Journal of dental research最新文献

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.

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.

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.

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.

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.

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.

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 (n = 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.

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.

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.