No abstract

Emergence angle of >30 degrees is a significant risk indicator for peri-implantitis and convex profile creates an additional risk for bone-level implants, but not for tissue-level implants.

Astrocytes play important roles in neurological disorders such as stroke, injury, and neurodegeneration. Most knowledge on astrocyte biology is based on studies of mouse models and the similarities and differences between human and mouse astrocytes are insufficiently characterized, presenting a barrier in translational research. Based on analyses of acutely purified astrocytes, serum-free cultures of primary astrocytes, and xenografted chimeric mice, we find extensive conservation in astrocytic gene expression between human and mouse samples. However, the genes involved in defense response and metabolism show species-specific differences. Human astrocytes exhibit greater susceptibility to oxidative stress than mouse astrocytes, due to differences in mitochondrial physiology and detoxification pathways. In addition, we find that mouse but not human astrocytes activate a molecular program for neural repair under hypoxia, whereas human but not mouse astrocytes activate the antigen presentation pathway under inflammatory conditions. Here, we show species-dependent properties of astrocytes, which can be informative for improving translation from mouse models to humans.

No abstract

Background: LipL32 induces a renal cell inflammatory response through the TLR2-signaling pathway. Results: Ca 2ϩ -binding LipL32 mutants showed attenuated TLR2-mediated inflammatory responses. Conclusion:The Ca 2ϩ -binding cluster of LipL32 is essential in regulating its interaction with TLR2 for subsequent inflammatory response induction. Significance: This investigation provides significant evidence for crucial roles of the Ca 2ϩ -binding cluster of LipL32 for pathogenesis via association with TLR2.

The advancement of intraoral scanners has allowed for more efficient workflow in the dental clinical setting. However, limited data exist regarding the accuracy of the digital impressions produced with various scanner settings and scanning approaches. The purpose of this in vitro study was to compare the accuracy of digital impressions at the crown preparation margin using different scanning resolutions of a specific intraoral scanner system. An all-ceramic crown preparation of a mandibular first molar was constructed in a typodont, and a scan (n = 3) was created with an industrial-grade laboratory scanner (3Shape D2000) as the control. Digital impressions were obtained with an intraoral scanner (3Shape TRIOS 3) under three settings—high resolution (HR), standard resolution (SR), and combined resolution (SHR). Comparative 3D analysis of scans was performed with Geomagic Control X software to measure the discrepancy between intraoral scans and the control scan along the preparation finish line. The scan time and number of images captured per scan were recorded. Statistical analysis was performed by one-way ANOVA, two-way repeated measures ANOVA, Pearson’s correlation, and Dunnett’s T3 test (α = 0.05). Significant differences were observed for scan time and for number of images captured among scan resolution settings (α < 0.05). The scan time for the SR group was, on average, 34.2 s less than the SHR group and 46.5 s less than the HR group. For discrepancy on the finish line, no significant differences were observed among scanning resolutions (HR: 31.5 ± 5.5 μm, SHR: 33.2 ± 3.7 μm, SR: 33.6 ± 3.1 μm). Significant differences in discrepancy were observed among tooth surfaces, with the distal surface showing the highest discrepancies. In conclusion, the resolution of the intra-oral scanner is primarily defined by the system hardware and optimized for default scans. A software high-resolution mode that obtains more data over a longer time may not necessarily benefit the scan accuracy, while the tooth preparation and surface parameters do affect the accuracy.

Accurate automatic quantitative cephalometry are essential for orthodontics. However, manual labeling of cephalometric landmarks is tedious and subjective, which also must be performed by professional doctors. In recent years, deep learning has gained attention for its success in computer vision field. It has achieved large progress in resolving problems like image classification or image segmentation. In this paper, we propose a two-step method which can automatically detect cephalometric landmarks on skeletal X-ray images. First, we roughly extract a region of interest (ROI) patch for each landmark by registering the testing image to training images, which have annotated landmarks. Then, we utilize pre-trained networks with a backbone of ResNet50, which is a state-of-the-art convolutional neural network, to detect each landmark in each ROI patch. The network directly outputs the coordinates of the landmarks. We evaluate our method on two datasets: ISBI 2015 Grand Challenge in Dental X-ray Image Analysis and our own dataset provided by Shandong University. The experiments demonstrate that the proposed method can achieve satisfying results on both SDR (Successful Detection Rate) and SCR (Successful Classification Rate). However, the computational time issue remains to be improved in the future. a global-context shape model. In 2017, Ibragimov et al. added a convolutional neural network for binary classification to their conventional method. They surpass the result of Lindner's a little bit, with around 75.3% prediction accuracy within a 2-mm range [9]. In 2017, Hansang Lee et al. proposed a deep learning based method which achieved not bad results but in a small resized image. He trained two networks to regress the landmark's x and y coordinate directly [10]. In 2019, Jiahong Qian et al. proposed a new architecture called CephaNet which improves the architecture of Faster R-CNN [11,12].Its accuracy is nearly 6% higher than other conventional methods.Despite the variety of techniques available, automatic cephalometric landmark detection remains insufficient due to its limited accuracy. In recent years, deep learning has gained attention for its success in computer vision field. For example, convolutional neural network models are widely used in problems like landmark detection [13,14], image classification [15][16][17] and image segmentation [18,19]. Trained models' performances surpass that of human beings in many applications. Since direct regression of several landmarks is a highly non-linear mapping, which is difficult to learn [20][21][22][23]. In our method, we only try to detect one key point in one patch image. We learn a non-linear mapping function for only one key point. Each key point has its corresponding non-linear mapping function. So we can achieve more accurate detection of key point than other methods.In this paper, we propose a two-step method for the automatic detection of cephalometric landmarks. First, we get the coarse landmark location by registering the test image to a most similar image in...

No abstract