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18 pages, 3062 KiB  
Article
Emergence of a Novel Dengue Virus Serotype-2 Genotype IV Lineage III Strain and Displacement of Dengue Virus Serotype-1 in Central India (2019–2023)
by Ashish Kumar Yadav, Rashmi Chowdhary, Arshi Siddiqui, Anvita Gupta Malhotra, Jagat R. Kanwar, Ashok Kumar, Debasis Biswas, Sagar Khadanga, Rajnish Joshi, Abhijit Pakhare and Sudhir Kumar Goel
Viruses 2025, 17(2), 144; https://doi.org/10.3390/v17020144 (registering DOI) - 23 Jan 2025
Abstract
Dengue fever remains a significant public health concern in tropical regions, including Central India, where outbreaks are frequent and associated with high morbidity and mortality. This study investigated the dynamics of dengue virus transmission and evolution in Central India from 2019 to 2023, [...] Read more.
Dengue fever remains a significant public health concern in tropical regions, including Central India, where outbreaks are frequent and associated with high morbidity and mortality. This study investigated the dynamics of dengue virus transmission and evolution in Central India from 2019 to 2023, focusing on the emergence of new strains and their impact on outbreak patterns. For this, 40 mosquito pools and 300 patient samples were recruited for the study. Phylogenetic and Bayesian evolutionary analyses performed on CPrM region and whole genome sequences generated by Sanger and Illumina sequencing, respectively, revealed the emergence and predominance of a novel DENV-2 genotype IV lineage III strain in the 2019 and 2023 outbreaks, which displaced the previously circulating DENV-1 genotype responsible for the 2016–2017 outbreak. Despite pre-existing DENV-1 neutralizing antibodies in the community (67 healthy volunteers), the novel DENV-2 strain exhibited higher viral loads and a greater reproduction number (R0), contributing to rapid disease spread. Molecular clock and Shannon entropy analyses suggest that DENV evolution occurred within the mosquito vector, driven by natural selection. Our findings highlight the importance of continuous DENV surveillance, including genetic characterization in both vectors and hosts, to understand viral evolution and predict future outbreaks. Rapid urbanization and inadequate sanitation in densely populated regions like India create ideal breeding grounds for mosquitoes, facilitating the introduction and establishment of novel DENV strains. Interrupting the vector–DENV–host cycle through targeted interventions is crucial for effective dengue control. Full article
(This article belongs to the Section Human Virology and Viral Diseases)
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Figure 1

Figure 1
<p>Map of Bhopal City showing hot-spot wards (52–61) for dengue cases and for mosquito sample collection (Blue dot represents the sample collection wards).</p>
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<p>Flow chart of the sample recruitment.</p>
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<p>Details of samples used in this study.</p>
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<p>Viral load difference between DF and DHF patients (**** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Phylogenetic tree based on CPrM gene of DENV-2 from a vector (<span class="html-italic">n</span> = 18) generated using Sanger sequencing. Each strain is identified by its GenBank accession number, country/state/city of origin, and the year of isolation. The analysis of DENV 2 was performed with the study isolates using the maximum likelihood and Tamura–Nei method in MEGA 10 software (Bootstrap = 1000).</p>
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<p>Phylogenetic tree-based CPrM gene of DENV-2 from hosts (<span class="html-italic">n</span> = 41) generated using Sanger sequencing. Each strain is identified by its GenBank accession number, country/state/city of origin, and the year of isolation. The analysis of DENV 2 was performed with the study isolates by using the maximum likelihood and Tamura–Nei method in MEGA 10 software (Bootstrap = 1000).</p>
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<p>Shannon entropy plot of the whole proteome of dengue virus. (<b>a</b>) DENV-1 and (<b>b</b>) DENV-2.</p>
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<p>Maximum clade credibility tree of DENV-1 (<span class="html-italic">n</span> = 58). The tree was generated with the best-fit strict clock, the Bayesian skyline model. Node ages are denoted at each node.</p>
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<p>Maximum clade credibility tree of DENV-2 (<span class="html-italic">n</span> = 62). The tree was generated with the best-fit strict clock and constant size model. Node ages are denoted at each node.</p>
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<p>Plot representing the distribution of neutralizing antibodies in the population.</p>
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19 pages, 23704 KiB  
Article
Morphology and Molecular Phylogeny of Four Anaerobic Ciliates (Protista, Ciliophora, Armophorea), with Report of a New Species and a Unique Arrangement Pattern of Dikinetids in Family Metopidae
by Song Li, Wenbao Zhuang, Xiaochen Feng, Alan Warren and Jun Gong
Microorganisms 2025, 13(2), 240; https://doi.org/10.3390/microorganisms13020240 (registering DOI) - 23 Jan 2025
Abstract
The diversity of anaerobic ciliates is greatly underestimated owing to the limitation in sampling and cultivation when compared with their aerobic counterparts. In this study, four anaerobic ciliates, viz. Brachonella abnormalis sp. nov., Brachonella contorta (Levander, 1894) Jankowski, 1964, Metopus contortus (Quennerstedt, 1867) [...] Read more.
The diversity of anaerobic ciliates is greatly underestimated owing to the limitation in sampling and cultivation when compared with their aerobic counterparts. In this study, four anaerobic ciliates, viz. Brachonella abnormalis sp. nov., Brachonella contorta (Levander, 1894) Jankowski, 1964, Metopus contortus (Quennerstedt, 1867) Kahl, 1932, and Metopus major Kahl, 1932, were investigated by live observation, protargol staining and 18S rRNA gene sequencing. B. abnormalis sp. nov. can be separated from its congeners by a combination of the following features: bullet-shaped cell with a life size of about 130–190 × 90–120 μm, dikinetids distributed along dorsal dome kineties, highly developed adoral zone comprised of 87–107 polykinetids, making about 450° spiralization around the long axis. The present work demonstrates that two known species, M. contortus and M. major, have a special trait never previously reported, viz. short, regularly arranged preoral dome dikinetids. Species with short, regularly arranged dome dikinetids appear in divergent clades in SSU rRNA gene trees, which may infer that this trait evolved several times. Phylogenetic analyses based on SSU rRNA gene sequence data also support the monophyly of the genus Brachonella and the paraphyly of the order Metopida, respectively. Full article
(This article belongs to the Special Issue Microbial Food Webs)
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Figure 1

Figure 1
<p>Location of sampling sites. (<b>A</b>) Partial map of China. (<b>B</b>) The stagnant freshwater pond in Qingdao where <span class="html-italic">Brachonella abnormalis</span> sp. nov. and <span class="html-italic">B. contorta</span> were isolated. (<b>C</b>) The estuary in Shenzhen where <span class="html-italic">Metopus contortus</span> was collected. (<b>D</b>) The lake in Jining where <span class="html-italic">B. contorta</span> was isolated. (<b>E</b>) The mangrove forest seawater pool in Haikou where <span class="html-italic">Metopus major</span> was collected.</p>
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<p><span class="html-italic">Brachonella abnormalis</span> sp. nov. from life with bright-field (<b>A</b>,<b>H</b>) and differential interference contrast (<b>B</b>,<b>C</b>,<b>G</b>) microscopy and after protargol staining (<b>D</b>–<b>F</b>,<b>I</b>,<b>J</b>). (<b>A</b>) Ventral view showing dense aggregation of brown-black refractive granules in the anterior region of the cell (arrow). (<b>B</b>) Right view showing caudal cilia (arrow). (<b>C</b>) Dorsal view showing contractile vacuole. (<b>D</b>) Left part of the cell, showing evenly distributed dome dikinetids (arrowheads). (<b>E</b>) Anterior portion of the cell showing dome dikinetids (arrowheads). (<b>F</b>) Proximal margin of the preoral dome showing perizonal ciliary stripe and dome kinety 1 (arrowhead). (<b>G</b>) General view of a cell showing refractive granules (arrowhead) and macronucleus (arrow). (<b>H</b>) Interkinetal cortical granules (white arrowheads) and kinetal furrows interval (red arrowheads) in a strongly compressed cell. (<b>I</b>,<b>J</b>) Ventral (<b>I</b>) and dorsal (<b>J</b>) views of the same specimen, showing dome kineties (black arrowheads), adoral membranelles (arrow), and postoral somatic kineties (white arrowheads). CV, contractile vacuole. Scale bars: 50 µm.</p>
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<p><span class="html-italic">Brachonella abnormalis</span> sp. nov. from life (<b>A</b>,<b>H</b>) and after protargol staining (<b>B</b>–<b>G</b>). (<b>A</b>) Ventral side of a representative specimen showing food vacuole (red arrow), aggregation of cytoplasmic granules (arrowhead), and caudal cilia (black arrow). (<b>B,C</b>) Ventral (<b>B</b>) and dorsal (<b>C</b>) views of the same specimen showing uneven arrangement of dome dikinetids (arrows in (<b>B</b>)), dome kineties 1 (black arrowhead), perizonal stripe row 5 (red arrowhead) and cytoproct area (arrow in (<b>C</b>)). (<b>D</b>) Posterior part of the cell showing adoral membranelles (black arrowhead), paroral membrane, somatic kineties (red arrowheads), and cytoproct area (arrow). (<b>E</b>,<b>F</b>) Different cell shapes in dorsal view. (<b>G</b>) Anterior part of the cell showing suture (arrowhead), dome kinety 1, and dome dikinetids (arrows). (<b>H</b>) Arrangement of cortical granules with arrowheads showing kinetal furrows interval (arrowheads). AM, adoral membranelles; CV, contractile vacuole; DK1, dome kinety 1; Ma, macronucleus; PM, paroral membrane; PS, perizonal ciliary stripe. Scale bars: 50 μm (<b>A</b>–<b>C</b>,<b>E</b>,<b>F</b>), 10 μm (<b>D</b>).</p>
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<p><span class="html-italic">Brachonella contorta</span> from life (<b>A</b>,<b>D</b>–<b>G</b>) with bright-field (<b>D</b>), differential interference contrast (<b>E</b>–<b>G</b>) microscopy, and after protargol staining (<b>B</b>,<b>C</b>). (<b>A</b>) Ventral view showing dense aggregation of brown-black refractive granules in the anterior region of the cell, contractile vacuole, and caudal cilia. (<b>B</b>,<b>C</b>) Ventral (<b>B</b>) and dorsal (<b>C</b>) views of the same specimen, showing dome kineties, adoral membranelles, and postoral kineties. (<b>D</b>) Left dorsal view showing contractile vacuole (arrow). (<b>E</b>,<b>F</b>) Ventral (<b>E</b>) and right (<b>F</b>) view of the same individual showing aggregation of granules (arrows), adoral membranelles, and caudal cilia (arrowhead). (<b>G</b>) Ventral view showing kinetal furrows interval (white arrowheads) and caudal cilia (black arrowheads). AG, aggregation of granules; AM, adoral membranelles; CC, cauda cilia; CV, contractile vacuole; DK, dome kineties; DK1, dome kinety 1; Ma, macronucleus; PK, postoral kinety; PS, perizonal ciliary stripe. Scale bars: 50 μm.</p>
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<p><span class="html-italic">Metopus contortus</span> from life (<b>A</b>) and after protargol staining (<b>B</b>–<b>F</b>). (<b>A</b>) Ventral side of a representative specimen showing general morphology, contractile vacuole, and elongated caudal cilia (arrow). (<b>B</b>,<b>C</b>) Ventral (<b>B</b>) and dorsal (<b>C</b>) views of the same specimen showing the uneven arrangement of dome dikinetids on the dorsal side (red arrows), suture (black arrow), dome kinety 1 (red arrowhead), perizonal stripe row 4 (black arrowhead) and cytoproct area (black arrows). (<b>D</b>) Detail view of the paroral membrane and perizonal ciliary stripe, showing dome kineties 1 (red arrowheads) and perizonal stripe row 4 (black arrowhead). (<b>E</b>) Shape variation of macronucleus (<b>i</b>–<b>iv</b>). (<b>F</b>) Different cell shapes in ventral view (<b>i</b>–<b>iii</b>). CV, contractile vacuole; Ma, macronucleus; PM, paroral membrane. Scale bars: 50 μm (<b>A</b>–<b>C</b>,<b>E</b>), 10 μm (<b>D</b>,<b>E</b>), 50 μm (<b>F</b>).</p>
