Journal of Dental Research

Optical coherence tomography (OCT) is a noninvasive imaging technology that provides high-resolution, cross-sectional views of biological tissues using near-infrared light. Since its introduction in the early 1990s, OCT has expanded from ophthalmology into dentistry. With micrometer-level resolution and no ionizing radiation, dental OCT offers significant advantages over conventional x-ray–based imaging modalities such as intraoral radiography and cone beam computed tomography, particularly for detecting early or subtle changes in hard and soft tissues. In this article, we review the evolution of OCT in periodontology and share 3 novel applications: assessment of postorthodontic fenestrations, preoperative planning for periapical lesions, and gingival revascularization after gingivectomy. These advances go beyond the reach of standard imaging tools and support the adoption of OCT for improving diagnosis, guiding treatment, and monitoring treatment response. As imaging protocols become increasingly sophisticated and targeted, OCT promises great advances in periodontal diagnostics and patient care.

Periodontitis is a complex, multifactorial inflammatory condition characterized by progressive destruction of the periodontal supporting structures. It profoundly affects oral health, esthetics, and masticatory function and is increasingly recognized as a contributing risk factor for systemic disorders. Deep periodontal pockets establish a severely hypoxic microenvironment induced by periodontal pathogens, increased oxygen consumption of infiltrated inflammatory cells, and accompanying periodontal vascular changes. The cellular response to hypoxia is centrally regulated by hypoxia-inducible factor 1 alpha (HIF-1α), which is also recognized as a critical factor driving the progression of periodontal tissue destruction. In vivo and in vitro studies have shown the upregulation of HIF-1α in both animal periodontitis models and clinical samples from individuals with periodontitis, where its expression correlates positively with deteriorating clinical periodontal parameters. Experimental and clinical studies using mouse conditional knockout models, selective small-molecule inhibitors, and human-derived materials have demonstrated clear causal roles for hypoxia-driven HIF-1α signaling in the progression of periodontitis. This conclusion is supported by mechanistic evidence demonstrating that HIF-1α induces aberrant neovascularization, enhances osteoclastogenesis leading to subsequent alveolar bone resorption, and promotes M1 macrophage polarization along with proinflammatory cytokine production. Hypoxia, via HIF-1α, synergistically acts with periodontal pathogens to amplify periodontal inflammation and oxidative stress, driving persistent extracellular matrix destruction in periodontal tissues. This critical review summarizes recent findings, using in vitro and in vivo approaches using animal and human-derived materials, on the role of hypoxia, primarily through the HIF-1α pathway, in periodontitis pathogenesis as well as the potential of hypoxia-based strategies and targeted modulation of HIF-1α signaling through HIF-1α stabilizers for managing periodontitis and promoting periodontal regeneration, while highlighting the existing gaps in our understanding and the limitations of current research, which can serve as a foundation for guiding future studies in this area.

Temporomandibular disorders (TMDs) encompass a heterogeneous group of musculoskeletal conditions involving the temporomandibular joint (TMJ), masticatory muscles, and associated structures. Diagnosis remains challenging due to overlapping symptoms, multifactorial etiology, and variability across clinical settings. To address these limitations, we developed the Gated Attention Tabular Transformer (GATT), a novel deep-learning model that uses masked self-supervised learning and gated attention mechanisms, to classify TMD subgroups based on the diagnostic criteria for TMD (DC/TMD). A total of 4,644 structured clinical records from a university-based registry were analyzed, comprising 3,524 female and 1,120 male patients (mean age 36.9 ± 14.7 y), across 12 core TMD subgroups. GATT achieved robust diagnostic performance with area under the receiver-operating characteristic curve values ranging from 0.815 to 1.000, sensitivity from 0.652 to 1.000, and specificity from 0.773 to 1.000. The model significantly outperformed conventional machine-learning methods including logistic regression, random forest, support vector machine, and XGBoost as well as advanced tabular deep-learning models such as TabNet, TabTransformer, AutoGluon Tabular Predictor, and FT-Transformer. Shapley additive explanations (SHAP) analysis revealed “pain-free opening” (SHAP = 6.78, P < 0.001) and “current TMJ noise” (SHAP = 2.87, P = 0.003) as key features of mechanical TMJ disorders. Co-occurrence network analysis uncovered side-specific clustering and potential time-lagged progression between bilateral TMJs. These findings demonstrate the feasibility of using deep learning to classify heterogeneous TMD subgroups using only structured clinical data, without the need for imaging. The GATT model offers an accurate, explainable, and scalable tool to support clinician-assisted diagnosis and reduce variability in TMD management in real-world practice. These results support the integration of AI-driven tools such as GATT into clinical workflows for standardized, efficient, and patient-specific TMD diagnosis.

