Chunbo Tang

Titanium mesh-supported guided bone regeneration (TM-GBR) has emerged as a clinically effective strategy for alveolar bone augmentation in complex defects, primarily through its ability to preserve osteogenic space and correct bone volume deficiencies. However, due to the inherent biological inertness of titanium mesh (TM), insufficient osteoinductive at the interface of TM remains a pressing issue to be addressed in alveolar bone reconstruction. Herein, silicon nitride nanofilms with low (SiN55), medium (SiN65), and high (SiN75) nitrogen contents were fabricated on TM substrates via reactive magnetron sputtering, exhibiting favorable hydrophilicity, superior corrosion resistance, and antibacterial activity. Moreover, the SiN65 nanofilms significantly promoted the macrophage polarization from the pro-inflammatory M1 phenotype to the pro-regenerative M2 phenotype and enhanced osteogenic differentiation through immune regulation of macrophages in co-culture assays. Mechanistically, RNA-eq analysis revealed that the SiN65 nanofilms modulated macrophage differentially expressed genes and reprogrammed the protein−protein interaction network, accompanied by downregulation of TNF, JAK-STAT, and Toll-like receptor signaling pathways. Additionally, the SiN65 nanofilms promoted bone regeneration in rat calvarial defect models. This study presents the SiN65 nanofilms as a rationally engineered coating integrating immunomodulatory and osteogenic functions, effectively mitigating peri-implant fibrosis and providing a promising strategy to improve the long-term outcomes of TM-GBR.

Periodontitis (PD) and ulcerative colitis (UC) are two common chronic inflammatory diseases increasingly connected via the “oral-gut axis,” yet their shared molecular mechanisms remain unclear. Oxidative stress, driven by excessive reactive oxygen species (ROS), represents a key pathogenic mechanism common to both PD and UC. In this study, we integrated transcriptomic datasets from patients with PD and UC to identify oxidative stress-related genes underlying their comorbidity. By combining weighted gene co-expression network analysis (WGCNA), machine learning, and single-cell RNA sequencing, we identified and validated a set of comorbidity-associated diagnostic biomarkers: CXCL1, XBP1, CD93, FYN, SELP, and CXCR4. These genes demonstrated high diagnostic accuracy across independent datasets, and gene set enrichment analysis (GSEA) revealed their involvement in inflammatory and immune-related pathways. Single-cell analysis further demonstrated endothelial-specific co-expression of SELP and CD93, highlighting their potential roles in intercellular communication and chronic inflammation. Moreover, molecular docking identified candidate therapeutic compounds with strong binding affinities for these targets. Collectively, our findings elucidate shared oxidative stress–driven mechanisms linking PD and UC and propose novel biomarkers and therapeutic targets for these interconnected diseases.

Objectives: To develop and validate an interpretable machine learning (ML) model for early prediction of peri‐implant mucositis (PIM). Material and Methods: This retrospective study enrolled patients receiving dental implants between October 2011 to June 2023 in our cohort to develop a ML‐based model. External testing was subsequently performed utilizing datasets from two other hospitals. The least absolute shrinkage and selection operator (LASSO) was utilized for feature selection. Six ML algorithms were applied to analyze and identify the optimal model. Shapley Additive exPlanations (SHAP) interpretation was developed for model interpretation and personalized risk assessment. Results: A total of 341 patients with 605 implants were included in our cohort and 101 patients with 170 implants were included in the external testing cohort. The Area Under Curves (AUCs) and Brier scores of the Support Vector Machine (SVM) model with 10 features were 0.902 and 0.129 in the validation set, while 0.797 and 0.256 in the external testing cohort, demonstrating superior discrimination and calibration compared to other algorithms. The SHAP algorithm interpreted that inadequate keratinized mucosa width, poor oral hygiene, restoration type (splinted crown/bridge), free‐end edentulism, gingivitis, smoking habit, thin gingival biotype, and cement‐retained restoration would elevate the occurrence of PIM. On the contrary, use of interproximal brushing/flossing and use of oral irrigator served as protective factors. Conclusion: This study developed the first interpretable ML‐based tool for predicting PIM in patients with dental implant placement after 1‐year, with the SVM model showing high discriminative ability and clinical utility for risk stratification.

Periodontal disease therapy faces complex pathological challenges, including persistent anaerobic bacterial infection, excessive inflammatory activation and impaired alveolar bone regeneration. Monofunctional biomaterials thus fail to meet comprehensive clinical needs. Herein, we developed a multifunctional thermosensitive hydrogel delivery system. By encapsulating polydopamine-coated magnesium peroxide (MgO2@PDA) nanocomposites within a Pluronic F127-sodium alginate (F127-SA) matrix, the system achieves integrated synergistic therapy targeting the ‘antibacterial-anti-inflammatory-osteogenic’ axis. Systematic characterization demonstrated that the hydrogel exhibits rapid thermosensitive gelation and excellent shear-thinning properties. In vitro experiments confirmed that MgO2@PDA enables the sustained release of oxygen (O2) and magnesium ions (Mg2+), effectively inhibiting the proliferation of key periodontal pathogens, including Porphyromonas gingivalis and Fusobacterium nucleatum. Furthermore, the system downregulates the secretion of pro-inflammatory cytokines by macrophages stimulated with lipopolysaccharide (LPS). Notably, it significantly enhances osteoblast migration, differentiation and mineralization in vitro. In a rat periodontitis model, this hydrogel effectively attenuated local inflammatory responses, inhibited anaerobic pathogen proliferation and upregulated the expression of bone regeneration markers. In summary, this hydrogel integrates sustained drug delivery, microenvironment modulation and regenerative guidance functions, providing a novel strategy with clinical translation potential for the comprehensive treatment of periodontitis and periodontal tissue regeneration.

