A midpalatal suture contention after rapid maxillary expansion (RME) is a major orthodontic challenge. The objective of this study is to evaluate the effect of systemic simvastatin on suture bone remodeling after disjunction. For that, 15 Wistar rats were used. In 10, orthodontic appliances were installed and activated for 5 days for RME. These animals were randomly divided into two groups: control (CT, n = 5) and simvastatin (SVT, n = 5). Also, animals without intervention (HG, n = 5) were used. In the SVT and CT groups, 5 mg/kg of simvastatin and distilled water were administered by gavage, respectively, for 20 consecutive days. Then, the animals were euthanized and scanned in micro-computed tomography (µCT). The images were analyzed through pixel linear measurement at four different points (P1, P2, P3 and P4), in the intra-incisor distance (DI) and in the suture distance (SD). Microtomographic parameters, such as cortical bone area (Ct.Ar), cortical area fraction (Ct.Ar/Tt.Ar), and cortical thickness (Ct.Th), were obtained. Also, bone volume fraction (BV/TV) and empty space (EV) were extracted. Then, histological slides were prepared for descriptive and histomorphometric analysis. There was a statistically significant difference in the linear measurements, microtomographic parameters, and histomorphometric results between the experimental groups. In conclusion, simvastatin demonstrated an osteoinductive and antiresorptive effect in the palatine suture region after RME.

Background: Preserving ridge volume after tooth extraction is essential for predictable implant outcomes. Various techniques and biomaterials have been proposed to minimize post-extraction dimensional changes. Melatonin, a pineal hormone, is an important mediator of new bone formation. The effect of melatonin gel incorporated into a porcine xenogeneic collagen sponge in post-extraction sockets has not been evaluated in humans, making this trial novel. Purpose: To assess the effect of melatonin gel incorporated into a collagen sponge on intra-socket bone formation and tissue characteristics in post-extraction sockets. Materials and methods: Twenty participants requiring extraction of premolars or molars with adjacent teeth were randomly allocated to test ( n = 10) and control ( n = 10) groups. After extraction, sockets were filled with xenogenic collagen sponge containing either melatonin gel (1.2 mg; test) or placebo gel (control). Ninety days later, during implant placement, bone samples were harvested using a trephine drill. Samples were processed for histomorphometric analysis of newly formed bone and connective tissue. Secondary outcomes included descriptive microscopic histology, quantitative microcomputed tomography (µCT) analyses and intra-alveolar volumetric analysis. Results: Histomorphometry revealed significantly greater bone density in the test group (44.0%) than in the control group (35.4%; p = 0.0099), with a mean difference of 8.6% ± 3.2%. Microscopic evaluation showed increased osteoblastic activity in the test group. Quantitative µCT analysis confirmed more new bone formation in the test group, although qualitative bone characteristics did not differ between groups. The intra-alveolar bone volume formed was numerically higher in the test group than in the control group. Conclusion: Incorporating melatonin gel into a collagen sponge enhanced bone repair in human post-extraction sockets over 90 days. This clinical trial is registered at the Brazilian Clinical Trials Registry (ReBEC; https://ensaiosclinicos.gov.br ): RBR-67jkxsj.

The clinical success of an implant largely depends on its ability to promote bone repair and facilitate osteointegration. While significant advancements have been made in designing new scaffolds, a thorough evaluation of implant success at various bone length scales has not been sufficiently investigated. Bone is a hierarchically organized tissue, with an intricate structure that ranges from the organization of collagen fibrils and their mineralization at the nanometer and micrometer scales to the macroscopic arrangement of compact and cancellous bone. This hierarchical organization is crucial for ensuring bone function and mechanical performance. In this context, a comprehensive assessment of the effects of biomaterials on bone repair at multiple length scales is essential for evaluating the effectiveness and safety of implanted materials. However, preparing samples for integrated multimodal analyses poses a technical challenge. In this work, we present a protocol designed to evaluate bone regeneration across various hierarchical levels and analyze the interface between newly formed bone and the implanted scaffold. This approach provides a robust method for assessing the effectiveness of scaffolds in inducing bone repair. The combination of selected 3D and 2D imaging techniques is crucial for a proper evaluation of the complex mineralized tissue that forms during the bone healing process

Volumetric imaging using X-ray microcomputed tomography (µCT) is a mainstream method for 3D structural studies of bones and teeth. Quantitative analysis of mineralized features in general relies on accurate segmentation. The larger the image and the higher the resolution, the greater the incentive to use (semi)automated high-throughput segmentation approaches, primarily using deep learning-aided methods. In this study we pretrained and evaluated two U-Net segmentation models, called Universal Bone and Universal Jaw, using a diverse 3D image library from contributing colleagues. Our assumption was that pretraining a U-Net on skeletal and dental features would result in higher accuracy and throughput, in comparison with a naïve U-Net trained by an end-user on a limited local dataset. The Universal Bone binary segmentor pretrained on a relatively heterogeneous subset of images showed satisfactory generalization capacity, and was applicable to unseen images with minimal tuning. The Universal Jaw four-class segmentor (background, bone, dentin, enamel) was pretrained on a narrower range of images and required rather extensive retraining on manually retouched image segmentations. The performance of pretrained segmentation U-Nets is defined not only by the number of segmentation classes (the fewer the better), similarity of the unseen image spacing to the training set (the closer the better), but even more so by class balance. For particularly large 3D images, extensive manual retouching and re-training was nevertheless valuable attributable to the high throughput of the pretrained segmentor. Despite achieving partial success in pretraining specialized segmentation models, we advocate for future concerted efforts to merge and expand image collections, share pretrained models and utilize expert segmentations for benchmarking.