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<p><span class="html-italic">Metopus contortus</span> from life with bright-field (<b>A</b>–<b>D</b>,<b>F</b>), differential interference contrast (<b>E</b>) microscopy, and after protargol staining (<b>G</b>–<b>K</b>). (<b>A</b>) Right ventral view showing aggregation of dark spherical particles, posterior cortical fold, and caudal cilia (arrows). (<b>B</b>) Right lateral view showing truncated distal end and caudal cilia (arrow). (<b>C</b>) Ventral view showing contractile vacuole (arrow). (<b>D</b>) Left view showing proximal margin of the preoral dome and contractile vacuole. (<b>E</b>) Right ventral view showing the contractile vacuole (arrow). (<b>F</b>) Left ventral view showing the contractile vacuole (arrow). (<b>G</b>) Anterior part of the cell showing the adoral membranelles and somatic kineties (arrows). (<b>H</b>) Details of perizonal ciliary stripe (arrow) and adoral membranelles. (<b>I</b>) Macronucleus surrounded by numerous irregular granules. (<b>J</b>) Left ventral view of a specimen showing adoral membranelles, perizonal ciliary stripe, and macronucleus. (<b>K</b>) Right ventral view of a specimen showing uneven arrangement of dome dikinetids (arrows). Scale bars: 50 µm.</p>
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<p><span class="html-italic">Metopus major</span> from life (<b>A</b>) and after protargol staining (<b>B</b>–<b>G</b>). (<b>A</b>) Ventral side of a representative specimen showing general morphology, contractile vacuole, elongated caudal cilia, and cytoproct area (arrow). (<b>B</b>) Showing a cluster of 4 (<b>i</b>) and 3 (<b>ii</b>) dikinetids. (<b>C</b>,<b>D</b>) Ventral (<b>C</b>) and dorsal (<b>D</b>) views of the same specimen, showing the ciliature, nuclear apparatus and cytoproct area (arrows). (<b>E</b>–<b>G</b>) Showing different shapes of the macronucleus and its length compared to the body length. AM, adoral membranelles; CC, caudal cilia; CV, contractile vacuole; FV, food vacuole; Ma, macronucleus; Mi, micronucleus; PD, preoral dome; PM, paroral membrane; PS, perizonal ciliary stripe. Scale bars: 50 μm (<b>A</b>,<b>C</b>,<b>D</b>).</p>
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<p><span class="html-italic">Metopus major</span> from life with bright-field (<b>A</b>,<b>C</b>), differential interference contrast (<b>B</b>,<b>G</b>,<b>H</b>) microscopy, and after protargol staining (<b>D</b>–<b>F</b>,<b>I</b>). (<b>A</b>) Ventral view showing the aggregate of spherical particles, food vacuole, contractile vacuole pore (arrow), and caudal cilia. (<b>B</b>) Ventral view showing macronucleus and elongated caudal cilia trailing behind the cell during swimming. (<b>C</b>) Showing oblong outline in left ventral view. (<b>D</b>) Showing an elongated macronucleus, which is about 2/3 of the length of the cell. (<b>E</b>,<b>F</b>) Ventral (<b>E</b>) and dorsal (<b>F</b>) views of the same specimen, showing the ciliature and macronucleus. Arrow indicates the cytoproct area (arrow). (<b>G</b>) Thick hyaline cortex (arrowheads). (<b>H</b>) Dense aggregation of spherical particles (arrowheads) around macronucleus. (<b>I</b>) Showing clusters of 2 (white arrowhead) and 3 (red arrowheads) dikinetids on the dorsal side of the preoral dome. AG, aggregation of granules; AM, adoral membranelles; CC, caudal cilia; CV, contractile vacuole; DK, dome kineties; FV, food vacuole; Ma, macronucleus; PM, paroral membrane; PS, perizonal ciliary stripe. Scale bars: 70 μm (<b>A</b>–<b>F</b>).</p>
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<p>Phylogenetic tree based on SSU rDNA sequences. Numbers near branches represent BI posterior probabilities and non-parametric bootstrap values from ML. Disagreements in BI and ML tree topologies are indicated by ‘*’. All branches are drawn to scale. The scale bar corresponds to 10 substitutions per 100 nucleotide position. GenBank accession numbers are given for each species.</p>
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33 pages, 2804 KiB  
Review
Preclinical Insights into the Role of Kir4.1 in Chronic Pain and Depression: Mechanisms and Therapeutic Potential
by Tingfeng Zha, Xinyi Fang, Jiamin Wan, Xiaoyan Chen, Jiu Lin and Qianming Chen
Biomolecules 2025, 15(2), 165; https://doi.org/10.3390/biom15020165 (registering DOI) - 23 Jan 2025
Abstract
Chronic pain and mental health disorders, such as depression and anxiety, frequently co-occur and share underlying mechanisms involving neuronal excitability and synaptic transmission. The inwardly rectifying potassium channel 4.1 (Kir4.1), predominantly expressed in glial cells, is crucial for maintaining extracellular potassium and glutamate [...] Read more.
Chronic pain and mental health disorders, such as depression and anxiety, frequently co-occur and share underlying mechanisms involving neuronal excitability and synaptic transmission. The inwardly rectifying potassium channel 4.1 (Kir4.1), predominantly expressed in glial cells, is crucial for maintaining extracellular potassium and glutamate homeostasis. Dysregulation of Kir4.1 leads to altered neuronal activity, contributing to both chronic pain and mental health disorders. In chronic pain, downregulation of Kir4.1 impairs potassium buffering and glutamate clearance, increasing neuronal excitability and enhancing pain signaling through peripheral and central sensitization. In mental health disorders, impaired Kir4.1 function disrupts neurotrophic factor secretion and neuroinflammatory pathways, leading to mood disturbances. This review primarily summarizes findings from preclinical studies to examine the relationship between Kir4.1 and the pathogenesis of chronic pain and mental health disorders, discussing its molecular structure, expression patterns, and functional roles. Furthermore, we explore therapeutic strategies targeting Kir4.1, including pharmacological modulators and gene therapy approaches, emphasizing its potential as a novel therapeutic target. Full article
(This article belongs to the Section Biological Factors)
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Figure 1
<p>The molecular structure of Kir4.1. (<b>A</b>) Kir4.1 is composed of four Kir4.1 subunits, while Kir4.1/5.1 consists of two Kir4.1 subunits and two Kir5.1 subunits. (<b>B</b>) The Kir4.1 subunit contains two transmembrane regions, an extracellular pore-forming loop, and intracellular N- and C-terminal domains. The pore-forming region features a G-Y-G motif that is responsible for selective K<sup>+</sup> permeation. Amino acid residues T128 and E158 serve as binding sites for certain antidepressants and small-molecule compounds that block Kir4.1. E158 is located at the center of the TM2 region, while T128 resides in the inner pore region below the G-Y-G sequence. The schematic was made using BioRender (<a href="https://app.biorender.com/" target="_blank">https://app.biorender.com/</a>, accessed on 18 November 2024).</p>
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<p>The expression profile of Kir4.1 in the rat nervous system. (<b>A</b>) Kir4.1 is mainly expressed in SGCs and Müller glia in the peripheral nervous system. (<b>B</b>) As for the CNS, Kir4.1 is mainly distributed in astrocytes, OLs, and OCPs. Moreover, the levels vary significantly across different regions, with notably higher abundance in areas such as the cerebellum, brainstem, spinal cord, hippocampus, and olfactory bulb. Note: In the upper section of (<b>B</b>), the red coloration simply indicates that Kir4.1 expression levels are higher in these brain regions compared to others. SGC: satellite glial cell. The schematic was made using BioRender (<a href="https://app.biorender.com/" target="_blank">https://app.biorender.com/</a>, accessed on 18 November 2024). The helical structure representing the Kir4.1 subunit in (<b>B</b>) was generated by ChimeraX-1.9 (San Francisco, CA, USA).</p>
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<p>The spatial K<sup>+</sup> buffering mediated by Kir4.1. During the repolarization phase of action potentials, neurons release a substantial amount of K<sup>+</sup>. Astrocytes absorb the excess K<sup>+</sup> through Kir4.1 and redistribute it to microcapillaries or transfer it through gap junctions to other astrocytes with lower K<sup>+</sup> concentrations, a process termed “spatial K<sup>+</sup> buffering.” Furthermore, Kir4.1 co-localizes with EAATs (e.g., EAAT1 and EAAT2) and AQP4, coupling with astrocytic uptake of glutamate and water. Gln: glutamine, Glu: glutamate, AP: action potential, VGPC: voltage-gated potassium channel, KAR: kainic acid receptor, NMADR: N-methyl-D-aspartate receptor, AMPAR: α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor, mGluR: metabotropic glutamate receptor. The schematic was made using BioRender (<a href="https://app.biorender.com/" target="_blank">https://app.biorender.com/</a>, accessed on 18 November 2024).</p>
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<p>The mechanism by which Kir4.1 on SGCs contributes to pain signal transmission. Following peripheral nerve injury or the application of Kir4.1 inhibitors, the function of Kir4.1 channels decreases, leading to an accumulation of extracellular K<sup>+</sup>. On one hand, the increase in [K<sup>+</sup>]<sub>o</sub> depolarizes neurons, enhancing their excitability; on the other hand, it causes SGC depolarization, promoting ATP release and activation of the Ras/ERK/CREB signaling pathway, which increases BDNF secretion. These signaling factors then act on P2X7R, P2X3R, and TrkB receptors, further boosting neuronal excitability. Additionally, elevated extracellular glutamate levels increase neuronal excitability directly by activating ionotropic glutamate receptors on neurons and indirectly by binding to mGluR on SGCs. This mGluR activation enhances intracellular Ca<sup>2</sup><sup>+</sup> signaling, further stimulating the Ras/ERK/CREB pathway. Together, these processes synergistically promote the transmission of pain signals. The schematic was made using BioRender (<a href="https://app.biorender.com/" target="_blank">https://app.biorender.com/</a>, accessed on 19 November 2024).</p>
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<p>The role of Kir4.1 in the pathogenesis of depression. In depression, Kir4.1 expression is upregulated. On one hand, the increased activity of Kir4.1 leads to a decrease in [K<sup>+</sup>]<sub>o</sub> and glutamate levels, as well as inhibition of BDNF secretion, ultimately reducing neuronal excitability, which contributes to the development of depression. On the other hand, upregulation of Kir4.1 in LHb induces neuronal hyperpolarization, which in turn triggers burst firing, a process highly associated with depressive-like behaviors. Furthermore, the high conductance property of Kir4.1 can limit lateral charge diffusion generated during glutamate transport. Elevated Kir4.1 expression disrupts this balance, intensifying glutamate uptake by astrocytes and further lowering extracellular glutamate concentration. The schematic was made using BioRender (<a href="https://app.biorender.com/" target="_blank">https://app.biorender.com/</a>, accessed on 19 November 2024).</p>
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15 pages, 4650 KiB  
Article
Arginine-Enhanced Termitomyces Mycelia: Improvement in Growth and Lignocellulose Degradation Capabilities
by Wenhui Yi, Jingfei Zhou, Qiwei Xiao, Wujie Zhong and Xuefeng Xu
Foods 2025, 14(3), 361; https://doi.org/10.3390/foods14030361 (registering DOI) - 23 Jan 2025
Abstract
Termitomyces mushrooms, known for their symbiotic relationship with termites and their high nutritional and medicinal value, are challenging to cultivate artificially due to their specific growth requirements. This study investigates the impact of arginine on the mycelial growth, development, and lignocellulolytic capabilities of [...] Read more.
