International endodontic journal最新文献

Aim: This study aims to comprehensively investigate the dynamic expression pattern of CXCR4 in human dental pulp stromal cells (hDPSCs) and stromal cells from the apical papilla (SCAPs) and assess the impact of different cell dissociation methods on its precise assessment. On this basis, a standardised CXCR4 detection and cell sorting strategy can be established to ensure the high purity and viability of the sorted population.

Methodology: hDPSCs and SCAPs were collected by the outgrowth method from human third molars from three independent healthy (ASA I) patients (< 18 years old). Cells were characterised as mesenchymal stromal cells (MSCs) and expanded to passage 3-6 for experiments. Three different dissociation methods (Enzymatic digestion, Non-enzymatic digestion, Mechanical collection) were used to collect single-cell suspensions from culture flasks. Afterwards, the cells were resuspended in culture medium and allowed to recover at 37°C/5% CO₂ for 0-4 h. Surface expression of CXCR4 on viable cells was assessed using flow cytometry. Cell metabolic activity and senescence after dissociation treatment were tested by XTT Assay and senescence staining. Cell migration capacity was evaluated by wound healing assay. The data were statistically analysed using p < 0.05 as a statistical significance reference.

Results: hDPSCs and SCAPs exhibited typical MSC profiles and comparable cell viability under the same dissociation methods and time points. hDPSCs showed higher CXCR4 expression than SCAPs. CXCR4 expression displayed time-dependent fluctuations in both cell types and cell viability was significantly affected by dissociation method and recovery period. Enzymatic digestion resulted in higher CXCR4 expression after 2-h recovery and preserved over 90% cell viability. In contrast, non-enzymatic dissociation and mechanical scraping impaired cell metabolic activity, migration capacity, and induced premature senescence.

Conclusion: In in vitro expanded hDPSCs and SCAPs, the cell-detachment method and recovery time can significantly affect the CXCR4 membrane detection and subsequent cell-survival performance. To balance optimal cell viability and detection reliability, enzymatic cell dissociation followed by a 2-h recovery time seems to be the best protocol for CXCR4 detection and subsequent downstream experiments within the 0-4 h time frame.

Aim: To delineate the mechanisms underlying the role of lactate-related lactylation, a novel post-translational modification, in the odontogenic differentiation of human dental pulp stem cells (hDPSCs).

Methodology: Dynamic levels of key glycolytic enzymes and histone lactylation along odontogenic differentiation in hDPSCs were evaluated by Western Blot and quantitative PCR (qPCR). Odontogenic differentiation was assessed via Alkaline Phosphatase and Alizarin Red S staining after treatment with sodium lactate, both with and without p300 knockdown. CUT&Tag sequencing of histone H3K18 lactylation (H3K18la) was performed, and genes identified through this analysis were subjected to pathway enrichment. WNT5A-knockdown hDPSCs were generated to elucidate the impact of both WNT5A and WNT5A/Ca²⁺ signalling axis in odontogenesis. Finally, we validated the critical function of lactylation in dentine regeneration using a subcutaneous ectopic transplantation model in nude mice with and without sodium lactate via intraperitoneal injection.

Results: The similar dynamic trends were observed in glycolytic enzyme expression, intracellular lactate and lactylation levels in hDPSCs. Exogenous lactate dose-dependently increased H3K18 lactylation and significantly promoted hDPSC odontogenic differentiation. Mechanistically, the key lactyltransferase p300 was demonstrated to be a regulator that enhanced lactate-induced H3K18la modification and odontogenic differentiation in hDPSCs. CUT&Tag sequencing identified WNT5A as a target gene whose promoter exhibited increased H3K18la enrichment upon lactate treatment. Lactate upregulated WNT5A expression and activated the WNT5A/Ca²⁺ signalling pathway via p300-mediated H3K18la, consequently accelerating the odontogenic differentiation. Further in vivo evidence revealed that lactate administration promoted pulp-like tissue regeneration, accompanied by increased hDPSC odontogenic marker expression and H3K18la levels in nude mice with subcutaneously transplanted dentine slices.

Conclusions: The lactate-driven mechanism acts through the p300-mediated H3K18la-WNT5A/Ca²⁺ signalling axis to promote the odontogenic differentiation of hDPSCs. The crosstalk between metabolism, epigenetic modifications and signalling pathways of hDPSCs might offer novel strategies for dental pulp regeneration.

Aim: To investigate the effects of locally applied liquid/gel mixed-phase concentrated growth factor (lgpCGF) on periodontal and pulpal healing following delayed replantation of avulsed immature permanent teeth and explore the potential of lgpCGF in mitigating root resorption.

