Short- and Long-Term Prediction of the Post-Pubertal Mandibular Length and Y-Axis in Females Utilizing Machine Learning.

The aim of this study was to create a novel machine learning (ML) algorithm for predicting the post-pubertal mandibular length and Y-axis in females. Cephalometric data from 176 females with Angle Class I occlusion were used to train and test seven ML algorithms. For all ML methods tested, the mean absolute errors (MAEs) for the 2-year prediction ranged from 2.78 to 5.40 mm and 0.88 to 1.48 degrees, respectively. For the 4-year prediction, MAEs of mandibular length and Y-axis ranged from 3.21 to 4.00 mm and 1.19 to 5.12 degrees, respectively. The most predictive factors for post-pubertal mandibular length were mandibular length at previous timepoints, age, sagittal positions of the maxillary and mandibular skeletal bases, mandibular plane angle, and anterior and posterior face heights. The most predictive factors for post-pubertal Y-axis were Y-axis at previous timepoints, mandibular plane angle, and sagittal positions of the maxillary and mandibular skeletal bases. ML methods were identified as capable of predicting mandibular length within 3 mm and Y-axis within 1 degree. Compared to each other, all of the ML algorithms were similarly accurate, with the exception of multilayer perceptron regressor.

A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration.

In the field of orthodontics, providing patients with accurate treatment time estimates is of utmost importance. As orthodontic practices continue to evolve and embrace new advancements, incorporating machine learning (ML) methods becomes increasingly valuable in improving orthodontic diagnosis and treatment planning. This study aimed to develop a novel ML model capable of predicting the orthodontic treatment duration based on essential pre-treatment variables. Patients who completed comprehensive orthodontic treatment at the Indiana University School of Dentistry were included in this retrospective study. Fifty-seven pre-treatment variables were collected and used to train and test nine different ML models. The performance of each model was assessed using descriptive statistics, intraclass correlation coefficients, and one-way analysis of variance tests. Random Forest, Lasso, and Elastic Net were found to be the most accurate, with a mean absolute error of 7.27 months in predicting treatment duration. Extraction decision, COVID, intermaxillary relationship, lower incisor position, and additional appliances were identified as important predictors of treatment duration. Overall, this study demonstrates the potential of ML in predicting orthodontic treatment duration using pre-treatment variables.

Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning.

The goal of this study was to create a novel machine learning (ML) model that can predict the magnitude and direction of pubertal mandibular growth in males with Class II malocclusion. Lateral cephalometric radiographs of 123 males at three time points (T1: 12; T2: 14; T3: 16 years old) were collected from an online database of longitudinal growth studies. Each radiograph was traced, and seven different ML models were trained using 38 data points obtained from 92 subjects. Thirty-one subjects were used as the test group to predict the post-pubertal mandibular length and y-axis, using input data from T1 and T2 combined (2 year prediction), and T1 alone (4 year prediction). Mean absolute errors (MAEs) were used to evaluate the accuracy of each model. For all ML methods tested using the 2 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.11-6.07 mm to 0.85-2.74° for the y-axis. For all ML methods tested with 4 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.32-5.28 mm to 1.25-1.72° for the y-axis. Besides its initial length, the most predictive factors for mandibular length were found to be chronological age, upper and lower face heights, upper and lower incisor positions, and inclinations. For the y-axis, the most predictive factors were found to be y-axis at earlier time points, SN-MP, SN-Pog, SNB, and SNA. Although the potential of ML techniques to accurately forecast future mandibular growth in Class II cases is promising, a requirement for more substantial sample sizes exists to further enhance the precision of these predictions.

The LRP5 high-bone-mass mutation causes alveolar bone accrual with minor craniofacial alteration.

