rotary blade group.</p>
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<p>Simulation model of double-ridge tillage and fertilization operation. 1. Soil trough; 2. retaining cover; 3. straight-groove wheel fertilizer; 4. fertilizer box; 5. fertilizer particles; 6. fertilizer axis; 7. fertilizer tube; 8. two-wing ditching shovel; 9. ordinary fertilizer shovel; 10. rotary tillage tools; 11. rotary blade shaft.</p>
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<p>Group 4 response surface simulation analysis test. (<b>a</b>) Axis mapping of double-ridge tillage fertilization; (<b>b</b>) double-ridge tillage fertilization front view; (<b>c</b>) double-ridge tillage fertilization left view and furrow fertilizer distribution; and (<b>d</b>) fertilizer distribution cross-section. The red circle represents the local enlarged image, and the pink particles represent the state and position of the fertilizer particles in the soil.</p>
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<p>Group 13 response surface simulation analysis test. (<b>a</b>) Axis mapping of double-ridge til-age fertilization; (<b>b</b>) double-ridge tillage fertilization front view; (<b>c</b>) double-ridge tillage fertilization left view and furrow fertilizer distribution; and (<b>d</b>) fertilizer distribution cross-section. The red circle represents the local enlarged image, and the pink particles represent the state and position of the fertilizer particles in the soil.</p>
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<p>The effect of the interaction of various factors on the qualified rate of seedbed tillage. The higher the position, the red the color, the greater the value, on the contrary, the bluer the color, the smaller the value. (<b>a</b>) The interaction between the forward speed of the combined machine and the rotation speed of the fertilizer; (<b>b</b>) the interaction between the forward speed of the combined machine and rotary tillage depth; and (<b>c</b>) the interaction between rotary tillage depth and fertilizer rotation speed.</p>
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<p>The effect of the interaction of various factors on the qualified rate of fertilization. The higher the position, the red the color, the greater the value, on the contrary, the bluer the color, the smaller the value. (<b>a</b>) The interaction between the forward speed of the combined machine and the rotation speed of the fertilizer; (<b>b</b>) the interaction between the forward speed of the combined machine and rotary tillage depth; and (<b>c</b>) the interaction between rotary tillage depth and fertilizer rotation speed.</p>
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<p>Numerical simulation process of whole-film double ridge-furrow ridging and fertilization forming operation. Different colours represent different layers of soil particles, the deeper the depth, the bluer the colour.</p>
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<p>The specific simulation process of double-ridge tillage fertilization operation. Different colours represent different layers of soil particles, the deeper the depth, the bluer the colour.</p>
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<p>Simulation results of optimal parameters. Different colours represent different layers of soil particles, the deeper the depth, the bluer the colour. (<b>a</b>) Axis mapping of double-ridge tillage fertilization; (<b>b</b>) double-ridge tillage fertilization front view; (<b>c</b>) double-ridge tillage fertilization left view and furrow fertilizer distribution; and (<b>d</b>) fertilizer distribution cross-section.</p>
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<p>Field experiment plot.</p>
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<p>Comparison of fertilization depth. Different colours represent different layers of soil particles, the deeper the depth, the bluer the colour.</p>
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10 pages, 1443 KiB  
Article
Optimizing Timing of Intraperitoneal Chemotherapy to Enhance Intravenous Carboplatin Concentration
by Kohei Tamura, Natsuka Kimura, Hideyuki Ohzawa, Hideyo Miyato, Naohiro Sata, Takahiro Koyanagi, Yasushi Saga, Yuji Takei, Hiroyuki Fujiwara, Ryozo Nagai, Joji Kitayama and Kenichi Aizawa
Cancers 2024, 16(16), 2841; https://doi.org/10.3390/cancers16162841 - 14 Aug 2024
Abstract
Despite advances in systemic chemotherapy, patients with gastric cancer (GC) and peritoneal metastases (PMs) continue to have poor prognoses. Intraperitoneal (IP) administration of Paclitaxel (PTX) combined with systemic chemotherapy shows promise in treating PMs from GC. However, methods of drug administration need to [...] Read more.
