The Effects of 3-Dimensional Bioprinting Calcium Silicate Cement/Methacrylated Gelatin Scaffold on the Proliferation and Differentiation of Human Dental Pulp Stem Cells.
Dakyung ChoiManfei QiuYun-Chan HwangWon-Mann OhJeong Tae KohChan ParkBin-Na LeePublished in: Materials (Basel, Switzerland) (2022)
A calcium silicate cement/methacrylated gelatin (GelMa) scaffold has been applied in tissue engineering; however, the research on its applications in dental tissue regeneration remains lacking. We investigate the effect of this scaffold on human dental pulp stem cells (hDPSCs). hDPSCs were cultured in 3D-printed GelMa and MTA-GelMa scaffolds. Cell adhesion was evaluated using scanning electron microscopy images. Cells were cultured in an osteogenic differentiation medium, which contained a complete medium or α-MEM containing aqueous extracts of the 3D-printd GelMa or MTA-GelMa scaffold with 2% FBS, 10 mM β-glycerophosphate, 50 μg/mL ascorbic acid, and 10 nM dexamethasone; cell viability and differentiation were shown by WST-1 assay, Alizarin Red S staining, and alkaline phosphatase staining. Quantitative real-time PCR was used to measure the mRNA expression of DSPP and DMP-1. One-way analysis of variance followed by Tukey's post hoc test was used to determine statistically significant differences, identified at p < 0.05. hDPSCs adhered to both the 3D-printed GelMa and MTA-GelMa scaffolds. There was no statistically significant difference between the GelMa and MTA-GelMa groups and the control group in the cell viability test. Compared with the control group, the 3D-printed MTA-GelMa scaffold promoted the odontogenic differentiation of hDPSCs. The 3D-printed MTA-GelMa scaffold is suitable for the growth of hDPSCs, and the scaffold extracts can better promote odontoblastic differentiation.
Keyphrases
- tissue engineering
- stem cells
- endothelial cells
- electron microscopy
- induced apoptosis
- cell adhesion
- mesenchymal stem cells
- high resolution
- deep learning
- low dose
- photodynamic therapy
- high throughput
- machine learning
- convolutional neural network
- ionic liquid
- endoplasmic reticulum stress
- oxidative stress
- optical coherence tomography
- wound healing