Weibin Sun

Background: Periodontal regeneration requires the coordinated reconstruction of mineralized and non-mineralized tissues. P35 polypeptide derived from Cementum protein-1(CEMP1) has been demonstrated to induce bio-mineralization in vitro, although in vivo applications still need to be further validated. In this study, we developed a novel mineralizing hydrogel system (P35/ACP@PEG Hy) to facilitate periodontal regeneration by encapsulating P35-loaded amorphous calcium phosphate nanoparticles (P35/ACP-NPs) within a four-arm polyethylene glycol (PEG) hydrogel. Methods: In vitro assays were performed to characterize the physicochemical properties, sustained-release behavior, osteogenic/cementogenic effects of P35/ACP-NPs on hPDLCs, together with the mechanical performance and biocompatibility of the PEG hydrogel. The regenerative capacity of the hydrogel was further assessed in a rat periodontal defect model using micro-CT and histological staining. The regenerative efficacy was evaluated in a rat periodontal defect model via micro-CT and histological staining, while transcriptome sequencing (RNA-seq) and immune-blotting were further performed to confirm the molecular mechanisms. Results: P35/ACP@PEG exhibited favorable physicochemical properties, featuring an initial burst release followed by a sustained gradual release profile, along with the excellent biocompatibility. In vitro, P35/ACP-NPs markedly promoted osteogenic and cementogenic differentiation of hPDLCs. Correspondingly, the P35/ACP@PEG hydrogel significantly accelerated periodontal tissue regeneration in the rat model with periodontal defect. And RNA-seq analysis and immune-blotting further revealed that these effects are highly linked to activation of the TGF-β/Smad2/3/LOX signaling pathway. Conclusions: P35/ACP@PEG Hy platform can promote periodontal regeneration via activation of TGF-β/Smad2/3/LOX signaling pathway in vivo. Its sustained bioactivity, favorable biocompatibility and robust regenerative efficacy highlight a potential of P35 polypeptide for periodontal tissue repair.

Acellular dermal matrix (ADM) is an alternative to autologous tissue grafts that are commonly utilized in soft tissue augmentation procedures. This randomized clinical trial evaluated the clinical effectiveness of allogeneic ADM versus connective tissue graft (CTG) in treating multiple recession type (RT) 2 gingival recessions using a modified tunnel technique. A total of 40 patients were randomly assigned to either the CTG group (n=20) or the ADM group (n=20). Clinical parameters, including the plaque index (PI), gingival index (GI), probing depth (PD), clinical attachment level (CAL), keratinized tissue width (KTW), recession depth (RD), and gingival thickness (GT), were assessed at baseline and 12 months. Both groups showed significant improvements in CAL, KTW, RD, and GT at 12 months compared to baseline. No significant differences in PI, GI, PD, mean root coverage (MRC), complete root coverage (CRC) or the Root Coverage Esthetic Score (RES) were observed between the CTG and ADM groups. The CTG group demonstrated greater gains in GT and KTW (pp<0.05). ADM and CTG yielded comparable root coverage and aesthetics. CTG provided superior tissue augmentation, whereas ADM offered better patient comfort. In conclusion, allogeneic ADM is a promising alternative for the treatment of RT2 gingival recessions.

A novel biomimetic peptide (P35) was designed and synthesized by combining the N-terminal 20 amino acids and the C-terminal 15 amino acids of cementum protein 1 (CEMP1), guided by molecular docking and plasmid-based molecular engineering (sequence: MGTSSTDSQQAGHRRCSTSNQGQGDTEDGRMTLMG). The peptide's role in biomineralization was investigated through in vitro mineralization assays of Type I collagen fibers and molecular dynamics simulations. In addition, we evaluated the ability of P35 to promote osteogenic and cementogenic differentiation in human periodontal ligament cells (hPDLCs). Our findings identified the N-terminal (N20) and C-terminal (C15) regions of CEMP1 as critical functional domains for regulating biomineralization. P35 exhibited superior binding affinity to both minerals and Type I collagen compared to individual N20 or C15 peptides, attributable to its enhanced structural flexibility and plasticity. Moreover, P35 significantly outperformed N20 and C15 in promoting osteogenic and cementogenic differentiation of hPDLCs. These results provide a strong theoretical basis for the potential clinical application of P35 in periodontal tissue and bone regeneration.

