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20 pages, 4395 KiB  
Article
Sex Differences in Circulating Inflammatory, Immune, and Tissue Growth Markers Associated with Fabry Disease-Related Cardiomyopathy
by Margarita M. Ivanova, Julia Dao, Andrew Friedman, Neil Kasaci and Ozlem Goker-Alpan
Cells 2025, 14(5), 322; https://doi.org/10.3390/cells14050322 (registering DOI) - 20 Feb 2025
Abstract
Fabry disease (FD) is a lysosomal disorder due to alpha-galactosidase-A enzyme deficiency, accumulation of globotriaosylceramide (Gb3) and globotriaosylsphingosine (lyso-Gb3) which lead to proinflammatory effects. Males develop progressive hypertrophic cardiomyopathy (HCM) followed by fibrosis; females develop nonconcentric hypertrophy and/or early fibrosis. The inflammatory response [...] Read more.
Fabry disease (FD) is a lysosomal disorder due to alpha-galactosidase-A enzyme deficiency, accumulation of globotriaosylceramide (Gb3) and globotriaosylsphingosine (lyso-Gb3) which lead to proinflammatory effects. Males develop progressive hypertrophic cardiomyopathy (HCM) followed by fibrosis; females develop nonconcentric hypertrophy and/or early fibrosis. The inflammatory response to Gb3/lyso-Gb-3 accumulation is one of the suggested pathogenic mechanisms in FD cardiomyopathy when the secretion of inflammatory and transforming growth factors with infiltration of lymphocytes and macrophages into tissue promotes cardiofibrosis. This study aims to evaluate inflammation-driving cytokines and cardio-hypertrophic remodeling biomarkers contributing to sex-specific HCM progression. Biomarkers were studied in 20 healthy subjects and 45 FD patients. IL-2, IL-10, TNF-α, and IFN-γ were elevated in all patients, while IL-1α, MCP-1, and TNFR2 showed sex-specific differences. The increased cytokines were associated with the NF-kB pathway in FD males with HCM, revealing a correlation between MCP-1, IFN-γ, VEGF, GM-CSF, IL-10, and IL-2. In female patients, the impaired TNFα/TNFR2/TGFβ cluster with correlations to MCP-1, VEGF, GM-CSF, and IL-1α was observed. The activation of cytokines and the NF-kB pathway indicates significant inflammation during HCM remodeling in FD males. The TNFα/TNFR2/TGFβ signaling cluster may explain early fibrosis in females with FD cardiomyopathy. Sex-specific inflammatory responses in FD influence the severity and progression of HCM. Full article
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Figure 1
<p>Onset diagnostic of hypertrophic cardiomyopathy (HCM) and relationship with enzyme replacement therapy (ERT) initiation in patients with FD. (<b>A</b>) The age range of female and male patients diagnosed with FD, with or without HCM, is categorized by individual values. BL—border line, patients without cardiomyopathy, but with abnormal EKG. (<b>B</b>,<b>C</b>) Correlation analysis between age at HCM diagnosis and ERT treatment in female (<b>B</b>) and male (<b>C</b>) patients with FD. Pearson correlation analysis is presented in the bottom right corner. BL (borderline), patients without HCM, but not normal EKG.</p>
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<p>Multivariate statistical analysis. Principle component analysis (PCA) was used to create a two-dimensional cluster plot of PC-1 and PC-2 for 18 biomarkers. The plot shows the PC1 vs. PC2 score scatter plot for female (<b>A</b>) and male (<b>B</b>) patients with FD, with each dot representing a different biomarker. The table represents biomarkers combined in Cluster 1, where PC1 is negative, and PC2 is positive. (<b>C</b>)<b>.</b> The list of biomarkers represents the categorization of inflammatory biomarkers into clusters using PCA. “Blue background” indicates the biomarkers with similar distribution in female and male patients with FD, while “white” refers to biomarkers in distinct clusters in the female and male groups.</p>
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<p>Circulated IL-2, IL-10, TNF-α, and IFN-γ levels are elevated in FD patients. (<b>A</b>) Interleukin 2 (IL-2) levels, control vs. FD. Statistical analysis using an unpaired <span class="html-italic">t</span>-test demonstrated a significant difference between control and FD cohorts. * <span class="html-italic">p</span> &lt; 0.05. (<b>B</b>) Interleukin 10 (IL-10) levels, control vs. FD. Statistical analysis using unpaired <span class="html-italic">t</span>-test and F-test to compare variance demonstrated a significant difference between control and FD cohorts. * <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) Tumor necrosis factor (TNF-α) levels, control vs. FD. The unpaired <span class="html-italic">t</span>-test demonstrated a significant difference between healthy control and FD cohorts. * <span class="html-italic">p</span> = 0.001. (<b>D</b>) Interferon (IFN-γ) levels, control vs. FD. The unpaired <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05. (<b>E</b>) Comparing the Il-2 levels in control females and female patients with FD, control males and male patients with FD. * <span class="html-italic">p</span> &lt; 0.05 <span class="html-italic">t</span>-test control females vs. FD females and control males vs. FD males. (<b>F</b>) Il-10 levels in control females vs. female patients, and control males vs. male patients with FD. * <span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.01. (<b>G</b>) Comparing TNF-α levels in control females vs. female patients, and control males vs. male patients with FD. * <span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.01. (<b>H</b>) IFN-γ levels in control females and female patients with FD, control males and male patients with FD. * <span class="html-italic">p</span> &lt; 0.05 <span class="html-italic">t</span>-test.</p>
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<p>Circulated Il-1α, MCP-1, and TNRF1 levels are elevated in patients with FD. (<b>A</b>) Interleukin 1α (Il-1 α) levels, control vs. FD. (<b>B</b>) Comparing the Il-1 α levels in female controls vs. FD, male controls vs. male patients with FD. * <span class="html-italic">p</span> &lt; 0.05. (<b>C</b>) Monocyte chemoattractant protein 1 (MCP1) levels, control vs. FD. <span class="html-italic">t</span>-test; * <span class="html-italic">p</span> &lt; 0.05. (<b>D</b>) Comparing the MCP-1 in female controls vs. FD, male controls vs. FD. * <span class="html-italic">p</span> &lt; 0.05. (<b>E</b>) TNFR2, control vs. FD. F test, * <span class="html-italic">p</span> &lt; 0.05. (<b>F</b>) Tumor necrosis factor α receptor (TNFR2) in female controls vs. FD, and comparing male controls vs. FD. F-test verified a significant difference between control males vs. FD males * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Circulated levels of FGF2 and IGF-1 are elevated in patients with FD. (<b>A</b>) Insulin-like growth factor 1 (IGF-1) levels control vs. FD. Statistical analysis using an unpaired <span class="html-italic">t</span>-test demonstrated a significant difference between control and FD cohorts. * <span class="html-italic">p</span> &lt; 0.05 <span class="html-italic">t</span>-test. (<b>B</b>) IGF-1 levels in control females and female patients with FD, as well as in control males and male patients with FD. * <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">t</span>-test comparison between control and FD females. (<b>C</b>) Fibroblast growth factor 2 (FGF2) levels control vs. FD. Statistical analysis using an unpaired <span class="html-italic">t</span>-test to compare cohorts demonstrated a significantly increased level in FD. * <span class="html-italic">p</span> &lt; 0.05 (<b>D</b>) FGF2 level in control females and female patients with FD, control males and male patients with FD. * <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">t</span>-test comparison between control and FD females.</p>
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<p>(<b>A</b>) Pearson correlation coefficient matrix presentation for 13 plasma biomarkers with plasma lyso-Gb-3 and urine levels of Gb-3 in cohorts: FD females without HCM(−) and with HCM(+), FD males with the absence of HCM(−), and patients with HCM(+). (<b>B</b>,<b>C</b>) Scatterplot analysis of correlation of TNFα and plasma lyso-Gb-3 in females (<b>B</b>) and males (<b>C</b>) with and without HCM. (<b>D</b>,<b>E</b>) Scatterplot analysis of correlation of NF-kB and plasma lyso-Gb-3 in females (<b>D</b>) and in FD male patients with and without HCM (<b>E</b>).</p>
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<p>Correlation matrix and hierarchical clustering. (<b>A</b>,<b>B</b>) Correlation coefficients for measurements of biomarkers are visualized by tile-color intensities (red color, strong; deep blue color, negative correlation). FD females without cardiomyopathy HCM(−) and with HCM(+). A correlation coefficient ≥ 0.8 indicates strong positive relationships; a correlation coefficient = between 0.5 and 0.7 indicates a moderate positive relationship; a correlation coefficient less than 0.5 indicates variables with a low correlation. Correlation is accepted as significant differences by Pearson’s correlation <span class="html-italic">p</span>-values &lt; 0.05. (<b>C</b>) A scatterplot analysis of the correlation of INF-γ and TNFR2 in FD female patients without and with HCM. (<b>D</b>) A scatterplot analysis of the correlation of IL-10 and NF-kB in FD male patients without and with HCM. (<b>E</b>) A scatterplot analysis examined the correlation between INF-γ and MCP-1 in females and male patients with and without HCM. (<b>F</b>) A scatterplot analysis examined the correlation between INF-γ and GM-CSF in male patients with and without HCM. On the left side, the scatterplot displays GM-CSF on the X-axis, with a maximum value of 22 pg/mL, and INF-γ on the Y-axis, with a maximum value of 110 pg/mL. On the right side, the scatterplot is displayed without the limitation of the X- and Y-axis.</p>
Full article ">Figure 8
<p>Signaling clusters and protein–protein interaction map staged the sex-related association with cardiomyopathy (HCM) in Fabry disease. (<b>A</b>) Multi-level networks in FD cardiomyopathy based on Pearson correlation analysis. (<b>B</b>) Protein–protein interaction networks suggest cytokine activation pathways play an essential role in HCM in male patients with FD. Protein–protein interaction network between NF-kB, Il-2, Il-20, INF-γ (IFNG), MCP-1 (CCL2), GM-CSF (CSF2), VEGF (KDR), and Il-1 α (IL1A) has been developed using STRING. (<b>C</b>,<b>D</b>) A bubble chart has been created using the scatter 3D plots in Excel and represents the correlation between TNFR2 (X-axis), MCP-1 (Y-axis), and INF-γ (bubble size represents the concentration of the biomarker) in female patients without (HCM−) and with HCM (HCM+). (<b>E</b>,<b>F</b>) The bubble scatter 3D plots represent the correlation between NF-kB (X-axis), INF-γ (Y-axis), and GM-CSF (bubble presentation) in male patients without (HCM−) and with HCM (HCM+).</p>
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20 pages, 7401 KiB  
Article
Genome-Wide Identification and Functional Analysis of AP2/ERF Gene Family in Passiflora edulis Sims
by Lanjun Luo, Liping Zhang, Ronghao Gu, Shihao Ni, Jingyao Yu, Yachao Gao and Chuanying Fang
Plants 2025, 14(5), 645; https://doi.org/10.3390/plants14050645 (registering DOI) - 20 Feb 2025
Abstract
The Apetala2/Ethylene Responsive Factor (AP2/ERF) family represents a critical group of transcription factors in plants, recognized for their roles in growth, development, fruit ripening, and postharvest processes. This study aimed to identify and characterize the AP2/ERF gene family in passion fruit (Passiflora [...] Read more.
