The recent World Health Organization (WHO) Oral Health Resolution and the subsequent WHO Global Oral Health Action Plan highlight the key relevance of providing information on the economic impacts of oral conditions. The purpose of this study was to provide updated estimates for the global, regional, and country-level economic impacts of oral conditions in 2019. Extending previously established methods, dental expenditures (costs for treatments) and productivity losses for 5 oral conditions (caries in deciduous and permanent teeth, periodontitis, edentulism, other oral diseases) were estimated for the year 2019. The estimated total worldwide economic impacts of oral conditions in 2019 were US $710B, of which US $387B (US $327B to US $404B) was due to direct costs and US $323B (US $186 to US $460) was due to productivity losses for the 5 main oral conditions. Low-income countries spent an average of US $0.52 (US $0.22 to US $0.96) per capita on dental care, while high-income countries spent an average of US $260 (US $257 to US $268) per capita-a 500-fold difference. These findings suggest that oral conditions continue to substantiate an enormous economic burden to individuals and society. The comprehensiveness of estimates supersedes that of previous work as the primary information on direct costs was identified for a larger number of countries. The need for more and better routine reporting and monitoring of the economic impact of oral conditions is emphasized. The relevance of such information is also highlighted by its inclusion in the first-ever WHO Global Oral Health Status Report and Global Strategy on Oral health 2023 to 2030. Given the persistently high economic burden of oral conditions, there is a key role for better prioritization of cost-efficient oral health programs as well as needs-responsive capacity planning.

Accurately delineating individual teeth in 3-dimensional tooth point clouds is an important orthodontic application. Learning-based segmentation methods rely on labeled datasets, which are typically limited in scale due to the labor-intensive process of annotating each tooth. In this article, we propose a self-supervised pretraining framework, named Geo-Net, to boost segmentation performance by leveraging large-scale unlabeled data. The framework is based on the scalable masked autoencoders, and 2 geometry-guided designs, curvature-aware patching algorithm (CPA) and scale-aware reconstruction (SCR), are proposed to enhance the masked pretraining for tooth point cloud segmentation. In particular, CPA is designed to assemble informative patches as the reconstruction unit, guided by the estimated pointwise curvatures. Aimed at equipping the pretrained encoder with scale-aware modeling capacity, we also propose SCR to perform multiple reconstructions across shallow and deep layers. In vitro experiments reveal that after pretraining with large-scale unlabeled data, the proposed Geo-Net can outperform the supervised counterparts in mean Intersection of Union (mIoU) with the same amount of annotated labeled data. The code and data are available at https://github.com/yifliu3/Geo-Net.

Several pieces of evidence have been reported regarding the association between periodontitis and systemic diseases. Despite the emphasized significance of prevention and early diagnosis of periodontitis, there is still a lack of a clinical tool for early screening of this condition. Therefore, this study aims to use explainable artificial intelligence (XAI) technology to facilitate early screening of periodontitis. This is achieved by analyzing various clinical features and providing individualized risk assessment using XAI. We used 1,012 variables for a total of 30,465 participants data from National Health and Nutrition Examination Survey (NHANES). After preprocessing, 9,632 and 5,601 participants were left for all age groups and the over 50 y age group, respectively. They were used to train deep learning and machine learning models optimized for opportunistic screening and diagnosis analysis of periodontitis based on Centers for Disease Control and Prevention/ American Academy of Pediatrics case definition. Local interpretable model-agnostic explanations (LIME) were applied to evaluate potential associated factors, including demographic, lifestyle, medical, and biochemical factors. The deep learning models showed area under the curve values of 0.858 ± 0.011 for the opportunistic screening and 0.865 ± 0.008 for the diagnostic dataset, outperforming baselines. By using LIME, we elicited important features and assessed the combined impact and interpretation of each feature on individual risk. Associated factors such as age, sex, diabetes status, tissue transglutaminase, and smoking status have emerged as crucial features that are about twice as important than other features, while arthritis, sleep disorders, high blood pressure, cholesterol levels, and overweight have also been identified as contributing factors to periodontitis. The feature contribution rankings generated with XAI offered insights that align well with clinically recognized associated factors for periodontitis. These results highlight the utility of XAI in deep learning-based associated factor analysis for detecting clinically associated factors and the assistance of XAI in developing early detection and prevention strategies for periodontitis in medical checkups.