BACKGROUND AND OBJECTIVE: Mutations in low-density lipoprotein receptor-related protein 5 (LRP5) cause various bone diseases. Several mouse models were generated to study the role of LRP5 in bone development. But most of the studies were confined to the appendicular skeleton. The role of LRP5 in the axial skeleton, especially in the craniofacial skeleton, is largely unknown. The aim of this study was to investigate the craniofacial phenotype with the LRP5G171V mutation. METHODS: To understand how LRP5 affects craniofacial bone properties, we analyzed LRP5 high-bone-mass mutant mice carrying the G171V missense mutation (LRP5HBM ). Quantitative microcomputed tomographic imaging and histomorphometric analyses were used to study craniofacial phenotypes and bone density. Histology, immunohistochemistry, and in vivo fluorochrome labeling were used to study molecular mechanisms. RESULTS: LRP5HBM mice showed overall minor changes in the craniofacial bone development but with increased bone mass in the interradicular alveolar bone, edentulous ridge, palatine bone, and premaxillary suture. Elevated osteocyte density was observed in LRP5HBM mice, along with increased Runx2 expression and unmineralized bone surrounding osteocytes. Meanwhile, LRP5HBM mice exhibited increased osteoprogenitors, but no significant changes were observed in osteoclasts. This led to a high-bone-mass phenotype, and an increased osteocyte density in the alveolar bone and edentulous ridge. CONCLUSION: LRP5HBM mice display increased bone mass in the alveolar bone with minor changes in the craniofacial morphology. Collectively, these data elucidated the important role of LRP5 in axial bone development and homeostasis and provided clues into the therapeutical potential of LRP5 signaling in treating alveolar bone loss.

Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study.

The aim was to predict the post-pubertal mandibular length and Y axis of growth in males by using various machine learning (ML) techniques. Cephalometric data obtained from 163 males with Class I Angle malocclusion, were used to train various ML algorithms. Analysis of variances (ANOVA) was used to compare the differences between predicted and actual measurements among methods and between time points. All the algorithms revealed an accuracy range from 95.80% to 97.64% while predicting post-pubertal mandibular length. When predicting the Y axis of growth, accuracies ranged from 96.60% to 98.34%. There was no significant interaction between methods and time points used for predicting the mandibular length (p = 0.235) and Y axis of growth (p = 0.549). All tested ML algorithms accurately predicted the post-pubertal mandibular length and Y axis of growth. The best predictors for the mandibular length were mandibular and maxillary lengths, and lower face height, while they were Y axis of growth, lower face height, and mandibular plane angle for the post-pubertal Y axis of growth. No significant difference was found among the accuracies of the techniques, except the least squares method had a significantly larger error than all others in predicting the Y axis of growth.

A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population.

INTRODUCTION: The purpose of the present study was to create a machine learning (ML) algorithm with the ability to predict the extraction/non-extraction decision in a racially and ethnically diverse sample. METHODS: Data was gathered from the records of 393 patients (200 non-extraction and 193 extraction) from a racially and ethnically diverse population. Four ML models (logistic regression [LR], random forest [RF], support vector machine [SVM], and neural network [NN]) were trained on a training set (70% of samples) and then tested on the remaining samples (30%). The accuracy and precision of the ML model predictions were calculated using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. The proportion of correct extraction/non-extraction decisions was also calculated. RESULTS: The LR, SVM, and NN models performed best, with an AUC of the ROC of 91.0%, 92.5%, and 92.3%, respectively. The overall proportion of correct decisions was 82%, 76%, 83%, and 81% for the LR, RF, SVM, and NN models, respectively. The features found to be most helpful to the ML algorithms in making their decisions were maxillary crowding/spacing, L1-NB (mm), U1-NA (mm), PFH:AFH, and SN-MP(̊), although many other features contributed significantly. CONCLUSIONS: ML models can predict the extraction decision in a racially and ethnically diverse patient population with a high degree of accuracy and precision. Crowding, sagittal, and vertical characteristics all featured prominently in the hierarchy of components most influential to the ML decision-making process.

Can we predict orthodontic extraction patterns by using machine learning?

