The surface characteristics of an implant that influence the speed and strength of osseointegration include crystal structure and bioactivity. The aim of this study was to evaluate the bioactivity of a novel natural hydroxyapatite/zircon (NHA/zircon) nanobiocomposite coating on 316L stainless steel (SS) dental implants soaking in simulated body fluid. A novel NHA/zircon nanobiocomposite was fabricated with 0 (control), 5, 10, and 15wt% of zircon in NHA using ball mill for 1h. The composite mixture was coated on SS implants using a plasma spray method. Scanning electron microscopy (SEM) was used to evaluate surface morphology, and X-ray diffraction (XRD) was used to analyze phase composition and crystallinity (Xc). Further, calcium ion release was measured to evaluate the coated nanobiocomposite samples. The prepared NHA/zircon coating had a nanoscale morphological structure with a mean crystallite size of 30–40nm in diameter and a bone-like composition, which is similar to that of the biological apatite of a bone. For the prepared NHA powder, high bioactivity was observed owing to the formation of apatite crystals on its surface. Both minimum crystallinity (Xc=41.1%) and maximum bioactivity occurred in the sample containing 10wt% of zircon because of minimum Xc and maximum biodegradation of the coating sample

Introduction. Evaluation of detailed features of the supporting bone is an important step in diagnosis and treatment planning for teeth with clinical attachment loss. Fractal analysis can be used as a method for evaluating the complexity of trabecular bone structures. The aim of this study was to evaluate the trabecular bone changes in periapical radiographs of patients with different stages of periodontitis using fractal analysis. Methods. This comparative cross-sectional study was performed on patients with and without clinical attachment loss in mandibular first molars. Teeth with clinical attachment loss were divided into mild, moderate, and severe periodontitis groups. Digital periapical radiographs were obtained from the mandibular first molars using the same exposure parameters. DICOM file of the radiographs was exported to ImageJ software for fractal analysis. Three regions of interest (ROIs) were considered in each radiograph: two proximal ROIs mesial and distal to the mandibular first molar and one apical ROI. Fractal dimension (FD) values were calculated using the fractal box counting approach. Statistical analysis was performed using the chi-square test, Mann–Whitney test, intraclass correlation coefficient, and ANOVA (α = 0.05). Results. FD values were significantly different between moderate and severe periodontitis and healthy periodontal bone ( ), except for the distal ROI for moderate periodontitis cases ( ). However, FD values of the supporting bone in periodontally healthy teeth and teeth with mild periodontitis did not show a statistically significant difference ( ). Conclusion. Fractal analysis is a useful tool for evaluation of bone alterations in moderate and severe periodontitis, but was not able to detect the most initial radiographic bone signs of mild periodontitis.

Using super-resolution (SR) algorithms, an image with a low resolution can be converted into a high-quality image. Our objective was to compare deep learning-based SR models to a conventional approach for improving the resolution of dental panoramic radiographs. A total of 888 dental panoramic radiographs were obtained. Our study involved five state-of-the-art deep learning-based SR approaches, including SR convolutional neural networks (SRCNN), SR generative adversarial network (SRGAN), U-Net, Swin for image restoration (SwinIr), and local texture estimator (LTE). Their results were compared with one another and with conventional bicubic interpolation. The performance of each model was evaluated using the metrics of mean squared error (MSE), peak signal-to-noise ratio (PNSR), structural similarity index (SSIM), and mean opinion score by four experts (MOS). Among all the models evaluated, the LTE model presented the highest performance, with MSE, SSIM, PSNR, and MOS results of 7.42 ± 0.44, 39.74 ± 0.17, 0.919 ± 0.003, and 3.59 ± 0.54, respectively. Additionally, compared with low-resolution images, the output of all the used approaches showed significant improvements in MOS evaluation. A significant enhancement in the quality of panoramic radiographs can be achieved by SR. The LTE model outperformed the other models.

The Coronavirus disease 2019 (COVID-19) pandemic is an ongoing global health crisis with unmatched outcomes and effects. This pandemic has caused an infodemic of article publication in scientific journals. Dental journals have been active in the publication of COVID-19 related articles from the beginning of the pandemic. In this cross-sectional survey, we present an analysis of the scientific output of dental journals on COVID-19. The PubMed COVID-19 database was searched with the “Dental Journals” filter. Data including journal name, country, month of publication and number of citations were recorded. Science mapping analysis of the most used keywords was also performed. The search retrieved a total of 659 articles, of which 28 were excluded. Oral Diseases has published the most COVID-19 articles (15.1%), followed by the British Journal of Oral and Maxillofacial Surgery (8.6%) and the Journal of Dental Education (7.9%). Most of the articles were from researchers from the United States (168), United Kingdom (120) and Brazil (83). The number of citations of the published articles ranged from 0 to 406, with most articles (64.2%) having no citations. Science Mapping analysis revealed that the most used keywords were coronavirus infections, pandemics and humans. The dental community has been active in the publication of COVID-19 articles from the beginning of the pandemic. The papers published by dental journals explore issues such as the management of clinical practices during the outbreak, infection control in the dental setting, signs and symptoms of COVID-19 affecting the oral cavity, and the impact of the COVID-19 pandemic on educational and clinical programs.