To present a minimally invasive approach to solve the impaction of palatal canines using computer-guided orthodontic miniscrews.

Miniscrew-supported appliances for palatal canine disimpaction are performed with CAD/CAM technology. With adequate software, it is possible to match the STL files of the dental arch with the DICOM images of the maxilla, previously transformed into STL files. The ideal points for miniscrew insertion can be identified on the STL 3D model file on the basis of the width and thickness of the palatal vault. A software application allows for the design of the surgical guide, which is printed using a 3D printer. The virtual position of the planned miniscrews is transferred onto a printed dental cast on which the orthodontic device is realized. On the day of surgery, both the surgical guide and the orthodontic appliance are ready for use.

Miniscrew insertion and palatal canine disimpaction can be achieved in one surgical procedure.

The use of computer-guided skeletal anchorage allows for both the reduction of the biomechanical side effects typical of conventional treatment and the risk of damaging adjacent anatomical structures, increasing the effectiveness of treatment. Controlled clinical trials are necessary to evaluate more fully any advantages of this minimally invasive technique.

The use of computer-guided skeletal anchorage allows for both the reduction of the biomechanical side effects typical of conventional treatment and the risk of damaging adjacent anatomical structures, increasing the effectiveness of treatment. Controlled clinical trials are necessary to evaluate more fully any advantages of this minimally invasive technique.

To assess the accuracy of DigiBrain4, Inc (DB4) Dental Classifier and DB4 Smart Search Engine* in recognizing, categorizing, and classifying dental visual assets as compared with Google Search Engine, one of the largest publicly available search engines and the largest data repository.

Dental visual assets were collected and labeled according to type, category, class, and modifiers. These dental visual assets contained radiographs and clinical images of patients’ teeth and occlusion from different angles of view. A modified SqueezeNet architecture was implemented using the TensorFlow r1.10 framework. The model was trained using two NVIDIA Volta graphics processing units (GPUs). A program was built to search Google Images, using Chrome driver (Google web driver) and submit the returned images to the DB4 Dental Classifier and DB4 Smart Search Engine. The categorical accuracy of the DB4 Dental Classifier and DB4 Smart Search Engine in recognizing, categorizing, and classifying dental visual assets was then compared with that of Google Search Engine.

The categorical accuracy achieved using the DB4 Smart Search Engine for searching dental visual assets was 0.93, whereas that achieved using Google Search Engine was 0.32.

The current DB4 Dental Classifier and DB4 Smart Search Engine application and add-on have proved to be accurate in recognizing, categorizing, and classifying dental visual assets. The search engine was able to label images and reject non-relevant results.

The current DB4 Dental Classifier and DB4 Smart Search Engine application and add-on have proved to be accurate in recognizing, categorizing, and classifying dental visual assets. this website The search engine was able to label images and reject non-relevant results.Background Age-related macular degeneration (AMD) is the most common cause of visual impairment in the developed world. Despite some treatment options for late AMD, there is no intervention that blocks early AMD proceeding to the late and blinding forms. This is partly due to the lack of precise drug targets, despite great advances in genetics, epidemiology, and protein-protein interaction (PPI) networks proposed to be driving the disease pathology. A systems approach to narrow down PPI networks to specific protein drug targets would provide new therapeutic options. Materials and Methods In this study we analyzed single cell RNAseq (RNA sequencing) datasets of 17 cell types present in choroidal, retinal pigment epithelium (RPE), and neural retina (NR) tissues to explore if a more granular analysis incorporating different cell types exposes more specific pathways and relationships. Furthermore, we developed a novel and systematic gene ontology database (SysGO) to explore if a subcellular classification of processes will further enhance the understanding of the pathogenesis of this complex disorder and its comorbidities with other age-related diseases. Results We found that 57% of the AMD (risk) genes are among the top 25% expressed genes in ∼1 of the 17 choroidal/RPE/NR cell types, and 9% were among the top 1% of expressed genes. Using SysGO, we identified an enrichment of AMD genes in cell membrane and extracellular anatomical locations, and we found both functional enrichments (e.g., cell adhesion) and cell types (e.g., fibroblasts, microglia) not previously associated with AMD pathogenesis. We reconstructed PPI networks among the top expressed AMD genes for all 17 choroidal/RPE/NR cell types, which provides molecular and anatomical definitions of AMD phenotypes that can guide therapeutic approaches to target this complex disease. Conclusion We provide mechanism-based AMD endophenotypes that can be exploited in vitro, using computational models and for drug discovery/repurposing.Background There is a growing body of literature showing that gender discrimination impacts physicians’ work and life experiences. Impact on income, promotion, and parenthood has been documented. Based on these findings, we hypothesized that the experiences of academic physicians who identify as women or gender nonconforming would be different from their counterparts who are men. This survey study explores the influences of gender on academic physicians’ experiences with discrimination in life and at work. Materials and Methods In the spring of 2017, academic physicians (n = 752) at a medical school in the West were invited to participate in a survey that measured experiences with discrimination using the Everyday Discrimination Scale and additional items. We used a mixed-methods approach to analyze the data, employing chi square and t-tests to analyze quantitative data and modified content analysis to code open-ended responses. Results The response rate was 24% (180/752). There was no significant difference between women and men in reported frequency of discrimination in everyday life (p = 0.