Concentrated polymer brush-modified cellulose nanofibers promote chondrogenic differentiation of human mesenchymal stem cells by controlling self-assembly

Research trends of bio-application of major components in lignocellulosic biomass (cellulose, hemicellulose and lignin) in orthopedics fields based on the bibliometric analysis: A review

Cellulose, hemicellulose, and lignin are the major bio-components in lignocellulosic biomass (BC-LB), which possess excellent biomechanical properties and biocompatibility to satisfy the demands of orthopedic applications. To understand the basis and trends in the development of major bio-components in BC-LB in orthopedics, the bibliometric technology was applied to get unique insights based on the published papers (741) in the Web of Science (WOS) database from January 1st, 2001, to February 14th, 2023. The analysis includes the annual distributions of publications, keywords co-linearity, research hotspots exploration, author collaboration networks, published journals, and clustering of co-cited literature. The results reveal a steady growth in publications focusing on the application of BC-LB in orthopedics, with China and the United States leading in research output. The "International Journal of Biological Macromolecules" was identified as the most cited journal for BC-LB research in orthopedics. The research hotspots encompassed bone tissue engineering, cartilage tissue engineering, and drug delivery systems, indicating the fundamental research and potential development in these areas. This study also highlights the challenges associated with the clinical application of BC-LB in orthopedics and provides valuable insights for future advancements in the field.

Graph Network-Based Simulation of Multicellular Dynamics Driven by Concentrated Polymer Brush-Modified Cellulose Nanofibers

Manipulating the three-dimensional (3D) structures of cells is important for facilitating to repair or regenerate tissues. A self-assembly system of cells with cellulose nanofibers (CNFs) and concentrated polymer brushes (CPBs) has been developed to fabricate various cell 3D structures. To further generate tissues at an implantable level, it is necessary to carry out a large number of experiments using different cell culture conditions and material properties; however this is practically intractable. To address this issue, we present a graph-neural network-based simulator (GNS) that can be trained by using assembly process images to predict the assembly status of future time steps. A total of 24 (25 steps) time-series images were recorded (four repeats for each of six different conditions), and each image was transformed into a graph by regarding the cells as nodes and the connecting neighboring cells as edges. Using the obtained data, the performances of the GNS were examined under three scenarios (i.e., changing a pair of the training and testing data) to verify the possibility of using the GNS as a predictor for further time steps. It was confirmed that the GNS could reasonably reproduce the assembly process, even under the toughest scenario, in which the experimental conditions differed between the training and testing data. Practically, this means that the GNS trained by the first 24 h images could predict the cell types obtained 3 weeks later. This result could reduce the number of experiments required to find the optimal conditions for generating cells with desired 3D structures. Ultimately, our approach could accelerate progress in regenerative medicine.