OBJECTIVE: To investigate the utility of machine learning (ML) in accurately predicting orthodontic extraction patterns in a heterogeneous population. MATERIALS AND METHODS: The material of this retrospective study consisted of records of 366 patients treated with orthodontic extractions. The dataset was randomly split into training (70%) and test sets (30%) and was stratified according to race/ethnicity and gender. Fifty-five cephalometric and demographic input data were used to train and test multiple ML algorithms. The extraction patterns were labelled according to the previous treatment plan. Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms were used to predict the patient's extraction patterns. RESULTS: The highest class accuracy percentages were obtained for the upper and lower 1st premolars (U/L4s) (RF: 81.63%, LR: 63.27%, SVM: 63.27%) and upper 1st premolars only (U4s) extraction patterns (RF: 61.11%, LR: 72.22%, SVM: 72.22%). However, all methods revealed low class accuracy rates (<50%) for the upper 1st and lower 2nd premolars (U4/L5s), upper 2nd and lower 1st premolars (U5/L4s), and upper and lower 2nd premolars (U/L5s) extraction patterns. For the overall accuracy, RF yielded the highest percentage with 54.55%, followed by SVM with 52.73% and LR with 49.09%. CONCLUSION: All tested supervised ML techniques yielded good accuracy in predicting U/L4s and U4s extraction patterns. However, they predicted poorly for the U4/L5s, U5/L4s, and U/L5s extraction patterns. Molar relationship, mandibular crowding, and overjet were found to be the most predictive indicators for determining extraction patterns.

Quantification of orthodontic loads on teeth in the correction of canine overeruption using different archwire designs.

INTRODUCTION: This study quantifies the effects of material, size of the continuous archwires, and level of overeruption on the loads on teeth in the correction of overerupted canines. METHODS: An orthodontic force test (OFT) was used to measure the 3-dimensional loads delivered by the archwires to the brackets attached to the maxillary right incisors, canine, and premolars. Dentoforms simulating canine overeruptions at the 0.5 mm and 1 mm levels were made from computerized tomography scans. Archwires with 2 types of material (stainless steel [SS] and nickel-titanium [NiTi]) and 2 sizes (0.014-in and 0.016-in) were tested, respectively, on the 0.022 × 0.028-in brackets through elastomeric ligatures. RESULTS: The forces were dominantly intrusive on the canines and extrusive on the first premolars and lateral incisors. The magnitudes of the extrusive forces were about 74% and 52% that of intrusive force on the canines, which range from -0.48 ± 0.01 N to -5.70 ± 0.14 N depending on the wire material, size, and severity of overeruption (P <0.01). The canine intrusive forces created by SS wires were about 3 times higher than that of NiTi wires with the same sizes, 0.016-in archwires were about twice higher than that of 0.014-in with the same materials, and 1 mm overeruption level doubled with respect to 0.5 mm. Significant second-order moment as coupled with the intrusive or extrusive forces. CONCLUSIONS: The intrusive and extrusive forces on teeth in the correction of canine overeruption can be quantified by the in vitro orthodontic force test, and the effects of the 3 factors significantly affect the loads on the teeth.

Type 1 diabetes mellitus leads to gingivitis and an early compensatory increase in bone remodeling.

BACKGROUND: Type 1 diabetes mellitus (T1DM) and periodontitis have long been thought to be biologically connected. Indeed, T1DM is a risk factor for periodontal disease. With the population of diabetic individuals growing, it is more important than ever to understand the negative consequences of diabetes on the periodontium and the mechanisms. The aim of this study was to find out the early effects of T1DM on the periodontium without any experimentally induced periodontitis. METHODS: We established the streptozotocin (STZ)-induced diabetic mouse model and examined the periodontium 8 weeks later by histology, molecular and cellular assays. Microcomputed tomographic (𝜇CT) imaging and in vivo fluorochrome labeling were also used to quantify bone volume and mineral apposition rates (MAR). RESULTS: The histologic appearance of epithelium tissue, connective tissue, and periodontal ligament in the diabetic condition was comparable with that of control mice. However, immune cell infiltration in the gingiva was dramatically elevated in the diabetic mice, which was accompanied by unmineralized connective tissue degeneration. Bone resorption activity was significantly increased in the diabetic mice, and quantitative 𝜇CT demonstrated the bone volume, the ratio of bone volume over tissue volume, and cemento-enamel junction to alveolar bone crest (CEJ-ABC) in the diabetic condition were equivalent to those in the control group. In vivo fluorochrome labeling revealed increased MAR and bone remodeling in the diabetic mice. Further investigation found the diabetic mice had more osteoprogenitors recruited to the periodontium, allowing more bone formation to balance the enhanced bone resorption. CONCLUSIONS: STZ-induced T1DM mice, at an early stage, have elevated gingival inflammation and soft tissue degeneration and increased bone resorption; but still the alveolar bone was preserved by recruiting more osteoprogenitor cells and increasing the rate of bone formation. We conclude that inflammation and periodontitis precede alveolar bone deterioration in diabetes.