Cementum, a bone-like tissue, is an essential component of periodontium, and periodontitis can lead to degenerative changes in the cementum, eventually resulting in tooth loss. The therapeutic strategy for advanced periodontitis is to achieve periodontal regeneration, of which cementum regeneration is a key criterion. Cementoblasts are responsible for cementogenesis, and their mineralization counts in cementum regeneration. However, research is still limited. Thus, novel treatment targets are required. The expression levels of lysine (K)-specific demethylase 6B (KDM6B), fatty acid oxidation (FAO), and cementogenic markers were detected by quantitative polymerase chain reaction, Western blot, immunofluorescence, and immunohistochemical assays. FAO levels were analyzed by assay kit. , injection of GSK-J4 into mice detected the influence of KDM6B on cementum formation. Chromatin immunoprecipitation sequencing, transcriptomic RNA sequencing, subsequent chromatin immunoprecipitation-quantitative polymerase chain reaction and overexpression of HADHA (hydroxyacyl-coA dehydrogenase trifunctional multienzyme complex subunit alpha) elucidated the KDM6B- axis. Global lactylation was detected by Western blot. Lactylation proteomics clarified the modified sites of HADHA. Mutating these sites and applying coimmunoprecipitation confirmed their significance. Knockdown of was utilized to assess its regulation on the lactylation of HADHA, FAO, and mineralization levels. FAO and KDM6B expression was elevated during cementoblast mineralization. KDM6B targeted and activated its transcription, thereby increasing FAO levels and promoting mineralization. Lactylation occurred in the process of mineralization, and KDM6B could regulate the lactylation of HADHA to promote FAO and mineralization. Overexpression of and the addition of lactate sodium could rescue the inhibition of mineralization by knockdown of . In summary, during cementoblast mineralization, KDM6B regulates HADHA by mediating histone demethylation and lactylation, thereby upregulating FAO and thus promoting mineralization.

The status of oral health research in the World Health Organization (WHO) African region is unclear, yet the need for such information is central to moving an oral health agenda forward. Such an agenda is essential for effectively translating research into actionable practices and supporting regional strategies. The aim of this scoping review was to provide data on the scope and output of oral health research in the WHO African region to be used as a starting point for establishing a research agenda that can affect oral health in the region. We conducted a systematic search in PubMed; EMBASE; Epistemonikos; Scopus; the International Association for Dental, Oral, and Craniofacial Research General and Regional Sessions; ProQUEST; PROSPERO; and African regional databases such as Regional African Index Medicus and the African Journal Online. We included primary and secondary studies published in English, French, or Portuguese between January 1, 2011, and December 31, 2022, addressing oral health-related research having individuals, groups, or populations as units of analysis. These reports either addressed a topic relevant to the WHO African region assessed using the title and study objective or were conducted in a country in the region. We excluded in vitro and in vivo studies focusing on cells, biomarkers, or animals. We assessed 24,014 records, and 1,379 proved eligible. Our findings indicate a preference for particular research designs less suitable for evidence-informed practice guidelines and oral policies, a limited scope of oral health research topics, and important regional differences in research capacity. Furthermore, publications by researchers in the WHO African region tend to be published in journals with a limited readership. A discussion of our findings among oral health researchers at academic institutions in the WHO African region on how to create within- and across-country collaborations could potentially improve both health and oral health in the region.

The objective of this study is to characterize how financial hardship related to oral health care (OHC) out-of-pocket (OOP) spending has been conceptualized, defined, and measured in the literature and to identify evidence gaps in this area. This scoping review follows Arksey and O'Malley's framework and synthesizes financial hardship from OHC concepts, methodologies, and evidence gaps. We searched Ovid-Medline, Ovid-Embase, PubMed, Web of Science, Scopus, EconLit, Business Source Premier, and the Cochrane Library. Gray literature was sourced from institutional websites (World Health Organization, United Nations, World Bank Group, Organisation for Economic Co-operation and Development, and governmental health agencies) as well as ProQuest Dissertations and Thesis Global. We used defined inclusion and exclusion criteria to select studies published between 2000 and 2023. Of the 1,876 records, 65 met our criteria. The studies conceptualized financial hardship as catastrophic spending, impoverishment, negative coping strategies, bankruptcy, financial burden, food insecurity, and personal financial hardship experience. We found heterogeneity in defining OHC OOP payments and services. Also, financial hardship was frequently measured as catastrophic health expenditure using cross-sectional designs and national household spending surveys from high-income and to a lesser extent lower-middle-income countries. We identify and discuss challenges in terms of conceptualizing financial hardship, study designs, and measurement instruments in the OHC context. Some of the common evidence gaps identified include studying the causal relationship in financial hardship from OHC, assessing the financial hardship and unmet dental needs due to cost relationship, and distinguishing the effect between pain/discomfort and esthetic/cosmetic dental treatments on financial hardship. Financial hardship in OHC needs further exploration and the use of consistent definitions as well must distinguish between treatments alleviating pain/discomfort from esthetic/cosmetic treatments. Our study is relevant for policy makers and researchers aiming to monitor financial protection of OOP payments on OHC in the wake of universal health coverage for oral health.

