One conserved feature of craniofacial development is that the first pharyngeal arch has two components, the maxillary and mandibular, which then form the upper and lower jaws, respectively. However, until now, there have been no tests of whether the maxillary cells originate entirely within the first pharyngeal arch or whether they originate in a separate condensation, cranial to the first arch. We therefore constructed a fate map of the pharyngeal arches and environs with a series of dye injections into stage 13-17 chicken embryos. We found that from the earliest stage examined, the major contribution to the maxillary bud is from post-optic mesenchyme with a relatively minor contribution from the maxillo-mandibular cleft. Cells labeled within the first pharyngeal arch contributed exclusively to the mandibular prominence. Gene expression data showed that there were different molecular codes for the cranial and caudal maxillary prominence. Two of the genes examined, Rarbeta (retinoic acid receptor beta) and Bmp4 (bone morphogenetic protein) were expressed in the post-optic mesenchyme and epithelium prior to formation of the maxillary prominence and then were restricted to the cranial half of the maxillary prominence. In order to determine the derivatives of the maxillary prominence, we performed focal injections of CM-DiI into the stage 24 maxillary prominence. Labeled cells contributed to the maxillary, palatine, and jugal bones, but not the other elements of the upper beak, the premaxilla and prenasal cartilage. We also determined that the cranial cells give rise to more distal parts of the upper beak, whereas caudal cells form proximal structures. Grafts of stage 24 maxillary prominences were also analyzed to determine skeletal derivatives and these results concurred with the DiI maps. These early and later fate maps indicate that the maxillary prominence and its skeletal derivatives are not derived from the first pharyngeal arch but rather from a separate maxillary condensation that occurs between the eye and the maxillo-mandibular cleft. These data also suggest that during evolution, recession of the first pharyngeal arch-derived palatoquadrate cartilage to a more proximal position gave way to the bony upper jaw of amniotes.

The embryonic vertebrate face is composed of similarly sized buds of neural crest-derived mesenchyme encased in epithelium. These buds or facial prominences grow and fuse together to give the postnatal morphology characteristic of each species. Here we review the role of neural crest cells and foregut endoderm in differentiating facial features. We relate the developing facial prominences to the skeletal structure of the face and review the signals and genes that have been shown to play an important role in facial morphogenesis. We also examine two experiments one at the genetic level and one at the signal level in which transformation of facial prominences and subsequent change of jaw identity was induced. We propose that signals such as retinoids and BMPs and downstream transcription factors such as Distal-less related genes specify jaw identity.

No abstract

The 3D architecture of the mandible contributes to the functional and morphological characteristics of the lower one third of craniofacial region. The mandible has six distinct functional units, and its architecture is the sum of balanced growth of each functional unit and surrounding matrix. A dentofacial deformity (DFD) with malocclusion can be interpreted as their unbalanced growth. In order to characterize the mandibular 3D architecture, we analyzed the 3D reconstructed computed tomography (CT) images in terms of functional units. We evaluated both sides of 30 datasets of 3D CT scans of normal controls (N = 6) and patients with prognathic (N = 17) or retrognathic (N = 7) mandibles. We first identified and evaluated reference points to define mandibular functional units and compared their linear and angular measurements of DFD with normal group. The condylar and body length, the ratio of condyle/coronoid length, and the condylar head axis angle showed the statistically significant differences between groups. From these results, we could define the 3D reference points for functional units and identify the 3D architectural characteristics of DFD mandibles. These models may help us improve diagnosis and treatment planning to let them return to the normal and balanced architecture for DFD.

The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.