The TMD-7 as a Brief Measure for Assessing Temporomandibular Disorder.

OBJECTIVES: The aim of this cross-sectional prospective study was to determine the internal consistency of the TMD-7, and compare prevalence of TMD symptoms in an adult population. MATERIALS AND METHODS: Upon presenting to the orthodontic screening appointment, a total of 440 subjects (316 females and 124 males) were asked to complete the TMD-7 questionnaire. A total of 108 of the participants were later excluded from the study either due to the duplicate or missing responses. The final sample consisted of data from 332 participants (232 females and 100 males), aged between 18 and 64 (mean age: 42.9 ± 9.0) years. STATISTICAL ANALYSIS: Cronbach's α statistics were calculated to assess internal consistency. Comparisons between genders, among age categories, and between subjects with versus without prior orthodontic treatment were performed using Wilcoxon ranks sum and Kruskal-Wallis tests. Comparisons for differences in the individual TMD-7 item ratings were performed using Mantel-Haenszel chi-square tests for ordered categorical responses. RESULTS: The calculated Cronbach's α for TMD-7 scale was 0.77. No statistically significant differences were found in the TMD-7 scale score or the individual TMD-7 item ratings between age categories (p = 0.993). Females had significantly higher TMD-7 scale score and higher ratings for headache, pain in jaw, pain in neck, pain in forehead, difficulty opening mouth, and difficulty while eating (p < 0.05). No statistically significant differences were found in the TMD-7 scale score or the individual TMD-7 item ratings between subjects with versus without previous orthodontic treatment (p = 0.075). CONCLUSION: The TMD-7 tool has good internal consistency and can be used reliably for assessment of TMD symptoms in adults. The use of this tool revealed no significant differences between age groups or between subjects with or without previous orthodontic treatment. However, a significant female gender predisposition for TMD symptoms in the adulthood was determined.

Vibrational Force on Accelerating Orthodontic Tooth Movement: A Systematic Review and Meta-Analysis.

This study aimed to systematically gather and analyze the current level of evidence for the effectiveness of the vibrational force in accelerating orthodontic tooth movement (OTM). This systematic review was conducted using three electronic databases: Scopus, PubMed, and Google Scholar until March 2022. The search was done through the following journals: European Journal of Orthodontics, American Journal of Orthodontics and Dentofacial Orthopedics, The Angle Orthodontist, Progress in Orthodontics, and Seminars in Orthodontics. Human or animal studies that have evaluated the effect of vibrational force on the rate of OTM were selected. A meta-analysis was performed for the rate of canine movement per month. Database research, elimination of duplicate studies, data extraction, and risk of bias assessment were performed by authors independently and in duplication. A fixed and random-effect meta-analysis was performed to evaluate the effect of vibrational forces. A total of 19 studies (6 animal and 13 human studies) that met the inclusion criteria were included. Meta-analysis was performed based on four human clinical trials. Three out of four studies showed no significant difference in the rate of canine movement between vibrational force and control groups. The limitation of this study was the small sample size and significant heterogeneity among the studies. Although vibrational forces have been shown to accelerate OTM in experimental studies, the results are inconsistent in clinical studies. The inability to apply desired peak load to the targeted teeth may be the main factor in inconsistent clinical outcomes.

A novel machine learning model for class III surgery decision.