Termitomyces mushrooms, known for their symbiotic relationship with termites and their high nutritional and medicinal value, are challenging to cultivate artificially due to their specific growth requirements. This study investigates the impact of arginine on the mycelial growth, development, and lignocellulolytic capabilities of Termitomyces. We found that arginine significantly promoted conidia formation, altered mycelial morphology, and enhanced biomass and polysaccharide content. The addition of arginine also upregulated the expression of the enzymes related to lignocellulose decomposition, leading to increased activities of cellulase, hemicellulase, and laccase, which accelerated the decomposition and utilization of corn straw. A transcriptome analysis revealed differential expression patterns of carbohydrate-active enzyme genes in arginine-supplemented Termitomyces mycelia, providing insights into the molecular mechanisms underlying these enhancements. The GO enrichment analysis and KEGG pathway analysis highlighted the role of arginine in transmembrane transport, fatty acid oxidation, and carbohydrate metabolism. This study offers a molecular basis for the observed phenotypic changes and valuable insights for developing optimal culture strategies for Termitomyces, potentially enhancing its artificial cultivation and application in the bioconversion of lignocellulosic waste. Full article
(This article belongs to the Section Food Biotechnology)
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Graphical abstract

Graphical abstract
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<p>Effects of different amino acids on growth rate, biomass, and spore germination rate of <span class="html-italic">Termitomyces</span> CK is the control medium.</p>
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<p>Macromorphology of liquid mycelia and micromorphology of plate mycelia. (<b>a</b>,<b>b</b>) The arginine-treated group; (<b>c</b>,<b>d</b>) the control group.</p>
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<p>(<b>a</b>) Changes in biomass; (<b>b</b>) changes in reducing sugar content; (<b>c</b>) changes in polysaccharide content; (<b>d</b>) changes in arginine content.</p>
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<p>Scanning electron micrographs of pre-treated corn straw on the 30th day of mycelial growth ((<b>a</b>): untreated; (<b>b</b>): <span class="html-italic">Termitomyces</span> group; (<b>c</b>): <span class="html-italic">Termitomyces</span> + Arg group).</p>
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<p>(<b>a</b>) Fourier infrared spectra; (<b>b</b>) X-ray diffractograms; (<b>c</b>) lignocellulose content of corn straw; data are mean earth standard error.</p>
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<p>Effect of arginine on related enzyme activities. The asterisk ‘*’ denotes significant difference (<span class="html-italic">p</span> &lt; 0.05), double asterisk ‘**’ denotes <span class="html-italic">p</span> ≤ 0.001, while ‘NS’ represents not significant.</p>
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<p>Heatmap of the effect of arginine on the predicted number of hits for representatives of different CAZyme families in the predicted proteomes of <span class="html-italic">Termitomyces</span>.</p>
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<p>(<b>a</b>) GO classification of total DEGs in <span class="html-italic">Termitomyces</span> mycelia between the CK- and Arg-treated groups; (<b>b</b>) KEGG enrichment analysis in <span class="html-italic">Termitomyces</span> mycelia between the CK- and Arg-treated groups.</p>
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<p>Reactions in carbohydrate metabolism, glycolysis/gluconeogenesis (ko00010), pentose and glucuronide interconversion (ko00040), and starch and sucrose metabolism (ko00500).</p>
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24 pages, 429 KiB  
Review
Risk Stratification in HPV-Associated Oropharyngeal Cancer: Limitations of Current Approaches and the Search for Better Solutions
by Bailey Fabiny Garb, Elham Mohebbi, Maria Lawas, Shaomiao Xia, Garett Maag, Peter H. Ahn, Nisha J. D’Silva, Laura S. Rozek and Maureen A. Sartor
Cancers 2025, 17(3), 357; https://doi.org/10.3390/cancers17030357 - 22 Jan 2025
Abstract
The rising incidence of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) necessitates advancements in risk stratification to optimize treatment outcomes and improve the quality of life for patients. Despite its favorable prognosis compared to HPV-negative OPSCC, current clinical staging and biomarkers, such [...] Read more.
The rising incidence of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) necessitates advancements in risk stratification to optimize treatment outcomes and improve the quality of life for patients. Despite its favorable prognosis compared to HPV-negative OPSCC, current clinical staging and biomarkers, such as p16 status, are limited in their ability to distinguish between high- and low-risk patients within HPV-associated OPSCC. This limitation results in the overtreatment of low-risk patients, exposing them to unnecessary toxicity, and the undertreatment of high-risk patients who require more aggressive interventions. This review critically evaluates current stratification methods, including clinical assessments, de-escalation trials, and candidate molecular biomarkers for risk stratification. Emerging approaches such as immune markers, viral genomic integration patterns, and other molecular markers offer promising avenues for enhanced prognostic accuracy. By integrating advanced risk stratification methods, tailored treatment approaches may one day be developed to balance oncologic efficacy with reduced treatment-related morbidity. This review underscores the need for continued research into predictive biomarkers and adaptive treatment strategies to better address the diverse risk profiles of HPV-associated OPSCC patients. Full article
(This article belongs to the Special Issue Head and Neck Cancers—Novel Approaches and Future Outlook)
20 pages, 18177 KiB  
Article
Identification of R2R3-MYB Transcription Factor Family Based on Amaranthus tricolor Genome and AtrMYB72 Promoting Betalain Biosynthesis by Directly Activating AtrCYP76AD1 Expression
by Yuwei Xue, Kexuan Li, Wenli Feng, Zhongxiong Lai and Shengcai Liu
Plants 2025, 14(3), 324; https://doi.org/10.3390/plants14030324 - 22 Jan 2025
Abstract
MYB (myeloblastosis) is one of the most abundant transcription factors in plants which regulates various biological processes. The molecular characteristics and function of R2R3-MYB transcription factors in amaranth remain unclear. In this study, 73 R2R3-MYB members were identified from the amaranth genome database [...] Read more.
MYB (myeloblastosis) is one of the most abundant transcription factors in plants which regulates various biological processes. The molecular characteristics and function of R2R3-MYB transcription factors in amaranth remain unclear. In this study, 73 R2R3-MYB members were identified from the amaranth genome database and we further analyzed their chromosome position, conserved motifs, physiological and biochemical features, collinearity relationships, gene structure, phylogeny and cis-acting element. Based on the phylogenetic and expression pattern analysis, 14 candidate R2R3-MYB genes might be involved in the betalain synthesis. Amongst the 14 candidate R2R3-MYB genes, the expression level of AtrMYB72 was higher in ‘Suxian No.1’ than ‘Suxian No.2’, and also higher in the red section than in the green section of the same leaf in Amaranthus. The overexpression vector pCambia1301-AtrMYB72-GUS and VIGS (virus-induced gene silencing) vector pTRV2- AtrMYB72 were transferred into leaves of ‘Suxian No.1’ via an Agrobacterium-mediated method. The results showed that AtrMYB72 overexpression could promote betalain synthesis. A yeast one-hybrid assay and dual luciferase reporter gene assay demonstrated that AtrMYB72 could bind to the AtrCYP76AD1 promoter to promote betalain synthesis. These results indicated that AtrMYB72 promoted betalain biosynthesis in amaranth by activating the AtrCYP76AD1 transcription. Our results could provide new insights into the betalain biosynthesis in amaranth. Full article
(This article belongs to the Special Issue Bioinformatics and Functional Genomics in Modern Plant Science)
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<p>Distribution of <span class="html-italic">Amaranthus tricolor R2R3-MYB</span> (<span class="html-italic">AtrMYB</span>) genes among 17 chromosomes. Gene positions and the size of each chromosome can be estimated using the scale on the right of the figure; the scale indicates 10 megabases (Mb).</p>
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<p>Comparison of DNA-binding domains of R2R3-MYB transcription factor in <span class="html-italic">Amaranthus tricolor</span>, <span class="html-italic">Hylocereus undatus</span>, <span class="html-italic">Beta vulgaris</span> and <span class="html-italic">Arabidopsis thaliana</span>. Sequence logos of the R2 and R3 repeats are based on conserved alignments from <span class="html-italic">Amaranthus tricolor</span> (<b>A</b>), <span class="html-italic">Hylocereus undatus</span> (<b>B</b>), <span class="html-italic">Beta vulgaris</span> (<b>C</b>) and <span class="html-italic">Arabidopsis thaliana</span> (<b>D</b>). The overall height of each stack indicates the conservation of the sequence at the position, whereas the height of letters within each stack represents the relative frequency of the corresponding amino acid. Highly conserved tryptophan (W) and phenylalanine (F) residues are indicated by yellow asterisks. The positions with different patterns between <span class="html-italic">Amaranthus tricolor</span>, <span class="html-italic">Hylocereus undatus</span>, <span class="html-italic">Beta vulgaris</span> and <span class="html-italic">Arabidopsis thaliana</span> are indicated by arrows. The positions with different patterns between <span class="html-italic">Amaranthus tricolor</span>, <span class="html-italic">Hylocereus undatus</span>, <span class="html-italic">Beta vulgaris</span> and <span class="html-italic">Arabidopsis thaliana</span> are indicated by red triangle.</p>
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<p>Collinearity analyses of <span class="html-italic">R2R3-MYB</span> genes. (<b>A</b>) Segmental duplication events of <span class="html-italic">R2R3-MYB</span> genes in amaranth. (<b>B</b>) Duplication events of <span class="html-italic">R2R3-MYB</span> genes between amaranth and pitaya. (<b>C</b>) Duplication events of <span class="html-italic">R2R3-MYB</span> genes between amaranth and beet. (<b>D</b>) Duplication events of <span class="html-italic">R2R3-MYB</span> genes between amaranth and <span class="html-italic">Arabidopsis thaliana</span>. Purple lines indicate duplication events of <span class="html-italic">R2R3-MYB</span> genes. Gray lines represent all synteny blocks in genomes.</p>
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<p>Phylogenetic relationships of R2R3-MYBs. <span class="html-italic">Arabidopsis thaliana</span>, amaranth, pitaya and beet R2R3-MYBs were used for the phylogenetic tree construction using the ML method. Red stars represent the R2R3-MYBs of amaranth, blue circles represent the R2R3-MYBs of pitaya, green circles symbolize the R2R3-MYBs of <span class="html-italic">Arabidopsis thaliana</span> and yellow circles represent the R2R3-MYBs of beet.</p>
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<p>AtrR2R3-MYBs phylogenetic relationship (<b>A</b>), conserved motifs (<b>B</b>), and conserved domains (<b>C</b>). Orange circles indicate the bootstrap value range from 81 to 100 in the tree, green is from 60 to 80, and blue is from 0 to 59.</p>
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<p>The regulatory element of R2R3-MYB gene promoters in amaranth.</p>
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<p>Expression patterns of the AtrMYBs.</p>
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<p>Quantitative analysis of selected <span class="html-italic">AtrR2R3-MYBs</span>. (<b>A</b>) Quantitative expression analysis in the leaves of ‘Suxian No.1’ and ‘Suxian No.2’; (<b>B</b>) Quantitative expression in the stems of ‘Suxian No.1’ and ‘Suxian No.2’; (<b>C</b>) Quantitative expression in the different sections of <span class="html-italic">Amaranthus</span> leaves. * indicates significant differences at <span class="html-italic">p</span> &lt; 0.05, ** indicates significant differences at <span class="html-italic">p</span> &lt; 0.01, *** indicates significant differences at <span class="html-italic">p</span> &lt; 0.001, and **** indicates significant differences at <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p><span class="html-italic">Agrobacterium</span>-mediated transient transformation into the amaranth leaves revealing that the overexpression of <span class="html-italic">AtrMYB72</span> promotes the betalain synthesis in amaranth. (<b>A</b>) Plant leaves after transient transformation for 7 days. (<b>B</b>) Plants after transient transformation for 7 days. (<b>C</b>) Relative expression of betalain synthesis-related genes in leaves of plants with different transient transformations. (<b>D</b>) Betalain contents in the leaves with different transient transformation plants. (a, b and c indicate significant differences at <span class="html-italic">p</span> &lt; 0.05; Bars: 1 cm).</p>
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<p>Silencing of <span class="html-italic">AtrMYB72</span> inhibited betalain synthesis. (<b>A</b>) control leaves (<b>left</b>), VIGS-empty leaves (<b>middle</b>) and VIGS-MYB72 leaves (<b>right</b>). (<b>B</b>) control plant (<b>left</b>), VIGS-empty plant (<b>middle</b>) and VIGS-MYB72 plant (<b>right</b>). (<b>C</b>) Relative expression levels of key genes involved in betalain synthesis in transgenic plants with gene silencing. (<b>D</b>) Betalain contents in leaves with gene silencing plants. Three biological replicates were performed for each sample (a, b and c indicate significant differences at <span class="html-italic">p</span> &lt; 0.01; Bar = 1 cm).</p>
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<p>Y1H assay of <span class="html-italic">AtrMYB72</span> with <span class="html-italic">AtrCYP76AD1</span> promoter. The promoter of <span class="html-italic">AtrCYP76AD1</span> was constructed in the pHis2 vector, and the ORF of <span class="html-italic">AtrMYB72</span> was constructed in the pGADT7 vector. Yeast cells were cultured on an SD/-Leu-Trp-His medium supplemented with 100 nm of 3-AT.</p>
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<p><span class="html-italic">AtrMYB72</span> promoted the <span class="html-italic">AtrCYP76AD1</span> transcription in <span class="html-italic">Nicotiana benthamiana</span> leaves. ** indicates significant differences at <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>A hypothetical model of <span class="html-italic">AtrMYB72</span> gene regulated <span class="html-italic">AtrCYP76AD1</span> involved in betalain biosynthesis in amaranth. <span class="html-italic">AtrMYB72</span> transcript factor activated <span class="html-italic">AtrCYP76AD1</span> transcription by binding the MBS elements of the <span class="html-italic">AtrCYP76AD1</span> promoter.</p>
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<p>Plant phenotype of amaranth. (<b>A</b>) represents ‘Suxian No.1’ and ‘Suxian No.2’. (<b>B</b>) represents different parts in full-red amaranth leaves. (<b>C</b>) represents ‘Suxian No.1’ in (2000 lux, 16 h light/8 h dark, temperature 26 ± 1 °C). (<b>D</b>) represents ‘Suxian No.1’ in (8000 lux, 16 h light/8 h dark, temperature 26 ± 1 °C).</p>
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12 pages, 4180 KiB  
Review
Reappraisal of the Genetic Diversity Patterns in Puya raimondii—The Queen of the Andes: Insights from Molecular Marker Analysis Reveal an Inbreeding Reproductive Strategy
by Samela Draga, Sergio Sgorbati and Gianni Barcaccia
Plants 2025, 14(3), 321; https://doi.org/10.3390/plants14030321 - 22 Jan 2025
Abstract
Puya raimondii Harms is a charismatic species discovered in the Cordillera Blanca (now Huascarán National Park, Peru) in 1867 by the great Italian-born Peruvian geographer and naturalist Antonio Raimondi. The importance of this plant is due to its imposing size, the rare and [...] Read more.
Puya raimondii Harms is a charismatic species discovered in the Cordillera Blanca (now Huascarán National Park, Peru) in 1867 by the great Italian-born Peruvian geographer and naturalist Antonio Raimondi. The importance of this plant is due to its imposing size, the rare and extreme ecosystem that depends on it, and the fact that it is linked to the name Antonio Raimondi. Four studies on its genetic diversity revealed a range of patterns, with a fixation index of 0.740 as weighted mean and gene flow as low as 0.02–0.03. In fact, the vast majority of the total genetic variation was documented between populations, with very low genetic variation found within populations (weighted mean genetic diversity as low as Hs = 0.072 and mean genetic similarity very high, ranging from 96% up to 99%). We hypothesize that the narrow genetic base of P. raimondii populations may be due to a combination of factors: (i) an inbreeding-based reproductive strategy (i.e., mating between individuals related by common ancestry), which leads to homozygosity and genomic uniformity; (ii) strong environmental selective pressure (e.g., day–night temperature excursion, long dry period, etc.), which favors only the highest fitness individual genotypes; and (iii) a long life cycle, which hampers recombination events and reduces genetic diversity. Overall, these factors suggest that P. raimondii is a genetically fragile, fragmented, and endangered species. Full article
(This article belongs to the Special Issue Genetics and Genomics of Plant Reproductive Systems)
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<p>Graphical illustration of the reported genetic data from the four studies in question. (<b>A</b>) UPGMA dendrogram displaying the genetic similarity among populations of <span class="html-italic">P. raimondii</span> from the work of Sgorbati et al. [<a href="#B5-plants-14-00321" class="html-bibr">5</a>]. Abbreviations of the populations are given in <a href="#plants-14-00321-t002" class="html-table">Table 2</a>, while the three other species of <span class="html-italic">Puya</span> are included as outgroups: fer-<span class="html-italic">P. ferruginea</span>, her-<span class="html-italic">P. herrerae</span>, and den-<span class="html-italic">P. densiflora</span>. (<b>B</b>) UPGMA dendrogram of the genetic similarity estimates from Hornung-Leoni et al. [<a href="#B6-plants-14-00321" class="html-bibr">6</a>], including the individuals from five populations reported as R1 to R5, followed by the genotype number. (<b>C</b>) STRUCTURE analysis, assuming the number of clusters (K) = 2 of the three populations (abbreviations as in <a href="#plants-14-00321-t002" class="html-table">Table 2</a>), of <span class="html-italic">P. raimondii</span> from the study of Tumi et al. [<a href="#B7-plants-14-00321" class="html-bibr">7</a>]. (<b>D</b>) STRUCTURE analysis of K = 9 (as the optimal solution) of the nine populations analyzed by Liu et al. [<a href="#B4-plants-14-00321" class="html-bibr">4</a>] (for population abbreviations, see <a href="#plants-14-00321-t002" class="html-table">Table 2</a>) and the well-defined regional divisions between the northern, central, and southern regions in the corresponding colors: blue, green and red.</p>
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<p>Geographical distribution of <span class="html-italic">P. raimondii</span> populations sampled in the Peruvian Andes, according to the four reported studies [<a href="#B4-plants-14-00321" class="html-bibr">4</a>,<a href="#B5-plants-14-00321" class="html-bibr">5</a>,<a href="#B6-plants-14-00321" class="html-bibr">6</a>,<a href="#B7-plants-14-00321" class="html-bibr">7</a>].</p>
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<p>Fascinating <span class="html-italic">P. raimondii</span> individuals of the Titancayocc population in Ayacucho, Peru.</p>
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16 pages, 1758 KiB  
Article
Longitudinal Circulating Tumor Cell Collection, Culture, and Characterization in Pancreatic Adenocarcinomas
by Jerry Xiao, Reetu Mukherji, George Sidarous, Shravanthy Suguru, Marcus Noel, Benjamin A. Weinberg, Aiwu He and Seema Agarwal
Cancers 2025, 17(3), 355; https://doi.org/10.3390/cancers17030355 - 22 Jan 2025
Abstract
Background/Objectives: Pancreatic adenocarcinoma (PDAC) remains one of the most lethal cancers, with limited advancements in treatment efficacy due to high rates of chemoresistance. Circulating tumor cells (CTCs) derived from liquid biopsies offer a non-invasive approach to monitoring tumor evolution and identifying molecular mechanisms [...] Read more.
Background/Objectives: Pancreatic adenocarcinoma (PDAC) remains one of the most lethal cancers, with limited advancements in treatment efficacy due to high rates of chemoresistance. Circulating tumor cells (CTCs) derived from liquid biopsies offer a non-invasive approach to monitoring tumor evolution and identifying molecular mechanisms of resistance. This study aims to longitudinally collect, culture, and characterize CTCs from PDAC patients to elucidate resistance mechanisms and tumor-specific gene expression profiles. Methods: Blood samples from 10 PDAC patients were collected across different treatment stages, yielding 16 CTC cultures. Differential gene expression, pathway dysregulation, and protein–protein interaction studies were utilized, highlighting patient-specific and disease progression-associated changes. Longitudinal comparisons within five patients provided further insights into dynamic molecular changes associated with therapeutic resistance. Results: CTC cultures exhibited the activation of key pathways implicated in PDAC progression and resistance, including TNFα/NF-kB, hedgehog signaling, and the epithelial-to-mesenchymal transition. Longitudinal samples revealed dynamic changes in signaling pathways, highlighting upregulated mechanisms of chemoresistance, including PI3K/Akt/mTOR and TGF-β pathways. Additionally, protein–protein interaction analysis emphasized the role of the immune system in PDAC progression and therapy response. Patient-specific gene expression patterns therefore suggest potential applications for precision medicine. Conclusions: This proof-of-concept study demonstrates the feasibility of longitudinally capturing and analyzing CTCs from PDAC patients. The findings provide critical insights into molecular drivers of chemoresistance and highlight the potential of CTC profiling to inform personalized therapeutic strategies. Future large-scale studies are warranted to validate these findings and further explore CTC-based approaches in PDAC management. Full article
(This article belongs to the Section Cancer Metastasis)
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<p>CTC samples were characterized via bulk RNA sequencing, comparing pooled CTC samples against pooled whole blood samples. (<b>A</b>) A heatmap of all samples with unbiased clustering demonstrating a clear separation between whole blood samples and processed CTC samples. (<b>B</b>) Selected genes associated with epithelial–mesenchymal transitions (EMT) depicted on a heatmap. Among the genes depicted, CTC samples had an overall higher expression of EMT-associated genes, consistent with the known CTC phenotype. (<b>C</b>) KEGG pathway analysis using differentially expressed genes between pooled CTCs and whole blood samples. GSEA depicting increased expression of the Hallmarks gene sets associated with (<b>D</b>) EMT, (<b>E</b>) KRAS signaling, and (<b>F</b>) TNFα via NF-κB signaling.</p>
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<p>Longitudinal collection of samples from five individuals. (<b>A</b>) General timeline of collection of longitudinal samples from the five individuals. More detailed timelines can be found in the <a href="#app1-cancers-17-00355" class="html-app">supplemental figures</a>. (<b>B</b>) A curated list of 15 signaling pathways and 5 broader gene-sets was evaluated using ssGSEA across all longitudinally collected samples, demonstrating various changes across signaling pathways and across therapies. Heatmaps depicting the log2-fold count of the 10 most differentially expressed genes in (<b>C</b>) TNFα via NF-κB signaling, (<b>D</b>) hedgehog signaling, and (<b>E</b>) PI3K/AKT/mTOR signaling within each pairwise comparison.</p>
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<p>ssGSEA evaluation of individual pairwise comparisons reveal intra-patient specific dysregulation of various signaling pathways. The heatmaps depicted represent the top 10 most dysregulated genes within each specified pathway. Pathways were selected to be the among the four most dysregulated pathways (not including TNFα via NF-κB, Hedgehog, or PI3K/AKT/mTOR signaling) within (<b>A</b>) patient 39, (<b>B</b>) patient 49, (<b>C</b>) patient 52, (<b>D</b>) patient 63, and (<b>E</b>) patient 64.</p>
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13 pages, 1099 KiB  
Article
Age-Related Changes in Caudate Glucose Metabolism: Insights from Normative Modeling Study in Healthy Subjects
by Zijing Zhang, Yuchen Li, Qi Xia, Qing Yu, Luqing Wei and Guo-Rong Wu
Metabolites 2025, 15(2), 67; https://doi.org/10.3390/metabo15020067 - 22 Jan 2025
Viewed by 90
Abstract
Background: As the global population ages, the prevalence of neurodegenerative conditions, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), dementia with Lewy bodies, and frontotemporal dementia, continues to rise. Understanding the impact of aging on striatal glucose metabolism is pivotal in identifying potential [...] Read more.