Methodology: A delayed replantation model in immature permanent teeth was established using eight healthy beagle dogs, with two additional dogs serving as normal controls. From each experimental animal, two mandibular canine teeth (33 and 43) were extracted and left dry for 60 min to simulate delayed replantation. Tooth 43 received freshly prepared lgpCGF in the alveolar socket before replantation (lgpCGF group), while tooth 33 underwent conventional replantation (CR group). Each group initially contained 8 teeth (n = 8); one tooth in the CR group was excluded due to extraction-related damage. Periapical radiographs were obtained immediately after replantation and again at 4 weeks postoperatively, and analysed utilising ImageJ software. Micro-computed tomography (micro-CT) combined with CT-Analyser, ImageJ, and Geomagic Wrap was performed for quantitative assessment of root resorption. Surface resorption (SRR) and/or inflammatory root resorption (IRR) were grouped together and quantified collectively as the proportion of total root surface area affected. Replacement resorption (RRR) was evaluated by measuring the arc length of resorbed areas relative to the total root perimeter on transverse sections. Histological evaluation of periodontal and pulpal tissues was conducted on haematoxylin and eosin-stained sections.

Results: Compared with the CR group, the lgpCGF group exhibited improved clinical signs with fewer high-pitched percussion responses and greater tooth stability. Radiographic and micro-CT analyses revealed that the lgpCGF group had a significantly longer root length and smaller apical foramen diameter compared to the CR group (p < 0.05). Additionally, the lgpCGF group showed greater root area and reduced root resorption compared to the CR group (p < 0.05). Histological observations further showed enhanced periodontal regeneration and pulpal tissue organisation in lgpCGF-treated teeth.

Conclusions: Local application of lgpCGF to the alveolar socket during delayed replantation of immature permanent teeth effectively reduced root resorption and enhanced periodontal and pulpal healing. These findings support lgpCGF's potential as a regenerative therapeutic strategy to improve the prognosis of delayed tooth replantation.

Background: Endodontic treatment varies in complexity, and accurate assessment of case difficulty is essential for clinical success, guiding referrals, and enhancing patient safety, particularly amongst general dental practitioners (GDPs).

Objective: This scoping review aimed to identify and appraise the available tools and indices developed to assess endodontic case difficulty.

Methods: The review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive search of seven databases (PubMed, MEDLINE, EMBASE, Scopus, Web of Science, Cochrane Library, and Google Scholar) was conducted for studies published between January 2000 and July 2025. Articles were included if they described, developed, evaluated, or validated tools specifically assessing endodontic case difficulty or complexity. Data were extracted and synthesised thematically to summarise tool characteristics, components, scoring systems, and validation methods.

Results: Out of 1134 initial records, 15 studies met the inclusion criteria. These studies were published between 2001 and 2025 across multiple countries. They evaluated various endodontic case difficulty assessment tools, including the American Association of Endodontists Case Difficulty Assessment (AAECDA) form, Endodontic Case Assessment Form (ECAF), Endodontic Complexity Assessment Tool (E-CAT), EndoApp, and Dutch Endodontic Treatment Index (DETI). Most studies identified tools designed for non-surgical root canal treatment; however, one specifically considered endodontic microsurgery. The tools varied in structure, scoring logic, and validation rigour. While most tools shared common domains, such as patient, tooth, and treatment-related factors, reliability assessments showed considerable variation in inter- and intra-rater agreement.

Conclusion: Endodontic difficulty assessment tools are diverse and evolving, supporting clinical decision-making and educational development. Continually refining the tools, conducting psychometric validation, and ensuring global applicability will help to enhance their utility across various settings and user groups.

Background: Understanding root canal morphology is crucial for successful endodontic treatment; however, the anatomy of mandibular first premolars (M1Ps) remains one of the most variable and challenging aspects. The Vertucci classification provides a standardised framework for describing canal configurations; however, population-level data integrating multiple countries are scarce. This study aimed to evaluate the global distribution and determinants of Vertucci canal morphology in M1Ps using a Bayesian hierarchical model.

Methods: Cone-beam computed tomography (CBCT) data of M1Ps from 21 countries were analysed. The Vertucci classification was used as the categorical outcome variable. The predictors included tooth side (34/44), voxel size, field of view (FOV), sex and age, with the country modelled as a random intercept. A Bayesian hierarchical multinomial logistic regression was fitted using the brms package (rstan backend) with weakly informative priors. Posterior estimates were expressed as odds ratios (OR) and 95% credible intervals (CrI), and model-based predicted probabilities were computed for each Vertucci type.

