Specific response of osteoblast-like cells on hydroxyapatite layer containing serum protein

Accelerations of bone-like apatite deposition and cell growth on an electrically polarized ceramic hydroxyapatite have been reported. A relationship between these phenomena was investigated in a previous report, and then it was suggested that osteoblast-like cell’s (MC3T3-E1) growth had relevance to the mineral growth. The effect of the formed apatite layer especially appeared to be on the cell adhesion. The acceleration of cell proliferation on the polarized HAp has been shown using fibroblastic cell (L929) and nerve cell (SK-N-SH) lines, therefore the effect of the layer on L929 and SK-N-SH was investigated to support the mechanism of acceleration of cell proliferation by polarization of HAp. In this study, the effect of the bone-like apatite layer was not confirmed on L929 cell’s growth. On the other hand, the acceleration of nerve cell’s proliferation was confirmed on the formed apatite layer. However, the remarkable improvement of the cell adhesion of SK-N-SH was not confirmed on the apatite layer. Consequently, it was considered that the bone-like apatite containing serum protein obtained by the coprecipitation of bone-like apatite and serum protein has a pronounced role only in the activity of osteoblast-like cells.     

Tailoring Silicon Nitride Surface Chemistry for Facilitating Odontogenic Differentiation of Rat Dental Pulp Cells

Silicon nitride (Si₃N₄) can facilitate bone formation; hence, it is used as a biomaterial in orthopedics. Nevertheless, its usability for dentistry is unexplored. The aim of the present study was to investigate the effect of Si₃N₄ granules for the proliferation and odontogenic differentiation of rat dental pulp cells (rDPCs). Four different types of Si₃N₄ granules were prepared, which underwent different treatments to form pristine as-synthesized Si₃N₄, chemically treated Si₃N₄, thermally treated Si₃N₄, and Si₃N₄ sintered with 3 wt.% yttrium oxide (Y₂O₃). rDPCs were cultured on or around the Si₃N₄ granular beds. Compared with the other three types of Si₃N₄ granules, the sintered Si₃N₄ granules significantly promoted cellular attachment, upregulated the expression of odontogenic marker genes (Dentin Matrix Acidic Phosphoprotein 1 and Dentin Sialophosphoprotein) in the early phase, and enhanced the formation of mineralization nodules. Furthermore, the water contact angle of sintered Si₃N₄ was also greatly increased to 40°. These results suggest that the sintering process for Si₃N₄ with Y₂O₃ positively altered the surface properties of pristine as-synthesized Si₃N₄ granules, thereby facilitating the odontogenic differentiation of rDPCs. Thus, the introduction of a sintering treatment for Si₃N₄ granules is likely to facilitate their use in the clinical application of dentistry.     

Project scheduling using a genetic algorithm with adaptable changing genetic operators

Genetic algorithms (GA) have been widely used to solve planning problems. However, they require one to determine the optimal values of many genetic parameters, such as population sizes, crossover probability, mutation probability, and so on. To make matters worse, the most suitable combination of parameters for one problem is not always optimal for others. Therefore, these parameters should be tuned whenever the problem changes. In this paper, we propose an adaptable GA mechanism that has autonomic parameter tuning for the composition of generic operators. This mechanism raises questions concerning the probability of genetic operators that acted effectively, that is, the probability that one operator created better individuals than the other operators. It also successively adjusts the combinations of genetic parameters suitable for the target problem. We applied the adaptable GA mechanism to a project scheduling model (PSM) and evaluated it with manual tuning methods. © 1998 Scripta Technica, Electr Eng Jpn, 124(2): 36–42, 1998     

Fracture toughness evaluation of silicon nitride from microstructures via convolutional neural network

The fracture toughness of silicon nitride (Si₃N₄) ceramics was evaluated directly from their microstructures via deep learning using convolutional neural network models. Totally 156 data sets containing microstructural images and relative densities were prepared from 45 types of Si₃N₄ samples as input feature quantities (IFQs) and were correlated to the fracture toughness as an objective variable. The data sets were divided into two groups. One was used for training, resulting in the creation of regression models for two kinds of IFQs: the microstructures only and a combination of the microstructures and the relative densities. The other group was used for testing the validity of the created models. As a result, the determination coefficient was approximately 0.8 even when using only the microstructures as the IFQs and was further improved when adding the relative densities. It was revealed that the fracture toughness of Si₃N₄ ceramics was well evaluated from their microstructures.