The aim was to compare the prevalence of molar incisor hypomineralisation (MIH) obtained from two cross-sectional surveys conducted in 2018 and 2023 and to evaluate their association with other variables in schoolchildren attending elementary schools in Tepatitlán de Morelos (Jalisco, Mexico). Children (5–13 years old) enrolled in elementary schools in Tepatitlán de Morelos, Mexico, were evaluated. Intra-oral examinations were performed in schools by calibrated examiners. In both surveys, the presence and characteristics (including severity) of MIH and hypomineralised second primary molars (HSPM) were scored according to the European Academy of Paediatric Dentistry (EAPD) MIH-index. In 2023, dental plaque and dental caries were scored additionally. Descriptive and statistical analyses were performed (α = 5
BACKGROUND:The objectives of this Focused Workshop were to update the epidemiology, aetiology, risk factors, diagnosis and management of gingival and periodontal diseases and conditions in children and adolescents, and to explore the applicability of the 2018 Classification in children and adolescents. METHODS:The Workshop discussions were informed by three specifically commissioned systematic reviews covering gingival and periodontal diseases and conditions, in systemically healthy children and adolescents, or in children and adolescents with systemic conditions. RESULTS:Over 70 genetic, congenital and acquired systemic conditions that impact the periodontal tissues were identified, with levels of evidence graded as very low, low or moderate. Gingival diseases and conditions in systemically healthy children and adolescents were identified, alongside local predisposing and systemic modifying factors. Periodontitis and other periodontal conditions in the 2018 Classification System also apply to children and adolescents; however, there are challenges with periodontal probing in the primary and mixed dentition. CONCLUSIONS:Periodontal tissues in children and adolescents differ from those in adults and require special consideration, accounting for their stage of development and predisposing and modifying factors unique to younger patients, which may confound accurate diagnosis, prognostication and management. Specific approaches to screening, examination and treatment are necessary for safe and effective management in this patient group.
Intracellular viscosity, a key mechanical property of cells, can significantly influence biochemical diffusion rates. Changes in cellular viscosity have been found in various human diseases, including diabetes and neurodegenerative diseases like Parkinson's disease, and are linked to cancer cell migration. Cell behavior changes in altered gravity and is suspected to contribute to the macroscale symptoms seen in astronauts. However, research on intracellular viscosity in altered gravity is limited. Therefore, in preparation for the European Space Agency (ESA)'s MechanoCell project, this pilot study investigates whether hypergravity affects the viscosity of HeLa cells and if this can be measured with Single-Particle Tracking (SPT). SPT analysis software was employed to track endogenous particles in cells subjected to hypergravity ranging from 1 to 12 g in the Large Diameter Centrifuge (LDC) at ESA ESTEC. The software tracks endogenous particles in the HeLa cells by comparing the video frames captured by the EVOS microscope inside the LDC while at hypergravity. The trajectories of the particles permit the calculation of the mean square displacement and subsequently the intracellular viscosity. Results indicate measurable changes in viscosity, although further research is needed to confirm these findings and account for other influencing factors.
Three-dimensional (3D) printing enables customized orthodontic aligners and retainers, but concerns persist regarding the biocompatibility of printable resins. Although ISO 10993-5 and ISO 10993-12 provide guidance for in vitro cytotoxicity testing and sample extraction, substantial flexibility in reporting and execution may hinder comparability across studies. To map how in vitro cytotoxicity testing is performed for materials used to 3D-print orthodontic aligners/retainers and to identify variation and reporting gaps in key ISO 10993-5/-12 testing domains. Following PRISMA-ScR, Ovid MEDLINE, Embase, and Web of Science were searched from database inception to 9 April 2025. Peer-reviewed studies assessing in vitro cytotoxicity of 3D-printed aligners/retainers (and closely related intraoral appliances where relevant) were included. Data were charted across extraction protocols (ISO 10993-12-related), cell exposure parameters, and cytotoxicity assays (ISO 10993-5-related). Twenty-five studies published between 2020 and 2025 were included. Seventeen studies (17/25, 68
Cone-beam computed tomography (CBCT) is widely used in dental diagnosis and implant planning. The accurate segmentation of teeth and alveolar bone from CBCT images is essential for precise diagnosis and treatment planning. In this study, we propose two distinct convolutional neural network models designed to effectively perform precise segmentation of alveolar bone and teeth. The first model is a multi-class segmentation convolutional neural network, termed ‘Attentional Relation U-Net++’ (AR U-Net++), specifically designed for slice-by-slice separation of the jawbone and tooth structures. The AR U-Net + + enhances the original U-Net + + model by incorporating a dual attention network and a local relation layer. This advancement allows the model to effectively integrate local features, as well as positional and channel attention. Complementing the AR U-Net++, we introduce the 3D merged selective U-Net (MS U-Net), designed specifically for segmenting individual teeth within volumetric CT images. The MS U-Net leverages a 2D/3D feature merge and selective kernel convolution to integrate across channels and diverse kernels. Our experiments utilized 9,330 axial slices and 973 individual tooth segments derived from forty anonymized CBCT volumes. The AR U-Net + + attained average values of 0.9892, 0.0223, and 0.0016 for the dice similarity coefficient (DSC), relative volumetric overlap error (VOE), and relative volume difference (RVD), respectively. In the experiments conducted with the 3D MS-Net, the performance indices observed were 0.9746 for the average DSC, 0.9508 for the average Jaccard coefficient (JC), 1.3263 mm for the average Hausdorff distance (HD), and 0.3180 mm for the average symmetric surface distance (ASSD). We proposed two novel CNNs: the attentional relational U-Net ++ (AR U-Net ++) and the 3D MS U-Net. The AR U-Net + + was designed to accurately delineate jawbones and teeth from CBCT images in a slice-to-slice manner. Their experimental results underline the substantial potential of the proposed system as an advanced tool for enhancing clinical examinations.
