Formation of functional skeletal tissues requires highly organized steps of mesenchymal progenitor cell differentiation. The dental follicle (DF) surrounding the developing tooth harbors mesenchymal progenitor cells for various differentiated cells constituting the tooth root–bone interface and coordinates tooth eruption in a manner dependent on signaling by parathyroid hormone-related peptide (PTHrP) and the PTH/PTHrP receptor (PPR). However, the identity of mesenchymal progenitor cells in the DF and how they are regulated by PTHrP-PPR signaling remain unknown. Here, we show that the PTHrP-PPR autocrine signal maintains physiological cell fates of DF mesenchymal progenitor cells to establish the functional periodontal attachment apparatus and orchestrates tooth eruption. A single-cell RNA-seq analysis revealed cellular heterogeneity of PTHrP+ cells, wherein PTHrP+ DF subpopulations abundantly express PPR. Cell lineage analysis using tamoxifen-inducible PTHrP-creER mice revealed that PTHrP+ DF cells differentiate into cementoblasts on the acellular cementum, periodontal ligament cells, and alveolar cryptal bone osteoblasts during tooth root formation. PPR deficiency induced a cell fate shift of PTHrP+ DF mesenchymal progenitor cells to nonphysiological cementoblast-like cells precociously forming the cellular cementum on the root surface associated with up-regulation of Mef2c and matrix proteins, resulting in loss of the proper periodontal attachment apparatus and primary failure of tooth eruption, closely resembling human genetic conditions caused by PPR mutations. These findings reveal a unique mechanism whereby proper cell fates of mesenchymal progenitor cells are tightly maintained by an autocrine system mediated by PTHrP-PPR signaling to achieve functional formation of skeletal tissues.

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Introduction The aims of this study were to evaluate how head orientation interferes with the amounts of directional change in 3-dimensional (3D) space and to propose a method to obtain a common coordinate system using 3D surface models. Methods Three-dimensional volumetric label maps were built for pretreatment (T1) and posttreatment (T2) from cone-beam computed tomography images of 30 growing subjects. Seven landmarks were labeled in all T1 and T2 volumetric label maps. Registrations of T1 and T2 images relative to the cranial base were performed, and 3D surface models were generated. All T1 surface models were moved by orienting the Frankfort horizontal, midsagittal, and transporionic planes to match the axial, sagittal, and coronal planes, respectively, at a common coordinate system in the Slicer software (open-source, version 4.3.1; http://www.slicer.org). The matrix generated for each T1 model was applied to each corresponding registered T2 surface model, obtaining a common head orientation. The 3D differences between the T1 and registered T2 models, and the amounts of directional change in each plane of the 3D space, were quantified for before and after head orientation. Two assessments were performed: (1) at 1 time point (mandibular width and length), and (2) for longitudinal changes (maxillary and mandibular differences). The differences between measurements before and after head orientation were quantified. Statistical analysis was performed by evaluating the means and standard deviations with paired t tests (mandibular width and length) and Wilcoxon tests (longitudinal changes). For 16 subjects, 2 observers working independently performed the head orientations twice with a 1-week interval between them. Intraclass correlation coefficients and the Bland-Altman method tested intraobserver and interobserver agreements of the x, y, and z coordinates for 7 landmarks. Results The 3D differences were not affected by the head orientation. The amounts of directional change in each plane of 3D space at 1 time point were strongly influenced by head orientation. The longitudinal changes in each plane of 3D space showed differences smaller than 0.5 mm. Excellent intraobserver and interobserver repeatability and reproducibility (>99%) were observed. Conclusions The amount of directional change in each plane of 3D space is strongly influenced by head orientation. The proposed method of head orientation to obtain a common 3D coordinate system is reproducible.