Background: As the global population ages, the prevalence of neurodegenerative conditions, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), dementia with Lewy bodies, and frontotemporal dementia, continues to rise. Understanding the impact of aging on striatal glucose metabolism is pivotal in identifying potential biomarkers for the early detection of these disorders. Methods: We investigated age-related changes in striatal glucose metabolism using both region of interest (ROI)-based and voxel-wise correlation analyses. Additionally, we employed a normative modeling approach to establish age-related metabolic trajectories and assess individual deviations from these normative patterns. In vivo cerebral glucose metabolism was quantified using a molecular neuroimaging technique, 18F-FDG PET. Results: Our results revealed significant negative correlations between age and glucose metabolism in the bilateral caudate. Furthermore, the normative modeling demonstrated a clear, progressive decline in caudate metabolism with advancing age, and the most pronounced reductions were observed in older individuals. Conclusions: These findings suggest that metabolic reductions in the caudate may serve as a sensitive biomarker for normal aging and offer valuable insights into the early stages of neurodegenerative diseases. Moreover, by establishing age-specific reference values for caudate glucose metabolism, the normative model provides a framework for detecting deviations from expected metabolic patterns, which may facilitate the early identification of metabolic alterations that could precede clinical symptoms of neurodegenerative processes. Full article
(This article belongs to the Special Issue Nutrition and Metabolic Changes in Aging and Age-Related Diseases)
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<p>Spatial distribution of brain regions exhibiting significant correlation between age and glucose metabolism (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </mrow> </semantics></math>, FWE correction).</p>
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<p>Lifespan trajectories and centiles of variation for the caudate glucose metabolism are plotted, with age on the <span class="html-italic">x</span>-axis and predicted standardized uptake values (SUV) on the <span class="html-italic">y</span>-axis. Blue circles represent the training data, while red dots denote the test data. The seven plotted fitted centile lines represent the 1st, 5th, 25th, 50th, 75th, 95th, and 99th percentiles.</p>
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21 pages, 4995 KiB  
Article
Ontogeny of Fetal Cardiometabolic Pathways: The Potential Role of Cortisol and Thyroid Hormones in Driving the Transition from Preterm to Near-Term Heart Development in Sheep
by Reza Amanollahi, Stacey L. Holman, Melanie R. Bertossa, Ashley S. Meakin, Kent L. Thornburg, I. Caroline McMillen, Michael D. Wiese, Mitchell C. Lock and Janna L. Morrison
J. Cardiovasc. Dev. Dis. 2025, 12(2), 36; https://doi.org/10.3390/jcdd12020036 (registering DOI) - 21 Jan 2025
Viewed by 260
Abstract
Understanding hormonal and molecular changes during the transition from preterm to near-term gestation is essential for investigating how pregnancy complications impact fetal heart development and contribute to long-term cardiovascular risks for offspring. This study examines these cardiac changes in fetal sheep, focusing on [...] Read more.
Understanding hormonal and molecular changes during the transition from preterm to near-term gestation is essential for investigating how pregnancy complications impact fetal heart development and contribute to long-term cardiovascular risks for offspring. This study examines these cardiac changes in fetal sheep, focusing on the changes between 116 days (preterm) and 140 days (near term) of gestation (dG, term = 150) using Western blotting, LC-MS/MS, and histological techniques. We observed a strong correlation between cortisol and T3 (Triiodothyronine) in heart tissue in near-term fetuses, highlighting the role of glucocorticoid signalling in fetal heart maturation. Protein expression patterns in the heart revealed a decrease in multiple glucocorticoid receptor isoforms (GRα-A, GR-P, GR-A, GRα-D2, and GRα-D3), alongside a decrease in IGF-1R (a marker of cardiac proliferative capacity) and p-FOXO1(Thr24) but an increase in PCNA (a marker of DNA replication), indicating a shift towards cardiomyocyte maturation from preterm to near term. The increased expression of proteins regulating mitochondrial biogenesis and OXPHOS complex 4 reflects the known transition from glycolysis to oxidative phosphorylation, essential for meeting the energy demands of the postnatal heart. We also found altered glucose transporter expression, with increased pIRS-1(ser789) and GLUT-4 but decreased GLUT-1 expression, suggesting improved insulin responsiveness as the heart approaches term. Notably, the reduced protein abundance of SIRT-1 and SERCA2, along with increased phosphorylation of cardiac Troponin I(Ser23/24), indicates adaptations for more energy-efficient contraction in the near-term heart. In conclusion, these findings show the complex interplay of hormonal, metabolic, and growth changes that regulate fetal heart development, providing new insights into heart development that are crucial for understanding pathological conditions at birth and throughout life. Full article
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Graphical abstract
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<p>Hormone concentrations of fetal cardiac tissue. The fetal cardiac concentration of cortisol (<b>A</b>) and cortisone (<b>B</b>) were not different between preterm and near-term fetuses. The cortisol: cortisone ratio (<b>C</b>) was higher, while 11-deoxycortisol (<b>D</b>) and corticosterone (<b>E</b>) were lower with no change in progesterone (<b>F</b>) in the near-term compared to preterm fetuses. T<sub>4</sub> (<b>G</b>) was lower with no change in T<sub>3</sub> (<b>H</b>) concentrations in the near-term compared to preterm fetuses. In the near-term fetuses only, there were positive linear relationships of T<sub>3</sub> with cortisol (<b>I</b>). Males (M) = circles, females (F) = triangles. preterm, left ventricle (LV) tissue from fetuses at 116 days of gestation (dG) (open symbols; hormone = 2M, 5F). Near-term, LV tissue from fetuses at 140 dG (closed symbols; hormone = 3M, 4F). One sample per animal was analysed via LC-MS/MS. Data were excluded due to a technical error in hormone concentration. Up to one outlier was excluded per group using the Grubbs method (Alpha = 0.05), when applicable. Data are expressed as mean ± SD and were analysed using either an unpaired <span class="html-italic">t</span>-test or simple linear regression. Data for progesterone, T<sub>3</sub>, and T<sub>4</sub> failed the normality test and were consequently analysed using the Mann–Whitney test. (*) indicates a statistically significant difference between the groups. <span class="html-italic">p</span> &lt; 0.05 was considered significant. AU: arbitrary unit.</p>
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<p>Abundance of glucocorticoid receptor isoforms in the fetal heart. The cardiac protein abundance of glucocorticoid receptors (GR) including GRα-A (<b>A</b>), GR-P (<b>B</b>), GR-A (<b>C</b>), GRα-D2 (<b>D</b>), and GRα-D3 (<b>E</b>) was lower in the near-term compared to preterm fetuses. In the near-term fetuses only, there was a positive linear relationship between cortisol and GRα-D2 (<b>F</b>). Males (M) = circles, females (F) = triangles. Preterm, left ventricle (LV) tissue from fetuses at 116 days of gestation (dG) (open symbols; protein = 3M, 5F; hormone = 2M, 5F). Near-term, LV tissue from fetuses at 140 dG (closed symbols; protein = 3M, 4F; hormone = 3M, 4F). One sample per animal was run per Western blot and LC-MS/MS. Data were excluded due to a technical error in hormone concentration. Up to one outlier was excluded per group using the Grubbs method (Alpha = 0.05) when applicable. Data were expressed as mean ± SD and were analysed using either an unpaired <span class="html-italic">t</span>-test or simple linear regression. (*) indicates a statistically significant difference between the groups. <span class="html-italic">p</span> &lt; 0.05 was considered significant. AU: arbitrary unit. (X) indicates data excluded from analysis (due to a defect on the band/s).</p>
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<p>Molecular markers of fetal cardiac growth. The cardiac protein expression of IGF-1R (<b>A</b>) and p-FOXO1:FOXO1 ratio (<b>B</b>) was lower, while PCNA (<b>C</b>) was higher in the near term compared to preterm fetuses. There was no difference in the p-mTOR:mTOR ratio (<b>D</b>), p-Akt:Akt ratio (<b>E</b>), and p-P70 S6K:P70 S6K ratio (<b>F</b>) between the groups. Males (M) = circles, females (F) = triangles. Preterm, left ventricle (LV) tissue from fetuses at 116 days of gestation (dG) (open symbols; protein = 3M, 5F). Near-term, LV tissue from fetuses at 140 dG (closed symbols; protein = 3M, 4F). One sample per animal was run per Western blot. Up to one outlier was excluded per group using the Grubbs method (Alpha = 0.05), when applicable. Data are expressed as mean ± SD and were analysed either using an unpaired <span class="html-italic">t</span>-test or simple linear regression. (*) indicates a statistically significant difference between the groups. <span class="html-italic">p</span> &lt; 0.05 was considered significant. AU: arbitrary unit. (X) indicates data excluded from analysis (due to a defect on the band/s).</p>
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<p>Molecular markers of fetal cardiac OXPHOS and mitochondrial content. The cardiac protein abundance of complex 4 (<b>D</b>) was higher in the near-term compared to preterm fetuses, while there was no difference in complex 1 (<b>A</b>), 2 (<b>B</b>), 3 (<b>C</b>), and 5 (<b>E</b>). The MT-COXI: SDHA ratio (<b>F</b>), a marker of mitochondrial content) was higher in the near-term compared to preterm fetuses. CS activity (<b>G</b>) did not differ between the groups, while CS activity: mitochondrial content ratio (<b>H</b>) was lower in the near-term compared to preterm fetuses. Males (M) = circles, females (F) = triangles. Preterm, left ventricle (LV) tissue from fetuses at 116 days of gestation (dG) (open symbols; protein/CS activity = 3M, 5F). Near-term, LV tissue from fetuses at 140 dG (closed symbols; protein/CS activity = 3M, 4F). One sample per animal was run per Western blot and CS activity. Up to one outlier was excluded per group using the Grubbs method (Alpha = 0.05), when applicable. Data are expressed as mean ± SD and were analysed using either an unpaired <span class="html-italic">t</span>-test or simple linear regression. (*) indicates a statistically significant difference between the groups. <span class="html-italic">p</span> &lt; 0.05 was considered significant. AU: arbitrary unit. (X) indicates data excluded from analysis (due to a defect on the band/s).</p>
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<p>Molecular markers of fetal cardiac glucose metabolism. The ratio of p-IRS-1:IRS-1 ratio (<b>A</b>) and GLUT-4 (<b>C</b>) were higher, while the ratio of p-AS160:AS160 (<b>B</b>) was not different, and GLUT-1 (<b>D</b>) was lower in the near-term compared to preterm fetuses. The abundance of PDK-4 protein (<b>E</b>), and activity of LDH (<b>F</b>) were not different in preterm and near-term fetuses. In the preterm fetuses only, there were positive linear relationships between GRα-D2 and GLUT-1 (<b>G</b>), as well as GRα-D3 and GLUT-1 (<b>H</b>). Males (M) = circles, females (F) = triangles. Preterm, left ventricle (LV) tissue from fetuses at 116 days of gestation (dG) (open symbols; protein/LDH activity = 3M, 5F). Near-term, LV tissue from fetuses at 140 dG (closed symbols; protein/LDH activity = 3M, 4F). One sample per animal was run per Western blot and LDH activity. Up to one outlier was excluded per group using the Grubbs method (Alpha = 0.05), when applicable. Data are expressed as mean ± SD and were analysed using either an unpaired <span class="html-italic">t</span>-test or simple linear regression. Data for p-AS160:AS160 ratio failed the normality test and were consequently analysed using the Mann–Whitney test. (*) indicates a statistically significant difference between the groups. <span class="html-italic">p</span> &lt; 0.05 was considered significant. AU: arbitrary unit. (X) indicates data excluded from analysis (due to a defect on the band/s).</p>