Oral cancer often develops from oral potentially malignant disorders. Oral leukoplakia (OL) is the most common oral potentially malignant disorder. However, not all patients with OL develop oral cancer in their lifetimes, and cancer risk assessment is challenging. This study developed a time-to-event artificial intelligence–based model (termed OralCancerPredict) that integrated patient characteristics, histologic features, and immunohistochemical indicators for precise prediction of cancer progression in OL. We performed a retrospective analysis of patient data (n = 456) and tissue samples (n = 1,312) obtained before cancer progression or at last follow-up for patients with OL. KRT13 and p53 immunohistochemistry and quantitative analyses were performed for tissue samples, while independent prognostic features, including demographic characteristics, clinical information, and histopathologic findings, were obtained for patients in the cohort. KRT13 and p53 expression was combined with histologic and patient data for model training, testing, and external validation. Performance evaluation for OralCancerPredict included model discriminative ability, calibration, explainability, and net benefit analysis via decision curves. External validation was also performed to ensure model generalizability based on patient data (n = 119) and tissue samples (n = 322) unused for training and testing. Our findings showed that OralCancerPredict trained on multidimensional data had good concordance indices (0.855 to 0.867), areas under the curve (0.877 to 0.882), and integrated Brier scores (0.046 to 0.069) at testing and external validation. Explainability analysis confirmed the importance of KRT13 and p53 deregulated expression and World Health Organization dysplasia grading to OralCancerPredict in predicting the risk of cancerization and cancer progression–free survival. Moreover, the model had a better net benefit than the World Health Organization and binary dysplasia grading systems alone, which represent the current risk stratification methods in OL management. Overall, OralCancerPredict can predict the risk of cancer development and cancer progression–free survival for patients with OL, with good discrimination, calibration, and net benefit. The explainable artificial intelligence model has the potential to streamline intervention and close monitoring in the clinical management of OL.

Accurate clinical records are fundamental to dental practice. Automatic speech recognition (ASR) has the capacity to convert spoken clinical language into written text within the electronic health record; however, the accuracy of ASR in natural language processing for clinical dentistry remains uncertain. The aim of this study was to investigate the transcriptional accuracy of ASR systems using orthodontic clinical records as the experimental model. Specifically, we used 4 commercial ASR systems (Heidi Health, DigitalTCO, Dragon Medical One, Dragon Professional Anywhere), 5 application programming interfaces (Amazon, Google, Speechmatics, Whisper, GPT4oTranscribe), and a 2-stage pipeline coupling GPT4oTranscribe with the GPT4o large language model (LLM) for generative error correction (GPT4oTranscribeCorrected). Orthodontic diagnostic and treatment planning summaries (n = 200; 10 subject domains; 43,408 words; 6 h of audio) were narrated and recorded for analysis. The primary outcome was domain word error rate (DWER), which investigates clinical terminological transcription errors against the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) database. Secondary outcomes included nondomain WER (N-DWER), lexical accuracy (Recall-Oriented Understudy for Gisting Evaluation [ROUGE] score), semantic similarity (Bidirectional Encoder Representations from Transformers [BERT] and Bidirectional and Auto-Regressive Transformer [BART] scores), hallucinations (transcribed text not in the spoken input), and qualitative error analysis. GPT4oTranscribeCorrected was transcriptionally most accurate (DWER = 3.5%; WER = 3.7%), with DWER decreasing by 54.9% versus GPT4oTranscribe. Heidi Health was the highest-performing commercial system (DWER = 6.2%; WER = 5.4%), with Dragon Professional Anywhere being the worst (WER = 33.9%). All systems were less accurate with technical vocabulary (DWER > N-DWER; P < 0.001), except GPT4oTranscribeCorrected. Significant differences were seen across systems for ROUGE, BERT, and BART scores (P < 0.001). Based on post hoc pairwise comparisons, GPT4oTranscribeCorrected performed best and Dragon Professional Anywhere was consistently worst for lexical and semantic errors. Hallucinations were absent except for Whisper (n = 57) and DigitalTCO (n = 1). Across systems, background noise increased DWER and WER (P < 0.001). Importantly, clinically significant errors were seen with all systems, ranging from 2% to 66% (GPT4oTranscribeCorrected clean; Dragon Medical One background noise, respectively). Variation in narrator accent had no effect in clean conditions (P = 0.65) and a small effect with background noise (P = 0.001). ASR systems deliver single-digit transcription error rates, particularly when coupled with LLM-based correction, but clinically significant errors persist. The verification of clinical records is essential when using current ASR systems.