The Apetala2/Ethylene Responsive Factor (AP2/ERF) family represents a critical group of transcription factors in plants, recognized for their roles in growth, development, fruit ripening, and postharvest processes. This study aimed to identify and characterize the AP2/ERF gene family in passion fruit (Passiflora edulis Sims) and investigate their potential roles in flavor enhancement. A total of 91 PeAP2/ERF genes were identified and classified into five subfamilies. Chromosome localization and collinearity analysis demonstrated their distribution across all nine chromosomes of passion fruit, with tandem duplication events identified as a key driver of family expansion. Exon–intron configurations and motif compositions were highly conserved among PeAP2/ERF genes. Promoter cis-acting element analysis indicated potential regulation by environmental signals, including abiotic and biotic stresses, as well as hormonal cues. Postharvest storage induced the expression of 59 PeAP2/ERF genes over time. Notably, PeAP2-10 was found to enhance the expression of PeSTP6, a gene associated with sugar transport, suggesting its potential influence on the flavor profile of passion fruit. These findings provide valuable insights into the functional roles of PeAP2/ERF genes in passion fruit, highlighting their significance in postharvest management and flavor quality enhancement strategies. Full article
24 pages, 1555 KiB  
Review
Therapeutic Effects of Alkaloids on Influenza: A Systematic Review and Meta-Analysis of Preclinical Studies
by Zhaoyuan Gong, Mingzhi Hu, Guozhen Zhao, Ning Liang, Haili Zhang, Huizhen Li, Qianzi Che, Jing Guo, Tian Song, Yanping Wang, Nannan Shi and Bin Liu
Int. J. Mol. Sci. 2025, 26(5), 1823; https://doi.org/10.3390/ijms26051823 (registering DOI) - 20 Feb 2025
Abstract
Experimental evidence suggests that alkaloids have anti-influenza and anti-inflammatory effects. However, the risk of translating existing evidence into clinical practice is relatively high. We conducted a systematic review and meta-analysis of animal studies to evaluate the therapeutic effects of alkaloids in treating influenza, [...] Read more.
Experimental evidence suggests that alkaloids have anti-influenza and anti-inflammatory effects. However, the risk of translating existing evidence into clinical practice is relatively high. We conducted a systematic review and meta-analysis of animal studies to evaluate the therapeutic effects of alkaloids in treating influenza, providing valuable references for future studies. Seven electronic databases were searched until October 2024 for relevant studies. The Review Manager 5.2 software was utilized to perform the meta-analysis. Our study was registered within the International Prospective Register of Systematic Reviews (PROSPERO) as number CRD42024607535. Alkaloids are significantly correlated with viral titers, pulmonary inflammation scores, survival rates, lung indices, and body weight. However, alkaloid therapy is not effective in reducing the levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). In addition, the therapeutic effects of alkaloids may be related to the inhibition of the Toll-like receptor 4 or 7/Nuclear factor (NF)-κB signaling pathway, NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome pathway, and the Antiviral innate immune response receptor RIG-I (RIG-I) pathway. Alkaloids are potential candidates for the prevention and treatment of influenza. However, extensive preclinical studies and clinical studies are needed to confirm the anti-influenza and anti-inflammatory properties of alkaloids. Full article
(This article belongs to the Section Molecular Immunology)
73 pages, 2073 KiB  
Systematic Review
From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications
by Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou and Constantinos Halkiopoulos
Brain Sci. 2025, 15(3), 220; https://doi.org/10.3390/brainsci15030220 (registering DOI) - 20 Feb 2025
Abstract
Background/Objectives: This systematic review presents how neural and emotional networks are integrated into EEG-based emotion recognition, bridging the gap between cognitive neuroscience and practical applications. Methods: Following PRISMA, 64 studies were reviewed that outlined the latest feature extraction and classification developments using deep [...] Read more.
Background/Objectives: This systematic review presents how neural and emotional networks are integrated into EEG-based emotion recognition, bridging the gap between cognitive neuroscience and practical applications. Methods: Following PRISMA, 64 studies were reviewed that outlined the latest feature extraction and classification developments using deep learning models such as CNNs and RNNs. Results: Indeed, the findings showed that the multimodal approaches were practical, especially the combinations involving EEG with physiological signals, thus improving the accuracy of classification, even surpassing 90% in some studies. Key signal processing techniques used during this process include spectral features, connectivity analysis, and frontal asymmetry detection, which helped enhance the performance of recognition. Despite these advances, challenges remain more significant in real-time EEG processing, where a trade-off between accuracy and computational efficiency limits practical implementation. High computational cost is prohibitive to the use of deep learning models in real-world applications, therefore indicating a need for the development and application of optimization techniques. Aside from this, the significant obstacles are inconsistency in labeling emotions, variation in experimental protocols, and the use of non-standardized datasets regarding the generalizability of EEG-based emotion recognition systems. Discussion: These challenges include developing adaptive, real-time processing algorithms, integrating EEG with other inputs like facial expressions and physiological sensors, and a need for standardized protocols for emotion elicitation and classification. Further, related ethical issues with respect to privacy, data security, and machine learning model biases need to be much more proclaimed to responsibly apply research on emotions to areas such as healthcare, human–computer interaction, and marketing. Conclusions: This review provides critical insight into and suggestions for further development in the field of EEG-based emotion recognition toward more robust, scalable, and ethical applications by consolidating current methodologies and identifying their key limitations. Full article
(This article belongs to the Section Computational Neuroscience and Neuroinformatics)
14 pages, 2558 KiB  
Review
Deciphering Host–Pathogen Interactions: Role of Cryptosporidium in Tumorigenesis
by Shakeel Hussain, Qurrat ul Ain, Muhammad Aamir, Khalid M. Alsyaad, Ahmed Ezzat Ahmed, Jude G. Zakai, Haytham Ahmed Zakai and Yongzhong Hou
Pathogens 2025, 14(3), 208; https://doi.org/10.3390/pathogens14030208 - 20 Feb 2025
Abstract
Cryptosporidium, a protozoan parasite affecting the gastrointestinal system, is primarily known for causing diarrhea, especially in those with weakened immune systems. However, there is increasingly persuasive evidence that it may be directly involved in tumorigenesis. This review examines some of the potential [...] Read more.
