Journal of dental research最新文献

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. In vivo, 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-Hadha 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 Kdm6b 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 Hadha 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 Hadha and the addition of lactate sodium could rescue the inhibition of mineralization by knockdown of Kdm6b. In summary, during cementoblast mineralization, KDM6B regulates HADHA by mediating histone demethylation and lactylation, thereby upregulating FAO and thus promoting mineralization.

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.

Dental caries, associated with plaque biofilm, is highly prevalent and significantly burdens public health. Streptococcus mutans is the main cariogenic bacteria that adheres to the tooth surface and forms an abundant extracellular polysaccharide matrix (EPS) as a cariogenic biofilm scaffold. S. mutans RNase III-encoding gene (rnc) and a putative chromosome segregation protein-encoding gene (smc) are potentially associated with EPS production. In addition, complex interactions between S. mutans and other oral microorganisms synergistically or antagonistically affect the cariogenicity. Commensal streptococci suppress the growth of cariogenic pathogens, whereas Candida albicans mediates the formation of cariogenic biofilm through aggregation and dual-species biofilm formation with S. mutans. However, label-free detection of cariogenic microbial interactions with the EPS matrix is still challenging during laboratory investigations. Herein, we hypothesized that the S. mutans rnc-smc operon affects EPS production and aimed to observe streptococci, S. mutans, and S. mutans-C. albicans 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, S. mutans smc/rnc mutants, and the dual-species of S. mutans-C. albicans were scanned by THz s-SNOM. This technique provided terahertz near-field scanning images of S. mutans smc/rnc mutants, streptococci, and dual-species of S. mutans-C. albicans. 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 S. mutans rnc-smc 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.

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.

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.

Due to its capacity to drive osteoclast differentiation, the receptor activator of nuclear factor kappa-β ligand (RANKL) is believed to exert a pathological influence in periodontitis. However, RANKL was initially identified as an activator of dendritic cells (DCs), expressed by T cells, and exhibits diverse effects on the immune system. Hence, it is probable that RANKL, acting as a bridge between the bone and immune systems, plays a more intricate role in periodontitis. Using ligature-induced periodontitis (LIP), rapid alveolar bone loss was detected that was later halted even though the ligature was still present. This late phase of LIP was also linked with immunosuppressive conditions in the gingiva. Further investigation revealed that the ligature prompted an immediate migration of RANK-expressing Langerhans cells (LCs) and EpCAM⁺ DCs, the antigen-presenting cells (APCs) of the gingival epithelium, to the lymph nodes, followed by an expansion of T regulatory (Treg) cells in the gingiva. Subsequently, the ligatured gingiva was repopulated by monocyte-derived RANK-expressing EpCAM⁺ DCs, while gingival epithelial cells upregulated RANKL expression. Blocking RANKL signaling with monoclonal antibodies significantly reduced the frequencies of Treg cells in the gingiva and prevented gingival immunosuppression. In addition, RANKL signaling facilitated the differentiation of LCs from bone marrow precursors. To further investigate the role of RANKL, we used K14-RANKL mice, in which RANKL is overexpressed by gingival epithelial cells. The elevated RANKL expression shifted the steady-state frequencies of LCs and EpCAM⁺ DCs within the epithelium, favoring LCs over EpCAM⁺ DCs. Following ligature placement, heightened levels of Treg cells were observed in the gingiva of K14-RANKL mice, and alveolar bone loss was significantly reduced. These findings suggest that RANKL-RANK interactions between gingival epithelial cells and APCs are crucial for suppressing gingival inflammation, highlighting a protective immunological role for RANKL in periodontitis that was overlooked due to its osteoclastogenic activity.

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.

Cisplatin resistance is one of the major causes of treatment failure in head and neck squamous cell carcinoma (HNSCC). There is an urgent need to uncover the underlying mechanism for developing effective treatment strategies. A quantitative proteomics assay was used to identify differential proteins in cisplatin-resistant cells. Mitochondrial topoisomerase I (TOP1MT) localization was determined using laser confocal microscopy and nucleocytoplasmic separation assay. Chromatin immunoprecipitation sequencing, dual-luciferase reporter assay, and RNA immunoprecipitation were used to identify the interaction between pseudogenes, miRNAs, and real genes. In vivo experiments verified the interaction between TOP1MT and pseudogenes on cisplatin resistance. TOP1MT was identified as a driving factor of cisplatin resistance in vitro, in vivo, and in HNSCC patients. Moreover, TOP1MT exceptionally translocated to the nucleus in cisplatin-resistant HNSCC cells in a signal peptide-dependent manner. Nuclear TOP1MT (nTOP1MT) transcriptionally regulated the mitochondrial functional pseudogene MTATP6P1, which bound to miR-137 and miR-491-5p as a competing endogenous RNA (ceRNA) and promoted the expression of MTATP6. An increase in MTATP6 enhanced mitochondrial oxidative phosphorylation (OXPHOS), which conferred cisplatin resistance in HNSCC. Our findings revealed that nTOP1MT transcriptionally activated MTAPT6P1 and increased MTATP6 expression via ceRNA, which facilitated OXPHOS and cisplatin resistance. These results provide novel insight for overcoming cisplatin resistance in HNSCC.

The introduction of immune checkpoint inhibitors (ICIs) to oncological care has transformed the management of various malignancies, including head and neck squamous cell carcinoma (HNSCC), offering improved outcomes. The first-line treatment of recurrent and malignant HNSCC for many years was combined platinum, 5-fluorouracil, and cetuximab. Recently, the ICI pembrolizumab was approved as a first-line treatment, with or without chemotherapy, based on tumor and immune cell percentage of programmed-death ligand 1 (PD-L1). Multiple head and neck (HN) cancer trials have subsequently explored immunotherapies in combination with surgery, chemotherapy, and/or radiation. Immunotherapy regimens may be personalized by tumor biomarker, including PD-L1 content, tumor mutational burden, and microsatellite instability. However, further clinical trials are needed to refine biomarker-driven protocols and standardize pathological methods to guide combined regimen timing, sequencing, and deescalation. Gaps remain for protocols using immunotherapy to reverse oral premalignant lesions, particularly high-risk leukoplakias. A phase II nonrandomized controlled trial, using the ICI nivolumab, showed a 2-y cancer-free survival of 73%, although larger trials are needed. Guidelines are also needed to standardize the role of dental evaluation and care before, during, and after immunotherapy, specifically in regard to oral immune-related adverse events and their impact on cancer recurrence. Standardized diagnostic and oral care coordination strategies to close these gaps are needed to ensure continued success of HN cancer immunotherapy.