To investigate the effects of titanium (Ti) ions on the cell viability, the cell differentiation and the gene expressions related to bone resorption including Receptor Activator of NF-kappaB Ligand (RANKL) and Osteoprotegerin (OPG) in the tissues around dental implants, the osteoblast-, osteoclast-, and gingival epithelial-like cells were exposed to Ti ions. An MTS assay was carried out to evaluate the viabilities of osteoblast-like MC3T3-E1, osteoclast-like RAW264.7 and epithelial cell-like GE-1 cells. The gene expressions in these cells were analyzed by the use of RT-PCR and real-time quantitative RT-PCR. Ti ions in the concentration range 1-9 ppm had little effect on the viabilities of MC3T3-E1, RAW264.7 and GE-1, whereas 20 ppm Ti ions significantly decreased the viabilities of all cells. Analyses of RT-PCR and real-time quantitative RT-PCR data revealed that Ti ions at 9 ppm remarkably inhibited the expressions of Runx2, Osterix and type I collagen in MC3T3-E1. In RAW264.7, Ti ions showed no effects on the levels of mRNAs for TRAP and cathepsin K enhanced by RANKL. Ti ions at the range of 1-9 ppm showed no effects on the levels of mRNAs for RANKL and OPG in GE-1, while Ti ions at 9 ppm enhanced the expression of these genes in MC3T3-E1. These results, taken together, suggested that Ti ions show the biological effects, both on the viabilities of osteoblast and osteoclast and on the differentiation of either the osteoblastic or osteoclastic cells, which may influence the prognosis of dental implants.

Temporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients with temporomandibular disorders includes patients with different forms of articular disc displacement and deformation. Here, we propose a fully automated articular disc detection and segmentation system to support the diagnosis of temporomandibular disorder on magnetic resonance imaging. This system uses deep learning-based semantic segmentation approaches. The study included a total of 217 magnetic resonance images from 10 patients with anterior displacement of the articular disc and 10 healthy control subjects with normal articular discs. These images were used to evaluate three deep learning-based semantic segmentation approaches: our proposed convolutional neural network encoder-decoder named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), U-Net, and SegNet-Basic. Of the three algorithms, 3DiscNet and SegNet-Basic showed comparably good metrics (Dice coefficient, sensitivity, and positive predictive value). This study provides a proof-of-concept for a fully automated deep learning-based segmentation methodology for articular discs on magnetic resonance images, and obtained promising initial results, indicating that the method could potentially be used in clinical practice for the assessment of temporomandibular disorders.

The present study was a molecular analysis of the initial differentiation of osteoclast precursor RAW264.7 cells on titanium specimens. RAW264.7 cell line was cultured on titanium specimens of which the surfaces were finished by wet grinding with 2000-, 1200-, 600-, or 180-grit waterproof abrasive paper. Total RNA was extracted from cells cultured in the presence or absence of Receptor Activator of NF-κB Ligand (RANKL), prior to cDNA synthesis for real-time quantitative reverse transcriptase-polymerase chain reaction analysis. Titanium surfaces initially enhanced the expression of osteoclast differentiation markers including tartrate-resistant acid phosphatase and cathepsin K in RAW264.7 cells cultured with RANKL stimulation, in a roughness-dependent manner. The mRNA expressions of both RANKL receptor, RANK, and its adapter protein TNF receptor-associated factor 6 (TRAF6) increased when RAW264.7 cells were cultured on titanium specimens with roughened surfaces, as compared with that of control specimen with a polished surface. These results, taken together, suggested that titanium surface roughness facilitated osteoclast differentiation through the activation of the RANK-TRAF6 signaling network.