Cryptosporidium, a protozoan parasite affecting the gastrointestinal system, is primarily known for causing diarrhea, especially in those with weakened immune systems. However, there is increasingly persuasive evidence that it may be directly involved in tumorigenesis. This review examines some of the potential mechanisms through which Cryptosporidium infections can induce cancer, specifically chronic inflammation, manipulation of the immune system, and alteration of cell signaling pathways. Persistent inflammation with immune system changes due to chronic infection, particularly among immunocompromised hosts, leads to a microenvironment that facilitates tumorigenesis. Cryptosporidium manipulates important cellular pathways such as PI3K, NF-κB, Wnt, and p38/MAPK to promote cell survival, regulate immune responses, and foster tissue remodeling, all of which contribute to a tumor-friendly microenvironment. Moreover, Cryptosporidium virulence factors such as ROP1, sPLA2, and microRNAs disrupt host cellular stability and significantly alter host cellular gene expression, which also exacerbates inflammation and tissue damage. Epidemiological data have indicated higher rates of Cryptosporidium infection in cancer patients, especially patients with gastrointestinal cancers. This, among other observations, raises the possibility that the infection may be connected to cancer progression. In animal models, especially studies with C. parvum-challenged rodents, chronic inflammation, immune repression, and genetic mutations related to neoplasia have been reported. While this has provided us with valuable information, we still have a long way to go to fully understand the long-term ramifications of Cryptosporidium infection. These cover aspects such as the contribution of latent infections and the genetic diversity of Cryptosporidium strains in cancer. Further investigation is urgently needed to understand the molecular processes by which Cryptosporidium might contribute to carcinogenesis and explore potential strategies for therapy and prevention especially among immunocompromised populations. Full article
(This article belongs to the Section Parasitic Pathogens)
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Figure 1
<p><b>Life cycle and transmission dynamics of <span class="html-italic">Cryptosporidium</span></b>: Infected individuals shed thick-walled oocysts in their feces, contaminating food and water. Ingestion of these leads to infection as the oocysts release sporozoites (Stages 1–2), which invade intestinal cells. Inside, they develop into trophozoites (Stage 3) that reproduce asexually to form type I meronts (Stage 4), and the type I meronts releasing merozoites (Stages 5–6). This triggers the sexual phase, forming microgamonts and macrogamonts (Stages 7–8). Their fusion creates diploid zygotes (Stage 9), which undergo meiosis and sporogony to produce oocysts (Stage 10). Thick-walled oocysts exit the body, spreading infection, while thin-walled oocysts can cause reinfection within the host (autoinfection). Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p><b>Hijacking cellular pathways and oncogenic mechanisms in <span class="html-italic">Cryptosporidium</span> infections:</b><span class="html-italic"> Cryptosporidium</span> manipulates several key host cell signaling pathways to ensure its survival and potentially contribute to cancer development. Upon contact with epithelial cells, the parasite activates multiple signaling proteins, including PI3K, Src, Cdc42, and Rho GTPases. This disruption of normal cellular signaling breaks down the epithelial barrier, helping the parasite form a specialized vacuole, the parasitophorous vacuole (PV), where it can thrive. <span class="html-italic">Cryptosporidium</span> also triggers inflammation by activating NF-κB signaling through Toll-like receptors (TLR2/4), which not only helps the parasite avoid cell death but also promotes its replication. In addition, <span class="html-italic">Cryptosporidium</span> interferes with the Wnt signaling pathway, altering the location of β-catenin. Disruptions to cell cycle regulation, adhesion, and migration create conditions more conducive to tumor development. Furthermore, <span class="html-italic">Cryptosporidium</span> may remodel host cytoskeletons using molecules like c-Src, PI3K and Rho GTPases, making entry easier while altering host cell structures that may promote cancer progression. Host microRNAs like miR-27b and ciRS7 further regulate immune responses and gene expression that foster oncogenesis. ITGA2 and ITGB1 play pivotal roles in signal transduction that enhance the migration/survival of host cell populations, thus encouraging tumorigenic processes that might support tumorigenic processes. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p><b>Mechanisms of apoptosis resistance in <span class="html-italic">Cryptosporidium</span> infections:</b><span class="html-italic"> Cryptosporidium</span> survives longer and may cause cancer by preventing apoptosis in infected cells. It activates NF-κB signaling as a crucial mechanism, which boosts cell-death-preventing proteins such Bcl-2, Bcl-xL, and survivin. <span class="html-italic">Cryptosporidium</span> keeps infected cells alive by upregulating anti-apoptotic proteins. The parasite also inhibits caspases and pro-apoptotic genes like Bax and Bak. Apoptosis resistance may cause cancer. <span class="html-italic">Cryptosporidium</span> also manipulates apoptosis inhibitors like cIAP1 and cIAP2 to help infected cells survive and become malignant. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p><b>Inflammatory responses and tumor microenvironment in <span class="html-italic">Cryptosporidium</span> infections:</b><span class="html-italic"> Cryptosporidium</span> infection causes chronic inflammation in the host, creating a tumor-promoting environment. Infected tissues release cytokines such as IL-6, IL-10, and TGF-β, promoting tissue remodeling, immune cell infiltration, and cell survival. <span class="html-italic">Cryptosporidium</span> also recruits immune cells like MDSCs and TAMs to assist tumor growth and escape the immune system. To avoid immunological identification, <span class="html-italic">Cryptosporidium</span> modifies cell surface proteins such as E-cadherin and immune checkpoints like PD-1/PD-L1. By suppressing MAPK signaling, reducing the activity of CD4+ and CD8+ T-cells, and drawing in regulatory immune cells, <span class="html-italic">Cryptosporidium</span> is able to maintain chronic infection and create an environment that suppresses immune responses, further enhancing its potential to drive tumorigenesis. <span class="html-italic">Cryptosporidium</span> also regulates chemokines in ways that support immune evasion and intensify the inflammation that promotes tumor growth. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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24 pages, 3487 KiB  
Review
A Comprehensive, Analytical Narrative Review of Polysaccharides from the Red Seaweed Gracilaria: Pharmaceutical Applications and Mechanistic Insights for Human Health
by Deepesh Khandwal, Sapna Patel, Abhay Kumar Pandey and Avinash Mishra
Nutrients 2025, 17(5), 744; https://doi.org/10.3390/nu17050744 - 20 Feb 2025
Abstract
Gracilaria species, a widely distributed genus of red macroalgae, have gathered significant attention for their diverse medical applications attributable to their bioactive sulphated polysaccharides (SPs). This review examines the global narrative of various Gracilaria SP applications in terms of their therapeutic potential and [...] Read more.
Gracilaria species, a widely distributed genus of red macroalgae, have gathered significant attention for their diverse medical applications attributable to their bioactive sulphated polysaccharides (SPs). This review examines the global narrative of various Gracilaria SP applications in terms of their therapeutic potential and mechanistic insights into the use of these SPs against a range of medical conditions, including cancer, inflammation, neurodegenerative disorders, diabetes, and immune dysfunctions. SPs extracted from G. lemaneiformis and G. fisheri have demonstrated potent anti-tumour activities by inducing apoptosis through various mechanisms, including the upregulation of CD8+ T cells and IL-2, inhibition of EGFR/MAPK/ERK signalling pathways, and activation of the Fas/FasL pathway. Selenium nanoparticles (SeNPs) conjugated with SPs further enhanced the targeted delivery and efficacy of these SPs against glioblastoma by the downregulation of ROS followed by the activation of p53, MAPK, and AKT pathways. The anti-inflammatory properties of SPs are evidenced by key suppressive inflammatory markers like NO, TNF-α, IL-1β, and IL-6 in mutant rodent models. SPs from G. cornea and G. birdiae effectively reduce neutrophil migration and vascular permeability, offering potential treatments for acute inflammation and conditions such as colitis by modulating pathways involving COX-2 and NF-κB. Neuroprotective effects by SPs (from G. cornea and G. gracili) studied in 6-OHDA-induced rats, which mitigate oxidative stress and enhance neuronal cell viability, facilitate the management of neurodegenerative diseases like Parkinson’s and Alzheimer’s. Regarding the hypoglycaemic effect, SPs from G. lemaneiformis exhibit a glucose-modulating response by improving insulin regulation, inhibiting α-amylase activity, repairing pancreatic β-cells, and modulating lipid metabolism. Moreover, immunomodulatory activities of Gracilaria-derived SPs include the stimulation of macrophages, T-cell proliferation, and cytokine production, underscoring their potential as functional food and immunotherapeutic agents. Recently, Gracilaria-derived SPs have been found to modulate gut microbiota, promote SCFA production, and enhance gut microbials, suggesting their potential as prebiotic agents (G. rubra and G. lemaneiformis). This review highlights the multifaceted medical applications of Gracilaria sulphated polysaccharides, providing detailed mechanistic insights and suggesting avenues for future clinical translation and therapeutic innovations. Full article
(This article belongs to the Special Issue Functional Foods and Sustainable Health (2nd Edition))
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<p>Schematic workflow for sulphated polysaccharides from <span class="html-italic">Gracilaria</span> spp.: extraction, purification, characterization and different biological activities including anti-cancer, anti-inflammatory, anti-diabetic, anti-neurodegenerative, immunomodulatory, and gut microbiota modulation.</p>
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<p>Multidimension approach of <span class="html-italic">Gracilaria</span> SP activities for different pharmaceutical and nutraceutical applications.</p>
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<p>A flowchart representing the molecular mechanism of <span class="html-italic">Gracilaria</span> SPs for anti-cancer activity.</p>
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<p>A flowchart representing the molecular mechanism of <span class="html-italic">Gracilaria</span> SPs for anti- inflammation activity.</p>
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<p>A flowchart representing the molecular mechanism of <span class="html-italic">Gracilaria</span> SPs for anti-hyperglycaemic effect.</p>
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<p>A flowchart representing the molecular mechanism of <span class="html-italic">Gracilaria</span> SPs for immunomodulation activity.</p>
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<p>A flowchart representing the molecular mechanism of <span class="html-italic">Gracilaria</span> SPs for neuroprotectant activity.</p>
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24 pages, 993 KiB  
Review
Targeting Cancer Stemness Using Nanotechnology in a Holistic Approach: A Narrative Review
by Melinda-Ildiko Mitranovici, Laura Georgiana Caravia, Liviu Moraru and Lucian Pușcașiu
Pharmaceutics 2025, 17(3), 277; https://doi.org/10.3390/pharmaceutics17030277 - 20 Feb 2025
Abstract
Increasing evidence shows that a very small population of cancer stem cells (CSCs) is responsible for cancer recurrence, drug resistance, and metastasis. CSCs usually reside in hypoxic tumor regions and are characterized by high tumorigenicity. Their inaccessible nature allows them to avoid the [...] Read more.