The severity of furcation involvement (FI) directly affected tooth prognosis and influenced treatment approaches. However, assessing, diagnosing, and treating molars with FI was complicated by anatomical and morphological variations. Cone-beam computed tomography (CBCT) enhanced diagnostic accuracy for detecting FI and measuring furcation defects. Despite its advantages, the high cost and radiation dose associated with CBCT equipment limited its widespread use. The aim of this study was to evaluate the performance of the Vision Transformer (ViT) in comparison with several commonly used traditional deep learning (DL) models for classifying molars with or without FI on panoramic radiographs. A total of 1,568 tooth images obtained from 506 panoramic radiographs were used to construct the database and evaluate the models. This study developed and assessed a ViT model for classifying FI from panoramic radiographs, and compared its performance with traditional models, including Multi-Layer Perceptron (MLP), Visual Geometry Group (VGG)Net, and GoogLeNet. Among the evaluated models, the ViT model outperformed all others, achieving the highest precision (0.98), recall (0.92), and F1 score (0.95), along with the lowest cross-entropy loss (0.27) and the highest accuracy (92%). ViT also recorded the highest area under the curve (AUC) (98%), outperforming the other models with statistically significant differences (p < 0.05), confirming its enhanced classification capability. The gradient-weighted class activation mapping (Grad-CAM) analysis on the ViT model revealed the key areas of the images that the model focused on during predictions. DL algorithms can automatically classify FI using readily accessible panoramic images. These findings demonstrate that ViT outperforms the tested traditional models, highlighting the potential of transformer-based approaches to significantly advance image classification. This approach is also expected to reduce both the radiation dose and the financial burden on patients while simultaneously improving diagnostic precision.

Background: To evaluate the performances of several advanced deep convolutional neural network models (AlexNet, VGG, GoogLeNet, ResNet) based on ensemble learning for recognizing chronic gingivitis from screening oral images. Methods: A total of 683 intraoral clinical images acquired from 134 volunteers were used to construct the database and evaluate the models. Four deep ConvNet models were developed using ensemble learning and outperformed a single model. The performances of the different models were evaluated by comparing the accuracy and sensitivity for recognizing the existence of gingivitis from intraoral images. Results: The ResNet model achieved an area under the curve (AUC) value of 97%, while the AUC values for the GoogLeNet, AlexNet, and VGG models were 94%, 92%, and 89%, respectively. Although the ResNet and GoogLeNet models performed best in classifying gingivitis from images, the sensitivity outcomes were not significantly different among the ResNet, GoogLeNet, and Alexnet models (p>0.05). However, the sensitivity of the VGGNet model differed significantly from those of the other models (p < 0.001). Conclusion: The ResNet and GoogLeNet models show promise for identifying chronic gingivitis from images. These models can help doctors diagnose periodontal diseases efficiently or based on self-examination of the oral cavity by patients.

