Inflammation and loss of articular cartilage are considered the major cause of temporomandibular joint osteoarthritis (TMJOA), a painful condition of the temporomandibular joint (TMJ). To determine the cause of TMJ osteoarthritis in these patients, synovial fluid of TMJOA patients was compared prior to and after hyaluronic lavage, revealing substantially elevated levels of interleukin (IL) 1β, reactive oxidative stress (ROS), and an overload of Fe³⁺ and Fe²⁺ prior to lavage, indicative of ferroptosis as a mode of chondrocyte cell death. To ask whether prolonged inflammatory conditions resulted in ferroptosis-like transformation in vitro, we subjected TMJ chondrocytes to IL-1β treatment, resulting in a shift in messenger RNA sequencing gene ontologies related to iron homeostasis and oxidative stress–related cell death. Exposure to rat unilateral anterior crossbite conditions resulted in reduced COL2A1 expression, fewer chondrocytes, glutathione peroxidase 4 (GPX4) downregulation, and 4-hydroxynonenal (4-HNE) upregulation, an effect that was reversed after intra-articular injections of the ferroptosis inhibitor ferrostatin 1 (Fer-1). Our study demonstrated that ferroptosis conditions affected mitochondrial structure and function, while the inhibitor Fer-1 restored mitochondrial structure and the inhibition of hypoxia-inducible factor 1α (HIF-1α) or the transferrin receptor 1 (TFRC) rescued IL-1β–induced loss of mitochondrial membrane potential. Inhibition of HIF-1α downregulated IL-1β–induced TFRC expression, while inhibition of TFRC did not downregulate IL-1β–induced HIF-1α expression in chondrocytes. Moreover, inhibition of HIF-1α or TFRC downregulated the IL-1β–induced MMP13 expression in chondrocytes, while inhibition of HIF-1α or TFRC rescued IL-1β–inhibited COL2A1 expression in chondrocytes. Furthermore, upregulation of TFRC promoted Fe²⁺ entry into chondrocytes, inducing the Fenton reaction and lipid peroxidation, which in turn caused ferroptosis, a disruption in chondrocyte functions, and an exacerbation of condylar cartilage degeneration. Together, these findings illustrate the far-reaching effects of chondrocyte ferroptosis in TMJOA as a mechanism causing chondrocyte death through iron overload, oxidative stress, and articular cartilage degeneration and a potential major cause of TMJOA.

Accurate segmentation of 3D dental models derived from intra-oral scanners (IOS) is one of the key steps in many digital dental applications such as orthodontics and implants. However, it is difficult to accurately segment individual teeth and gums in 3D dental models due to the following problems: 1) the shape and appearance of adjacent teeth are very similar, which is easy to be misidentified; 2) the boundary between teeth and gums is often indistinct, especially in orthodontic patients with abnormalities such as missing and crowded teeth. To solve such problems, a Dual-Branch Geometric Attention Network (DBGANet) for 3D tooth segmentation is proposed, which can capture tooth geometric structure and detailed boundary information from multi-view geometric features encoded by 3D coordinates and normal vectors. The framework contains two branches, i.e., C-branch and N-branch. First, centroid-guided separable attention is designed in the C-branch to learn global context information by modeling the spatial dependencies of tooth point clouds, which can capture the overall geometric structure of teeth to better distinguish adjacent teeth with similar appearance. Then, Gaussian neighbor attention is designed in the N-branch to encode normal vectors to highlight detailed differences between geometric features at different points, which helps to refine the boundaries of teeth and gingiva for more accurate and smooth tooth segmentation. Extensive experiments on the real-patient datasets of 3D dental models demonstrate that the proposed DBGANet significantly outperforms state-of-the-art methods.

Objective: To investigate the differences in mandibular retromolar space among skeletal Class I subjects with different vertical divergence using cone-beam computed tomography (CBCT). Materials and Methods: A total of 123 skeletal Class I patients (aged 20–40 years) were categorized into hypodivergent, normodivergent, and hyperdivergent groups based on S-N/Go-Me and facial height index (FHI). Mandibular retromolar space was measured at four planes parallel to the occlusal plane along the sagittal line and molar cuspal line, respectively. The mandibular retromolar space was compared among the three vertical groups. Results: The hyperdivergent group had a significantly smaller mandibular retromolar space compared with the other two groups, while the hypodivergent group had the largest retromolar space. In addition, the hyperdivergent group had a larger number of subjects whose roots contacted the lingual cortex of the mandibular body. Conclusions: The hyperdivergent group tends to exhibit the smallest mandibular retromolar space and highest risk of cortex contact. Clinicians should keep in mind that successful molar distalization requires sufficient retromolar space, especially for hyperdivergent subjects, which should be verified with CBCT.

Dental abnormality (DA) detection is of great significance to orthodontic treatment. However, it is difficult to detect abnormal teeth from the oral cavity due to the following problems: (1) The crowding dentition often overlaps with normal teeth; (2) The lesion regions are small on the tooth surface. To address such problems, a Category-Guide Attention U-Net (CGA-UNet) is proposed, where a deformable attention convolution (DAC) module is first devised to discriminate crowding teeth from normal ones by learning dentition spatial distribution information; then, a differential variable convolution (DVC) module is designed to perform pathological tooth identification by extracting the small lesion features; finally, an attentional feature fusion (AFF) module is developed to integrate the spatial information and lesion features to obtain the abnormal tooth region. Experiments conducted on the benchmark show excellent performance of CGA-UNet for dental abnormality detection, and it can further assist orthodontists in formulating orthodontic treatment plans.

Tissue engineering is a promising approach to restore or replace a damaged temporomandibular joint (TMJ) disc. However, constructing a scaffold that can mimic biomechanical and biological properties of the natural TMJ disc remains a challenge. In this study, three-dimensional (3D) printing technology was used to fabricate polycaprolactone (PCL)/polyurethane (PU) scaffolds and PU scaffolds to imitate the region-specific biomechanical properties of the TMJ disc. The scaffolds were coated with polydopamine (PDA) and combined with a decellularized matrix (dECM). Then, rat costal chondrocytes and mouse L929 fibroblasts, respectively, were suspended on the composite scaffolds and the biological functions of the cells were studied. The properties of the scaffolds were characterized by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), contact angle analysis, and biomechanical testing. To verify the biocompatibility of the scaffolds, the viability, proliferation, and extracellular matrix (ECM) production of the cells seeded on the scaffolds were assessed by LIVE/DEAD staining, Cell Counting Kit-8 assay, biochemical content analysis, immunofluorescence staining, and qRT-PCR. The functionalized hybrid scaffolds were then implanted into the subcutaneous space of nude mice for 6 weeks, and the regenerated tissue was evaluated by histological staining. The biomechanical properties of PCL/PU and PU scaffolds were comparable to that of the central and peripheral zones, respectively, of a native human TMJ disc. The PDA-coated scaffolds displayed superior biomechanical, structural, and functional properties, creating a favorable microenvironment for cell survival, proliferation, ECM production, and tissue regeneration. In conclusion, 3D-printed polymer scaffolds coated with PDA and combined with dECM hydrogel were found to be a promising substitute for TMJ disc tissue engineering.