Results: Bayesian modelling estimated the posterior probability of Vertucci Type I configuration at 73.4% (95% CrI: 63.8%-81.5%). Non-Type I configurations showed lower but credible probabilities, including Type V (8.2%, 3.6%-15.9%), Type III (3.7%, 1.6%-7.7%), Type IV (2.9%, 1.2%-6.3%) and Type II (1.3%, 0.5%-3.1%). Unclassified canal patterns accounted for approximately one-tenth of the MnP1s (9.9%, 3.9%-19.2%). Substantial variability was observed between countries for non-Type I and unclassified configurations, whereas Type I remained consistently predominant. Sex and age exerted modest effects, whereas tooth side and field of view showed no meaningful associations. Increasing the voxel size was associated with a slight reduction in the probability of Type I and marginal increases in Type V and unclassified configurations.

Conclusions: Although Vertucci Type I configuration predominates globally in MnP1s, clinically relevant non-Type I and unclassified canal patterns occur with non-negligible frequency and vary across populations. Bayesian hierarchical modelling enables the robust quantification of anatomical heterogeneity and uncertainty, supporting more reliable cross-country comparisons and cautious interpretation of less common canal configurations.

Aim: This study investigated the transfer of implicit anatomical features from micro-CT to periapical radiographs using fused-rooted mandibular second molars (MSMs) as a model. The objective was to evaluate the feasibility and effectiveness of multimodal transfer learning for the three-dimensional (3D) morphological identification of root canals, and to examine how task complexity influences transfer performance.

Methodology: Fused-rooted MSMs were scanned using high-resolution micro-CT to generate virtual radiographs. Clinically simulated periapical radiographs (CSPRs) were obtained from ex vivo mandibles to reproduce realistic clinical conditions. Based on micro-CT classification, root canals were divided into merging, symmetrical and asymmetrical types. Four convolutional neural network (CNN) architectures (VGG19, ResNet18, ResNet50 and EfficientNet-b5) were trained under three conditions: (1) CSPRs with ImageNet-pretrained CNNs, (2) virtual radiographs with ImageNet-pretrained CNNs, and (3) CSPRs with CNNs pretrained on virtual radiographs. Grad-CAM visualisation was used to interpret model attention, and results were compared with those of four endodontic residents. To reduce task complexity, symmetrical and asymmetrical types were later merged into a "separating" group to generate a two-class classification task.

Results: In the three-class task, CNNs pretrained on virtual radiographs achieved an average accuracy of 69.68% (95% CI: 64.61%-74.76%), significantly higher than ImageNet-pretrained models (64.36%, 95% CI: 61.12%-67.61%) and endodontic residents (61.17%, 95% CI: 56.09%-66.25%) (p < 0.05). Grad-CAM visualisation revealed that virtual radiograph-pretrained models concentrated attention on root structures, whereas ImageNet-pretrained networks showed diffuse or misplaced focus. In the two-class task, accuracies were 79.79% (95% CI: 73.30%-86.27%) for CNNs pretrained on virtual radiographs, 73.41% (95% CI: 67.54%-79.27%) for ImageNet-pretrained models and 76.60% (95% CI: 69.28%-83.91%) for residents, with no significant differences (p > 0.05). The overall diagnostic balance improved following transfer learning, indicating better feature representation across classes.

Conclusions: Implicit 3D features extracted from micro-CT-based virtual radiographs can be effectively transferred to CSPRs through transfer learning. This approach enhances CNN interpretability and diagnostic precision in identifying root canal morphology. The benefits of transfer learning are greater for complex, multi-class tasks that require the extraction of intricate morphological features, whereas its effect diminishes in simplified binary classifications. These findings provide a theoretical and experimental foundation for applying multimodal transfer learning to clinical dental imaging.

Aim: The dental pulp is an immunologically active tissue that responds dynamically to cariogenic challenge. Peripheral pulp cells adjacent to dentine encounter bacterial stimuli earlier than cells located in the central pulp. To investigate signalling and immune interactions, this study profiled the transcriptomes of dentine-adherent cells (DACs) and central dental pulp cells (DPCs) cocultured with Streptococcus mutans.

Methodology: Primary cultures of both DACs and DPCs were obtained from healthy third molars of three female and three male donors aged 13-16. Cells were cocultured with viable S. mutans (2 × 10⁸ CFU/mL) for 6 h (n = 6). Controls included γ-inactivated bacteria and unexposed cells. RNA libraries (Illumina Stranded mRNA Prep) were sequenced on a NextSeq2000. Bioinformatic analysis included differential gene expression (DESeq2), gene set enrichment analysis (GSEA), and protein-protein interaction (PPI) network construction. Batch effects were corrected, and significantly regulated genes (|log₂FC| > 1.5, p_adj < 0.05) were identified. Validation of DEGs was performed via reverse transcription quantitative polymerase chain reaction (RT-qPCR).

