Performance of 3D printed porous polyetheretherketone composite scaffolds combined with nano-hydroxyapatite/carbon fiber in bone tissue engineering: a biological evaluation

3D printed Gel/PTH@PAHA scaffolds with both enhanced osteogenesis and mechanical properties for repair of large bone defects

The repair of large bone defects continues to pose a significant challenge in clinical orthopedics. Successful repairs require not only adequate mechanical strength but also exceptional osteogenic activity for successful clinical translation. Composite materials based on polyamide 66 (PA66) and hydroxyapatite have been widely used in various clinical settings. However, existing PA66/hydroxyapatite composites often lack sufficient osteogenic stimulation despite their favorable mechanical properties, which limit their overall clinical efficacy. In this study, we fabricated a polyamide 66/nano-hydroxyapatite (PAHA) scaffold using an extruder and fused deposition modeling-based 3D printing technology. Subsequently, gelatin methacrylamide (GelMA) containing teriparatide (PTH) was incorporated into the PAHA scaffold to construct the Gel/PTH@PAHA scaffold. Material characterization results indicated that the compressive modulus of elasticity and compressive strength of the Gel/PTH@PAHA scaffold were 172.47 ± 5.48 MPa and 25.55 ± 2.19 MPa, respectively. In vitro evaluations demonstrated that the Gel/PTH@PAHA scaffold significantly enhanced osteoblast adhesion and proliferation while promoting osteogenic differentiation of BMSCs. In vivo studies further revealed that this scaffold notably promoted new bone regeneration in rabbit femoral defects. These findings suggest that the 3D-printed Gel/PTH@PAHA scaffold exhibits excellent mechanical properties alongside remarkable osteogenic activity, thereby meeting the dual requirements for load-bearing applications and bone regeneration. This innovative approach may be a promising candidate for customized orthopedic implants with substantial potential for clinical application.

Machine Learning and Mendelian Randomization Reveal Molecular Mechanisms and Causal Relationships of Immune-Related Biomarkers in Periodontitis

This study aimed to investigate the molecular mechanisms of periodontitis and identify key immune-related biomarkers using machine learning and Mendelian randomization (MR). Differentially expressed gene (DEG) analysis was performed on periodontitis datasets GSE16134 and GSE10334 from the Gene Expression Omnibus (GEO) database, followed by weighted gene co-expression network analysis (WGCNA) to identify relevant gene modules. Various machine learning algorithms were utilized to construct predictive models, highlighting core genes, while MR assessed the causal relationships between these genes and periodontitis. Additionally, immune infiltration analysis and single-cell sequencing were employed to explore the roles of key genes in immunity and their expression across different cell types. The integration of machine learning, MR, and single-cell sequencing represents a novel approach that significantly enhances our understanding of the immune dynamics and gene interactions in periodontitis. The study identified 682 significant DEGs, with WGCNA revealing seven gene modules associated with periodontitis and 471 core candidate genes. Among the 113 machine learning algorithms tested, XGBoost was the most effective in identifying periodontitis samples, leading to the selection of 19 core genes. MR confirmed significant causal relationships between CD93, CD69, and CXCL6 and periodontitis. Further analysis showed that these genes were correlated with various immune cells and exhibited specific expression patterns in periodontitis tissues. The findings suggest that CD93, CD69, and CXCL6 are closely related to the progression of periodontitis, with MR confirming their causal links to the disease. These genes have potential applications in the diagnosis and treatment of periodontitis, offering new insights into the disease's molecular mechanisms and providing valuable resources for precision medicine approaches in periodontitis management. Limitations of this study include the demographic and sample size constraints of the datasets, which may impact the generalizability of the findings. Future research is needed to validate these biomarkers in larger, diverse cohorts and to investigate their functional roles in the pathogenesis of periodontitis.

Biphasic Calcium Phosphate and Activated Carbon Microparticles in a Plasma Clot for Bone Reconstruction and In Situ Drug Delivery: A Feasibility Study

The development of bone-filling biomaterials capable of delivering in situ bone growth promoters or therapeutic agents is a key area of research. We previously developed a biomaterial constituting biphasic calcium phosphate (BCP) microparticles embedded in an autologous blood or plasma clot, which induced bone-like tissue formation in ectopic sites and mature bone formation in orthotopic sites, in small and large animals. More recently, we showed that activated carbon (AC) fiber cloth is a biocompatible material that can be used, due to its multiscale porosity, as therapeutic drug delivery system. The present work aimed first to assess the feasibility of preparing calibrated AC microparticles, and second to investigate the properties of a BCP/AC microparticle combination embedded in a plasma clot. We show here, for the first time, after subcutaneous (SC) implantation in mice, that the addition of AC microparticles to a BCP/plasma clot does not impair bone-like tissue formation and has a beneficial effect on the vascularization of the newly formed tissue. Our results also confirm, in this SC model, the ability of AC in particle form to adsorb and deliver large molecules at an implantation site. Altogether, these results demonstrate the feasibility of using this BCP/AC/plasma clot composite for bone reconstruction and drug delivery.