Heel bone mineral density and various oral diseases: a bidirectional Mendelian randomization.

INTRODUCTION: Observational studies demonstrated that the relationship between bone mineral density and oral diseases is mixed. To access the association between heel bone mineral density and various oral diseases, we conducted the Mendelian randomization analysis to explore the association. MATERIALS AND METHODS: Two-sample bidirectional Mendelian analysis was used to explore the relationship between heel bone mineral density and various oral diseases. The inverse-variance weighted (IVW) was used as the primary effect estimate, and various methods were applied to test the reliability and stability of the results, namely MR-Egger, weighted median, simple mode, and weighted mode. RESULTS: This study showed that there was a negative relationship between heel BMD and periodontitis when heel BMD was used as an exposure factor and periodontitis as an outcome factor (IVW OR = 0.85; 95% CI, 0.75-0.95; p = 0.005). Bidirectional Mendelian randomization showed that there was no statistically significant association between periodontitis and heel bone mineral density when chronic periodontitis was the exposure factor (p > 0.05). And there was no significant relationship between heel bone mineral density and other oral diseases (dental caries, diseases of pulp and periapical tissues, impacted teeth, cleft lip, and cleft palate, oral and oropharyngeal cancer) (p > 0.05). CONCLUSION: This study showed that there was a negative relationship between heel bone density and periodontitis, and the decrease in heel bone density could promote the occurrence of periodontitis. In addition, there was no statistically significant relationship between heel bone density and other oral diseases.

Deep learning-based automatic sella turcica segmentation and morphology measurement in X-ray images.

BACKGROUND: Although the morphological changes of sella turcica have been drawing increasing attention, the acquirement of linear parameters of sella turcica relies on manual measurement. Manual measurement is laborious, time-consuming, and may introduce subjective bias. This paper aims to develop and evaluate a deep learning-based model for automatic segmentation and measurement of sella turcica in cephalometric radiographs. METHODS: 1129 images were used to develop a deep learning-based segmentation network for automatic sella turcica segmentation. Besides, 50 images were used to test the generalization ability of the model. The performance of the segmented network was evaluated by the dice coefficient. Images in the test datasets were segmented by the trained segmentation network, and the segmentation results were saved in binary images. Then the extremum points and corner points were detected by calling the function in the OpenCV library to obtain the coordinates of the four landmarks of the sella turcica. Finally, the length, diameter, and depth of the sella turcica can be obtained by calculating the distance between the two points and the distance from the point to the straight line. Meanwhile, images were measured manually using Digimizer. Intraclass correlation coefficients (ICCs) and Bland-Altman plots were used to analyze the consistency between automatic and manual measurements to evaluate the reliability of the proposed methodology. RESULTS: The dice coefficient of the segmentation network is 92.84%. For the measurement of sella turcica, there is excellent agreement between the automatic measurement and the manual measurement. In Test1, the ICCs of length, diameter and depth are 0.954, 0.953, and 0.912, respectively. In Test2, ICCs of length, diameter and depth are 0.906, 0.921, and 0.915, respectively. In addition, Bland-Altman plots showed the excellent reliability of the automated measurement method, with the majority measurements differences falling within ± 1.96 SDs intervals around the mean difference and no bias was apparent. CONCLUSIONS: Our experimental results indicated that the proposed methodology could complete the automatic segmentation of the sella turcica efficiently, and reliably predict the length, diameter, and depth of the sella turcica. Moreover, the proposed method has generalization ability according to its excellent performance on Test2.

Recombinant human insulin-like growth factor-1 promotes osteoclast formation and accelerates orthodontic tooth movement in rats.

BACKGROUND: IGF-1 may be an important factor in bone remodeling, but its mechanism of action on osteoclasts during orthodontic tooth movement is complex and unclear. METHODOLOGY: The closed-coil spring was placed between the left maxillary first molar and upper incisors with a force of 50 g to establish an orthodontic movement model. Eighty SD rats were randomized to receive phosphate buffer saline or 400 ng rhIGF-1 in the lateral buccal mucosa of the left maxillary first molar every two days. Tissue sections were stained for tartrate-resistant acidic phosphatase (TRAP), the number of TRAP-positive cells was estimated and tooth movement measured. RESULTS: The rhIGF-1 group exhibited evidential bone resorption and lacuna appeared on the alveolar bone compared to the control group. Moreover, the number of osteoclasts in compression side of the periodontal ligament in the rhIGF-1 group peaked at day 4 (11.37±0.95 compared to 5.28±0.47 in the control group) after the orthodontic force was applied and was significantly higher than that of the control group (p<0.01). Furthermore, the distance of tooth movement in the rhIGF-1 group was significantly larger than that of the control group from day 4 to day 14 (p<0.01), suggesting that rhIGF-1 accelerated orthodontic tooth movement. CONCLUSION: Our study has showed that rhIGF-1 could stimulate the formation of osteoclasts in the periodontal ligament, and accelerate bone remodeling and orthodontic tooth movement.