Objective: To provide an automated risk assessment for lingual plate perforation (LPP) and mandibular canal injury (MCI) in dental implants. Also, to reduce interoperator variability in risk classification and eliminate manual measurements during implant planning. Methods: A dataset of 896 CBCT‐implant records was used for training our model (DentaRisk‐Net). CBCT images were processed using ResNet‐18 encoders for each implant site view. Patient‐specific metadata was processed through a fully connected network. These modalities were fused using a gated fusion mechanism (GFM). Deep metric learning (DML) was employed to enhance distinguish of risk classes. The model was trained on two risk tasks: bone plate (BP) and mandibular canal (MC) tasks. Classification and regression metrics (accuracy, precision, recall, F 1‐score, R ² , and ablation studies) were used to evaluate performance. True assessment rate (TAR) was used to assess the model’s agreement with human annotators. Grad‐CAM visualizations were conducted for qualitative evaluation. Results: The model achieved an accuracy exceeding 0.87 in detecting risk in implant sites, with precision and recall above 0.84. The GFM increased F 1‐scores by 3%–5% and R ² by 5%–8% for both risk tasks. The model achieved high performance for the Safe and Risk classes, whereas the Caution class was comparatively lower in BP and MC. TAR ≤1 values demonstrated acceptable alignment with human (BP: r = 0.63; MC: r = 0.72). Grad‐CAM visualizations confirmed the model’s ability for feature localization. Conclusion: By integrating imaging and metadata through DML and GFM, the model attempted to reduce interoperator variability in decision‐making and enhance risk‐classified assessment in the treatment planning process.

Oral squamous cell carcinoma (OSCC) is a highly aggressive head and neck malignancy with a poor prognosis associated with its complex tumor microenvironment. Cancer-associated fibroblasts (CAFs) contribute to tumor progression by secreting various signaling molecules. This study investigates the molecular mechanism through which Z-DNA-binding protein 1 (ZBP1) promotes OSCC development through CAF regulation. To this end, orthotopic MOC1 transplantation and 4NQO-induced carcinogenesis OSCC models were established with Zbp1 ^−/− mice. Single-cell RNA sequencing (scRNA-seq) analyzed cellular heterogeneity and signaling network alterations in the tumor microenvironment. An in vitro CAF induction model combined with a Transwell co-culture system clarified the molecular mechanism of ZBP1. Finally, the role of the ZBP1–CCL7/CCR1 signaling axis in promoting OSCC progression was evaluated via in vivo recombinant CCL7 protein rescue and CCR1 antagonist (BX471) intervention. ZBP1 is highly expressed in OSCC tissues, while its deficiency inhibits tumor growth and proliferation. Proliferation-related pathways (e.g., E2F targets, MYC targets, cell cycle) are downregulated while immune activation signatures (e.g., interferon response, p53 pathway, TNF-α/NF-κB signaling) are upregulated in Zbp1 ^−/− tumor cells. Cellular interaction analysis and ligand–receptor network profiling demonstrated significant attenuation of the CCL7–CCR1 signaling axis between CAFs and tumor cells. ZBP1 deficiency reduces CCL7 expression in CAFs, diminishing their ability to promote tumor cell proliferation, migration, and invasion via the CCL7/CCR1 axis. Exogenous CCL7 supplementation partially restores tumor growth in Zbp1 ^−/− mice, indicating that ZBP1 bridges CAF–tumor cell communication through the CCL7–CCR1 axis. This study highlights ZBP1 as crucial for OSCC progression by regulating CCL7 expression in CAFs to activate CCR1 signaling in tumor cells. This provides insights into the regulatory mechanisms within the OSCC microenvironment, offering a potential therapeutic strategy for targeted interventions.

Background: Growing evidence indicates a correlation between heavy metal exposure and various diseases. However, studies examining the impact of heavy metal exposure on periodontitis remain limited. The research investigated potential links between heavy metal exposure and periodontal disease development. Methods: Using the 2009–2014 NHANES, we employed generalized additive models (GAM), multivariate logistic regression, quartile logistic regression, weighted quantile sum (WQS) regression, and subgroup analysis to investigate the association between heavy metal exposure and periodontitis. Mechanisms of antimony's effects were identified by GO/KEGG analysis combined with experimental validation. Results: Lead and antimony showed significant dose–response relationships with periodontitis. The subgroup analysis revealed more pronounced effects among female participants compared to males, and individuals under 60 years had a higher number of significant associations than those over 60. WQS regression indicated that antimony contributed most to periodontitis among mixed heavy metal exposures. GO/KEGG enrichment analysis suggested that antimony exacerbates periodontitis via the ferroptosis pathway. Conclusion: Exposure to heavy metals, particularly lead and antimony, is associated with elevated risks of periodontitis, with significant effect modification by gender and age. Based on these results, antimony was further investigated and found to exacerbate inflammation via ferroptosis, as confirmed experimentally.