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<p>Molecular markers of fetal cardiac contractility. The expression of SIRT-1 (<b>A</b>) and SERCA2 (<b>B</b>) in cardiac tissue was lower, while there was no difference in the ratio of p-PLN:PLN (<b>C</b>) in the near-term compared to preterm fetuses. The ratio of p-TroponinI:TroponinI (<b>D</b>) was higher, while NOX-2 (<b>E</b>) was lower in near-term compared to preterm fetuses. Males (M) = circles, females (F) = triangles. Preterm, left ventricle (LV) tissue from fetuses at 116 days of gestation (dG) (open symbols; protein = 3M, 5F). Near-term, LV tissue from fetuses at 140 dG (closed symbols; protein = 3M, 4F). One sample per animal was run per Western blot. Up to one outlier was excluded per group using the Grubbs method (Alpha = 0.05), when applicable. Data are expressed as mean ± SD and were analysed using either an unpaired <span class="html-italic">t</span>-test or simple linear regression. (*) indicates a statistically significant difference between the groups. <span class="html-italic">p</span> &lt; 0.05 was considered significant. AU: arbitrary unit.</p>
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<p>Fetal cardiac glycogen, collagen, and Ki67 staining: 20× magnification representative micrograph of glycogen staining using PAS (black arrow indicates glycogen stained in magenta) in preterm (<b>A</b>) and near term (<b>B</b>). 20× magnification representative micrograph of collagen staining using Masson’s trichrome (black arrow indicates collagen stained in blue) in preterm (<b>D</b>) and near term (<b>E</b>). 40× magnification representative micrograph of Ki67 staining using IHC (black arrow) in preterm (<b>G</b>) and near term (<b>H</b>). The fetal cardiac glycogen (<b>C</b>), collagen (<b>F</b>), and Ki67 (<b>I</b>) staining were not different between preterm and near-term fetuses. Males (M) = circles, females (F) = triangles. Preterm, left ventricle (LV) tissue from fetuses at 116 days of gestation (dG) (open symbols; histology/IHC = 3M, 2F). Near-term, LV tissue from fetuses at 140 dG (closed symbols; histology/IHC = 2M, 3F). One sample per animal was run per histology and IHC. A smaller subset of animals was included in this analysis due to missing fixed tissue samples. Scale bars = 100 μm. Data are expressed as mean ± SD and were analysed using an unpaired <span class="html-italic">t</span>-test. <span class="html-italic">p</span> &lt; 0.05 was considered significant.</p>
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53 pages, 9820 KiB  
Review
Surface Functionalization of Nanocarriers with Anti-EGFR Ligands for Cancer Active Targeting
by Alessandra Spada and Sandrine Gerber-Lemaire
Nanomaterials 2025, 15(3), 158; https://doi.org/10.3390/nano15030158 - 21 Jan 2025
Viewed by 329
Abstract
Active cancer targeting consists of the selective recognition of overexpressed biomarkers on cancer cell surfaces or within the tumor microenvironment, enabled by ligands conjugated to drug carriers. Nanoparticle (NP)-based systems are highly relevant for such an approach due to their large surface area [...] Read more.
Active cancer targeting consists of the selective recognition of overexpressed biomarkers on cancer cell surfaces or within the tumor microenvironment, enabled by ligands conjugated to drug carriers. Nanoparticle (NP)-based systems are highly relevant for such an approach due to their large surface area which is amenable to a variety of chemical modifications. Over the past decades, several studies have debated the efficiency of passive targeting, highlighting active targeting as a more specific and selective approach. The choice of conjugation chemistry for attaching ligands to nanocarriers is critical to ensure a stable and robust system. Among the panel of cancer biomarkers, the epidermal growth factor receptor (EGFR) stands as one of the most frequently overexpressed receptors in different cancer types. The design and development of nanocarriers with surface-bound anti-EGFR ligands are vital for targeted therapy, relying on their facilitated capture by EGFR-overexpressing tumor cells and enabling receptor-mediated endocytosis to improve drug accumulation within the tumor microenvironment. In this review, we examine several examples of the most recent and significant anti-EGFR nanocarriers and explore the various conjugation strategies for NP functionalization with anti-EGFR biomolecules and small molecular ligands. In addition, we also describe some of the most common characterization techniques to confirm and analyze the conjugation patterns. Full article
(This article belongs to the Special Issue The Future of Nanotechnology: Healthcare and Manufacturing)
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<p>Schematic representation of active targeting vs. passive targeting of cancer cells by nano-delivery systems. In passive targeting, the size and surface properties of nanocarriers facilitate their accumulation within tumor tissues through the EPR effect, resulting from the leaky vasculature of tumors. Conversely, active targeting involves the conjugation of specific ligands to the NP surface, enabling targeted delivery to cancer cells by binding to overexpressed receptors or markers on their surface. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Classification of bioconjugation strategies based on covalent and non-covalent strategies.</p>
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<p>An overview of the EGFR signaling pathway. Upon binding of specific ligands, EGFR undergoes dimerization and phosphorylation, leading to the activation of downstream signaling cascades and resulting in enhanced tumor growth, invasion and metastasis. Some of the main activated pathways include PI3K/AKT, RAS/MAPK, and JAK/STAT, which cross-regulate and interact, contributing to cell proliferation, migration and survival. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Molecular mechanism for the effect of EGF conjugation on TiO<sub>2</sub> PEG NPs uptake levels and the cell proliferation effect via interaction with EGFRs proposed in [<a href="#B101-nanomaterials-15-00158" class="html-bibr">101</a>]. (<b>A</b>) Non-treated A431 cells; (<b>B</b>) TiO<sub>2</sub> PEG NPs-treated A431 cells; (<b>C</b>) EGF-TiO<sub>2</sub> PEG NPs-treated cells. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Confocal microscopy images of bare PMNPs and GE11-PMNPs in red (Cy5.5), nucleus in blue (DAPI), and their combination (merged) in SW620 and HCT116 cells. Adapted with permission from [<a href="#B117-nanomaterials-15-00158" class="html-bibr">117</a>]. Copyright 2020, American Chemical Society.</p>
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<p>Therapeutic efficacy of C-P-DM1 in orthotopic A549-Luc NSCLC-bearing mice. (<b>A</b>) Establishment, therapy and monitoring scheme of the orthotopic A549-Luc NSCLC model. (<b>B</b>) Bioluminescence imaging and (<b>C</b>) quantitative bioluminescence levels of mice from day 0 to day 24 (<span class="html-italic">n</span> = 3). (<b>D</b>) Body weight change and (<b>E</b>) survival curves of mice (<span class="html-italic">n</span> = 6, C-P-DM1 vs. P-DM1 and PBS, ** <span class="html-italic">p</span> &lt; 0.01). (<b>F</b>) Photographs and (<b>G</b>) H&amp;E stained images of lung isolated from different groups on day 24. Scale bars are 50 μm. Adapted with permission from [<a href="#B157-nanomaterials-15-00158" class="html-bibr">157</a>]. Copyright 2021, American Chemical Society.</p>
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<p>Preparation of polymeric micelles conjugated with EGa1 (targeted) or Cys (non-targeted). Adapted with permission from [<a href="#B209-nanomaterials-15-00158" class="html-bibr">209</a>]. Copyright 2020, American Chemical Society.</p>
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<p>Mean fluorescent intensity from human colorectal Caco-2 cancer cells after incubation with Alexa Fluor 555-modified UC-ACD (targeted) and UC-CD (non-targeted), with and without NIR irradiation. MFI of UCNP-protein complex is 4.38 ± 0.32 times higher in the cells treated with UC-ACD-AF (+) IR than that of the non-irradiated control (UC-ACD-AF, <span class="html-italic">n</span> = 3, * <span class="html-italic">p</span> &lt; 0.05). Inset: schematic showing the UC-ACD conjugate structure and the NIR activation of the UCNPs, followed by upconversion to UV, resulting in a photoreaction between the affibody-enzyme ACD and EGF receptors. This chemical association allows enzymatic conversion of systematically delivered prodrug 5-FC to active drug 5-FU at the cancer site. Adapted with permission from [<a href="#B227-nanomaterials-15-00158" class="html-bibr">227</a>]. Copyright 2022, American Chemical Society.</p>
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<p>Selective tumor targeting of Cy7@PNPs-CL4 compared to Cy7@PNPs-SCR and Cy7@PNPs. Nude mice bearing subcutaneous MDA-MB-231 xenografts were i.v. injected with Cy7@PNPs-CL4, Cy7@PNPs or Cy7@PNPs-SCR (5 nmol Cy7/100 μL) and analyzed by in vivo FRI imaging at the indicated time points (i.e., Pre: before injection, 30 min, 1, 3 and 24 h acquisitions). Adapted from [<a href="#B256-nanomaterials-15-00158" class="html-bibr">256</a>]. Copyright 2021, Springer Nature.</p>
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<p>Schematic representation of the directional Ab conjugation chemistry. A glycosylated Ab is first fluorescently labelled, and then aldehyde groups are created on the Fc region. The hydrazide portion of an EMCH cross-linker binds to the aldehyde groups while the maleimide portion attaches to the thiolated BTNP surface. Adapted with permission from [<a href="#B173-nanomaterials-15-00158" class="html-bibr">173</a>]. Copyright 2020, American Chemical Society.</p>
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<p>Nbs targeting MET or EGFR were successfully conjugated to zinc phthalocyanine-loaded micelles. (<b>A</b>) Schematic of Nb-targeted micelles prepared via thiol-maleimide chemistry. (<b>B</b>) SDS-PAGE with silver staining of (1) Non-targeted micelles, (2) MET-Nb, (3) MET-targeted micelles, (4) EGFR-Nb, (5) EGFR-targeted micelles before the centrifugation step. (<b>C</b>) Size and polydispersity of non-targeted micelles, MET-targeted micelles and EGFR-targeted micelles (N = 3). Adapted with permission from [<a href="#B211-nanomaterials-15-00158" class="html-bibr">211</a>]. Copyright 2024, Elsevier.</p>
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<p>Schematic illustration of step-by-step liposome synthesis, surface functionalization and site-selective Ab attachment. Adapted with permission from [<a href="#B294-nanomaterials-15-00158" class="html-bibr">294</a>]. Copyright 2020, Royal Society of Chemistry.</p>
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<p>Schematic representation of the functionalization of neutravidin-coated gold nanorods with biotinylated anti-EGFR Abs. (i) Neutravidin-coated 10 nm × 67 nm gold nanorods, (ii) biotinylated 5.2 nm × 5.2 nm anti-EGFR Abs, (iii) Biotinylated anti-EGFR Abs-coated neutravidin gold nanorods. Adapted with permission from [<a href="#B308-nanomaterials-15-00158" class="html-bibr">308</a>]. Copyright 2021, MDPI.</p>
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<p>Characterization of multifunctional NPs. (<b>A</b>) Schematic illustration of the synthesis procedures for Apt/(siRNA + GEF)@ZIF-8 NPs. (<b>B</b>–<b>E</b>) TEM analysis of ZIF-8, GEF@ZIF-8, (siRNA + GEF)@ZIF-8 and Apt/(siRNA + GEF)@ZIF-8 NPs, respectively. Red circle highlighting the white dots in (<b>C</b>) gives evidence of the successful encapsulation of GEF. (<b>F</b>) Dark-field TEM image and elemental mappings of Apt/(siRNA + GEF)@ZIF-8 NPs. Adapted with permission from [<a href="#B321-nanomaterials-15-00158" class="html-bibr">321</a>]. Copyright 2022, American Chemical Society.</p>
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<p>Characterization of aptamer-functionalized NPs at different steps of functionalization. (<b>A</b>) UV-Vis spectra. (<b>B</b>) Hydrodynamic diameters. (<b>C</b>) Zeta potential values of the systems dispersed in water. Adapted with permission from [<a href="#B258-nanomaterials-15-00158" class="html-bibr">258</a>]. Copyright 2024, Springer Nature.</p>
