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Bladder cancer is one of the concerning malignancies worldwide, which is lacking effective targeted therapy. Gene therapy is a potential approach for bladder cancer treatment. While, a safe and effective targeted gene delivery system is urgently needed for prompting the bladder cancer treatment in vivo. In this study, we confirmed that the bladder cancer had CD44 overexpression and small interfering RNAs (siRNA) with high interfere to Bcl2 oncogene were designed and screened. Then hyaluronic acid dialdehyde (HAD) was prepared in an ethanol-water mixture and covalently conjugated to the chitosan nanoparticles (CS-HAD NPs) to achieve CD44 targeted siRNA delivery. The in vitro and in vivo evaluations indicated that the siRNA-loaded CS-HAD NPs (siRNA@CS-HAD NPs) were approximately 100 nm in size, with improved stability, high siRNA encapsulation efficiency and low cytotoxicity. CS-HAD NPs could target to CD44 receptor and deliver the therapeutic siRNA into T24 bladder cancer cells through a ligand-receptor-mediated targeting mechanism and had a specific accumulation capacity in vivo to interfere the targeted oncogene Bcl2 in bladder cancer. Overall, a CD44 targeted gene delivery system based on natural macromolecules was developed for effective bladder cancer treatment, which could be more conducive to clinical application due to its simple preparation and high biological safety.
Pubmed ID: 32995671
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Open source Java based image processing software program designed for scientific multidimensional images. ImageJ has been transformed to ImageJ2 application to improve data engine to be sufficient to analyze modern datasets.
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View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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