Increasing evidence shows that a very small population of cancer stem cells (CSCs) is responsible for cancer recurrence, drug resistance, and metastasis. CSCs usually reside in hypoxic tumor regions and are characterized by high tumorigenicity. Their inaccessible nature allows them to avoid the effects of conventional treatments such as chemotherapy, radiotherapy, and surgery. In addition, conventional chemo- and radiotherapy is potentially toxic and could help CSCs to spread and survive. New therapeutic targets against CSCs are sought, including different signaling pathways and distinct cell surface markers. Recent advances in nanotechnology have provided hope for the development of new therapeutic avenues to eradicate CSCs. In this review, we present newly discovered nanoparticles that can be co-loaded with an apoptosis-inducing agent or differentiation-inducing agent, with high stability, cellular penetration, and drug release. We also summarize the molecular characteristics of CSCs and the signaling pathways responsible for their survival and maintenance. Controlled drug release targeting CSCs aims to reduce stemness-related drug resistance, suppress tumor growth, and prevent tumor relapse and metastases. Full article
(This article belongs to the Special Issue Customized and Designed Micro- and Nanocarriers for Drug Delivery)
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<p>A flow diagram showing the selection mode [<a href="#B16-pharmaceutics-17-00277" class="html-bibr">16</a>]. * The number of records identified from the Google Scholar, Web of Science, and PubMed databases. ** Records excluded by a human. Reason 1: Records excluded as they were publications in a language other than English. Reason 2: Records excluded by a human reviewer due to inaccurate or inappropriate titles. Reason 3: Records excluded based on the study’s research design.</p>
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<p>Nanoparticles targeting cancer stemness pathways.</p>
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18 pages, 7039 KiB  
Article
The Impact of Uterus-Derived Prostaglandins on the Composition of Uterine Fluid During the Period of Conceptus Elongation in Dairy Heifers
by Beibei Zhang, Yuan Han, Shengxiang Wang, Ming Cheng, Longgang Yan, Dong Zhou, Aihua Wang, Pengfei Lin and Yaping Jin
Int. J. Mol. Sci. 2025, 26(5), 1792; https://doi.org/10.3390/ijms26051792 - 20 Feb 2025
Abstract
In ruminants, the survival and development of the conceptus are heavily dependent on the composition of the uterine lumen fluid (ULF), which is influenced by prostaglandins (PGs). However, the variations in underlying PG-mediated ULF remain unclear. Herein, cycling heifers received an intrauterine infusion [...] Read more.
In ruminants, the survival and development of the conceptus are heavily dependent on the composition of the uterine lumen fluid (ULF), which is influenced by prostaglandins (PGs). However, the variations in underlying PG-mediated ULF remain unclear. Herein, cycling heifers received an intrauterine infusion of vehicle as a control (CON) or meloxicam (MEL) on days 12–14 of the estrous cycle. Then, the ULF was collected on day 15 and alternations in its protein and lipid levels were analyzed. The suppression of prostaglandins induced by meloxicam resulted in 1343 differentially abundant proteins (DAPs) and 59 differentially altered lipids. These DAPs were primarily associated with vesicle-mediated transport, immune response, and actin filament organization, and were mainly concentrated on the ribosome, complement and coagulation cascades, cholesterol metabolism, chemokine signal pathway, regulation of actin cytoskeleton and starch and sucrose metabolism. These differential lipids reflected a physiological metabolic shift as the abundance of cell membrane-related lipids was modulated, including an accumulation of triacylglycerols and reductions in lysophosphatidylcholines, hexosyl ceramides, ceramides, and sphingomyelins species. Integration analysis of the DAPs and differentially altered lipid metabolites revealed that glycerophospholipid metabolism and choline metabolism were the core pathways. These findings highlight the potential roles of prostaglandins in ULF, providing new insights into the contributions of prostaglandins in the development of the conceptus. Full article
(This article belongs to the Section Molecular Endocrinology and Metabolism)
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<p>Hormone concentrations in blood samples. (<b>A</b>) Circulating concentrations of progesterone on different days, between the CON and MEL groups. (<b>B</b>) Plasma progesterone concentration between the CON and MEL groups. (<b>C</b>) Plasma PGF<sub>2α</sub> concentration between the CON and MEL groups. (<b>D</b>) Plasma PGE<sub>2</sub> concentration between the CON and MEL groups. “ns” as <span class="html-italic">p</span> &gt; 0.05; “*” as <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Proteomic profile of ULF samples using quantitative proteomics based on DIA technology. (<b>A</b>) The heatmap of all identified proteins between the CON and MEL groups. (<b>B</b>) Volcano plot of DAPs between the CON and MEL groups. (<b>C</b>) Subcellular localization map of DAPs between the CON and MEL groups.</p>
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<p>The analysis of DAPs between the CON and MEL groups. (<b>A</b>) GO enrichment analysis of DAPs between the CON and MEL groups. (<b>B</b>) The network diagram of biological processes of the DAPs between the CON and MEL groups. (<b>C</b>) The BP classification of the DAPs between the CON and MEL groups. “**” as <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The enriched pathways of the DAPs between the CON and MEL groups. (<b>A</b>) The enriched KEGG pathways of the DAPs between the CON and MEL groups. (<b>B</b>) The top 20 KEGG pathways of the up-regulated DAPs in the ULF between the CON and MEL groups. (<b>C</b>) The top 20 KEGG pathways of the down-regulated DAPs in the ULF between the CON and MEL groups. The analyses of PPI with the DAPs involved in the (<b>D</b>) ribosome, (<b>E</b>) complement and coagulation cascades, (<b>F</b>) regulation of actin cytoskeleton, (<b>G</b>) cholesterol metabolism, (<b>H</b>) chemokine signal pathway, (<b>I</b>) starch and sucrose metabolism. The GSEA of the DAPs in the ULF between the CON and MEL groups, including the (<b>J</b>) ribosome, (<b>K</b>) complement and coagulation cascades, (<b>L</b>) cholesterol metabolism, (<b>M</b>) regulation of actin filament organization, (<b>N</b>) chemokine signal pathway, (<b>O</b>) starch and sucrose metabolism.</p>
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<p>The relative abundance of the different lipid subclasses. Quantities of lipids class detected in the ULF between the CON and MEL groups: (<b>A</b>) the positive ion mode, (<b>B</b>) the negative ion mode. TG: triglyceride; PC: phosphatidylcholine; PE: phosphatidylethanolamine; Cer: Ceramides; DG: diglyceride; SM: sphingomyelin; Hex1Cer: hexosyl ceramide; Hex2Cer: Hex2-ceramide; Hex3Cer: Hex3-ceramide; LPC: lysophosphatidylcholine; PS: phosphatidylserine; ChE: cholesterol ester; CL: cardiolipin; PI: phosphatidylinositol.</p>
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<p>The analyses of the lipid profiles in the ULF between the CON and MEL groups. (<b>A</b>) The OPLS-DA analyses between the CON and MEL groups. (<b>B</b>) Volcano plot of different lipid metabolites between the CON and MEL groups. (<b>C</b>) The top 20 differential lipid metabolites based on the value of VIP. (<b>D</b>) Pathway enrichment analysis of differential lipid metabolites between the CON and MEL groups.</p>
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<p>Integrative analysis of proteome and metabolome in the ULF between the CON and MEL groups. (<b>A</b>) The strong correlation heatmap of DAPs and the differential lipid metabolites between the CON and MEL groups (r ≥ 0.9 or r ≤ −0.9). (<b>B</b>) The correlation network of the DAPs and differential lipid metabolites between the CON and MEL groups. (<b>C</b>) Chord diagram of biological processes of the DAPs strongly related to differential lipid metabolites between the CON and MEL groups. (<b>D</b>) The common KEGG pathways between the DAPs and differential lipid metabolites.</p>
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19 pages, 3737 KiB  
Article
Heterozygosity-Rich Regions in Canine Genome: Can They Serve as Indicators of Balancing Selection?
by Adrián Halvoník, Nina Moravčíková, Luboš Vostrý, Hana Vostra-Vydrova, Gábor Mészáros, Eymen Demir, Monika Chalupková and Radovan Kasarda
Animals 2025, 15(4), 612; https://doi.org/10.3390/ani15040612 - 19 Feb 2025
Abstract
Compared to the negative effect of directional selection on genetic diversity, balancing selection acts oppositely and maintains variability across the genome. This study aims to articulate whether balancing selection leads to heterozygosity-rich region islands (HRRIs) forming in the canine genome by investigating 1000 [...] Read more.