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<p>Microscopy images of the secondary Ab labeled with fluorescein bound to mAb-functionalized PLGA NPs. (<b>A</b>) mAb-functionalized PLGA NPs were incubated with fluorescein-conjugated secondary Ab. (<b>B</b>) mAb-functionalized PLGA NPs without fluorophore. Adapted with permission from [<a href="#B333-nanomaterials-15-00158" class="html-bibr">333</a>]. Copyright 2020, Elsevier.</p>
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<p>Characterization of aptamer-decorated erlotinib-loaded chitosan NPs. Agarose gel electrophoresis of (1) erlotinib-loaded chitosan NPs and (2) aptamer-decorated erlotinib-loaded chitosan NPs. Adapted with permission from [<a href="#B337-nanomaterials-15-00158" class="html-bibr">337</a>]. Copyright 2020, Elsevier.</p>
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<p>FTIR spectra of the aptamer-functionalized NPs at different steps of functionalization. Adapted with permission from [<a href="#B321-nanomaterials-15-00158" class="html-bibr">321</a>]. Copyright 2023, American Chemical Society.</p>
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<p>TGA curves of Ab-functionalized NPs at different steps of functionalization. The analyses were performed under N<sub>2</sub> using a ramp of 10 °C/min. The level of functionalization was calculated from the weight loss values at 650 °C. Adapted with permission from [<a href="#B357-nanomaterials-15-00158" class="html-bibr">357</a>]. Copyright 2020, Elsevier.</p>
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23 pages, 4681 KiB  
Article
Ammopiptanthus nanus Population Dynamics: Bridging the Gap Between Genetic Variation and Ecological Distribution Patterns
by Jingdian Liu, Mengmeng Wei, Jiayi Lu, Shiqing Liu, Xuerong Li, Xiyong Wang, Jiancheng Wang, Daoyuan Zhang, Ting Lu and Wei Shi
Biology 2025, 14(2), 105; https://doi.org/10.3390/biology14020105 - 21 Jan 2025
Viewed by 217
Abstract
Ammopiptanthus nanus, a Tertiary-era endangered plant, is of great scientific value. In this research, we focus on A. nanus population dynamics in an effort to bridge the divide between micro genetic variation and a macroscopic ecological pattern of distribution. The population structure [...] Read more.
Ammopiptanthus nanus, a Tertiary-era endangered plant, is of great scientific value. In this research, we focus on A. nanus population dynamics in an effort to bridge the divide between micro genetic variation and a macroscopic ecological pattern of distribution. The population structure of 129 wild specimens of A. nanus from eight populations was analyzed using EST-SSR molecular markers in this research. The Mantel test and RDA analysis have been used in this research to investigate the factors that influence the genetic diversity of A. nanus. Using 15 pairs of SSR primers, a total of 227 alleles were detected in 129 samples from 8 populations. The mean number of alleles was 17, and the average expected heterozygosity was 0.405. It is shown that wild A. nanus is divided into six individual populations. A. nanus are significantly affected by wind speed in terms of the variation of genetics. It is suggested that a nature conservation area for A. nanus be established as soon as possible, based on our results and the current natural distribution of the species. It is necessary to focus on the issue of pests and diseases while simultaneously preventing the continuation of anthropogenic woodcutting and disaster. Manual seedling collection should be employed in regions where the environment permits. Through making use of manual breeding techniques, this will contribute to the growth of the natural population of A. nanus. Full article
(This article belongs to the Special Issue Genetic Variability within and between Populations)
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<p>Distribution point and elevation data from 8 <span class="html-italic">A. nanus</span> populations.</p>
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<p>Principal coordinate analysis of 129 <span class="html-italic">A. nanus</span> base on genetic distance.</p>
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<p>Bayesian model-based genetic structure analysis of <span class="html-italic">A. nanus</span>. The blue dots show the Delta K values corresponding to different K values. The red vertical line marks the peak of Delta K at K = 6.</p>
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<p>Ancestral clustering for K = 2 to K = 8. Each <span class="html-italic">A. nanus</span> individual is expressed as a line segment, where black lines are used to distinguish from 8 different populations. Different colors represent different clustering groups. For example, at K = 2, there are only two colors (e.g., blue and orange), indicating that all individuals are divided roughly into two clustering groups. When K = 3, a third color (e.g., purple) is present, which indicates that individuals are divided into three clustering groups, and so on.</p>
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<p>Pairwise population matrix of Nei genetic distance and Fst values of <span class="html-italic">A. nanus</span> populations.</p>
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<p>Phylogenetic tree of 129 <span class="html-italic">A. nanus</span> samples. Different colors represent different populations.</p>
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<p>Mantel test of <span class="html-italic">A. nanus</span> based on SSR molecular markers. An asterisk (*) indicates statistical significance: * represents statistical significance at the 95% confidence level (<span class="html-italic">p</span>-value &lt; 0.05), ** at the 99% confidence level (<span class="html-italic">p</span>-value &lt; 0.01), and *** at the 99.9% confidence level (<span class="html-italic">p</span>-value &lt; 0.001).</p>
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<p>Redundancy analysis of <span class="html-italic">A. nanus</span> based on SSR molecular markers. On the horizontal axis, 92.91% of the data variance is explained, while on the vertical axis, 5.64% of the variance is revealed.</p>
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<p>Bilinear analysis of the effect of wind speed on genetic diversity in <span class="html-italic">A. nanus</span>.</p>
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17 pages, 3581 KiB  
Article
Role of GmFRI-1 in Regulating Soybean Nodule Formation Under Cold Stress
by Hongcai Zhang, Lin He, Huiyun Li, Nengfu Tao, Tianda Chang, Dongmei Wang, Yichu Lu, Zhenying Li, Chunhai Mai, Xiaorui Zhao, Bingjie Niu, Junkui Ma and Lixiang Wang
Int. J. Mol. Sci. 2025, 26(3), 879; https://doi.org/10.3390/ijms26030879 - 21 Jan 2025
Viewed by 251
Abstract
Symbiotic nitrogen fixation, recognized as the most efficient nitrogen assimilation system in ecosystems, is essential for soybean growth, as nodulation provides critical nitrogen to host cells. Soybeans thrive in warm and moist environments. However, they are highly susceptible to low temperatures, which impede [...] Read more.
Symbiotic nitrogen fixation, recognized as the most efficient nitrogen assimilation system in ecosystems, is essential for soybean growth, as nodulation provides critical nitrogen to host cells. Soybeans thrive in warm and moist environments. However, they are highly susceptible to low temperatures, which impede the formation and development of root nodules. The genetic basis and molecular mechanism underlying the inhibition of nodulation induced by low temperatures remain unclear. In this study, we conducted a comparative transcriptomic analysis of soybean roots inoculated with rhizobium at 1 DPI (Day Post Inoculation) under normal or cold treatments. We identified 39 up-regulated and 35 down-regulated genes associated with nodulation and nitrogen fixation. Notably, cold-responsive genes including three FRI (Frigida) family genes were identified among differentially expressed genes (DEGs). Further expression pattern analysis of GmFRI-1 demonstrated it being significantly responsive to rhizobium inoculation and its highest expression in nodules. Further investigation revealed that overexpression of GmFRI-1 led to an increase in the nodule number, while RNA interference (RNAi)-mediated gene editing of GmFRI-1 suppressed nodule formation. Additionally, GmFRI-1 overexpression may regulate soybean nodulation by modulating the expression of GmNIN (NODULE INCEPTION), GmNSP1 (nodulation signaling pathway 1), and GmHAP2-2 (histone- or haem-associated protein domain) in the nod factor signaling pathway. This study offers new insights into the genetic basis of nodulation regulation under cold stress in legumes and indicates that GmFRI-1 may serve as a key regulator of nodule formation under cold stress. Full article
(This article belongs to the Special Issue Plant–Microbe Interactions)
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<p>Transcriptomic analysis of soybean root infected by rhizobium under cold treatment or control at 1 DAI. (<b>A</b>) Principal component analysis of cold treatment or control soybean root samples infected by rhizobium. (<b>B</b>) Volcano map of DEGs between cold treated or control inoculated soybean root. (<b>C</b>) Differential expressed genes related to cold stress.</p>
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<p>(<b>A</b>) Expression of <span class="html-italic">FRI</span> family members under cold treatment (4 °C) or control (room temperature). (<b>B</b>) Heat map shown the relative expression of soybean <span class="html-italic">FRI</span> family genes at different time points after inoculated with rhizobium USDA110. (<b>C</b>) The levels of relative expression of the <span class="html-italic">FRI</span> family genes within different tissues. (<b>D</b>) The relative expression level of <span class="html-italic">GmFRI-1</span> in different tissues at 28 DAI. The expression levels were normalized against the housekeeping gene of soybean <span class="html-italic">GmCYP2.</span> Student’s <span class="html-italic">t</span>-test was performed (*** <span class="html-italic">p</span> &lt; 0.001, <span class="html-italic">n</span> = 10). Note: “<span class="html-italic">n</span>” represents the technical replicates of transgenic events used for statistics.</p>
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<p>Root hair deformation status of <span class="html-italic">GmFRI-1</span> overexpression and knocking down soybean roots. (<b>A</b>) Expression level of transgenic hairy roots harboring empty vector and 35S: <span class="html-italic">GmFRI-1</span>. The expression levels were normalized against the housekeeping gene of soybean <span class="html-italic">GmCYP2</span>. Student’s <span class="html-italic">t</span>-test was performed (*** <span class="html-italic">p</span> &lt; 0.001, <span class="html-italic">n</span> = 20). (<b>B</b>) At 7 DAI, 2 cm root segments of hairy roots overexpressing <span class="html-italic">GmFRI-1</span> or expressing EV below the root–hypocotyl junction were cut and stained with 1% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) methylene blue. Deformed root hairs were counted (<span class="html-italic">n</span> = 20). Root hair deformation in transgenic roots harboring EV and 35S: <span class="html-italic">GmFRI-1</span> vector. Bar = 40 μm. The red star represents the typical root hair deformation beside it. (<b>C</b>) Quantification of deformed root hairs in the transgenic lines (<span class="html-italic">n</span> = 10 to 12). Values are averages ± SD from three independent experiments. Asterisks represent statistically significant differences. (<span class="html-italic">n</span> = 20, Student’s <span class="html-italic">t</span>-test; ** <span class="html-italic">p</span> &lt; 0.01). (<b>D</b>) Expression level of transgenic hairy roots harboring empty vector and <span class="html-italic">GmFRI-1</span>-RNAi. The expression levels were normalized against the housekeeping gene of soybean <span class="html-italic">GmCYP2</span>. Student’s <span class="html-italic">t</span>-test was performed (*** <span class="html-italic">p</span>  &lt;  0.001, <span class="html-italic">n</span> = 20). (<b>E</b>) Root hair deformation in transgenic roots harboring EV and RNAi-<span class="html-italic">GmFRI-1</span>, Bar = 40 μm. The red star represents the typical root hair deformation beside it. (<b>F</b>) Quantification of deformed root hairs in the transgenic root harboring EV and RNAi-<span class="html-italic">GmFRI-1</span> (<span class="html-italic">n</span> = 20). Values are averages ± SD from three independent experiments. Asterisks represent statistically significant differences. (<span class="html-italic">n</span> = 20, Student’s <span class="html-italic">t</span>-test; *** <span class="html-italic">p</span>  &lt;  0.001). Note: “<span class="html-italic">n</span>” represents the technical replicates of transgenic events used for statistics.</p>
