Liyuan Fan

Dental implant abutments face dual clinical challenges of early‐stage biological contamination and late‐stage inflammatory microenvironments. To address the issues, this study developed a smart drug‐release coating based on a superhydrophobic‐wettable transition mechanism. The coating on the abutment surface was constructed by hydrophobic micelles self‐assembling on mesoporous silica nanoparticles loaded with berberine. In the early stage, the superhydrophobic surface can effectively resist the adhesion of proteins and bacteria. With protein deposition, this process induced a transition of the superhydrophobic surface to a hydrophilic state, leading to a sigmoidal drug release profile that passively triggered sustained microenvironment regulation in the later stage. In vivo experiments demonstrated that this coating not only had strong immediate antibacterial efficacy (22.5‐fold enhancement) but also significantly reduced inflammation and promoted soft tissue closure. This work provides a sequential functionalization strategy of “early‐antifouling‐enabled late active regulation” for abutments, offering a potential solution to prevent peri‑implant inflammation and soft tissue dehiscence.

Peri-implantitis (PI) is a leading cause of dental implant failure, characterized by inflammatory soft tissue lesions and progressive bone loss, necessitating reliable animal models for mechanistic and therapeutic research. This study established a novel rat model that synergistically combines mechanical irritation with bacterial infection to better mimic the pathogenesis of human PI. Following the extraction of the maxillary first molars in 12 male Sprague-Dawley rats (4-week-old) and an 8-week healing period, titanium implants were placed. After 4 weeks of osseointegration, the PI group (n = 6) received ligatures soaked with Porphyromonas gingivalis (P. gingivalis) around the implant necks, while the negative controls (NC) group (n = 6) received no ligature treatment. Two weeks post-induction, analyses via Micro-computed tomography (micro-CT) and histology revealed significant alveolar bone loss and severe soft tissue inflammation, including marked inflammatory cell infiltration and tissue destruction, in the PI group compared to the NC group. This validated model effectively recapitulated key features of clinical PI. Its limitations include a simplified mono-pathogen challenge and an accelerated disease course compared to human PI. Nonetheless, it provides a robust and clinically relevant tool for investigative and therapeutic studies.

Peri-implantitis (PI) is a leading cause of dental implant failure, characterized by inflammatory soft tissue lesions and progressive bone loss, necessitating reliable animal models for mechanistic and therapeutic research. This study established a novel rat model that synergistically combines mechanical irritation with bacterial infection to better mimic the pathogenesis of human PI. Following the extraction of the maxillary first molars in 12 male Sprague-Dawley rats (4-week-old) and an 8-week healing period, titanium implants were placed. After 4 weeks of osseointegration, the PI group (n = 6) received ligatures soaked with Porphyromonas gingivalis (P. gingivalis) around the implant necks, while the negative controls (NC) group (n = 6) received no ligature treatment. Two weeks post-induction, analyses via Micro-computed tomography (micro-CT) and histology revealed significant alveolar bone loss and severe soft tissue inflammation, including marked inflammatory cell infiltration and tissue destruction, in the PI group compared to the NC group. This validated model effectively recapitulated key features of clinical PI. Its limitations include a simplified mono-pathogen challenge and an accelerated disease course compared to human PI. Nonetheless, it provides a robust and clinically relevant tool for investigative and therapeutic studies.

Background: Osteoradionecrosis of the jaw (ORNJ) is a common complication following radiotherapy for head and neck cancer. Thus, this study aimed to explore the effects of active components in Lycium barbarum on ORNJ through network pharmacology and to conduct experimental verification to identify potential therapeutic targets. Methods: The main active ingredients in Lycium barbarum (Gouqi), a traditional Chinese herbal medicine, was used in this study. After identifying ferroptosis-related genes associated with ORNJ, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and constructed an interaction network. A molecular interaction force analysis was then performed on eight binding conformations to identify the optimal conformation with the lowest binding free energy. We established a model of ORNJ in Sprague-Dawley (SD) rats and administered oral Lycium barbarum glycopeptide (LbGP) to the experimental group. Moreover, reverse transcription quantitative polymerase chain reaction (RT-qPCR), enzyme-linked immunosorbent assay (ELISA), and immunohistochemical staining techniques were then employed to detect the mRNA and protein levels of various relevant cytokines. Results: Based on network pharmacology predictions, this study identified three potential active components in Lycium barbarum, namely β-sitosterol, glycitein, and quercetin. The possible effects of these components in the treatment of ORNJ were analyzed. Seven hub genes related to ferroptosis and ORNJ were identified, and LbGP was selected for in vivo verification. The expression levels of TP53, EGFR, IL-6, and TNF were significantly altered in the LbGP group compared to the untreated group, as revealed by the qPCR, ELISA, and immunohistochemistry assays. Conclusion: The use of Lycium barbarum extract may exert therapeutic effects on mandible injury following ORNJ by regulating the expression of TP53, EGFR, IL-6, and TNF.