Dental caries is closely associated with microbiome dysbiosis. Incorporating antimicrobial agents can enhance the efficacy of fluoride toothpaste. Our previous studies showed that caffeic acid phenethyl ester (CAPE), derived from propolis, effectively inhibited cariogenic bacteria. To formulate a novel CAPE-containing fluoridated toothpaste and establish a multistage evaluation system assessing its caries-controlling efficacy. The CAPE toothpaste’s physicochemical properties were characterized. Its in vitro antimicrobial activity against Streptococcus mutans was examined using quantitative suspension and checkerboard microdilution assays. In vivo anticaries efficacy and biosafety were evaluated in a rat caries model (n = 9/group) comparing 5 groups: untreated control (group NC), base toothpaste without CAPE (group B, 600 ppm F⁻), CAPE–fluoride toothpaste (group C, 0.16 mg/mL CAPE + 600 ppm F⁻), fluoride control (group F, DARLIE^®, 600 ppm F⁻), and propolis nonfluoride control (group P, Red Seal^®). Caries severity was scored using the Keyes method. Finally, in situ enamel repair (n = 24 enamel blocks) and plaque microbiome modulation (n = 6 samples) were assessed in a 7-d clinical study (ChiCTR2400089643) with 4 groups (groups NC, C, F, and P). The novel formulation showed stable physicochemical properties. Group C reduced S. mutans by 1.5-log10 within 3 min (P = 0.0062 vs NC), and CAPE plus fluoride exhibited additive antibacterial properties (fractional inhibitory concentration index = 0.75). In rats, group C showed the lowest smooth-surface score (21.0 ± 1.7, P < 0.005 vs. all) and reduced sulcal lesion severity (slight dentinal lesions: P = 0.0181 vs. NC and P = 0.0318 vs. F). In situ, group C achieved 27.58% surface microhardness recovery (P < 0.01 vs. all), with significant reductions in mineral loss and lesion depth (P < 0.001 vs. all). Microbiome analysis revealed a preserved microbial diversity, increased Streptococcus oralis, and reduced cariogenic populations. The novel CAPE-containing fluoridated toothpaste effectively inhibited the onset and development of caries. Wider-ranging and longer-term clinical investigations are still needed.

Enhancing the durability of dentin bonding remains a significant challenge in dental restoration. This study introduces an innovative approach using aldehyde-grafted polyaspartate (PACA) to achieve spatiotemporal regulation of biomimetic mineralization through collagen cross-linking. The collagen cross-linking capability of PACA was confirmed by sodium dodecyl sulfate–polyacrylamide gel electrophoresis analysis. Its ability to induce intrafibrillar mineralization of collagen fibrils was investigated using a rat tail collagen model through dynamic light scattering, zeta potential measurements, and transmission electron microscopy. In addition, the remineralization efficacy of PACA on demineralized dentin collagen was evaluated using scanning electron microscopy, energy dispersive X-ray spectroscopy, and atomic force microscopy. These analyses revealed that PACA facilitates intrafibrillar mineralization by creating an amorphous calcium phosphate–rich microenvironment in the cross-linked region. Subsequently, the mineralization encapsulates the covalently cross-linked polymer and collagen fibrils within a mineralized matrix, forming an organized crystalline structure. Furthermore, PACA was used as a single-component primer in a dentin bonding model, and its impact on bonding durability was assessed through micro-tensile bond strength testing, nanoleakage analysis, and in situ zymography. These assessments demonstrate that PACA could simultaneously achieve collagen cross-linking, matrix metalloproteinase inhibition, and enhanced adhesive penetration while facilitating temporally regulated mineralization. Due to this multifunctionality, the PACA primer significantly improves micro-tensile bond strength and exhibits favorable durability after the aging experiment. This innovative approach provides a promising solution to the durability limitations of conventional adhesive systems.

