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  • Result 1-10 of 505
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1.
  • Aad, G, et al. (author)
  • 2015
  • swepub:Mat__t
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  • 2019
  • Journal article (peer-reviewed)
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4.
  • Duyen, Nguyen Thi, et al. (author)
  • Steroid glycosides isolated from Paris polyphylla var. chinensis aerial parts and paris saponin II induces G1/S-phase MCF-7 cell cycle arrest
  • 2022
  • In: Carbohydrate Research. - : Elsevier BV. - 0008-6215. ; 519
  • Journal article (peer-reviewed)abstract
    • In our previous research on Vietnamese medicinal plants, we found that the ethanolic extract of the aerial parts of Paris polyphylla var. chinensis exhibited cytotoxic effects in vitro in the MCF-7 human cancer cell line. Here, we used combined chromatographic separations to isolate six compounds including a new steroid glycoside, paripoloside A (3), and five known compounds, from the butanol extract of the aerial parts of P. polyphylla. We unambiguously elucidated their structures based on spectroscopic data (proton and carbon-13 nuclear magnetic resonance, heteronuclear single quantum coherence, heteronuclear multiple bond correlation, correlation spectroscopy, and high-resolution electrospray ionization mass spectroscopy data), and chemical reactions. Among the isolated compounds, paris saponin II (PSII) had the strongest cytotoxic effects against MCF-7 breast cancer cells. Interestingly, PSII significantly increased the expression of p53, p21, p27, and Bax protein levels and significantly suppressed the expression of cyclin D1 and retinoblastoma protein. These data suggest that PSII may induce G1/S phase cell cycle arrest and apoptosis pathway development in MCF-7 cells. Furthermore, the MCF-7 breast cancer cells mechanism of PSII was also investigated using molecular docking. Together, our results demonstrate that isolated compounds from P. polyphylla are promising candidates as breast cancer inhibitors.
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5.
  • Tran, Xuan-Truc Dinh, et al. (author)
  • Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model : an in silico investigation of apelin agonists
  • 2024
  • In: Frontiers in Chemistry. - : Frontiers Media S.A.. - 2296-2646. ; 12
  • Journal article (peer-reviewed)abstract
    • Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design.Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening.Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031.Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.
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  • Result 1-10 of 505
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