Compared to the negative effect of directional selection on genetic diversity, balancing selection acts oppositely and maintains variability across the genome. This study aims to articulate whether balancing selection leads to heterozygosity-rich region islands (HRRIs) forming in the canine genome by investigating 1000 animals belonging to 50 dog breeds via 153,733 autosomal SNPs. A consecutive SNP-based approach was used to identify heterozygosity-rich regions (HRRs). Signals of balancing selection in the genome of studied breeds were then assessed with Tajima’s D statistics. A total of 72,062 HRRs with an average length of 324 kb were detected to be unevenly distributed across the genome. A total of 509 and 450 genomic regions were classified as HRRIs and balancing selection signals, respectively. Although the genome-wide distributions of HRRIs varied across breeds, several HRRIs were found in the same locations across multiple breeds. A total of 109 genomic regions were classified as both HRRIs and signals of balancing selection. Even though the genomic coordinates of HRRIs and balancing selection signals did not fully overlap across all genomic regions, balancing selection may play a significant role in maintaining diversity in regions associated with various cancer diseases, immune response, and bone, skin, and cartilage tissue development. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Pairwise Pearson correlations (*** indicates <span class="html-italic">p</span> &lt; 0.001) between <span class="html-italic">H<sub>o</sub></span>, percentage of genome covered by HRRs, and number of HRRs (<b>A</b>) and violin plot showing variability in the length of detected HRRs (<b>B</b>).</p>
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<p>Distribution of HRRIs per chromosome in the whole population (<b>A</b>) and distribution of HRRIs (blue) and ROHIs (red) per breed (<b>B</b>). The gaps between visualised regions within and between chromosomes do not correspond to the real distance.</p>
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<p>Distribution of balancing selection signals derived from Tajima’s D statistics per chromosome in the whole population (<b>A</b>) and per breed (<b>B</b>). The gaps between visualised regions within and between chromosomes do not correspond to the real distance.</p>
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<p>Venn diagrams showing overlaps between balancing selection signatures and HRRIs in regions identified as hot spots of balancing selection signatures and HRRIs.</p>
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19 pages, 5304 KiB  
Article
Hepatotoxicity in Carp (Carassius auratus) Exposed to Perfluorooctane Sulfonate (PFOS): Integrative Histopathology and Transcriptomics Analysis
by Lin Tang, Guijie Hao, Dongren Zhou, Yunpeng Fan, Zihao Wei, Dongsheng Li, Yafang Shen, Haoyu Fang, Feng Lin, Meirong Zhao and Haiqi Zhang
Animals 2025, 15(4), 610; https://doi.org/10.3390/ani15040610 - 19 Feb 2025
Abstract
Perfluorooctane sulfonate (PFOS) contamination poses a significant environmental threat due to its widespread distribution and persistence. However, the hepatotoxic effects of PFOS on key aquatic species, such as crucian carp, remain understudied. This study systematically investigated the hepatotoxicity and underlying molecular mechanisms associated [...] Read more.
Perfluorooctane sulfonate (PFOS) contamination poses a significant environmental threat due to its widespread distribution and persistence. However, the hepatotoxic effects of PFOS on key aquatic species, such as crucian carp, remain understudied. This study systematically investigated the hepatotoxicity and underlying molecular mechanisms associated with PFOS exposure in crucian carp over a 21 day period. We determined a 96 h 50% lethal concentration (LC50) of 23.17 mg/L. Histopathological and transcriptomic analyses confirmed PFOS-induced liver damage in the carp, characterized by venous congestion, nucleolar dissolution and cellular vacuolation. Transcriptomic profiling further identified 1036 differentially expressed genes (DEGs), involving critical pathways related to lipid and energy metabolism, immunity, and endocrine regulation. These pathways are integral to the development of nonalcoholic fatty liver disease (NAFLD). Specifically, DEGs related to lipid metabolism showed significant changes, while those involved in energy metabolism indicated disrupted ATP production and mitochondrial function. Genes associated with immune response revealed an upregulation of pro-inflammatory markers, and hormone regulation genes highlighted alterations in endocrine signaling. Our findings emphasized that PFOS exhibits acute toxicity to crucian carp, potentially inducing hepatotoxicity by disrupting multiple physiological systems. This research provides a theoretical foundation for mitigating aquatic pollution and protecting eco-health, contributing to broader ecological and conservation biology discussions. Full article
(This article belongs to the Section Aquatic Animals)
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<p>PFOS damages the liver of carp: (<b>A</b>): control; (<b>B</b>): 0.1 mg/L PFOS treatment group; (<b>C</b>): 0.5 mg/L PFOS treatment group. (<b>D</b>): 1.0 mg/L PFOS treatment group. (1, 2, 3): Microstructure of carp liver under 25×, 50× and 100× microscope. The red arrow: adipose tissue was significantly increased compared to the control group; the yellow arrow: compression deformation of hemoglobin compared to controls; the green arrow: deformation and lysis of nuclei compared to controls. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article).</p>
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<p>Annotation of the single genes based on the GO database: (<b>A</b>): 0.1 mg/L PFOS treatment group vs. control comparison (L vs. C); (<b>B</b>): 0.5 mg/L PFOS treatment group vs. control comparison (M vs. C); (<b>C</b>): 1.0 mg/L PFOS treatment group vs. control comparison (H vs. C).</p>
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<p>Heat map of inter-sample correlation (<b>A</b>) and Venn diagram analysis of DEGs (<b>B</b>). (<b>A</b>): High differential gene expression (red), low differential gene expression (blue). (<b>B</b>): Venn diagrams showing the DEGs between H vs. C (red), M vs. C (blue), and L vs. C (green).</p>
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<p>The volcano map shows the number of genes that have changed in the liver (FC ≥ 1.5 or ≤−1.5, <span class="html-italic">p</span> ≤ 0.05): (<b>A</b>): 0.1 mg/L PFOS treatment group vs. control comparison (L vs. C); (<b>B</b>): 0.5 mg/L PFOS treatment group vs. control comparison (M vs. C); (<b>C</b>): 1.0 mg/L PFOS treatment group vs. control comparison (H vs. C). Each dot represents one gene. Red and blue dots represent DEGs. Gray dots represent no differential expressed genes.</p>
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<p>Comparative KEGG pathways analysis between (<b>A</b>) L vs. C, (<b>B</b>) M vs. C, and (<b>C</b>) H vs. C: (<b>A</b>): 0.1 mg/L PFOS treatment group vs. control comparison (L vs. C); (<b>B</b>): 0.5 mg/L PFOS treatment group vs. control comparison (M vs. C); (<b>C</b>): 1.0 mg/L PFOS treatment group vs. control comparison (H vs. C).</p>
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<p>Comparison of gene expression data between RNA-Seq and qRT-PCR.</p>
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<p>KEGG pathway of crucian liver treated with PFOS. Red boxes: significantly upregulated genes; blue boxes: significantly downregulated genes; yellow background color: known genes; green background color: new genes. The solid red boxes are the major signaling pathways and differential genes leading to non-alcoholic liver disease.</p>
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16 pages, 5538 KiB  
Article
Establishing Minimum Criteria for Stem Cells from Human Exfoliated Deciduous Teeth (SHEDs) Cultured in Human Platelet Lysate (hPL)-Contained Media as Cell Therapy Candidates: Characterization and Predictive Analysis of Secretome Effects
by Ji-Young Yoon, Bình Do Quang, Ji-Sun Shin, Jong-Bin Kim, Jun Hee Lee, Hae-Won Kim and Jung-Hwan Lee
Cells 2025, 14(4), 316; https://doi.org/10.3390/cells14040316 - 19 Feb 2025
Abstract
SHEDs have demonstrated significant potential in cell therapy due to their superior proliferation rate, self-renewal and differentiation capacity (particularly neurogenesis attributed to their neural crest origin), and the less invasive procedure required for tissue collection compared to other stem cells. However, there is [...] Read more.