Resin–dentin bonding technology is the primary method for tooth restoration in clinical practice. However, the formation of a defect zone at the bonding interface due to inadequate moisture control at the interface remains a major challenge. Inspired by mussel wet-adhesion mechanisms, this study functionalized phosphoric acid etchants with catechol–lysine–methacrylate (CLM), a polymerizable small-molecule monomer. During dentin demineralization, catechol binding to demineralized dentin was confirmed using an incubation-rinsing technique, while lysine’s role in facilitating rapid water release at the bonding interface was validated by freeze-drying mass loss, surface charge distribution, and thermogravimetric analysis. Confocal laser scanning microscopy revealed increased resin tag lengths in the CLM-treated groups, indicating enhanced resin infiltration. Ultimate tensile strength, dry mass loss, sodium dodecyl sulfate–polyacrylamide gel electrophoresis, and in situ zymography demonstrated catechol-enabled collagen stabilization via Schiff base/ester bonds and endogenous enzyme inhibition. Micro-tensile testing revealed that the 4- to 5-mg/mL CLM groups maintained bond strength after 10,000 thermal cycles. Thus, CLM-functionalized etchants may enhance bonding durability in vitro by improving moisture control and collagen stability.

The dental pellicle is a continuously forming layer present at the interface between oral surfaces and saliva. It protects the dental surfaces by shielding against chemical and mechanical damages. The pellicle represents the basis for further biofilm development, and its formation starts after oral hygiene through the adsorption of mostly salivary proteins to all exposed oral surfaces. In spite of its important physiologic role, its formation process and composition are not yet revealed in all details. The objective of the current study was 1) to visualize and elucidate the individual proteomic composition of the very early (only a few seconds) formed in situ pellicle, named seconds-pellicle, and 2) to analyze changes in its ultrastructure and proteomic composition over time. Transmission electron microscopic analyses demonstrated the seconds-pellicle to consist of an already continuous electron-dense layer without a detectable increase in thickness within the first minutes of formation. Proteomic analyses showed it to consist of up to 841 proteins on an individual level, without elevating protein content or diversity over the first minutes. Analyses of several pellicle formation times enabled the detection of changes over time and the first direct verification of protein desorptions occurring during pellicle formation. Comparisons between the salivary and pellicle proteomes at the different formation times revealed insights in the amount of substance of single pellicle proteins over time. The present study provides first evidence that the pellicle forms instantaneously and dynamically on solid surfaces in the oral cavity. Furthermore, the data suggest these first proteins to be replaced by proteins adsorbing with higher selectivity to stabilize the pellicle within the first minutes of its formation. These insights represent the basis for selective modifications of the pellicle layer to control the originating biofilm and develop preventive strategies for oral biofilm management.