PURPOSE: The primary purpose of this study was to develop a new machine learning model for the surgery/non-surgery decision in class III patients and evaluate the validity and reliability of this model. METHODS: The sample consisted of 196 skeletal class III patients. All the cases were allocated randomly, 136 to the training set and the remaining 60 to the test set. Using the test set, the success rate of the artificial neural network model was estimated, along with a 95% confidence interval. To predict surgical cases, we trained a binary classifier using two different methods: random forest (RF) and logistic regression (LR). RESULTS: Both the RF and the LR model showed high separability when classifying each patient for surgical or non-surgical treatment. RF achieved an area under the curve (AUC) of 0.9395 on the test set. 95% confidence intervals were computed by bootstrap sampling as lower bound = 0.7908 and higher bound = 0.9799. On the other hand, LR achieved an AUC of 0.937 on the test set. 95% confidence intervals were computed by bootstrap sampling as lower bound = 0.8467 and higher bound = 0.9812. CONCLUSIONS: RF and LR machine learning models can be used to generate accurate and reliable algorithms to successfully classify patients up to 90%. The features selected by the algorithms coincide with the clinical features that we as clinicians weigh heavily when determining a treatment plan. This study further supports that overjet, Wits appraisal, lower incisor angulation, and Holdaway H angle can be used as strong predictors in assessing a patient's surgical needs.

Precision and accuracy assessment of single and multicamera three-dimensional photogrammetry compared with direct anthropometry.

OBJECTIVES: To assess the precision and accuracy of single-camera photogrammetry (SCP) and multicamera photogrammetry (MCP) compared with direct anthropometry (DA). MATERIALS AND METHODS: A total of 30 participants were recruited, and 17 soft tissue landmarks were identified and used to complete a total of 16 measurements. Using SCP and MCP, two three-dimensional (3D) images were acquired from each participant. All 3D measurements and direct measurements were measured twice by the same operator to assess intraexaminer repeatability. Intraclass coefficients (ICCs) were used to evaluate intraexaminer repeatability and interexaminer agreement of the methods. Nonparametric bootstrap analyses were used to compare the means of the measurements among the three methods. RESULTS: All three methods showed excellent intraexaminer repeatability (ICCs > 0.90), except interpupillary distance (ICC = 0.86) measured by SCP. Both SCP and MCP showed excellent interexaminer agreement (ICCs > 0.90), except interpupillary distance (ICC = 0.79), left gonion-pogonion (ICC = 0.74), and columella-subnasale-labrale superior angle (ICC = 0.86) measured by SCP. Overall, there was good agreement between methods, except for columella-subnasale-labrale superior angle (ICC = 0.40) between SCP and MCP. CONCLUSIONS: Both SCP and MCP techniques were found to be reliable and valid options for 3D facial imaging. SCP produced slightly larger mean values for several measurements, but the differences were within a clinically acceptable range. Because of the larger margin of errors, measurements including the gonial area and subnasale should be assessed with caution.

Three-dimensional analysis of the posttreatment displacements of mandibular anterior teeth with rigid and flexible lingual retainers.

INTRODUCTION: The purpose of this study was to analyze any posttreatment changes in the positions of mandibular anterior teeth retained by 2 different fixed retention methods (rigid or flexible) and their effectiveness in minimizing postorthodontic tooth movement. METHODS: Thirty patients were selected for inclusion in this retrospective study. Group I consisted of 15 patients with 0.032-in stainless steel rigid retainers bonded to canines only. Group II consisted of 15 patients with 0.0215-in twisted stainless steel flexible retainer bonded to all mandibular anterior teeth. Geomagic Control (3DS Systems, Rock Hill, SC) software was used to quantify the changes between selected points. Paired t test was used for intragroup comparisons in all 3 coordinates, and an independent-sample t test was used for intergroup comparison. RESULTS: In the transverse dimension, significant intergroup differences were found in displacements of distoincisal points of the right lateral (P <0.05) and left central incisors (P <0.01). In the sagittal dimension, significant intergroup differences were found in the displacements of mesioincisal point of the right central incisor (P <0.05) and distoincisal point of the left central incisor (P <0.01). Examining total displacement, there were significant intergroup differences in mesioincisal point on the right central incisor, distoincisal point on the left lateral incisor (P <0.05). CONCLUSIONS: Our results suggested that central incisor contacts were more likely to shift with the rigid retainers, especially in sagittal and transverse dimensions. However, no statistically or clinically significant changes were noted in the third-order or vertical positions of the teeth with either retention method.

Pyk2 deficiency enhances bone mass during midpalatal suture expansion.