SHEDs have demonstrated significant potential in cell therapy due to their superior proliferation rate, self-renewal and differentiation capacity (particularly neurogenesis attributed to their neural crest origin), and the less invasive procedure required for tissue collection compared to other stem cells. However, there is no established criterion to verify the minimum qualification to select one from numerous candidates, especially for SHEDs’ cultured FBS-free medium for clinic application. For that, we performed a characteristic analysis containing the growth rate, colony-forming unit (CFU) number, average colony size, and migration capacity with hPL-cultured SHEDs from 21 different donors, and we suggest the result as a minimum standard to filter out unqualified candidates. In addition, in the secretome analysis to predict the paracrine effect, it was found that upregulated proteins compared to the control were related to angiogenesis, immune response, and BMP signaling, and this was found to have a strong correlation only with protein concentration. This study presents a minimum standard for selecting cell therapy candidates and suggests the protein concentration of a conditioned medium as a cost-effective tool to expect the paracrine effect of SHEDs. Full article
(This article belongs to the Special Issue Human Dental Pulp Stem Cells: Isolation, Cultivation and Applications)
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<p>Proliferation and self-renewal capacity analysis of SHEDs derived from 21 different donors. (<b>A,B</b>) Doubling times (<b>A</b>) and growth rates (<b>B</b>) were calculated based on cell counts at 24 and 72 h post 50,000 cell seeding. (<b>C</b>,<b>D</b>) Results from colony formation unit (CFU) assay show colony numbers (<b>C</b>) and average colony sizes (<b>D</b>) measured 10 days after seeding 500 cells. The light gray area and red bar in panels (<b>A</b>–<b>D</b>) indicate the average ± standard deviation (s.d.), with values above or below the average categorized as Good (red) or Poor (blue). (<b>E</b>,<b>F</b>) Representative images of CFU assays are shown at low magnification ((<b>E</b>), full 100 mm dish scan) and high magnification (<b>F</b>). Colonies were stained with crystal violet for visualization. Scale bar: 500 μm. (<b>G</b>) Correlation plots with linear regression showing the relationship between colony sizes and growth rates (left) or doubling times (right). The shaded blue area represents the 95% confidence interval (CI) for the regression line. Pearson’s correlation coefficient (r) values and two-tailed <span class="html-italic">p</span>-value are indicated (<span class="html-italic">p</span>). (<b>A</b>–<b>D</b>) Data are presented as mean ± s.d. (n = 3), with statistical significance determined using one-way ANOVA followed by Dunnett’s multiple comparisons test. Significance levels: * <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.</p>
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<p>Proliferation and self-renewal capacity analysis of SHEDs derived from 21 different donors. (<b>A,B</b>) Doubling times (<b>A</b>) and growth rates (<b>B</b>) were calculated based on cell counts at 24 and 72 h post 50,000 cell seeding. (<b>C</b>,<b>D</b>) Results from colony formation unit (CFU) assay show colony numbers (<b>C</b>) and average colony sizes (<b>D</b>) measured 10 days after seeding 500 cells. The light gray area and red bar in panels (<b>A</b>–<b>D</b>) indicate the average ± standard deviation (s.d.), with values above or below the average categorized as Good (red) or Poor (blue). (<b>E</b>,<b>F</b>) Representative images of CFU assays are shown at low magnification ((<b>E</b>), full 100 mm dish scan) and high magnification (<b>F</b>). Colonies were stained with crystal violet for visualization. Scale bar: 500 μm. (<b>G</b>) Correlation plots with linear regression showing the relationship between colony sizes and growth rates (left) or doubling times (right). The shaded blue area represents the 95% confidence interval (CI) for the regression line. Pearson’s correlation coefficient (r) values and two-tailed <span class="html-italic">p</span>-value are indicated (<span class="html-italic">p</span>). (<b>A</b>–<b>D</b>) Data are presented as mean ± s.d. (n = 3), with statistical significance determined using one-way ANOVA followed by Dunnett’s multiple comparisons test. Significance levels: * <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.</p>
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<p>Migration capacity analysis of SHEDs derived from 21 different donors. (<b>A</b>,<b>B</b>) SHED (40,000 cells per well) were seeded in the upper chamber of a transwell insert and allowed to migrate for 24 h. (<b>A</b>) Quantification of migrated cell numbers based on DAPI-stained images. The red bar and the light gray area indicate the average ± standard deviation (s.d.). Statistical significance was determined using one-way ANOVA followed by Dunnett’s multiple comparisons test against the average. Data are presented as mean ± s.d. (* <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). (<b>B</b>) Representative images of migrated cells stained with crystal violet (upper). Scale bar: 100 μm. (<b>C</b>) Correlation plots with linear regression showing the relationship between colony size and migrated cell numbers. The shaded blue area represents the 95% confidence interval (CI). Pearson’s correlation coefficient (r = 0.46) and <span class="html-italic">p</span>-value (<span class="html-italic">p</span> = 0.03) were used to evaluate statistical significance.</p>
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<p>Secretome analysis using conditioned media from SHEDs derived from 21 different donors. Differentially secreted proteins relative to blank (FC &gt; 2.5, normalized value (log2) &gt; 2) were analyzed. (<b>A</b>) Heatmap of secretome. (<b>B</b>) PCA analysis: #15, #17, #18, and #19 show distinct characteristics compared to others (yellow circle). (<b>C</b>) Biological process gene ontology from David analysis, with particularly interesting biological processes marked with different colored lines. (<b>D</b>) Heatmap of secretome from 21 different donors (FC &gt; 2.5, normalized value (log2) &gt; 2) assigned to angiogenesis, immune response, and BMP signaling pathway (as representative differentiation). FC: Fold change.</p>
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<p>Total protein amounts from conditioned media can predict general secretome factors. (<b>A</b>) Schematic summary of secretome factor calculation. Fold changes of proteins upregulated over 2.5-fold compared to blank medium were summed to calculate the secretome factor. (<b>B</b>–<b>F</b>) Correlation plots with linear regression showing the relationship between secretome factor and various stem cell parameters including growth rate, CFU number, average colony size, migrated cell numbers, and total secreted proteins amounts. The blue shaded areas represent the 95% confidence interval of the regression line, illustrating the uncertainty in the correlation. Pearson’s correlation coefficient (r) was used to evaluate relationships, and statistical significance was assessed with two-tailed <span class="html-italic">p</span>-values. (<b>G</b>) Pearson’s correlation matrix showing relationships among the secretome factor, total protein amounts, and stem cell parameters. Only the total protein amount showed significant correlation with the secretome factor (*, <span class="html-italic">p</span> &lt; 0.0001). (<b>H</b>) Pearson’s correlation matrix depicting associations between secretome factors and stem cell parameters. Total protein amount showed significant positive correlation with angiogenesis factors, immune response factors, and BMP signaling factor (*, <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Schematic image of the current study. We conducted assays of proliferation, self-renewal, and migration on SHEDs obtained from 21 different donors. Additionally, we found that the secretome concentration related to angiogenesis, immunomodulation, and differentiation could be predicted by protein concentration. Based on these results, we propose criteria for the minimum qualification as a candidate for cell therapy. Cell candidates passing these criteria can undergo further studies, including preclinical studies, clinical research, and clinical trials, for eventual clinical application.</p>
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35 pages, 1692 KiB  
Review
Impact of Nutrient Stress on Plant Disease Resistance
by Héctor Martín-Cardoso and Blanca San Segundo
Int. J. Mol. Sci. 2025, 26(4), 1780; https://doi.org/10.3390/ijms26041780 - 19 Feb 2025
Abstract
Plants are constantly exposed to abiotic and biotic stresses that seriously affect crop yield and quality. A coordinated regulation of plant responses to combined abiotic/biotic stresses requires crosstalk between signaling pathways initiated by each stressor. Interconnected signaling pathways further finetune plant stress responses [...] Read more.