Recombinant human insulin-like growth factor-1 promotes osteoclast formation and accelerates orthodontic tooth movement in rats

Abstract Background: IGF-1 may be an important factor in bone remodeling, but its mechanism of action on osteoclasts during orthodontic tooth movement is complex and unclear. Methodology: The closed-coil spring was placed between the left maxillary first molar and upper incisors with a force of 50 g to establish an orthodontic movement model. Eighty SD rats were randomized to receive phosphate buffer saline or 400 ng rhIGF-1 in the lateral buccal mucosa of the left maxillary first molar every two days. Tissue sections were stained for tartrate-resistant acidic phosphatase (TRAP), the number of TRAP-positive cells was estimated and tooth movement measured. Results: The rhIGF-1 group exhibited evidential bone resorption and lacuna appeared on the alveolar bone compared to the control group. Moreover, the number of osteoclasts in compression side of the periodontal ligament in the rhIGF-1 group peaked at day 4 (11.37±0.95 compared to 5.28±0.47 in the control group) after the orthodontic force was applied and was significantly higher than that of the control group (p<0.01). Furthermore, the distance of tooth movement in the rhIGF-1 group was significantly larger than that of the control group from day 4 to day 14 (p<0.01), suggesting that rhIGF-1 accelerated orthodontic tooth movement. Conclusion: Our study has showed that rhIGF-1 could stimulate the formation of osteoclasts in the periodontal ligament, and accelerate bone remodeling and orthodontic tooth movement.

Association between Tooth Loss and Gastric Cancer: A Meta-Analysis of Observational Studies.

Observational studies showed that tooth loss is associated with gastric cancer, but the findings are inconsistent. In this study, a meta-analysis was conducted to evaluate the relationship between tooth loss and gastric cancer. Relevant studies were screened in PubMed and Embase databases, and nine observational studies were considered eligible for the analysis. The combined relative risks for the highest versus the lowest categories of tooth loss were 1.86 (95% CI: 1.08-3.21) and 1.31 (95% CI: 1.12-1.53) in case control and cohort studies, respectively. However, unstable results were observed in the stratified and sensitivity analysis. The current evidence, based solely on four case-control studies and five cohort studies, suggested that tooth loss is a potential marker of gastric cancer. However, we can not concluded at this time that tooth loss may be a risk factor for gastric cancer due to significant heterogeneity among studies and mixed results between case-control studies and cohort studies. Additional large-scale and high-quality prospective studies are required to evaluate the association between tooth loss and risk of gastric cancer.

[Preliminary evaluation on 3-demension changes of facial soft tissue with structure light scanning technique before and after orthognathic surgery of Class III deformities].

OBJECTIVE: To evaluate facial soft tissue 3-deminsion changes of skeletal Class III malocclusion patients after orthognathic surgery using structure light scanning technique. METHODS: Eight patients [3 males and 5 females, aged (27.08 ± 4.42) years] with Class III dentoskeletal relationship who underwent a bimaxillary orthognathic surgical procedure involving advancement of the maxilla by Le Fort I osteotomy and mandibular setback by bilateral sagittal split ramus osteotomy (BSSO) and genioplasty to correct deformity were included. 3D facial images were obtained by structure light scanner for all the patients 2 weeks preoperatively and 6 months postoperatively. The facial soft tissue changes were evaluated in 3-dimension. The linear distances and angulation changes for facial soft tissue landmarks were analyzed. The soft tissue volumetric changes were assessed too. RESULTS: There were significant differences in the sagittal and vertical changes of soft tissue landmarks. The greatest amount of soft tissue change was close to lips. There were more volumetric changes in the chin than in the maxilla, and fewer in the forehead. CONCLUSION: After biomaxillary surgery, there were significant facial soft tissue differences mainly in the sagittal and vertical dimension for skeletal Class III patients. The structure light 3D scanning technique can be accurately used to estimate the soft tissue changes in patients who undergo orthognathic surgery.