ABSTRACT
Dental pulp is composed of nerves, blood vessels, and various types of cells and surrounded by a thick and hard enamel-dentin matrix. Due to its importance in the maintenance of tooth vitality, there have been intensive efforts to analyze the complex cellular-level organization of the dental pulp in teeth. Although conventional histologic analysis has provided microscopic images of the dental pulp, 3-dimensional (3D) cellular-level visualization of the whole dental pulp in an intact tooth has remained a technically challenging task. This is mainly due to the inevitable disruption and loss of microscopic structural features during the process of mechanical sectioning required for the preparation of the tooth sample for histological observation. To accomplish 3D microscopic observation of thick intact tissue, various optical clearing techniques have been developed mostly for soft tissue, and their application for hard tissues such as bone and teeth has only recently started to be investigated. In this work, we established a simple and rapid optical clearing technique for intact mouse teeth without the time-consuming process of decalcification. We achieved 3D cellular-level visualization of the microvasculature and various immune cell distributions in the whole dental pulp of mouse teeth under normal and pathologic conditions. This technique could be used to enable diverse research methods on tooth development and regeneration by providing 3D visualization of various pulpal cells in intact mouse teeth.
ABSTRACT
Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.
Subject(s)
Complement System Proteins , Image Processing, Computer-Assisted , Learning , Machine Learning , Magnetic Resonance Imaging , Quality ImprovementABSTRACT
Human dental pulp stem cells (DPSCs) are multi-potent mesenchymal stem cells that have several differentiation potentials. An understanding of the tissues that differentiate from these cells can provide insights for future regenerative therapeutics and tissue engineering strategies. The mesiodens is the most frequent form of supernumerary tooth from which DPSCs can differentiate into several lineages similar to cells from normal deciduous teeth. Recently, it has been shown that nanoscale structures can affect stem cell differentiation. In our presentstudy, we investigated the effects of a 250-nm nanoscale ridge/groove pattern array on the osteogenic and adipogenic differentiation of dental pulp cells from mesiodenscontaining human DPSCs. To this end, the expression of lineage specific markers after differentiation induction was analyzed by lineage specific staining and RT-PCR. The nanoscale pattern arrayed surface showed apositive effect on the adipogenic differentiation of DPSCs. There was no difference between nanoscale pattern arrayed surface and conventional surface groups onosteogenic differentiation. In conclusion, the nanoscale ridge/groove pattern arrayed surface can be used to enhance the adipogenic differentiation of DPSCs derived from mesiodens. This finding provides an improved understanding of the effects of topography on cell differentiation as well as the potential use of supernumerary tooth in regenerative dental medicine.