Oral leukoplakia (OL) has an inherent disposition to develop oral cancer. OL with epithelial dysplasia (OED) is significantly likely to undergo malignant transformation; however, routine OED assessment is invasive and challenging. This study investigated whether a deep learning (DL) model can predict dysplasia probability among patients with leukoplakia using oral photographs. In addition, we assessed the performance of the DL model in comparison with clinicians' ratings and in providing decision support on dysplasia assessment. Retrospective images of leukoplakia taken before biopsy/histopathology were obtained to construct the DL model ( = 2,073). OED status following histopathology was used as the gold standard for all images. We first developed, fine-tuned, and internally validated a DL architecture with an EfficientNet-B2 backbone that outputs the predicted probability of OED, OED status, and regions-of-interest heat maps. Then, we tested the performance of the DL model on a temporal cohort before geographical validation. We also assessed the model's performance at external validation with opinions provided by human raters on OED status. Performance evaluation included discrimination, calibration, and potential net benefit. The DL model achieved good Brier scores, areas under the curve, and balanced accuracies of 0.124 (0.079-0.169), 0.882 (0.838-0.926), and 81.8% (76.5-87.1) at testing and 0.146 (0.112-0.18), 0.828 (0.792-0.864), and 76.4% (72.3-80.5) at external validation, respectively. In addition, the model had a higher potential net benefit in selecting patients with OL for biopsy/histopathology during OED assessment than when biopsies were performed for all patients. External validation also showed that the DL model had better accuracy than 92.3% (24/26) of human raters in classifying the OED status of leukoplakia from oral images (balanced accuracy: 54.8%-79.7%). Overall, the photograph-based intelligent model can predict OED probability and status in leukoplakia with good calibration and discrimination, which shows potential for decision support to select patients for biopsy/histopathology, obviate unnecessary biopsy, and assist in patient self-monitoring.

Dental caries, associated with plaque biofilm, is highly prevalent and significantly burdens public health. is the main cariogenic bacteria that adheres to the tooth surface and forms an abundant extracellular polysaccharide matrix (EPS) as a cariogenic biofilm scaffold. RNase III-encoding gene () and a putative chromosome segregation protein-encoding gene () are potentially associated with EPS production. In addition, complex interactions between and other oral microorganisms synergistically or antagonistically affect the cariogenicity. Commensal streptococci suppress the growth of cariogenic pathogens, whereas mediates the formation of cariogenic biofilm through aggregation and dual-species biofilm formation with . However, label-free detection of cariogenic microbial interactions with the EPS matrix is still challenging during laboratory investigations. Herein, we hypothesized that the operon affects EPS production and aimed to observe streptococci, , and using terahertz scanning near-field optical microscopy (THz s-SNOM). The light in the 0.1- to 0.3-THz frequency range interacted with the sample through a nano-probe tip by a point-by-point scanning process. Additional noise reduction of the original image was achieved by a dual kernel Gaussian filter. The monospecies of streptococci, mutants, and the dual-species of were scanned by THz s-SNOM. This technique provided terahertz near-field scanning images of mutants, streptococci, and dual-species of . Additional analysis of the original images potentially revealed the structures of the strains, such as cell diameters and cell wall thickness. In conclusion, the results suggested that the operon regulates EPS production. Furthermore, this novel label-free detection of a THz near-field scanning technique had the potential to observe the morphologies of bacterial cells and EPS matrix.

