These findings suggest that AFSs are a conducive microenvironment for bone regeneration and are well tolerated in vivo. The results provide valuable baseline data for the design of implant studies using tissue-engineered bone constructed by RFB.

Calcium phosphates are key biomaterials in dental treatment and bone regeneration. Biomaterials must exhibit antibacterial properties to prevent microbial infection in implantation frameworks. Previously, we developed various types of calcium phosphate powders (amorphous calcium phosphate, octacalcium phosphate (OCP), dicalcium phosphate anhydrate, and hydroxyapatite) with adsorbed protamine (which is a protein with antibacterial property) and confirmed their antibacterial property. In this study, as foundational research for the development of novel oral care materials, we synthesized calcium phosphate composite powders from three starting materials: i) OCP, which intercalates organic compounds, ii) protamine, which has antibacterial properties, and iii) F– ion, which promotes the formation of apatite crystals. Through investigating the preparation concentration of the F– ions and their loading into OCP, it was found that more F– ion could be loaded at higher concentrations regardless of the loading method. It was also observed that the higher the preparation concentration, the more the OCP converted to fluorapatite. The synthesized calcium phosphate composite powders were evaluated for biocompatibility through proliferation of MG-63 cells, with none of the powders exhibiting any growth inhibition. Antimicrobial tests showed that the calcium phosphate composite powders synthesized with protamine and F– ion by precipitation had enhanced antimicrobial properties than those synthesized by protamine adsorption. Thus, the calcium phosphate composite powder prepared from OCP, protamine, and F– ion forms the basis for promising antimicrobial biomaterials.

With the limitation of autografts, the development of alternative treatments for bone diseases to alleviate autograft-related complications is highly demanded. In this study, a tissue-engineered bone was formed by culturing rat bone marrow cells (RBMCs) onto porous apatite-fiber scaffolds (AFSs) with three-dimensional (3D) interconnected pores using a radial-flow bioreactor (RFB). Using the optimized flow rate, the effect of different culturing periods on the development of tissue-engineered bone was investigated. The 3D cell culture using RFB was performed for 0, 1 or 2 weeks in a standard medium followed by 0, 1 or 2 weeks in a differentiation medium. Osteoblast differentiation in the tissue-engineered bone was examined by alkaline phosphatase (ALP) and osteocalcin (OC) assays. Furthermore, the tissue-engineered bone was histologically examined by hematoxylin and eosin and alizarin red S stains. We found that the ALP activity and OC content of calcified cells tended to increase with the culture period, and the differentiation of tissue-engineered bone could be controlled by varying the culture period. In addition, the employment of RFB and AFSs provided a favorable 3D environment for cell growth and differentiation. Overall, these results provide valuable insights into the design of tissue-engineered bone for clinical applications.

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Bioceramics, such as hydroxyapatite and β-tricalcium phosphate, are widely used in orthopedics and oral surgery because they are free in shape and size and are not harvested from patients or donors. General development of bioceramics requires a great deal of effort, a long time, and many animal experiments. Because an animal experiment takes several months and is currently regarded as an ethical problem, the number of experiments should be reduced. In this study, machine learning was applied to construct mathematical models to predict the material properties, including the porosity, compressive strength, Ca2+ dissolution rate, and bone formation rate, from the synthesis conditions and to design synthesis conditions of bioceramics with desired bone formation rates. We propose two types of models: model 1 to predict the material properties, crystallite sizes, and second selected Fourier transform infrared wavenumbers of the resulting bioceramics from the synthesis conditions, such as the starting powder conditions, and model 2 to predict the bone formation rate from the material properties, crystallite sizes, second selected Fourier transform infrared wavenumbers, and animal experimental conditions of bioceramics. Both models were constructed using Gaussian mixture regression, enabling direct inverse analysis of the models. Furthermore, by visualization of the models, the relationships among the bone formation rate, material properties, and animal experimental conditions can be understood to establish guidelines for designing the synthesis conditions. We succeeded in designing artificial bone synthesis conditions with bone formation rate exceeding existing bone formation rates.