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<p>Nodulation status of <span class="html-italic">GmFRI-1</span> overexpression and knocking down soybean roots. (<b>A</b>) Expression level of transgenic hairy roots harboring empty vector and 35S: <span class="html-italic">GmFRI-1</span>. The expression levels were normalized against the housekeeping gene of soybean <span class="html-italic">GmCYP2</span>. Student’s <span class="html-italic">t</span>-test was performed (*** <span class="html-italic">p</span> &lt; 0.001, <span class="html-italic">n</span> = 20). (<b>B</b>) Nodulation status of individual transgenic roots expressing EV and 35S: <span class="html-italic">GmFRI-1</span> at 28 DAI. Bar = 2 cm. (<b>C</b>) Quantification of deformed root hairs in the transgenic root harboring EV and <span class="html-italic">GmFRI-1</span>-RNAi (<span class="html-italic">n</span> = 20). Values are averages ± SD from three independent experiments. Asterisks represent statistically significant differences. (<span class="html-italic">n</span> = 20, Student’s <span class="html-italic">t</span>-test; *** <span class="html-italic">p</span>  &lt;  0.001). (<b>D</b>) qRT-PCR analysis of transgenic hairy roots harboring empty vector and RNAi-<span class="html-italic">GmFRI-1</span>. The expression levels were normalized against the housekeeping gene of soybean <span class="html-italic">GmCYP2</span>. Student’s <span class="html-italic">t</span>-test was performed (*** <span class="html-italic">p</span> &lt; 0.001, <span class="html-italic">n</span> = 15). (<b>E</b>) Nodule status of individual transgenic roots expressing empty vector and RNAi-<span class="html-italic">GmFRI-1</span> at 28 DAI. Bar = 2 cm. (<b>F</b>) Quantitative analysis of nodule number per hairy root carrying EV and RNAi-<span class="html-italic">GmFRI-1</span> at 28 DAI. Values are the mean ± SD. A total of 36 hairy roots were collected for each biological replicate (<span class="html-italic">n</span> = 12, Student’s <span class="html-italic">t</span>-test; *** <span class="html-italic">p</span> &lt; 0.001). Note: “<span class="html-italic">n</span>” represents the technical replicates of transgenic events used for statistics.</p>
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<p><span class="html-italic">GmFRI-1</span> affected the transcript levels of nodulation-related genes. (<b>A</b>) qRT-PCR analysis of <span class="html-italic">GmHAP2-1</span>, <span class="html-italic">GmHAP2-2</span>, <span class="html-italic">GmENOD40</span>, <span class="html-italic">GmNIN</span>, and <span class="html-italic">GmNSP1</span> in roots transformed with empty vector and <span class="html-italic">GmFRI-1</span>-OE at 1 DAI (<span class="html-italic">n</span> = 6). (<b>B</b>) qRT-PCR analysis of <span class="html-italic">GmHAP2-1</span>, <span class="html-italic">GmHAP2-2</span>, <span class="html-italic">GmENOD40</span>, <span class="html-italic">GmNIN</span>, and <span class="html-italic">GmNSP1</span> in roots transformed with empty vector and <span class="html-italic">GmFRI-1</span> knock out at 1 DAI (<span class="html-italic">n</span> = 6). (<b>C</b>) qRT-PCR analysis of <span class="html-italic">GmHAP2-1</span>, <span class="html-italic">GmHAP2-2</span>, <span class="html-italic">GmENOD40</span>, <span class="html-italic">GmNIN,</span> and <span class="html-italic">GmNSP1</span> in roots transformed with empty vector and <span class="html-italic">GmFRI-1</span>-OE at 5 DAI (<span class="html-italic">n</span> = 6). (<b>D</b>) qRT-PCR analysis of <span class="html-italic">GmHAP2-1</span>, <span class="html-italic">GmHAP2-2</span>, <span class="html-italic">GmENOD40</span>, <span class="html-italic">GmNIN,</span> and <span class="html-italic">GmNSP1</span> in roots transformed with empty vector and <span class="html-italic">GmFRI-1</span> knock out at 5 DAI (<span class="html-italic">n</span> = 6). The transcript amounts in each sample were normalized to those of <span class="html-italic">GmCYP2</span> (<span class="html-italic">n</span> = 6, Student’s <span class="html-italic">t</span>-test; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001; ns, no significance). Note: “<span class="html-italic">n</span>” represents the technical replicates of transgenic events used for statistics.</p>
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18 pages, 1856 KiB  
Review
fMRI Insights into Visual Cortex Dysfunction as a Biomarker for Migraine with Aura
by Damian Pikor, Natalia Banaszek-Hurla, Alicja Drelichowska, Mikołaj Hurla, Jolanta Dorszewska, Tomasz Wolak and Wojciech Kozubski
Neurol. Int. 2025, 17(2), 15; https://doi.org/10.3390/neurolint17020015 - 21 Jan 2025
Viewed by 220
Abstract
Migraine with aura (MwA) is a common and severely disabling neurological disorder, characterised by transient yet recurrent visual disturbances, including scintillating scotomas, flickering photopsias, and complex geometric patterns. These episodic visual phenomena significantly compromise daily functioning, productivity, and overall quality of life. Despite [...] Read more.
Migraine with aura (MwA) is a common and severely disabling neurological disorder, characterised by transient yet recurrent visual disturbances, including scintillating scotomas, flickering photopsias, and complex geometric patterns. These episodic visual phenomena significantly compromise daily functioning, productivity, and overall quality of life. Despite extensive research, the underlying pathophysiological mechanisms remain only partially understood. Cortical spreading depression (CSD), a propagating wave of neuronal and glial depolarisation, has been identified as a central process in MwA. This phenomenon is triggered by ion channel dysfunction, leading to elevated intracellular calcium levels and excessive glutamate release, which contribute to widespread cortical hyperexcitability. Genetic studies, particularly involving the CACNA gene family, further implicate dysregulation of calcium channels in the pathogenesis of MwA. Recent advances in neuroimaging, particularly functional magnetic resonance imaging (fMRI), have provided critical insights into the neurophysiology of MwA. These results support the central role of CSD as a basic mechanism behind MwA and imply that cortical dysfunction endures beyond brief episodes, possibly due to chronic neuronal dysregulation or hyperexcitability. The visual cortex of MwA patients exhibits activation patterns in comparison to other neuroimaging studies, supporting the possibility that it is a disease-specific biomarker. Its distinctive sensory and cognitive characteristics are influenced by a complex interplay of cortical, vascular, and genetic factors, demonstrating the multifactorial nature of MwA. We now know much more about the pathophysiology of MwA thanks to the combination of molecular and genetic research with sophisticated neuroimaging techniques like arterial spin labelling (ASL) and fMRI. This review aims to synthesize current knowledge and analyse molecular and neurophysiological targets, providing a foundation for developing targeted therapies to modulate cortical excitability, restore neural network stability, and alleviate the burden of migraine with aura. The most important and impactful research in our field has been the focus of this review, which highlights important developments and their contributions to the knowledge and treatment of migraine with aura. Full article
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Figure 1
<p>(<b>A</b>–<b>C</b>) Cortical spreading depression causes migraine aura and consists of a wave of depolarization of allcortical elements that spreads at rate of 2–6 mm per’min (red).</p>
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<p>This representation illustrates the key components of the trigeminovascular system, comprising meningeal blood vesselsand trigeminal nerve fibres, which are actively involved during migraine headache attacks. The binding of calcitoningene-related peptide (CGRP) and amylin to their respective receptors induces vasodilation within meningeal vesselsFurthermore, CGRP activates mast cells, prompting the release of numerous pro-nociceptive mediators, ineludingserotonin, adenosine triphosphate (ATP), prostaglandins, and nitric oxide (NO), These mediators further excitenociceptive fibres, leading to the amplification of CGRP release and the enhancement of pain signalling pathwaysassociated with migraines.</p>
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<p>(<b>A</b>) fMRl imaging of occipital cortex of patient with migraine during aura episode, cluster size- 8068voxels, (<b>B</b>) fMRl imaging of occipital cortex of patient with tension type headaches, cluster size- 2595 voxels.</p>
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<p>(<b>A</b>) fMRl imaging of occipital cortex of patient with migraine during aura episode, (<b>B</b>) fRl imaging ofoccipital cortex of patient with tension type headaches. In this study, both patients were presented withidentical visual stimuli during the fMRI session.</p>
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36 pages, 1941 KiB  
Review
Current Landscape of Molecular Biomarkers in Gastroesophageal Tumors and Potential Strategies for Co-Expression Patterns
by Martin Korpan, Hannah Christina Puhr, Julia M. Berger, Alexander Friedrich, Gerald W. Prager, Matthias Preusser and Aysegül Ilhan-Mutlu
Cancers 2025, 17(3), 340; https://doi.org/10.3390/cancers17030340 - 21 Jan 2025
Viewed by 522
Abstract
The treatment of metastasized gastroesophageal adenocarcinoma largely depends on molecular profiling based on immunohistochemical procedures. Therefore, the examination of HER2, PD-L1, and dMMR/MSI is recommended by the majority of clinical practice guidelines, as positive expression leads to different treatment approaches. Data from large [...] Read more.
The treatment of metastasized gastroesophageal adenocarcinoma largely depends on molecular profiling based on immunohistochemical procedures. Therefore, the examination of HER2, PD-L1, and dMMR/MSI is recommended by the majority of clinical practice guidelines, as positive expression leads to different treatment approaches. Data from large phase-III trials and consequent approvals in various countries enable physicians to offer their patients several therapy options including immunotherapy, targeted therapy, or both combined with chemotherapy. The introduction of novel therapeutic targets such as CLDN18.2 leads to a more complex decision-making process as a significant number of patients show positive results for the co-expression of other biomarkers besides CLDN18.2. The aim of this review is to summarize the current biomarker landscape of patients with metastatic gastroesophageal tumors, its direct clinical impact on daily decision-making, and to evaluate current findings on biomarker co-expression. Furthermore, possible treatment strategies with multiple biomarker expression are discussed. Full article
(This article belongs to the Special Issue Oesogastric Cancer: Treatment and Management)
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<p>Comparison of the TAP to TPS and CPS. The visual estimation of tumor area positivity is less time consuming than counting each cell. CPS = combined positive score; IC = immune cell; <span class="html-italic">n</span>, number; PD-L1 = programmed death-ligand 1; TAP = tumor area positivity; TC = tumor cell; TPS = tumor proportion score.</p>
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<p>During the process of carcinogenesis, the loss of cellular polarity leads to aberrant Claudin 18.2 expression, which is associated with irregular proliferation and invasion.</p>
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