Tissue-specific immune responses are critical determinants of health-maintaining homeostasis and disease-related dysbiosis. In the context of COVID-19, oral immune responses reflect local host-pathogen dynamics near the site of infection and serve as important "windows to the body," reflecting systemic responses to the invading SARS-CoV-2 virus. This study leveraged multiplex technology to characterize the salivary SARS-CoV-2-specific immunological landscape (37 cytokines/chemokines and 11 antibodies) during early infection. Cytokine/immune profiling was performed on unstimulated cleared whole saliva collected from 227 adult SARS-CoV-2+ participants and 37 controls. Statistical analysis and modeling revealed significant differential abundance of 25 cytokines (16 downregulated, 9 upregulated). Pathway analysis demonstrated early SARS-CoV-2 infection is associated with local suppression of oral type I/III interferon and blunted natural killer-/T-cell responses, reflecting a potential novel immune-evasion strategy enabling infection. This virus-associated immune suppression occurred concomitantly with significant upregulation of proinflammatory pathways including marked increases in the acute phase proteins pentraxin-3 and chitinase-3-like-1. Irrespective of SARS-CoV-2 infection, prior vaccination was associated with increased total α-SARS-CoV-2-spike (trimer), -S1 protein, -RBD, and -nucleocapsid salivary antibodies, highlighting the importance of COVID-19 vaccination in eliciting mucosal responses. Altogether, our findings highlight saliva as a stable and accessible biofluid for monitoring host responses to SARS-CoV-2 over time and suggest that oral-mucosal immune dysregulation is a hallmark of early SARS-CoV-2 infection, with possible implications for viral evasion mechanisms.

Periodontitis (PD) potentiates systemic inflammatory diseases and fuels a feed-forward loop of pathogenic inflammation in obesity and type 2 diabetes (T2D). Published work in this area often conflates obesity with obesity-associated T2D; thus, it remains unclear whether PD similarly affects the inflammatory profiles of these 2 distinct systemic diseases. We collected peripheral blood mononuclear cells (PBMCs) from cross-sectionally recruited subjects to estimate the ability of PD to affect cytokine production in human obesity and/or T2D. We analyzed 2 major sources of systemic inflammation: T cells and myeloid cells. Bioplex quantitated cytokines secreted by PBMCs stimulated with T cell- or myeloid-targeting activators, and we combinatorially analyzed outcomes using partial least squares discriminant analysis. Our data show that PD significantly shifts peripheral T cell- and myeloid-generated inflammation in obesity. PD also changed myeloid- but not T cell-generated inflammation in T2D. T2D changed inflammation in samples from subjects with PD, and PD changed inflammation in samples from subjects with T2D, consistent with the bidirectional relationship of inflammation between these 2 conditions. PBMCs from T2D subjects with stage IV PD produced lower amounts of T cell and myeloid cytokines compared with PBMCs from T2D subjects with stage II to III PD. We conclude that PD and T2D affect systemic inflammation through overlapping but nonidentical mechanisms in obesity, indicating that characterizing both oral and metabolic status (beyond obesity) is critical for identifying mechanisms linking PD to systemic diseases such as obesity and T2D. The finding that stage IV PD cells generate fewer cytokines in T2D provides an explanation for the paradoxical findings that the immune system can appear activated or suppressed in PD, given that many studies do not report PD stage. Finally, our data indicate that a focus on multiple cellular sources of cytokines will be imperative to clinically address the systemic effects of PD in people with obesity.

Childhood is considered crucial in the establishment of future oral microbiota. However, the precise period of oral microbiota development remains unclear. This study aimed to identify the progression of oral microbiota formation in children. We longitudinally investigated the salivary microbiota of 54 children across 13 time points from 1 wk to 60 mo (5 y) old and their parents at 2 time points as a representative sample of the adult microbiota. Using next-generation sequencing, we obtained 10,000 gene sequences of the 16s rRNA V1-V2 region for each sample. The detection rate in children of 110 operational taxonomic units commonly detected in more than 85% of mothers and fathers, defined as the main constituent bacteria, was 25% at 1 wk old, increased to 80% between 6 and 18 mo old, and reached approximately 90% by 36 mo old. Early main constituent bacteria detected at 1 wk old were limited to , , and . At 6 to 18 mo old, the detection rates of various main constituent bacteria, including , , and , increased. UniFrac distance analysis showed that the oral microbiota of children approached that of adults at 6 to 18 mo old. In the weighted UniFrac distance index, unlike the unweighted index, there were no significant changes in children between 36 and 60 mo old from adults, and microbiota formation at 60 mo old was sufficiently advanced to be included within the range of adult individual differences. Our findings suggest that the initial 36 mo, particularly the period from 6 to 18 mo old, consists of a time window for oral microbiota maturation. In addition, the development of microbiota during this period may be critical for future oral disease prevention.