Results: RNA-Seq revealed a dynamic shift in the transcriptome of DACs and DPCs stimulated with S. mutans, while cells exposed to γ-inactivated or no bacteria did not. Although DACs and DPCs shared common DEGs (33 up, 8 down), several regulations were exclusive to DACs (22 up, 9 down) and DPCs (9 up, 25 down), highlighting a donor-independent functional specificity of the pulp subpopulations. Functional enrichment analysis revealed a strong and comparable activation of hypoxia-related pathways in both DPCs and DACs. However, DACs additionally showed enrichment in extracellular matrix organisation and cytokine signalling, while DPCs were characterised by intracellular stress responses and protein folding pathways. Additionally, protein-protein interaction analysis identified IL-6 as a key hub in DACs, while ANGPTL4 was central in DPCs.

Conclusion: Following exposure to S. mutans, mechanically isolated DACs and DPCs displayed distinct transcriptomic profiles, indicating functional heterogeneity in the pulpal immune response. DACs engaged immunomodulatory pathways, while DPCs were marked by cellular stress responses, suggesting divergent contributions to tissue defence and homeostasis.

Aim: Autotransplantation of mature teeth is often complicated by pulp necrosis due to compromised blood supply. This study aimed to establish a reproducible rat model for mature molar autotransplantation and to explore, within this model, the histological effects of root-end resection (RER).

Methodology: In rats, the maxillary third molar (M3) was autotransplanted into the socket of the extracted second molar (M2). Animals were randomly allocated to a control group (direct transplantation) or an RER group (transplantation following apical third root resection). Tooth positioning and structural integrity were assessed using micro-computed tomography (micro-CT). At 4 weeks postoperatively, pulp and periodontal tissues were evaluated by haematoxylin and eosin (H&E) staining, and vascular-associated markers were examined using CD31/α-SMA dual immunofluorescence.

Results: The recipient M2 socket was significantly larger than the donor M3 (p < 0.05), allowing stable placement of the transplanted tooth. No significant differences were observed between groups in transplantation success rate (75.0% Control vs. 70.0% RER), operative time, or extra-oral time (p > 0.05). H&E staining demonstrated extensive pulp necrosis in the control group, whereas the RER group showed ingrowth of vascularized connective tissue and cementum-like tissue formation. Immunofluorescence revealed sparse CD31/α-SMA-positive signals in controls and limited apical signals in the RER group, with a significantly greater double-positive area in the latter (p < 0.001).

Conclusion: A stable and reproducible rat model for autotransplantation of mature molars was successfully established. Within the constraints of this model, RER was associated with early histological features suggestive of vascular ingrowth. Further studies are required to determine the biological significance and clinical relevance of these observations.

Background: Recent advances in Artificial Intelligence (AI) and machine learning algorithms have transformed diagnostic tasks in dentistry and endodontics. However, the development of robust AI systems remains constrained by the scarcity of annotated, high-quality datasets along with issues like class imbalance and privacy concerns. Synthetic data, replicating the statistical and visual characteristics of real data, may offer a promising solution to these limitations.

Objective: The scoping review aimed to map (i) existing synthetic datasets in dentistry/endodontics, (ii) commonly used synthetic data generation techniques, (iii) their fidelity and diagnostic utility, and (iv) current limitations and future research directions in endodontics.

Methods: A comprehensive search was conducted across PubMed, Scopus, EBSCO, and Google Scholar using keywords related to synthetic datasets in dentistry and endodontics. Data regarding synthetic image modality most used, model architecture, training dataset size, evaluation metrics, and the real-world clinical utility (use-cases) of synthetic datasets generated were extracted and analysed descriptively.

Results: Eleven studies met the inclusion criteria, primarily using Generative Adversarial Networks (GANs), with only one utilising a hybrid GAN-diffusion model. The most common synthetic imaging modalities were panoramic radiographs (n = 4), intraoral clinical images and lateral cephalograms (n = 2 each), followed by periapical, bitewing, and TMJ Magnetic Resonance Imaging (MRI) (n = 1 each). Training datasets ranged from 509 to 35 254 images, with stable generation achieved using as few as 1720 images in the hybrid model. Evaluation metrics varied, with Fréchet Inception Distance (FID), Signal-to-Noise Ratio (SNR), and visual Turing tests being most common. Only four studies evaluated clinical utility, using synthetic images to train AI models. While synthetic data alone showed moderate fidelity, combining it with real images consistently improved accuracy across tasks like canal classification, implant segmentation, tooth numbering, and skeletal pattern recognition.

Conclusion: Synthetic data use in endodontics is still emerging but holds strong potential. While GANs dominate current research, diffusion and hybrid models may yield more realistic images. Future efforts should focus on generating diverse, clinically representative datasets to improve diagnostics, education, and AI-driven endodontic care.