Poly(NIPAAm-co-hydroxyethylmethacarylate (HEMA)) acrylate and poly(NIPAAm-co-cysteine ethyl ester (CysOEt)) were synthesized and characterized by GPC(gel permeation chromatography), rheology, NMR (nuclear magnetic resonance), and Ellman's method. Upon mixing of these materials in aqueous solution, they formed gels immediately at body temperature owing to temperature-driven physical gelling, and gradually cured by chemical cross-linking through Michael-type addition reactions between thiols and acrylates. The rate of nucleophilic attack in the Michael-type addition reaction was shown to be highly dependent on the mole ratio of thiol to acrylate at neutral pH. Physical and chemical gelation improved the mechanical properties of the materials compared to purely physical gels. In vitro and in vivo results revealed that chemical and physical gels formed stiffer less viscoelastic materials compared to purely physical gels. Physical and chemical gel systems using thermosensitive polymer with acrylates and thermosensitive polymer with thiols showed minimum toxicity.
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http://dx.doi.org/10.1080/09205063.2013.781939 | DOI Listing |
Soft Matter
January 2025
Research Center for Macromolecules & Biomaterials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan.
We developed a facile one-pot method for fabricating physical gels consisting of ultrahigh molecular weight (UHMW) polymers and highly concentrated lithium salt electrolytes. We previously reported physical gels formed from the entanglement of UHMW polymers by radical polymerisation in aprotic ionic liquids. In this study, we found that the molecular weight of methacrylate polymers formed by radical polymerisation increased with the concentration of lithium salts in the organic solvents.
View Article and Find Full Text PDFJ Nanobiotechnology
January 2025
State Key Laboratory of Pharmaceutical Biotechnology, Division of Sports Medicine and Adult Reconstructive Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, People's Republic of China.
RNA interference (RNAi) and oxidative stress inhibition therapeutic strategies have been extensively utilized in the treatment of osteoarthritis (OA), the most prevalent degenerative joint disease. However, the synergistic effects of these approaches on attenuating OA progression remain largely unexplored. In this study, matrix metalloproteinase-13 siRNA (siMMP-13) was incorporated onto polyethylenimine (PEI)-polyethylene glycol (PEG) modified FeO nanoparticles, forming a nucleic acid nanocarrier termed si-Fe NPs.
View Article and Find Full Text PDFNature
January 2025
Department of Chemistry, University of Manchester, Manchester, UK.
Cells display a range of mechanical activities generated by motor proteins powered through catalysis. This raises the fundamental question of how the acceleration of a chemical reaction can enable the energy released from that reaction to be transduced (and, consequently, work to be done) by a molecular catalyst. Here we demonstrate the molecular-level transduction of chemical energy to mechanical force in the form of the powered contraction and powered re-expansion of a cross-linked polymer gel driven by the directional rotation of artificial catalysis-driven molecular motors.
View Article and Find Full Text PDFMater Today Bio
February 2025
Department of Physical Chemistry and Materials Science, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111, Budapest, Hungary.
Mucosal membranes with strong variability in their viscoelastic properties line numerous organs and are often targeted by mucoadhesive formulations, e.g., highly swellable hydroxypropylmethylcellulose (HPMC) and slightly cross-linked poly(acrylic acid) (PAA) tablets.
View Article and Find Full Text PDFEur Phys J E Soft Matter
January 2025
Institut für Theoretische Physik 1, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, 91058, Bavaria, Germany.
We employ graph neural networks (GNN) to analyse and classify physical gel networks obtained from Brownian dynamics simulations of particles with competing attractive and repulsive interactions. Conventionally such gels are characterized by their position in a state diagram spanned by the packing fraction and the strength of the attraction. Gel networks at different regions of such a state diagram are qualitatively different although structural differences are subtile while dynamical properties are more pronounced.
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