Oral leukoplakia (OLK) is the most representative oral potentially malignant disorder, with a high risk of malignant transformation and unclear mechanisms of occurrence. Recently, photodynamic therapy (PDT) has exhibited great potential in the treatment of OLK. However, the efficacy of PDT is difficult to predict and varies from person to person. Ferroptosis-related pathways are upregulated in many cancers, and ferroptosis induction is considered to be a potential synergistic strategy for various antitumor therapies, but its role in OLK treatment remains unclear. This study aimed to determine whether ferroptosis induction can enhance the efficacy of PDT in OLK treatment. Our study revealed that solute carrier family 7 member 11 (SLC7A11), a component of a crucial amino acid transporter and a key negative regulator of ferroptosis, was found to be highly expressed in OLK patients with no response to PDT. 5-Aminolevulinic acid (ALA)-PDT is known to cause apoptosis and necrosis, but ferroptosis also occurred under ALA-PDT in OLK cells in our study. Using erastin to induce ferroptosis enhanced the efficacy of ALA-PDT on OLK cells by disrupting the antioxidant system and further elevating intracellular reactive oxygen species levels, leading to increased apoptosis. Furthermore, this combined modality also enhanced the efficacy of ALA-PDT on 4-nitroquinoline-1-oxide (4NQO)-induced OLK lesions in mice. In summary, ferroptosis induction may serve as a potential strategy to enhance the efficacy of ALA-PDT for OLK treatment.

Nonresolving inflammation causes irreversible damage to periodontal ligament stem cells (PDLSCs) and impedes alveolar bone restoration. The impaired tissue regeneration ability of stem cells is associated with abnormal mitochondrial metabolism. However, the impact of specific metabolic alterations on the differentiation process of PDLSCs remains to be understood. In this study, we found that inflammation altered the metabolic flux of the tricarboxylic acid cycle and induced the accumulation of fumarate through metabolic testing and metabolic flux analysis. Transcriptome sequencing revealed the potential of fumarate in modulating epigenetics. Specifically, histone methylation typically suppresses the expression of genes related to osteogenesis. Fumarate was found to impede the osteogenic differentiation of PDLSCs that exhibited high levels of H3K9me3. Various techniques, including assay for transposase-accessible chromatin with high-throughput sequencing, chromatin immunoprecipitation sequencing, and RNA sequencing, were used to identify the target genes regulated by H3K9me3. Mechanistically, accumulated fumarate inhibited lysine-specific demethylase 4B (KDM4B) activity and increased H3K9 methylation, thus silencing asporin gene transcription. Preventing fumarate from binding to the histone demethylase KDM4B with α-ketoglutarate effectively restored the impaired osteogenic capacity of PDLSCs and improved alveolar bone recovery. Collectively, our research has revealed the significant impact of accumulated fumarate on the regulation of osteogenesis in stem cells, suggesting that inhibiting fumarate production could be a viable therapeutic approach for treating periodontal diseases.

The fibrocartilage stem cells (FCSCs) on the surface of the condyle play an essential role in cartilage homeostasis and regeneration. However, few well-defined stem cell markers have been identified for the analysis of FCSCs' cell fate and regulation mechanism. In this study, we first mapped the transcriptional landscape of the condylar cartilage and identified a Gli1+ subset. Label-retaining cells and our lineage-tracing study showed that Gli1 labeled a group of FCSCs. Conditional knockout β-catenin inhibited Gli1+ cells differentiating into hypertrophic chondrocytes. In discectomy-induced temporomandibular joint osteoarthritis (TMJOA), Gli1+ cells were further activated, and their differentiation into hypertrophic chondrocytes was accelerated, which induced stem cell pool depletion. The deletion of β-catenin in Gli1+ cells preserved the FCSC pool and alleviated TMJOA cartilage degeneration. Collectively, we uncovered that a Gli1+ FCSC subpopulation and Wnt/β-catenin signaling orchestrate the Gli1+ cell fate in condyle postnatal development and TMJOA.