Despite advances in systemic chemotherapy, patients with gastric cancer (GC) and peritoneal metastases (PMs) continue to have poor prognoses. Intraperitoneal (IP) administration of Paclitaxel (PTX) combined with systemic chemotherapy shows promise in treating PMs from GC. However, methods of drug administration need to be optimized to maximize efficacy. In this study, we utilized a mouse model with PMs derived from a human GC cell line, administering PTX either IP or intravenously (IV), and Carboplatin (CBDCA) IV 0, 1, and 4 days after PTX administration. The PMs were resected 30 min later, and concentrations of PTX and CBDCA in resected tumors were measured using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Results indicated that PTX concentrations were higher with IP administration than with IV administration, with significant differences observed on days 0 and 1. CBDCA concentrations 4 days post-IP PTX administration were higher than with simultaneous IV PTX administration. These findings suggest that IP PTX administration enhances CBDCA concentration in peritoneal tumors. Therefore, sequential IV administration of anti-cancer drugs appears more effective than simultaneous administration with IP PTX, a strategy that may improve prognoses for patients with PMs. Full article
(This article belongs to the Section Cancer Drug Development)
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Figure 1
<p>Experimental design to evaluate concentrations of PTX and CBDCA in peritoneal tumors. BALB/c nude mice were injected IP with 3 × 10⁶ MKN45P. After three weeks, PTX was administered IP or IV and vehicle was given IP. Right, 24 h and 96 h after PTX administration, CBDCA was injected IV and mice were sacrificed 30 min thereafter. Tumors were resected and PTX and CBDCA concentrations were measured with LC-MS/MS.</p>
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<p>Concentrations of PTX in tumors after IV or IP PTX injection. The number of PMs from 14 to 18 was obtained from both groups at days 0 and 4 after PTX administration, and PTX concentrations were measured with LC-MS/MS. Both groups involved 3 or more mice. Day 1 groups comprised 6 to 9 samples. Both groups involved 1 or 2 mice. Data show the mean ± SD. *: <span class="html-italic">p</span> &lt; 0.05, ns: not significant.</p>
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<p>Concentrations of CBDCA in tumors after IV or IP PTX injection. Numbers of PMs from 12 to 18 were obtained at days 0 and 4, respectively, after PTX administration and CBDCA concentrations were measured. Each group involved 3 or more mice. Day 1 groups each comprised 1 or 2 mice. Data show the mean ± SD. *: <span class="html-italic">p</span> &lt; 0.05, ***: <span class="html-italic">p</span> &lt; 0.001, ns: not significant.</p>
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<p>Chromatograms of Paclitaxel and Carboplatin spiked in mouse tumor samples. Representative chromatograms of (<b>a</b>) blank mouse tumor samples spiked with only paclitaxel-d5; (<b>b</b>) PTX (<span class="html-italic">m</span>/<span class="html-italic">z</span> 854 &gt; 286; 3.7 min) added to blank mouse tumor samples at the LLOQ (lower limit of quantification) concentration of 1 ng; (<b>c</b>) blank mouse tumor samples spiked only with carboplatin-d4; and (<b>d</b>) CBDCA (<span class="html-italic">m</span>/<span class="html-italic">z</span> 372 &gt; 294; 2.9 min) added to blank mouse tumor samples at the LLOQ concentration of 5 ng.</p>
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<p>Schematic illustration of a hypothesis for increasing IV CBDCA concentrations in tumors 4 days after IP PTX injection. The bold black line in the tumor margin indicates apoptosis induced by IP PTX. Microvessels open and reduce interstitial pressure in PMs, improving drug delivery at day 4 because of apoptosis of peripheral tumor cells.</p>
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