Plants are constantly exposed to abiotic and biotic stresses that seriously affect crop yield and quality. A coordinated regulation of plant responses to combined abiotic/biotic stresses requires crosstalk between signaling pathways initiated by each stressor. Interconnected signaling pathways further finetune plant stress responses and allow the plant to respond to such stresses effectively. The plant nutritional status might influence disease resistance by strengthening or weakening plant immune responses, as well as through modulation of the pathogenicity program in the pathogen. Here, we discuss advances in our understanding of interactions between nutrient stress, deficiency or excess, and immune signaling pathways in the context of current agricultural practices. The introduction of chemical fertilizers and pesticides was a major component of the Green Revolution initiated in the 1960s that greatly boosted crop production. However, the massive application of agrochemicals also has adverse consequences on the environment and animal/human health. Therefore, an in-depth understanding of the connections between stress caused by overfertilization (or low bioavailability of nutrients) and immune responses is a timely and novel field of research with important implications for disease control in crop species. Optimizing nutrient management practices tailored to specific environmental conditions will be crucial in maximizing crop production using environmentally friendly systems. Full article
(This article belongs to the Special Issue New Insights into Plant Pathology and Abiotic Stress)
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<p>Major abiotic stresses in plants and their effects on physiological and biochemical processes affecting plant growth and development.</p>
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<p>MiRNAs involved in the regulation of nutrient homeostasis and immunity against the rice blast fungus <span class="html-italic">M. oryzae</span> are shown. A schematic representation of canonical miRNA biogenesis pathway is shown. MiRNA biogenesis is a multistep process in which primary precursor transcripts (pri-miRNAs) are cleaved into pre-miRNA precursors and miRNA duplexes (miRNA/miRNA*) by the activity of DICER-LIKE1 (DCL) proteins. MiRNAs are exported from the nucleus to the cytosol where the functional strand of the miRNA/miRNA* duplex exerts its regulatory function on target transcripts, either transcriptional regulation or translational repression. The miRNA target genes and the biological processes that they regulate are indicated. AGO, ARGONAUTE; CCSD, copper chaperone for superoxide dismutase; CDS, Cu/Zn-superoxidase dismutase; DAMPs, damage-associated molecular patterns; GRFs, growth-regulating factors; NRAMP6, natural resistance-associated macrophage protein 6; PAMPs; pathogen-associated molecular patterns; PHO2, phosphate 2; PHT1, phosphate transporter 1 family; pre-miRNA, precursor microRNA; PRRs, pattern recognition receptors; RBOHs, respiratory burst oxidase homologs; RNA Pol II, RNA polymerase II; SODX, superoxide dismutase X; SPX-MFS, SYG1/PHO81/XPR1-major facilitator superfamily.</p>
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<p>Plant disease resistance depends on many factors, including the type of host–pathogen combination and environmental conditions. Balanced nutrition is an important factor in controlling plant diseases. Nutrient supply might affect disease resistance either by improving plant vigor or by modulating plant defense responses. The bioavailability of nutrients in the soil as well as fertilization practices might also have an effect on the incidence and severity of a particular disease. Beneficial microbes enhance nutrient uptake, improve plant immune responses, and help plants to manage pathogen infection.</p>
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16 pages, 3757 KiB  
Article
Folic Acid Supplementation Inhibits Proliferative Retinopathy of Prematurity
by Shen Nian, Yan Zeng, Katarina E. Heyden, Gaël Cagnone, Hitomi Yagi, Myriam Boeck, Deokho Lee, Victoria Hirst, Zhanqing Hua, Jeff Lee, Chaomei Wang, Katherine Neilsen, Jean-Sébastien Joyal, Martha S. Field and Zhongjie Fu
Biomolecules 2025, 15(2), 309; https://doi.org/10.3390/biom15020309 - 19 Feb 2025
Abstract
Background: Retinopathy of prematurity (ROP) is the major cause of blindness in children. It is a biphasic disease with retinal vessel growth cessation and loss (Phase I) followed by uncontrolled retinal vessel growth (Phase II). Folate is an essential nutrient for fetal development [...] Read more.
Background: Retinopathy of prematurity (ROP) is the major cause of blindness in children. It is a biphasic disease with retinal vessel growth cessation and loss (Phase I) followed by uncontrolled retinal vessel growth (Phase II). Folate is an essential nutrient for fetal development and growth. Premature infants have a high risk for folate deficiency. However, the contribution of folate to ROP risk remains unknown. Methods: In mouse oxygen-induced retinopathy (OIR), the nursing dams were fed with a folic acid-deficient or control diet after delivery until the end of hyperoxia. Alternatively, pups received direct injection of either folic acid or vehicle during Phase I hyperoxia. Genes involved in the folate cycle and angiogenic responses were examined using real-time PCR. Total retinal folate levels were measured with the Lactobacillus casei assay. Results: Maternal folic acid deficiency in early life exacerbated pathological retinal vessel growth, while supplementation with folic acid suppressed it. Genes involved in the folate cycle were downregulated in Phase I OIR retinas and were highly expressed in Müller glia. Folic acid reduced pro-angiogenic signaling in cultured rat retinal Müller glia in vitro. Conclusions: Appropriate supplementation of folic acid might be a new and safe treatment for ROP at an early stage. Full article
(This article belongs to the Section Molecular Medicine)
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<p>Expression of metabolic genes involved in folate cycle at OIR vs. normal retinas. (<b>A</b>) Schematics of mouse model of oxygen-induced retinopathy (OIR). Hyperoxia-induced vessel loss and growth cessation and relative hypoxia-induced uncontrolled vessel proliferation. Peak neovascularization (NV) occurs at P17. (<b>B</b>) Schematics of the folate cycle. Metabolic enzymes were highlighted in bold. Dihydrofolate reductase (DHFR), serine hydroxymethyltransferase (SHMT1, SHMT2), methenyltetrahydrofolate cyclohydrolase (MTHFD1, MTHFD2), NAD-dependent methylenetetrahydrofolate dehydrogenase 2-like protein (MTHFD2L), 10-formyltetrahydrofolate dehydrogenase (ALDH1L1), aldehyde dehydrogenase 1 family, member L2 (ALDH1L2), mitochondrial methionyl-tRNA formyltransferase (MTFMT), methionine synthase (MTR), and methylenetetrahydrofolate reductase (MTHFR). (<b>C</b>,<b>D</b>) qPCR of metabolic genes involved in the folate cycle. Retinas were isolated from P12 (<b>C</b>) and P17 (<b>D</b>) OIR vs. normal control mice. <span class="html-italic">n</span> = 8 mice per group. (<b>E</b>) Western blot of MTR in P12 OIR vs. control retinas. <span class="html-italic">n</span> = 7–8 mice per group, two independent litters for each group. β-ACTIN was used as internal control. Ratio of change was calculated over P12N (averaged value of the intensity of MTR over β-ACTIN) on the same blot. Analysis was conducted using combined values from two blots. Normality (quantile–quantile plot) and F-test were first conducted; unpaired <span class="html-italic">t</span>-test or Mann–Whitney test was used to compare the groups. ns, not significant. Original Western blot images can be found in <a href="#app1-biomolecules-15-00309" class="html-app">Figure S4</a>.</p>
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<p>Early folic acid deficiency worsened hypoxia-induced retinal neovascularization in OIR mice. (<b>A</b>) Mice were fed on folic acid-deficient or control diet from P1 to P12 and exposed to 75% oxygen from P7 to P12. Mice from both groups were fed on folic acid control diet from P12. At P17, the retinas were collected and stained with isolectin. The neovascular area was highlighted in white. <span class="html-italic">n</span> = 20–24 retinas per group from four independent litters (two litters for each diet). (<b>B</b>) Mice were fed on folic acid-deficient or control diet from P1 to P12 and exposed to 75% oxygen from P7 to P12. At P12, the retinas were collected immediately after hyperoxia and stained with isolectin. The vaso-obliterated area was outlined in yellow. <span class="html-italic">n</span> = 8–12 retinas per group from two independent litters (one litter for each diet). Scale bar, 1 mm. Retinal vaso-obliteration (VO) and neovascularization (NV) were quantified using Image J 1.47v. The ratio of change was calculated and compared with the control group. Normality (quantile–quantile plot) and F-test were first conducted, and unpaired <span class="html-italic">t</span>-test (or Welch’s test) or Mann–Whitney test was used to compare the groups. ns, not significant.</p>
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<p>Folic acid supplementation during hyperoxia decreased retinal neovascularization in OIR mice. Mouse pups were treated with either folic acid (0.5 mg/kg, i.p.) or vehicle from P7 to P11. Retinas were collected and stained with isolectin at P17 (<b>A</b>) and P12 (<b>B</b>). The vaso-obliterated area was outlined in yellow (<b>B</b>). Retinal VO and NV were quantified using Image J 1.47v and compared in mice. Scale bar, 1 mm. <span class="html-italic">n</span> = 37–38 retinas per group from five independent litters (<b>A</b>). <span class="html-italic">n</span> = 12–13 retinas per group from two independent litters (<b>B</b>). Ratio of change was calculated and compared with littermate control group. Normality (quantile–quantile plot) and F-test were first conducted, and unpaired <span class="html-italic">t</span>-test (or Welch’s test) was used to compare the groups. ns, not significant.</p>
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<p>Folic acid supplementation during hypoxia did not change retinal neovascularization in OIR mice. Mouse pups were treated with folic acid (0.5 mg/kg, i.p.) or vehicle from P12 to P16. At P17, the retinas were collected and stained with isolectin. Retinal vaso-obliteration (VO) and neovascularization (NV) were quantified using Image J 1.47v. Scale bar, 1 mm. <span class="html-italic">n</span> = 10–12 retinas per group from two independent litters. The ratio of change was calculated and compared with littermate control group. Normality (quantile–quantile plot) and F-test were first conducted, and unpaired <span class="html-italic">t</span>-test was used to compare the groups. ns, not significant.</p>
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<p>Folic acid decreased Müller glial <span class="html-italic">Epo</span> in vitro. Immortalized rat retinal Müller cell line rMC-1 was cultured and treated with folic acid for 24 h. (<b>A</b>) Cell proliferation was measured using MTT assay. <span class="html-italic">n</span> = 6 per group. ANOVA with Dunnett’s multiple comparison posttest. (<b>B</b>) Western blot of HIF-1α in rMC-1 cells treated with folic acid (FA) treatment (1 µM) or vehicle (0.02% DMSO) for 24 h. CoCl<sub>2</sub> was used to induce HIF-1α stabilization in these cells. <span class="html-italic">n</span> = 3–4 per group. The band intensity of HIF-1α was divided by that of β-ACTIN. Unpaired <span class="html-italic">t</span>-test or ANOVA. * <span class="html-italic">p</span> &lt; 0.05. ns, not significant. Original Western blot images can be found in <a href="#app1-biomolecules-15-00309" class="html-app">Figure S5</a>. (<b>C</b>) qPCR of rat <span class="html-italic">Epo</span> in rMC-1 cells treated with folic acid treatment (1 µM) or vehicle (0.02% DMSO) for 24 h. <span class="html-italic">n</span> = 4–5 per group. ANOVA. *** <span class="html-italic">p</span> &lt; 0.001, * <span class="html-italic">p</span> &lt; 0.05. ns, not significant. (<b>D</b>) qPCR of <span class="html-italic">Epo</span> in P12 and P17 OIR retinas from mice fed on folic acid-deficient or control diets from P1 to P12. <span class="html-italic">n</span> = 8 mice per group. Normality (quantile–quantile plot) and F-test were first conducted, and Mann–Whitney test was used to compare the groups. ns, not significant. (<b>E</b>) qPCR of rat <span class="html-italic">Vegfa</span> in rMC-1 cells treated with folic acid treatment (1 µM) or vehicle (0.02% DMSO) for 24 h. <span class="html-italic">n</span> = 4–6 per group. ANOVA. ns, not significant. (<b>F</b>) qPCR of <span class="html-italic">Vegfa</span> in P12 and P17 OIR retinas from mice fed on folic acid-deficient or control diets from P1 to P12. <span class="html-italic">n</span> = 8 mice per group. Normality (quantile–quantile plot) and F-test were first conducted, and an unpaired <span class="html-italic">t</span>-test was used to compare the groups. ns, not significant.</p>
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16 pages, 3926 KiB  
Article
Active Vibration Control Study of Harmonic Excitation for Voigt–Kelvin System
by Ovidiu Vasile and Mihai Bugaru
Appl. Sci. 2025, 15(4), 2226; https://doi.org/10.3390/app15042226 - 19 Feb 2025
Abstract
This paper presents research on active vibration control (A-V-C), which is being carried out to reduce structural vibration in the field of active vibration control and describes the most important method of implementation. Non-adaptive and adaptive systems feedback with adaptive algorithms are outlined. [...] Read more.