Dental caries among long-term care (LTC) residents is a persistent and complex problem driven by social and structural factors. Systems thinking may be useful in considering novel approaches to reducing disease. This study aimed to develop a system dynamics model to simulate the progression of dentate older adults in LTC through caries severity states and estimate the effects of 3 intervention scenarios on the progression of caries: preventive topical fluoride (TF), arrest of caries with silver diamine fluoride (SDF), and a combination of TF and SDF. Dentate older adults in LTC were categorized into 4 caries severity states by their number of untreated carious lesions. The model assumed that changes in severity states were consistent with incidence rates reported in the literature and available billing data for dental care and that individuals move in and out of the system by entering and exiting the facility or experiencing edentulism. For all scenarios, the proportion of dentate older adults in LTC with 1 or more untreated lesions stays stable, the distribution of disease shifts from a high severity state, and the system approaches equilibrium after 4 y. The TF intervention predicts minimal impacts on decreasing the proportion of dentate older adults with 1 or more untreated lesions (2.5% decrease), while the SDF intervention and the combination interventions were most disruptive. There was a 29.6% and 33.6% decrease, respectively. Given the specific population dynamics in LTC, these findings suggest that long-term (greater than 4 y) interventions should be designed to address both the management of existing lesions and their incidence. This system dynamics model allows researchers to render institution-specific data points from LTCs to estimate the effects of proposed interventions at the respective site.

Spatial transcriptomics (ST) is a cutting-edge methodology that enables the simultaneous profiling of global gene expression and spatial information within histological tissue sections. Traditional transcriptomic methods lack the spatial resolution required to sufficiently examine the complex interrelationships between cellular regions in diseased and healthy tissue states. We review the general workflows for ST, from specimen processing to ST data analysis and interpretations of the ST dataset using visualizations and cell deconvolution approaches. We show how recent studies used ST to explore the development or pathogenesis of specific craniofacial regions, including the cranium, palate, salivary glands, tongue, floor of mouth, oropharynx, and periodontium. Analyses of cranial suture patency and palatal fusion during development using ST identified spatial patterns of bone morphogenetic protein in sutures and osteogenic differentiation pathways in the palate, in addition to the discovery of several genes expressed at critical locations during craniofacial development. ST of salivary glands from patients with Sjögren's disease revealed co-localization of autoimmune antigens with ductal cells and a subpopulation of acinar cells that was specifically depleted by the dysregulated autoimmune response. ST of head and neck lesions, such as premalignant leukoplakia progressing to established oral squamous cell carcinomas, oral cancers with perineural invasions, and oropharyngeal lesions associated with HPV infection spatially profiled the complex tumor microenvironment, showing functionally important gene signatures of tumor cell differentiation, invasion, and nontumor cell dysregulation within patient biopsies. ST also enabled the localization of periodontal disease-associated gene expression signatures within gingival tissues, including genes involved in inflammation, and the discovery of a fibroblast subtype mediating the transition between innate and adaptive immune responses in periodontitis. The increased use of ST, especially in conjunction with single-cell analyses, promises to improve our understandings of craniofacial development and pathogenesis at unprecedented tissue-level resolution in both space and time.

Intraoral scanners (IOSs) have emerged as a cornerstone technology in digital dentistry. This article examines the recent advancements and multifaceted applications of IOSs, highlighting their benefits in patient care and addressing their current limitations. The IOS market has seen a competitive surge. Modern IOSs, featuring continuous image capture and advanced software for seamless image stitching, have made the scanning process more efficient. Patient comfort with IOS procedures is favorable, mitigating the discomfort associated with conventional impression taking. There has been a shift toward open data interfaces, notably enhancing interoperability. However, the integration of IOSs into large dental institutions is slow, facing challenges such as compatibility with existing health record systems and extensive data storage management. IOSs now extend beyond their use in computer-aided design and manufacturing, with software solutions transforming them into platforms for diagnostics, patient communication, and treatment planning. Several IOSs are equipped with tools for caries detection, employing fluorescence technologies or near-infrared imaging to identify carious lesions. IOSs facilitate quantitative monitoring of tooth wear and soft-tissue dimensions. For precise tooth segmentation in intraoral scans, essential for orthodontic applications, developers are leveraging innovative deep neural network-based approaches. The clinical performance of restorations fabricated based on intraoral scans has proven to be comparable to those obtained using conventional impressions, substantiating the reliability of IOSs in restorative dentistry. In oral and maxillofacial surgery, IOSs enhance airway safety during impression taking and aid in treating conditions such as cleft lip and palate, among other congenital craniofacial disorders, across diverse age groups. While IOSs have improved various aspects of dental care, ongoing enhancements in usability, diagnostic accuracy, and image segmentation are crucial to exploit the potential of this technology in optimizing patient care.