OBJECTIVE: To determine if Pyk2 deficiency increases midpalatal suture bone mass and preserves sutural integrity after maxillary expansion. SETTING AND SAMPLE: Thirty-six male Pyk2 knockout (KO) and control (WT) mice at 6 weeks of age. MATERIALS AND METHODS: Mice received nickel-titanium spring expanders delivering 0 g (no intervention control), 10 or 20 g force for 14 days. High-resolution micro-CT was used to determine bone volume/tissue volume (BV/TV), sutural width and intermolar width. Effects on osteoclasts, chondrocytes and suture morphology were determined by histomorphometry. RESULTS: Pyk2-KO controls (0 g) had 7% higher BV/TV compared with WT controls. Expanded Pyk2-KO maxillae also exhibited 12% (10 g) and 18% (20 g) higher BV/TV than WT mice. Although bone loss following expansion occurred in both genotypes, BV/TV was decreased to a greater extent in WT maxillae (-10% at 10g; -22% at 20 g) compared with Pyk2-KO maxillae (-11% only at 20 g). Expanded WT maxillae also showed a greater increase in sutural width, intermolar width and fibrous connective tissue width compared with expanded Pyk2-KO maxillae. Moreover, osteoclast number was increased 77% (10 g) and 132% (20 g) in expanded WT maxillae, but remained unchanged in expanded Pyk2-KO, compared to their respective controls. Cartilage area and chondrocyte number were increased to the same extent in expanded WT and Pyk2-KO sutures. CONCLUSIONS: These findings suggest that midpalatal suture expansion increases osteoclast formation in WT but not Pyk2-KO mice, leading to higher BV/TV in expanded Pyk2-KO maxillae. These studies suggest Pyk2-targeted strategies may be beneficial to increase bone density and preserve sutural integrity during maxillary expansion.

Root resorption and ensuing cementum repair by Wnt/ß-catenin dependent mechanism.

INTRODUCTION: Physiological root resorption is a common occurrence in mammalian teeth, which suggests that there must be a corollary consisting of physiological cementum repair. The mechanism(s) responsible for this physiological repair process is unknown and was the focus of this study. METHODS: Using a rat model, we explored first the prevalence of physiological root resorption and then asked whether this prevalence changed as a result of an osteoporotic phenotype. The cellular mechanisms of resorption were characterized using a combination of finite element modeling coupled with in-vivo histologic, molecular, and cellular analyses in rats. A potential molecular mechanism for cementum repair was uncovered using a strain of transgenic mice in which Wnt-responsive cells could be labeled and followed over time. RESULTS: In rats, most resorption lacunae were concentrated on the distal surfaces of the roots. Rat molars undergo a physiological tooth drift distally, and using finite element modeling, we calculated the magnitude of the compressive strains that accumulated on these surfaces in response to mastication. Although the overall strain magnitudes were low, they were constant and coincided with the presence of resorption lacunae. Where resorption lacunae were present, progeny from a Wnt-responsive population of stem cells, embedded in the periodontal ligament, directly contributed to the repair of the lacunae. CONCLUSIONS: Despite the fact that both are clastic conditions, an osteoporotic phenotype in rats was not associated with an increase in the prevalence of physiological root resorption. The location of the resorption lacunae corresponded to sites of low but constant compressive strains produced by physiological distal drift. At least 1 mechanism responsible for physiological cementum repair involved the contribution of Wnt-responsive stem or progenitor cells originating in the periodontal ligament. These data point toward a potential Wnt-based strategy to regenerate cementum in subjects with disease or damage.

A novel hypothesis based on clinical, radiological, and histological data to explain the dentinogenesis imperfecta type II phenotype.

Purpose/Aim: The aim of this study was to explore whether dentinogenesis imperfecta (DGI)-related aberrations are detectable in odontogenic tissues. Materials and Methods: Morphological and histological analyses were carried out on 3 teeth (two maxillary 1st molars, one maxillary central incisor) extracted from a patient with DGI Type II. A maxillary 2nd molar teeth extracted from a healthy patient was used as control. A micro-computed tomographic (µCT) data-acquisition system was used to scan and reconstruct samples. Pentachrome and picrosirius red histologic stains were used to analyze odontogenic tissues and their collagenous matrices. Results: Our findings corroborate DGI effects on molar and incisor root elongation, and the hypo-mineralized state of DGI dentin. In addition to these findings, we discovered changes to the DGI pulp cavity: Reactionary dentin formation, which we theorize is exacerbated by the early loss of enamel, nearly obliterated an acellular but still-vascularized DGI pulp cavity. We also discovered an accumulation of lamellated cellular cementum at the root apices, which we hypothesize compensates for the severe and rapid attrition of the DGI tooth. Conclusions: Based on imaging and histological data, we propose a novel hypothesis to explain the complex dental phenotypes observed in patients with DGI Type II.