This paper presents research on active vibration control (A-V-C), which is being carried out to reduce structural vibration in the field of active vibration control and describes the most important method of implementation. Non-adaptive and adaptive systems feedback with adaptive algorithms are outlined. Electrodynamic shakers, used to excite an SDOF system to study its dynamic characteristics, are introduced. Signal analysis determines the response of a system under known excitation and presents it in a convenient form. The proposed method directly measures the payload displacement relative to the ground. We carry out a detailed investigation based on a realistic single-degree-of-freedom (SDOF), demonstrate the effectiveness of the proposed adaptive control law, estimate the control parameters, and show that the target dynamics of the isolator are attained. Full article
(This article belongs to the Section Mechanical Engineering)
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Figure 1

Figure 1
<p>Voigt–Kelvin system with active control.</p>
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<p>Chart of the excitation forces and the results of their <math display="inline"><semantics> <mrow> <msub> <mi>F</mi> <mi>T</mi> </msub> </mrow> </semantics></math> composition: (<b>a</b>) time representation; (<b>b</b>) frequency representation.</p>
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<p>Displacement versus time representation for Case 1: (<b>a</b>) solution of the free response equation without excitation; (<b>b</b>) solution of the particular equation with composed force; (<b>c</b>) general solution of displacement.</p>
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<p>Displacement versus time representation for Case 2: (<b>a</b>) solution of the free response equation without excitation; (<b>b</b>) solution of the particular equation with composed force; (<b>c</b>) general solution of displacement.</p>
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<p>Displacement versus time representation for Case 3: (<b>a</b>) solution of the free response equation without excitation; (<b>b</b>) solution of the particular equation with composed force; (<b>c</b>) general solution of displacement.</p>
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<p>Blok diagram of closed-loop control.</p>
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<p>Variations in control parameters in Case 1, in four different situations and total displacement <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>min</mi> </mrow> </msub> </mrow> </semantics></math> depending on the variation of: (<b>a</b>) parameters <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>r</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>r</mi> </msub> </mrow> </semantics></math>; (<b>b</b>) parameters <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>r</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>r</mi> </msub> </mrow> </semantics></math>; (<b>c</b>) parameters <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>r</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>r</mi> </msub> </mrow> </semantics></math>; (<b>d</b>) parameters <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>r</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>r</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Variations in control parameters in Case 2, for two different situations and total displacement <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>min</mi> </mrow> </msub> </mrow> </semantics></math> depending of the variation of: (<b>a</b>) parameters <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>r</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>r</mi> </msub> </mrow> </semantics></math>; (<b>b</b>) parameters <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>r</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>r</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Variations in control parameters in Case 3 for two situations and total displacement <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mrow> <mn>3</mn> <mo>,</mo> <mi>min</mi> </mrow> </msub> </mrow> </semantics></math> depending of the variation of: (<b>a</b>) parameters <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>r</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>r</mi> </msub> </mrow> </semantics></math>; (<b>b</b>) parameters <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>r</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>r</mi> </msub> </mrow> </semantics></math>.</p>
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<p>The effect of the weighting control on the amplitude of displacement variation, showing results with and without the control for (<b>a</b>) Case 1, (<b>b</b>) Case 2, and (<b>c</b>) Case 3.</p>
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10 pages, 1797 KiB  
Article
Laser Power Modulation of Fiber Coated with Multilayer-Graphene Based on Lithium Intercalation Method
by Zhenyu Fang, Ganying Zeng, Yijie Li, Zixuan Wang, Liantuan Xiao, Suotang Jia and Chengbing Qin
Photonics 2025, 12(2), 169; https://doi.org/10.3390/photonics12020169 - 19 Feb 2025
Abstract
Dynamic manipulation of light in optical fibers has attracted extensive interest due to its compatibility with various fiber-optic systems. The integration of two-dimensional (2D) materials on the surface of optical fibers is an effective method to manipulate light beams. However, it is still [...] Read more.
Dynamic manipulation of light in optical fibers has attracted extensive interest due to its compatibility with various fiber-optic systems. The integration of two-dimensional (2D) materials on the surface of optical fibers is an effective method to manipulate light beams. However, it is still a huge challenge to acquire dynamic modulation for light signals in fiber. In this work, we develop electrically manipulable in-line multilayer graphene (MLG) devices by integrating a graphene-based lithium-ion (Li-ion) battery on a side-polished fiber. Through charge and discharge processes with a current of 400 µA, the output power of a 1550 nm laser can be cyclically tuned in the range of ~120 and ~240 µW with a response time of about 1.8 min. After 100 cycles of testing, the modulation power of the laser system remains nearly unchanged, exhibiting good stability. The optical modification of MLG is due to the shift of Fermi energy (Ef), which results from charge transfer between Li and graphene layers. Therefore, the light in the fiber can be modulated due to the change in the optical absorbance of MLG. Our findings imply potential value in fabricating fiber-intergraded 2D intercalation materials with high tunability. Full article
(This article belongs to the Special Issue Thermal Radiation and Micro-/Nanophotonics)
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Figure 1

Figure 1
<p>Device structure and working principle. (<b>a</b>) Schematic diagram of the device structure consisting of anode MLG film, PE separator soaked in electrolyte, and cathode Li-NMC. Two pieces of Al foil are the current collector for cathode and anode materials. (<b>b</b>) Demonstration of Li-ions and electrons behavior during charge and discharge processes. (<b>c</b>) Schematic diagram of the Ef of MLG before and after Li intercalation. (<b>d</b>) Electrochemical potential versus lithiation time of MLG during charge process. (<b>e</b>) Detail of voltage profile versus time corresponding to the curve in the red box of (<b>d</b>). (<b>f</b>) Optical images of MLG, LiC<sub>18</sub>, LiC<sub>12</sub>, and LiC<sub>6</sub>, respectively.</p>
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<p>Characterization of MLG and Li-intercalated MLG. (<b>a</b>,<b>b</b>) Raman spectra of MLG and Li-GIC at different stages. (<b>c</b>) XRD test of MLG before (black line) and after intercalation (red line). (<b>d</b>) Four-probe I versus V curves for MLG before (black) and after intercalation (red line). (<b>e</b>) The absorbance spectra as a function of wavelength for MLG at different electrochemical potentials. (<b>f</b>) The absorbance at 1550 nm wavelength as a function of electrochemical potential versus Li/Li<sup>+</sup>.</p>
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<p>(<b>a</b>,<b>b</b>) Fiber laser system configuration includes a graphene-based Li-ion battery. (<b>c</b>) The output power of the laser corresponds to intercalation (red line) and no intercalation (black line) during 100 charge and discharge cycles. The current is 400 µA during the charge and discharge process. (<b>d</b>) Typical voltage profile of MLG during the first 25th charge and discharge cycles, with a constant current of 400 µA. The other voltage profile of MLG during the first 25th to 100th charge and discharge cycles is shown in <a href="#app1-photonics-12-00169" class="html-app">Figure S4</a>.</p>
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<p>(<b>a</b>) The output power of a laser during charge (orange area) and discharge (green area) for the 10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, 90th, and 100th cycles, respectively. The current is 400 µA during the charge and discharge process. (<b>b</b>) The difference of output power vs. the number of cycles. The difference of output power between 3.6 V and 3.8 V is extracted from (<b>a</b>).</p>
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