IntroductionThe aim was to evaluate three regions of reference (Björk, Modified Björk and mandibular Body) for mandibular registration testing them in a patients’ CBCT sample.MethodsMandibular 3D volumetric label maps were built from CBCTs taken before (T1) and after treatment (T2) in a sample of 16 growing subjects and labeled with eight landmarks. Registrations of T1 and T2 images relative to the different regions of reference were performed, and 3D surface models were generated. Seven mandibular dimensions were measured separately for each time-point (T1 and T2) in relation to a stable reference structure (lingual cortical of symphysis), and the T2-T1 differences were calculated. These differences were compared to differences measured between the superimposed T2 (generated from different regions of reference: Björk, Modified Björk and Mandibular Body) over T1 surface models. ICC and the Bland-Altman method tested the agreement of the changes obtained by nonsuperimposition measurements from the patients’ sample, and changes between the overlapped surfaces after registration using the different regions of reference.ResultsThe Björk region of reference (or mask) did work properly only in 2 of 16 patients. Evaluating the two other masks (Modified Björk and Mandibular body) on patients’ scans registration, the concordance and agreement of the changes obtained from superimpositions (registered T2 over T1) compared to results obtained from non superimposed T1 and T2 separately, indicated that Mandibular Body mask displayed more consistent results.ConclusionsThe mandibular body mask (mandible without teeth, alveolar bone, rami and condyles) is a reliable reference for 3D regional registration.

Introduction The aim of this study was to 3-dimensionally assess the treatment outcomes of bone-anchored maxillary protraction (BAMP) in patients with unilateral cleft lip and palate. Methods The cleft group comprised 24 patients with unilateral cleft lip and palate and Class III malocclusion with mean initial and final ages of 11.8 and 13.2 years, respectively. The noncleft group comprised 24 noncleft patients with Class III malocclusion with mean initial and final ages of 11.9 and 12.9 years, respectively. Cone-beam computed tomography examinations were performed before and after BAMP therapy in both groups and superimposed at the cranial base. Three-dimensional displacements of maxillary landmarks were quantified and visualized with color-coded maps and semitransparent superimpositions. The t test corrected for multiple testing (Holm-Bonferroni method), and the paired t test was used for statistical comparison between groups and sides, respectively (P < 0.05). Results BAMP produced anterior (1.66 mm) and inferior (1.21 mm) maxillary displacements in the cleft group with no significant differences compared with the noncleft group. The maxillary first molars of the cleft group showed significantly greater medial displacement than did those in the noncleft group. The zygoma showed significantly greater lateral displacement at the cleft side compared with the noncleft side. Conclusions BAMP caused similar amounts of maxillary protraction in patients with and without unilateral cleft lip and palatem with discrete differences between the cleft side and the noncleft side.

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.

Introduction the aim of this study was to evaluate the differences between 2 regions of maxillary voxel-based registration and to test the reproducibility of the registration. Methods 3D models were built for before treatment (T1) and after treatment (T2) Cone Beam CTs for 16 growing subjects. Landmarks were labeled in all T2 models of the maxilla, and voxel-based registration was performed independently by two observers, at two different times, using two different reference regions: 1) the Maxilla region (MAX) included the maxillary bone clipped inferiorly at the dentoalveolar processes, superiorly at the plane passing through the right and left orbitale points, laterally at the zygomatic processes through the orbitale point, and posteriorly at a plane passing through the distal surface of the second molars. 2) the Palate and Infra-zygomatic region (PIZ) had different posterior and anterior limits (at the plane passing through the distal of the first molar and distal of the canines, respectively). The differences between the registration regions were measured by comparing the distances between corresponding landmarks in the T2 registered models and comparing corresponding x,y,z coordinates from corresponding landmarks. Statistical analysis of the differences between T2 surface models was performed by evaluating the means and standard deviations of the distances between landmarks and by testing the agreement between coordinates from corresponding landmarks (ICC and Bland-Altman method). Results The means of the differences between landmarks from PIZ to MAX 3D T2 surface models for all of the regions of reference, times of registrations and observers combinations were smaller than 0.5 mm. The ICC and the Bland-Altman plots indicated adequate concordance. Conclusions Both regions of regional maxillary registration (MAX and PIZ) showed similar results and adequate intra- and inter-observer reproducibility.

The findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis.