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Keywords = NSMN

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13 pages, 1841 KB  
Article
The Interaction Network of NSm and Its Role as a Movement Protein in the Tomato Zonate Spot Virus
by Xingyue Zhao, Jianbin Chen, Limin Zheng, Jiajia Tu, Xin Wang, Xiaobin Shi, Yu Zhang, Shue Sun, Jie Zhang, Xue Zheng and Deyong Zhang
Viruses 2025, 17(12), 1570; https://doi.org/10.3390/v17121570 - 30 Nov 2025
Viewed by 594
Abstract
The tomato zonate spot virus (TZSV) poses a significant threat to agriculture. Therefore, the elucidation of the functional roles and interactions of its encoded proteins is crucial for the development of effective control strategies. The aim of this study was to investigate the [...] Read more.
The tomato zonate spot virus (TZSV) poses a significant threat to agriculture. Therefore, the elucidation of the functional roles and interactions of its encoded proteins is crucial for the development of effective control strategies. The aim of this study was to investigate the interaction network between the TZSV nucleocapsid (N), the non-structural M-segment (NSm) and the non-structural S-segment (NSs) proteins, with a focus on the functional characterization of the NSm protein. Yeast two-hybrid (Y2H) analysis indicated that both the N protein (N-N) and the NSm protein (NSm-NSm) exhibit self-interaction in vitro, with successful expression of all fusion proteins confirmed by Western blotting. Subsequently, we used bimolecular fluorescence complementation (BiFC) and luciferase complementation imaging (LCI) assays in epidermal cells of Nicotiana benthamiana to confirm that N and NSm proteins self-interact. In addition, heterologous interactions between NSs-N, N-NSm and NSs-NSm were also detected. BiFC and co-localization experiments with fusion proteins elucidated the interaction place of the cell: N-N and NSm-N interactions occurred in both the cytoplasm and nucleus, with NSm-NSm interaction occurring in the nucleus, whereas NSs-N and NSs-NSm interactions only occurred in the cytoplasm. Subcellular localization studies showed that the N protein is distributed in both the cytoplasm and the nucleus, whereas the NSm and NSs proteins are predominantly localized in the cytoplasm. In particular, NSm was found to specifically target plasmodesmata (PD) and co-localize with the known PD marker protein PDLP8. Interestingly, TZSV NSm was demonstrated to mediate the cell-to-cell movement of a cucumber mosaic virus mutant (ΔCMV-GFP) lacking its native movement protein (3a). This was evidenced by the spread of approximately 50 fluorescent foci to neighboring cells observed at 6 dpi. This study comprehensively describes the intricate interaction network between the N, NSm and NSs proteins of TZSV and clarifies their subcellular localizations within plant cells. Crucially, we provide conclusive evidence that the NSm protein of TZSV is a functional movement protein essential for facilitating viral intercellular transport which promotes viral spread within the host during systemic infection. These findings offer important insights into the infection mechanism of TZSV and provide potential targets for the control of TZSV. Full article
(This article belongs to the Section Viruses of Plants, Fungi and Protozoa)
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46 pages, 7647 KB  
Article
Harnessing Nature for Breast Cancer Management: Effects of Fisetin-Loaded Nigellasomes Embedded in Microneedles Improve Tumor Suppression and Reduce Oxidative Stress
by Sammar Fathy Elhabal, Eman Mohammed Ali, Sandra Hababeh, Fatma E. Hassan, Suzan Awad AbdelGhany Morsy, Dalia Ahmed Elbahy, Sahar K. Ali, Khaled M. Allam, Ibrahim Mousa, Marwa A. Fouad and Ahmed Mohsen Elsaid Hamdan
Pharmaceutics 2025, 17(11), 1392; https://doi.org/10.3390/pharmaceutics17111392 - 27 Oct 2025
Cited by 4 | Viewed by 1847
Abstract
Background: Natural compounds such as fisetin have promising in breast cancer treatment, but their poor pharmacokinetics limit their therapeutic application. This study utilized a synergistic approach by combining fisetin-loaded Nigella sativa (N.S.) oil nanovesicles (FIS-NSs) and carbohydrate-based microneedles (FIS-NSs-MNs) to improve breast [...] Read more.