Objective: This study investigates the effects of ZNF862 on the proliferation and apoptosis of human gingival fibroblasts and their related mechanisms. Background: As a major transcription factor family, zinc finger proteins (ZFPs) regulate cell differentiation, growth, and apoptosis through their conserved zinc finger motifs, which allow high flexibility and specificity in gene regulation. In our previous study, ZNF862 mutation was associated with hereditary gingival fibromatosis. Nevertheless, little is known about the biological function of ZNF862. Therefore, this study was aimed to reveal intracellular localization of ZNF862, the influence of ZNF862 on the growth and apoptosis of human gingival fibroblasts (HGFs) and its potential related mechanisms. Methods: Immunohistochemistry, immunofluorescence staining, and western blotting were performed to determine the intracellular localization of ZNF862 in HGFs. HGFs were divided into three groups: ZNF862 overexpression group, ZNF862 interference group, and the empty vector control group. Then, the effects of ZNF862 on cell proliferation, migration, cell cycle, and apoptosis were evaluated. qRT-PCR and western blotting were performed to further explore the mechanism related to the proliferation and apoptosis of HGFs. Results: ZNF862 was found to be localized in the cytoplasm of HGFs. In vitro experiments revealed that ZNF862 overexpression inhibited HGFs proliferation and migration, induced cell cycle arrest at the G0/G1-phase and apoptosis. Whereas, ZNF862 knockdown promoted HGFs proliferation and migration, accelerated the transition from the G0/G1 phase into the S and G2/M phase and inhibited cell apoptosis. Mechanistically, the effects of ZNF862 on HGFs proliferation and apoptosis were noted to be dependent on inhibiting the cyclin-dependent kinase inhibitor 1A (p21)-retinoblastoma 1 (RB1) signaling pathway and enhancing the B-cell lymphoma-extra- large (Bcl-xL)- Caspase 3 signaling pathway. Conclusion: Our results for the first time reveal that ZNF862 is localized in the cytoplasm of HGFs. ZNF862 can inhibit the proliferation of HGFs by inhibiting the

Objectives: Instance-level tooth segmentation extracts abundant localization and shape information from panoramic radiographs (PRs). The aim of this study was to evaluate the performance of a mask refinement network that extracts precise tooth edges. Methods: A public dataset which consists of 543 PRs and 16211 labelled teeth was utilized. The structure of a typical Mask Region-based Convolutional Neural Network (Mask RCNN) was used as the baseline. A novel loss function was designed focus on producing accurate mask edges. In addition to our proposed method, 3 existing tooth segmentation methods were also implemented on the dataset for comparative analysis. The average precisions (APs), mean intersection over union (mIoU), and mean Hausdorff distance (mHAU) were exploited to evaluate the performance of the network. Results: A novel mask refinement region-based convolutional neural network was designed based on Mask RCNN architecture to extract refined masks for individual tooth on PRs. A total of 3311 teeth were correctly detected from 3382 tested teeth in 111 PRs. The AP, precision, and recall were 0.686, 0.979, and 0.952, respectively. Moreover, the mIoU and mHAU achieved 0.941 and 9.7, respectively, which are significantly better than the other existing segmentation methods. Conclusions: This study proposed an efficient deep learning algorithm for accurately extracting the mask of any individual tooth from PRs. Precise tooth masks can provide valuable reference for clinical diagnosis and treatment. This algorithm is a fundamental basis for further automated processing applications.

Porphyromonas gingivalis (P. gingivalis) is a key pathogen of chronic periodontitis. Aryl hydrocarbon receptor (AhR) is essential in immune homeostasis via modulation of pro‐inflammatory cytokines production and indoleamine 2,3‐dioxygenase (IDO). In this study, it is demonstrated that P. gingivalis may regulate AhR signalling in periodontitis, which provides a potential target for further immune regulation studies in periodontitis. Experimental periodontitis was induced in C57BL/6 mice by silk ligature and P. gingivalis oral inoculation. The alveolar bone resorption was examined using Micro‐CT. Histological structures were observed and related cytokines involved in AhR signalling pathway were analysed. RAW264.7 cells were pretreated with AhR agonist (FICZ) and antagonist (CH223191) and infected with P. gingivalis subsequently. The levels of IDO, AhR and other related cytokines were measured. To demonstrate IDO activity, the concentrations of tryptophan (Trp) and kynurenine (Kyn) were assessed by HPLC. Histological analysis of periodontitis mice showed distinct alveolar bone resorption and inflammatory cell infiltration. The level of AhR and its downstream target factors were significantly decreased in inflamed gingival tissue. Furthermore, RAW 264.7 cells incubated by P. gingivalis exhibited increased pro‐inflammatory cytokines production and decreased AhR, CYP1A1, CYP1B1, and IDO expression. Decreased IDO activity was observed as decreased Kyn/Trp ratio in the supernatant. Moreover, FICZ decreased the pro‐inflammatory cytokines levels in P. gingivalis infected cells. It is concluded that P. gingivalis may promote inflammatory responses via inhibiting the AhR signalling pathway in periodontitis.