Histomorphometric and Histopathologic Evaluation of the Effects of Systemic Fluoride Intake on Orthodontic Tooth Movement.

OBJECTIVES: The aim of this study was to determine the effects of systemic fluoride intake on orthodontic tooth movement with histomorphometric and histopathologic methods. MATERIALS AND METHODS: Forty-eight Wistar albino rats were randomly divided into four groups of 12 rats each. Group I received fluoridated water and underwent orthodontic tooth movement. Group II received fluoridated water and did not undergo orthodontic tooth movement. Group III received nonfluoridated water and underwent orthodontic tooth movement. Group IV received nonfluoridated water and did not undergo orthodontic tooth movement. At the beginning of the experiment (T1), impressions were taken from the maxilla of the rats in groups I and III under general anesthesia, and a NiTi closed coil spring appliance was ligated between the left maxillary central incisors and maxillary first molar. The orthodontic force applied was approximately 75 g, and the duration of the experimental period was 18 days. During the experimental period, appliances were controlled daily. At the end of the experimental period (T2), the rats were sacrificed with an overdose of a ketamine/xylasine combination, and their impressions were obtained. The upper first molars were subsequently dissected for histological examination. Incisor-molar distance, number of osteoblasts, number of osteoclasts and periodontal ligament (PDL) space widths on the compression and tension sides were measured. STATISTICAL ANALYSIS: All measurements were statistically analyzed with SPSS for Windows version 18.0 (SPSS Inc., Chicago, IL, USA). Repeated measures ANOVA and posthoc Tukey tests were used to compare the groups. RESULTS: No statistically significant difference was found with respect to the amount of tooth movement between the fluoridated and nonfluoridated groups (p > 0.05). Orthodontic force application increased the number of osteoblasts at the tension sides and reduced it at the compression sides (p < 0.001). An increased number of osteoclasts was observed in the nonfluoridated group relative to the fluoridated group (p < 0.01). CONCLUSIONS: No difference was observed with respect to the amount of tooth movement between the fluoridated and nonfluoridated groups. Fluoride significantly reduced the number of osteoclasts in the experimental groups.

Effects of Rapid Maxillary Expansion and Facemask Therapy on the Soft Tissue Profiles of Class III Patients at Different Growth Stages.

OBJECTIVES: The aim of this study was to evaluate the effects of rapid maxillary expansion (RME) and facemask therapy on the soft tissue profiles of class III patients at different growth stages. MATERIALS AND METHODS: Forty-five subjects (23 females and 22 males) were divided into prepubertal, pubertal, and postpubertal groups. Bonded type RME appliances and Petit-type facemasks were fitted to each patient, and intraoral elastics were applied from the hooks of the RME appliance to the facemask. STATISTICAL ANALYSIS: All measurements were statistically analyzed with SPSS version 18.0 (SPSS Inc., Chicago, IL, United States) for Windows. Repeated-measures of ANOVA and posthoc Tukey tests were used to compare the groups. RESULTS: The soft tissue nasion, pronasale, subnasale, soft tissue A point, and labrale superior landmarks were all displaced forward and downward, and the most dramatic changes were recorded in the pubertal group. The labrale inferior, soft tissue B point, soft tissue pogonion, and soft tissue menton landmarks moved backward and downward in all groups, and the greatest displacements were observed in the pubertal group. CONCLUSIONS: The soft tissue profiles improved significantly and became more convex in all treatment groups. Although, the most favorable facial changes were observed in the pubertal growth stage, the treatments applied in the postpubertal stage also elicited significant changes and should thus be considered viable treatment options.