Background Head and Neck Squamous Cell Carcinoma (HNSCC) is an aggressive malignancy with poor prognosis and low five-year survival rates. Ferroptosis, an iron-dependent form of cell death, has emerged as a potential factor in cancer therapy. This study aimed to identify differentially expressed genes (DEGs) associated with HNSCC, ferroptosis, and radiotherapy, and to develop a diagnostic model to establish novel biomarkers, early diagnosis and individualized therapy. Methods We analyzed TCGA-HNSCC, GSE6631, and GSE107591 datasets to identify Ferroptosis- and Radiotherapy-Related Differentially Expressed Genes (F&RRDEGs).Ferroptosis & radiotherapy scores were calculated, followed by immune infiltration and immunotherapy analyses. A Cox prognostic model was evaluated using nomograms, calibration curves, and decision curve analysis. Real-time quantitative PCR (RT-qPCR) and Western blotting (WB) were used for experimental validation. Results 11 F&RRDEGs were identified classifying two subtypes related to HNSCC.Significant differences were found in immune cell infiltration and immunophenoscore in CTLA4 and PD1/PD-L1/PD-L2 + CTLA4 between high and low ferroptosis & radiotherapy scores(F&Rs) groups. The 1-year calibration curve of the prognostic risk model showed better and more accurate performance and performance for the five-year prediction. RT-qPCR identified significant differences in the genes ALOX12, EMP1, PIM1, and CA9 between the control and HNSCC groups, with WB confirming radiotherapy-induced upregulation of VDAC1/TFRC (pro-ferroptosis) and downregulation of GPX4/SLC7A11/FTH1 (anti-ferroptosis) in irradiated HNSCC cells. Conclusion ALOX12, EMP1, PIM1, and CA9 exhibited promising potential as diagnostic biomarkers. This study applies immune infiltration analysis to HNSCC research, offering fresh perspectives on patient-specific treatment plans, and accelerating the creation of focused therapies.

Purpose: A systematic review and meta-analysis were conducted to evaluate the diagnostic precision of radiomics in the differential diagnosis of parotid tumors, considering the increasing utilization of radiomics in tumor diagnosis. Although some researchers have attempted to apply radiomics in this context, there is ongoing debate regarding its accuracy.Methods: Databases of PubMed, Cochrane, EMBASE, and Web of Science up to May 29, 2024 were systematically searched. The quality of included primary studies was assessed using the Radiomics Quality Score (RQS) checklist. The meta-analysis was performed utilizing a bivariate mixed-effects model.Results: A total of 39 primary studies were incorporated. The machine learning model relying on MRI radiomics for diagnosis malignant tumors of the parotid gland, demonstrated a sensitivity of 0.80 [95% CI: 0.74, 0.86], SROC of 0.89 [95% CI: 0.27-0.99] in the validation set. The machine learning model based on MRI radiomics for diagnosis malignant tumors of the parotid gland, exhibited a sensitivity of 0.83[95% CI: 0.76, 0.88], SROC of 0.89 [95% CI: 0.17-1.00] in the validation set. The models also demonstrated high predictive accuracy for benign lesions.Conclusion: There is great potential for radiomics-based models to improve the accuracy of diagnosing benign and malignant tumors of the parotid gland. To further enhance this potential, future studies should consider implementing standardized radiomics-based features, adopting more robust feature selection methods, and utilizing advanced model development tools. These measures can significantly improve the diagnostic accuracy of artificial intelligence algorithms in distinguishing between benign and malignant tumors of the parotid gland.Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023434931.

Background Peri-implantitis (PI） is a common complication of dental implants that can result in implant failure. The study aims to identify genes related to PI and Ferroptosis, analyze relevant molecules in related pathways, and perform immune infiltration analysis, with the goal of investigating the classification of PI and creating a diagnostic model using its common genes. Methods The study first identified Ferroptosis-related genes (FRGs) by downloading datasets GSE33774 and GSE57631 and intersecting them with the FRGs set. Enrichment analysis, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), was conducted to compare enrichment pathways in the Control team and the PI team. Weighted Gene Co-Expression Network Analysis (WGCNA) was then used to search for genes and build logistic regression models, Support Vector Machine (SVM) models, and Least Absolute Shrinkage and Selection Operator (LASSO) regression models. A diagnostic model of PI composed of three genes HLF, PLIN2, and MAP1LC3A was obtained. The study utilized periodontal samples from patients with normal periodontal health and those with peri-implantitis to identify significant differences in the gene MAP1LC3A between the normal team and the PI team, further verifying this clinical correlation of FRHGs. Results The calibration curves of the diagnostic model showed close results, indicating better and more accurate performance. Immune cells exhibited a strong negative connection with one of the FRHGs——MAP1LC3A in both highly and low-risk categories of PI. Additionally, the FRHGs PLIN2 and HLF had strong positive and negative correlations with immune cells called monocytes, respectively. Two categories of PI, Cluster1 and Cluster2, were determined by the transcript levels of FRHGs in PI samples from the GEO Combined Datasets. Conclusion This study identified FRGs linked to PI and established a diagnostic model (HLF, PLIN2, MAP1LC3A). The accurate model revealed immunological characteristics of PI through immune infiltration analysis. Transcription levels of FRHGs classified PI samples into Cluster1 and Cluster2, enabling personalized treatment plans. Significant transcription difference of MAP1LC3A gene between normal and PI groups suggests its potential as a diagnostic biomarker. Integrating immune infiltration analysis offers new insights for PI research, enhancing understanding of its molecular mechanisms and targeted interventions.