Keqian Zhi

Objective: The fibula flap has gained popularity in jaw reconstruction in recent years. The restoration of masticatory function after jaw reconstruction is the ultimate goal of repair and reconstruction. Therefore, how to restore the occlusal function of patients to the greatest extent is an urgent problem to be solved clinically. Methods: This retrospective study included 50 patients (2018–2023) who underwent computer-assisted fibula flap reconstruction for jaw tumors. Inclusion criteria were fibula flap reconstruction, postoperative CT within 1 week, and normal preoperative occlusion. Patients were divided into experimental and control groups. 3D CT and CT angiography data were used to design guides. The experimental group underwent virtual occlusal reconstruction. Postoperative CT scans were analyzed, and implants were placed according to the preoperative plan (experimental) or existing contour (control). Occlusal efficiency was evaluated, and statistical analysis was performed using SPSS 25 ( P < 0.05). Results: The experimental and control groups showed no significant differences in baseline characteristics or operative time. The experimental group had a higher implant rate (88.00% vs. 72.00%, P = 0.036) and masticatory efficiency (92.6% vs. 74.50%, P = 0.024). Morphometric analysis revealed significantly smaller intercondylar distance, intermandibular angle, ramus length, and body length in the experimental group (all P < 0.05). Additionally, 88.00% of patients in the experimental group achieved complete occlusal reconstruction, compared to 72.00% in the control group ( P < 0.05). Conclusion: Our data show that the concept of occlusion-reconstruction-oriented jaw repair has good positional stability of the reconstructed jaw after surgery and a good effect of occlusal reconstruction for patients after surgery. Trial registration: QYFYWZJJ26812.

Bone-invasive solid tumors (BISTs) represent a clinically devastating and therapeutically challenging group of malignancies characterized by aggressive bone destruction, high morbidity, and poor prognosis due to limited effective treatments. Although bone invasion frequently occurs in advanced oral, breast, and prostate cancers, its underlying molecular mechanisms remain poorly understood, impeding the development of targeted therapies. Tumor-associated macrophages (TAMs), as the predominant immune cells within the tumor microenvironment, play central roles in promoting tumor progression, immune suppression, and pathological bone remodeling. Growing evidence identifies O-GlcNAcylation, a nutrient-sensitive post-translational modification, as a critical regulator of TAM polarization, metabolic reprogramming, and functional interactions with bone cells such as osteoclasts, osteoblasts, and mesenchymal stem cells. Emerging evidence suggests that dysregulated O-GlcNAcylation in TAMs is closely associated with an osteoclastogenic microenvironment, potentially facilitating bone invasion across various solid tumors. To address the broad knowledge gap regarding the molecular drivers of macrophage plasticity, this review systematically summarize currently identified O-GlcNAcylated protein substrates within TAMs. We highlight how these specific substrates remodel cellular energy cascades to induce a phenotypic transition in TAMs, thereby modulating the secretion of bone-resorbing cytokines and antitumor immune responses. We also explore emerging therapeutic strategies, including O-GlcNAcylation, macrophage repolarization, and advanced nanomedicine-based delivery systems designed for spatially and cell-type-specific targeting within the bone tumor microenvironment. Elucidating O-GlcNAc-mediated metabolic control of TAMs offers transformative insights and paves the way for novel combinatorial therapies aimed at suppressing bone invasion and improving outcomes in patients with advanced BISTs.

Oral squamous cell carcinoma (OSCC), the predominant pathological subtype of oral cancer accounting for approximately 90% of cases, is characterized by a high recurrence rate and metastatic propensity. Consequently, the 5-year survival rate of affected patients has long remained at a relatively low level of 50%–60%. Microbial dysbiosis is also recognized to have potential associations with enhanced genomic instability of tumor cells and epithelial-mesenchymal transition (EMT). Emerging evidence suggests that intracellular bacteria may act as cryptic pathogenic factors, participating in the initiation and progression of OSCC by driving chronic inflammation, facilitating tumor immune escape, and remodeling the metabolic patterns of tumor cells. Despite advancements in microbiome multi-omics analysis technologies and the development of synthetic biology-driven engineered bacteria, which have offered novel possibilities for precise interventions, the vast majority of these strategies are still in the preclinical validation phase. Their in vivo safety, delivery efficiency, and adaptability to the heterogeneity of the tumor microenvironment all await systematic evaluation. This review aims to systematically synthesize the multiple lines of evidence regarding the involvement of intracellular bacteria in the malignant progression of OSCC, highlighting their potential role as key “collaborators” in tumor evolution. Meanwhile, it explicitly points out that current research is mostly confined to correlation analyses or in vitro functional validation, lacking large-sample prospective cohorts and confirmation of causal mechanisms. Future studies should, on the basis of clarifying the molecular mechanisms underlying microbe-host interactions, prudently assess the potential and risks of microbiome-targeted intervention strategies in clinical applications.

Background: Oral squamous cell carcinoma (OSCC) remains a major clinical challenge, with delayed diagnosis, frequent resistance to therapy, and poor long‐term survival. Methods: This review systematically evaluates the methodological framework for applying AI to oral microbiome data in OSCC. Emerging paradigms, including self‐supervised learning for leveraging unlabelled data and explainable AI (XAI) techniques for model interpretability, are also discussed. Model evaluation relies on cross‐validation, hyperparameter optimisation, and performance metrics such as AUC, accuracy, sensitivity, specificity, and F1‐score. Results: Multiple studies demonstrate that AI‐based classifiers, especially random forest models built on salivary or tissue‐derived microbial features, achieve outstanding discrimination between OSCC patients and healthy controls in retrospective, single‐centre cohorts, with reported AUC values exceeding 0.99 and accuracy >95%. However, these exceptional metrics should be interpreted with caution, as they are susceptible to cohort size, sampling site heterogeneity, batch effects, feature‐selection bias, and the absence of independent external validation. Beyond binary diagnosis, AI has been successfully applied to predict lymph node metastasis, explore tumour metabolic reprogramming, and assess environmental interactions. Integrated multi‐omics approaches further enhance the specificity and clinical relevance of microbial biomarkers. Conclusions: The convergence of AI and oral microbiome analysis is reshaping the diagnostic and therapeutic landscape of OSCC, and explore microbiome‐targeted combination therapies. Addressing these challenges will be pivotal to realising truly intelligent, personalised management and ultimately improving outcomes for OSCC patients.