Timing of orthodontic tooth movement in bone defects repaired with synthetic scaffolds: A scoping review of animal studies

NF-κB Decoy Oligodeoxynucleotide-Loaded Poly Lactic-co-glycolic Acid Nanospheres Facilitate Socket Healing in Orthodontic Tooth Movement

Orthodontic space closure following tooth extraction is often hindered by alveolar bone deficiency. This study investigates the therapeutic use of nuclear factor-kappa B (NF-κB) decoy oligodeoxynucleotides loaded with polylactic-co-glycolic acid nanospheres (PLGA-NfDs) to mitigate alveolar bone loss during orthodontic tooth movement (OTM) following the bilateral extraction of maxillary first molars in a controlled experiment involving forty rats of OTM model with ethics approved. The decreased tendency of the OTM distance and inclination angle with increased bone volume and improved trabecular bone structure indicated minimized alveolar bone destruction. Reverse transcription-quantitative polymerase chain reaction and histomorphometric analysis demonstrated the suppression of inflammation and bone resorption by downregulating the expression of tartrate-resistant acid phosphatase, tumor necrosis factor-α, interleukin-1β, cathepsin K, NF-κB p65, and receptor activator of NF-κB ligand while provoking periodontal regeneration by upregulating the expression of alkaline phosphatase, transforming growth factor-β1, osteopontin, and fibroblast growth factor-2. Importantly, relative gene expression over the maxillary second molar compression side in proximity to the alveolus highlighted the pharmacological effect of intra-socket PLGA-NfD administration, as evidenced by elevated osteocalcin expression, indicative of enhanced osteocytogenesis. These findings emphasize that locally administered PLGA-NfD serves as an effective inflammatory suppressor and yields periodontal regenerative responses following tooth extraction.

Bone Tissue Engineering Strategies for Alveolar Cleft: Review of Preclinical Results and Guidelines for Future Studies

The current standard of care for an alveolar cleft defect is an autogenous bone graft, typically from the iliac crest. Given the limitations of alveolar bone graft surgery, such as limited supply, donor site morbidity, graft failure, and need for secondary surgery, there has been growing interest in regenerative medicine strategies to supplement and replace traditional alveolar bone grafts. Though there have been preliminary clinical studies investigating bone tissue engineering methods in human subjects, lack of consistent results as well as limitations in study design make it difficult to determine the efficacy of these interventions. As the field of bone tissue engineering is rapidly advancing, reconstructive surgeons should be aware of the preclinical studies informing these regenerative strategies. We review preclinical studies investigating bone tissue engineering strategies in large animal maxillary or mandibular defects and provide an overview of scaffolds, stem cells, and osteogenic agents applicable to tissue engineering of the alveolar cleft. An electronic search conducted in the PubMed database up to December 2021 resulted in 35 studies for inclusion in our review. Most studies showed increased bone growth with a tissue engineering construct compared to negative control. However, heterogeneity in the length of follow up, method of bone growth analysis, and inconsistent use of positive control groups make comparisons across studies difficult. Future studies should incorporate a pediatric study model specific to alveolar cleft with long-term follow up to fully characterize volumetric defect filling, cellular ingrowth, bone strength, tooth movement, and implant support.

Impact of Bone-Grafting Materials on the Rate of Orthodontic Tooth Movement: A Systematic Review

Orthodontists may encounter patients with alveolar bony defects, which are often treated with various bone-grafting materials. The effects of different bone-grafting materials on orthodontic tooth movement (OTM) are of concern to orthodontists. Therefore, we aimed to evaluate the current status of the literature that reports on the effects of different bone-grafting materials on OTM in terms of the rate and side effects. An electronic search of the PubMed and Scopus databases and Google Scholar was performed. Two reviewers independently conducted the screening process using COVIDENCE™, and a third reviewer resolved any conflicts. SYRCLE's (Systematic Review Centre for Laboratory Animal Experimentation's) risk-of-bias tool for animal studies was utilized to assess the quality of the included studies. Out of 457 initial titles, 11 studies were finally included for data extraction. All of the included studies were animal experiments, and none of them were considered to have a low risk of bias. The included studies had varied results. However, a general tendency existed, whereby OTM in surgically treated areas with no bone grafting presented the highest OTM rate. In cases where a bone graft was used, xenografts revealed the highest OTM rate, followed by alloplasts. Lastly, the use of allografts resulted in the slowest OTM rates. The most common side effect was root resorption. In conclusion, there is a lack of high-quality evidence regarding the effects of bone-grafting materials on OTM rate. Due to the lack of human subjects, RCTs, and the heterogeneity of subjects in the current literature, the impact of bone-grafting materials on OTM deserves further investigations using more rigorous scientific methodologies.

Extraction and Analysis of Foot Bone Shape Features Based on Deep Learning

With the rapid development of artificial intelligence, more and more researchers and research institutions begin to pay attention to the bone feature recognition field. Human bone movement is very complex, and human bone shape recognition technology can be widely used in medical treatment, sports, and other fields. At present, there are mainly two kinds of methods for extracting the shape features of human foot bone based on optical image acquisition technology and sensor information perception technology. However, due to the interference factors such as target posture change, camera shake, and individual behavior differences, it is still a very challenging task to design a robust algorithm for extraction and analysis of foot bone shape features. In recent years, convolutional neural network- (CNN-) based foot contour feature recognition methods emerge one after another and have made breakthrough progress. How to use and how to fully explore the potential relationship of various characteristics contained in the foot bone data and how to enhance the robustness of view changes and other aspects need to be further studied. In this context, this paper proposed an improved CNN model, which not only has the capability of deep feature extraction of the CNN model but also can obtain the optimal model parameters with the combination of particle swarm optimization algorithm. The effectiveness of the proposed method in the extraction and analysis of foot bone shape features is verified in the simulation experiment.