Titanium particles as a product of degradation have been detected in periimplant oral tissues and it has been assumed that implants were the source of these particles. Periimplantitis sites had higher concentrations of particles in comparison to healthy implant sites. Several factors have been identified in the degradation of dental implant surface, such as mechanical wear, contact with chemical agents, and the effects of biofilm adhesion. Titanium particles silently prompt the immune-system activation and generate a pro-inflammatory response in macrophages, T lymphocytes and monocytes. During the activation, inflammatory cytokines are released including, granulocyte–macrophage colony-stimulating factor (GM-CSF), prostaglandin, and TNF-α, IL-1β, IL-6. The nanoparticles depict unique features such as high level of biological reactivity and potentially harmful compared to microparticles since they have a relatively greater surface area to volume ratio. Allergic response to titanium as a cause of implant failure has not been well documented. Evidence demonstrating biological complication due to titanium particles release includes peri-implant tissue inflammation that lead terminally to implant loss. There is a biological probability for a relation between the presence of titanium particles and ions, biological complication, and corrosion, but there is no justifiable evidence for unidirectional series of causative actions.

Titanium dental implants are one of the modalities to replace missing teeth. The release of titanium particles from the implant’s surface may modulate the immune cells, resulting in implant failure. However, little is known about the immune microenvironment that plays a role in peri-implant inflammation as a consequence of titanium particles. In this study, the peri-implant gingival tissues were collected from patients with failed implants, successful implants and no implants, and then a whole transcriptome analysis was performed. The gene set enrichment analysis confirmed that macrophage M1/M2 polarization and lymphocyte proliferation were differentially expressed between the study groups. The functional clustering and pathway analysis of the differentially expressed genes between the failed implants and successful implants versus no implants revealed that the immune response pathways were the most common in both comparisons, implying the critical role of infiltrating immune cells in the peri-implant tissues. The H&E and IHC staining confirmed the presence of titanium particles and immune cells in the tissue samples, with an increase in the infiltration of lymphocytes and macrophages in the failed implant samples. The in vitro validation showed a significant increase in the level of IL-1β, IL-8 and IL-18 expression by macrophages. Our findings showed evidence that titanium particles modulate lymphocyte and macrophage polarization in peri-implant gingival tissues, which can help in the understanding of the imbalance in osteoblast–osteoclast activity and failure of dental implant osseointegration.

Objective: The aim of this study was to measure the fracture resistance and failure modes of root-filled teeth restored with three different computer-aided design/computer-aided manufacturing (CAD/CAM)-fabricated post and core assemblies. Methods: Thirty extracted intact maxillary central incisors were used in the study, and the teeth were endodontically treated and sectioned to a root length of 16 mm. The samples were divided into three groups (n = 10) according to the mean of the dimensions at the cervical portion of the root. The posts and cores were fabricated with CAD/CAM technology using metal, zirconia, and polymer-infiltrated ceramic network (PICN) material. The posts were luted using resin cement, and then, metal crowns were fabricated, cemented, and then subjected to a compression to determine the fracture resistance force. Fracture of the post and core or fracture of the root above the level of the acrylic resin was considered as a favorable fracture, while nonfavorable fractures were those where the root fracture occurred below the level of the acrylic resin. Statistical analysis was carried out using SPSS software, and one-way analysis of variance was used to analyze root fracture resistance in Newton. The number of failure modes, post–core fractures and/or root fractures was calculated. The differences between study groups were revealed using the chi-square test. Results: The results of ANOVA revealed a nonsignificant difference in the resistance to fracture among the study groups (P = 0.114). The failure modes were nonfavorable for all metal and zirconia samples and seven PICN samples. However, three PICN samples exhibited a favorable represented by core fracture without root fracture. Conclusion: There was no significant difference between metal and zirconia samples in terms of nonfavorable fracture, while few PICN samples exhibited a favorable fracture. PICN material can be used in the fabrication of post and core assemblies using CAD/CAM.

Introduction: The COVID-19 pandemic is likely to increase anxiety levels within the community and in particular medical students who are already considered psychologically vulnerable groups. Since the COVID-19 outbreak, no study has yet estimated the effect of this pandemic on university students in the UAE or its impact on the psychological well-being of medical students. Methods: In this study, we surveyed 1485 medical (comprising medical and dental) and non-medical university students across 4 emirates within the UAE. We used an online platform to assess knowledge, sources of information, changes in hygienic behavior, perceptions of fear and worry and anxiety levels using the generalized anxiety disorder 7 (GAD-7) scale. The GAD-7 score was measured at three time points; during hospital visits for medical/dental students, before the introduction of online learning and after online learning for all students. Results: The majority of students demonstrated high levels of knowledge and utilized reliable sources of information. Non-medical students exercised higher compliance with social restrictions, while medical students practiced better hand hygiene. Almost half of students reported anxiety levels ranging from mild to severe with females reporting higher anxiety scores during hospital visits (OR=2.02, 95% CI,1.41 to 2.91) and medical students reporting lower anxiety levels in comparison to dental students (OR=0.61, 95% CI,0.45 to 0.84). Medical students reported higher levels of anxiety during their clinical rotations which decreased with the introduction of online learning, yet, non-medical student's anxiety levels increased with online learning. Conclusions: This is the first study to provide important information on the initial response and anxiety levels in university students across the UAE during the COVID-19 pandemic. The findings from our study can be used to support the development of effective screening strategies and interventions to build psychological resilience among university students during the COVID-19 pandemic or any other public health emergencies in the future.

Radiographs play a key role in diagnosis of periodontal diseases. Deep learning models have been explored for image analysis in periodontal diseases. However, there is lacuna of research in the deep learning model-based detection of furcation involvements [FI]. The objective of this study was to determine the accuracy of deep learning model in the detection of FI in axial CBCT images. We obtained initial dataset 285 axial CBCT images among which 143 were normal (without FI) and 142 were abnormal (with FI). Data augmentation technique was used to create 600(300 normal and 300 abnormal) images by using 200 images from the training dataset. Remaining 85(43 normal and 42 abnormal) images were kept for testing of model. ResNet101V2 with transfer learning was used employed for the analysis of images. Training accuracy of model is 98%, valid accuracy is 97% and test accuracy is 91%. The precision and F1 score were 0.98 and 0.98 respectively. The Area under curve (AUC) was reported at 0.98. The test loss was reported at 0.2170. The deep learning model (ResNet101V2) can accurately detect the FI in axial CBCT images. However, since our study was preliminary in nature and carried out with relatively smaller dataset, a study with larger dataset will further confirm the accuracy of deep learning models.