The development of artificial intelligence (AI) in dentistry requires large and well-annotated datasets. However, the availability of public dental imaging datasets remains unclear. This study aimed to provide a comprehensive overview of all publicly available dental imaging datasets to address this gap and support AI development. This observational study searched all publicly available dataset resources (academic databases, preprints, and AI challenges), focusing on datasets/articles from 2020 to 2023, with PubMed searches extending back to 2011. We comprehensively searched for dental AI datasets containing images (intraoral photos, scans, radiographs, etc.) using relevant keywords. We included datasets of >50 images obtained from publicly available sources. We extracted dataset characteristics, patient demographics, country of origin, dataset size, ethical clearance, image details, FAIRness metrics, and metadata completeness. We screened 131,028 records and extracted 16 unique dental imaging datasets. The datasets were obtained from Kaggle (18.8%), GitHub, Google, Mendeley, PubMed, Zenodo (each 12.5%), Grand-Challenge, OSF, and arXiv (each 6.25%). The primary focus was tooth segmentation (62.5%) and labeling (56.2%). Panoramic radiography was the most common imaging modality (58.8%). Of the 13 countries, China contributed the most images (2,413). Of the datasets, 75% contained annotations, whereas the methods used to establish labels were often unclear and inconsistent. Only 31.2% of the datasets reported ethical approval, and 56.25% did not specify a license. Most data were obtained from dental clinics (50%). Intraoral radiographs had the highest findability score in the FAIR assessment, whereas cone-beam computed tomography datasets scored the lowest in all categories. These findings revealed a scarcity of publicly available imaging dental data and inconsistent metadata reporting. To promote the development of robust, equitable, and generalizable AI tools for dental diagnostics, treatment, and research, efforts are needed to address data scarcity, increase diversity, mandate metadata completeness, and ensure FAIRness in AI dental imaging research.

Small nucleolar RNAs (snoRNAs), a distinct class of noncoding RNAs, encompass highly diverse structures and have a range of 60 to 300 nucleotides in length. About 90% of human snoRNAs are intronic and embedded within introns of their host gene transcripts. Most snoRNAs enriched in specific tissue correlate in abundance with their parental host genes. Advancements in high-throughput sequencing have facilitated the discovery of dysregulated snoRNA expression in numerous human malignancies including head and neck squamous cell carcinoma (HNSCC). Hundreds of differentially expressed snoRNAs have been identified in HNSCC tissues. Among 1,524 snoRNA genes in a 567 HNSCC cohort, 113 snoRNAs were found to be survival related. As for snoRNA's roles in HNSCC, based on the available evidence, dysregulated snoRNAs are closely associated with the carcinogenesis and development of HNSCC. Upregulated snoRNAs have been shown to augment the expression of other oncogenes or activate the Wnt/β-catenin signaling pathway, thereby promoting tumor cell viability, glycolysis, migration, and the epithelial-mesenchymal transition while inhibiting apoptosis in vitro. In vivo animal studies have further elucidated the functional roles of snoRNAs. Knockdown of host genes of these snoRNAs suppressed the Wnt/β-catenin signaling pathway and restrained tumor proliferation and aggressiveness in mice. The putative mechanisms underlying these observations are associated with the biological functions of snoRNAs, primarily involving microRNA-like functions through the generation of microRNA-like fragments and regulation of alternative splicing to yield diverse transcripts. While most of the snoRNAs are upregulated in HNSCC, 4 downregulated snoRNAs have been identified and annotated. SNORA36B (implicated in the regulation of DNA templates) and U3 (chr17, influencing cell proliferation) may serve as protective factors associated with prolonged overall survival. This review describes the viable structures of snoRNAs, endeavors to refine snoRNA sequencing technology, and summarizes snoRNAs' expression profile as well as their role in HNSCC progression for potential diagnostic and therapeutic strategies for HNSCC management.

Advances in imaging technologies combined with artificial intelligence (AI) are transforming dental, oral, and craniofacial research. This editorial highlights breakthroughs ranging from gene expression mapping to visualizing the availability of global AI data, providing new insights into biological complexity and clinical applications.