Dental pulp stem cells (DPSCs) are neural crest–derived stem cells endowed with multipotency and self-renewal. While processes orchestrating DPSC differentiation have been studied extensively, mechanisms underpinning the differentiation of human DPSCs in vivo remain unclear. Here, we induced vasculogenic, odontoblastic, or neurogenic differentiation of human DPSCs for 7 d in vitro and performed single-cell RNA sequencing. Then, human DPSCs tagged with green fluorescent protein (DPSC-GFP) seeded in human tooth slice/scaffolds were transplanted into the subcutaneous space of immunodeficient mice. DPSC-GFP were sorted by flow cytometry 7 and 21 d after transplantation, and single-cell RNA sequencing was performed. In addition, a time course study was performed to investigate the sequence of differentiation events triggered upon transplantation of DPSC-GFP into mice. Here, we observed 8 distinct clusters of DPSCs at baseline, indicating a high level of cell heterogeneity. When DPSCs were induced to undergo vasculogenic, odontoblastic, or neurogenic differentiation in vitro, we observed distinct shifts in patterns of gene expression. Although some DPSCs retained mesenchymal stem cell markers likely due to asymmetric cell division and self-renewal, each differentiation protocol resulted in a unique gene expression signature. Stem cell markers that were highly expressed in DPSCs pretransplantation were progressively downregulated after 7 and 21 d in vivo. In contrast, endothelial cell markers presented high expression levels 7 d after transplantation, while neuronal markers showed upregulation 21 d after transplantation. Notably, while DPSC-derived functional blood vessels (i.e., blood-carrying vessels) can be clearly seen 2 wk after transplantation, well-defined DPSC-derived neural structures can be observed only after 5 wk. In conclusion, DPSCs are heterogeneous stem cells with distinct cell clusters, all of which contain progenitor cells with unique differentiation potential. Furthermore, this work demonstrated that microenvironment cues generated within human root canals are sufficient to induce vasculogenic differentiation, followed by neurogenic differentiation of DPSCs in vivo.

Temporomandibular joint osteoarthritis (TMJOA) is a progressive and debilitating degenerative joint disorder characterized by cartilage degradation. Its pathogenesis remains poorly understood, and current treatment strategies are insufficient to restore normal joint structure. Lipid metabolism disorders in condylar chondrocytes have been identified as key contributors to TMJOA development, with peroxisomes playing an essential regulatory role in this metabolic process. Although previous studies have suggested a role for peroxisomes in chondrocyte biology, their specific involvement in TMJOA pathogenesis remains unclear. This study is the first to demonstrate the involvement of peroxisomes in TMJOA and to elucidate the associated molecular mechanisms. A TMJOA mouse model was established via unilateral anterior crossbite surgery, revealing abnormal peroxisome quantity and function. In vitro experiments demonstrated that inhibiting peroxisome function alleviated mechanical stress-induced OA-like damage to chondrocytes. In Acan-CreER^T2 Pex2^f/f conditional knockout (KO) mice, Pex2 KO inhibited peroxisome function and significantly attenuated TMJOA pathology. Mechanistically, peroxisome functional inhibition led to decreased levels of palmitic acid (PA), whereas exogenous PA exposure induced an OA-like phenotype in chondrocytes. Further investigation revealed that PA activated the WNT/PCP pathway by activating the JNK/c-JUN signaling axis. Multiomics analysis revealed S100a4 as a key downstream effector gene, and further CUT&RUN quantitative polymerase chain reaction and dual-luciferase reporter assays confirmed that c-JUN directly bound to the S100a4 promoter region (−101 to −94 bp) to regulate its transcription. Knockdown of S100a4 expression significantly reduced PA-induced Mmp13 expression in chondrocytes. In vivo experiments confirmed that intra-articular injection of PA upregulated S100a4 levels and promoted TMJOA development. In conclusion, this study is the first to elucidate the critical role of the peroxisome/PA/JNK/c-JUN/S100a4 axis in cartilage degradation in TMJOA, providing a novel and promising therapeutic target for TMJOA.

Graphical abstract

Immune alterations, such as neutrophil dysfunction, significantly affect the progression and outcome of periodontitis, a prevalent inflammatory disease. Despite this, the molecular mechanisms driving neutrophil dysregulation in periodontitis remain poorly understood. In this study, we demonstrate that CD300lf, a critical immune regulator, is markedly downregulated in neutrophils from a periodontitis mouse model and human patients. The loss of CD300lf accelerates neutrophil aging, as evidenced by increased reactive oxygen species production, the senescence-associated secretory phenotype with elevated IL-1β and S100A8/A9 levels, and heightened neutrophil extracellular trap formation. Mechanistically, CD300lf deficiency leads to MyD88 upregulation, indicating a shift toward a proinflammatory state. Inhibition of MyD88 effectively reduces periodontal inflammation in CD300lf-deficient mice. Furthermore, targeting CD300lf with its known ligand ceramide alleviates periodontitis and mitigates the aging phenotype of neutrophils. These findings underscore the critical role of the CD300lf/MyD88 axis in neutrophil homeostasis and suggest that modulation of CD300lf through ceramide presents a promising therapeutic strategy for periodontitis.