Background: Natural compounds such as fisetin have promising in breast cancer treatment, but their poor pharmacokinetics limit their therapeutic application. This study utilized a synergistic approach by combining fisetin-loaded Nigella sativa (N.S.) oil nanovesicles (FIS-NSs) and carbohydrate-based microneedles (FIS-NSs-MNs) to improve breast cancer management. Methods: Chemical composition of NS petroleum ether extract using gas chromatography–mass spectrometry (GC/MS). FIS-NSs were prepared and characterized for particle size, polydispersity, zeta potential, encapsulation efficiency, and stability. These vesicles were embedded into gelatin, hyaluronic acid, and carboxymethyl cellulose microneedles. In vitro drug release, ex vivo permeation, cytotoxicity against breast cancer cells, and in vivo antitumor efficacy in Ehrlich tumor models were evaluated. Results: Optimized FIS-NSs displayed nanoscale size (190 ± 0.74 nm), low P.D.I (0.25 ± 0.07), high surface charge (+37 ± 0.57 mV), and high encapsulation (88 ± 0.77%). In vitro investigations showed sustained FIS release (~85% over 72 h), while ex vivo permeation showed higher absorption than free fisetin. Both FIS-NSs and FIS-NSs-MNs showed dose-dependent cytotoxicity against breast cancer cells, with lower IC50 than free fisetin (24.7 µM). In vivo, FIS-NSs-MNs and tumor burden inhibition (~77%), reduced oxidative stress (54%), restored antioxidant defenses, and decreased inflammatory markers. Immunohistochemical analysis for caspase-3 showed apoptosis activation within tumor tissues. Conclusions: These findings demonstrate that FIS administration via NS-MNs improves drug stability, penetration, and apoptotic activity, resulting in enhanced anticancer effects. This innovative nanovesicle–microneedle platform provides a non-invasive, effective, and patient-friendly approach for the effective treatment of breast cancer, with potential for broader applications in oncological nanomedicine. Full article
(This article belongs to the Special Issue Advanced Drug Delivery Systems for Natural Products)
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23 pages, 1523 KB  
Article
Deep One-Directional Neural Semantic Siamese Network for High-Accuracy Fact Verification
by Muchammad Naseer, Jauzak Hussaini Windiatmaja, Muhamad Asvial and Riri Fitri Sari
Big Data Cogn. Comput. 2025, 9(7), 172; https://doi.org/10.3390/bdcc9070172 - 30 Jun 2025
Viewed by 1627
Abstract
Fake news has eroded trust in credible news sources, driving the need for tools to verify the accuracy of circulating information. Fact verification addresses this issue by classifying claims as Supports (S), Refutes (R), or Not Enough Info (NEI) based on evidence. Neural [...] Read more.
Fake news has eroded trust in credible news sources, driving the need for tools to verify the accuracy of circulating information. Fact verification addresses this issue by classifying claims as Supports (S), Refutes (R), or Not Enough Info (NEI) based on evidence. Neural Semantic Matching Networks (NSMN) is an algorithm designed for this purpose, but its reliance on BiLSTM has shown limitations, particularly overfitting. This study aims to enhance NSMN for fact verification through a structured framework comprising encoding, alignment, matching, and output layers. The proposed approach employed Siamese MaLSTM in the matching layer and introduced the Manhattan Fact Relatedness Score (MFRS) in the output layer, culminating in a novel algorithm called Deep One-Directional Neural Semantic Siamese Network (DOD–NSSN). Performance evaluation compared DOD–NSSN with NSMN and transformer-based algorithms (BERT, RoBERTa, XLM, XL-Net). Results demonstrated that DOD–NSSN achieved 91.86% accuracy and consistently outperformed other models, achieving over 95% accuracy across diverse topics, including sports, government, politics, health, and industry. The findings highlight the DOD–NSSN model’s capability to generalize effectively across various domains, providing a robust tool for automated fact verification. Full article
(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainable Development)
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