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Systemic modulation of skeletal mineralization by magnesium implant promoting fracture healing: Radiological exploration enhanced with PCA-based machine learning in a rat femoral model
Journal of Magnesium and Alloys ( IF 17.6 ) Pub Date : 2024-01-29 , DOI: 10.1016/j.jma.2024.01.012
Yu Sun , Heike Helmholz , Regine Willumeit-Römer

The clinical application of magnesium (Mg) and its alloys for bone fractures has been well supported by and trials. However, there were studies indicating negative effects of high dose Mg intake and sustained local release of Mg ions on bone metabolism or repair, which should not be ignored when developing Mg-based implants. Thus, it remains necessary to assess the biological effects of Mg implants in animal models relevant to clinical treatment modalities. The primary purpose of this study was to validate the beneficial effects of intramedullary Mg implants on the healing outcome of femoral fractures in a modified rat model. In addition, the mineralization parameters at multiple anatomical sites were evaluated, to investigate their association with healing outcome and potential clinical applications. Compared to the control group without Mg implantation, postoperative imaging at week 12 demonstrated better healing outcomes in the Mg group, with more stable unions in 3D analysis and high-mineralized bridging in 2D evaluation. The bone tissue mineral density (TMD) was higher in the Mg group at the non-operated femur and lumbar vertebra, while no differences between groups were identified regarding the bone tissue volume (TV), TMD and bone mineral content (BMC) in humerus. In the surgical femur, the Mg group presented higher TMD, but lower TV and BMC in the distal metaphyseal region, as well as reduced BMC at the osteotomy site. Principal component analysis (PCA)-based machine learning revealed that by selecting clinically relevant parameters, radiological markers could be constructed for differentiation of healing outcomes, with better performance than 2D scoring. The study provides insights and preclinical evidence for the rational investigation of bioactive materials, the identification of potential adverse effects, and the promotion of diagnostic capabilities for fracture healing.

中文翻译:

镁植入物对骨骼矿化的系统调节促进骨折愈合:在大鼠股骨模型中使用基于 PCA 的机器学习增强放射学探索

镁(Mg)及其合金治疗骨折的临床应用得到了很好的支持和试验。然而,有研究表明,高剂量镁摄入和镁离子持续局部释放对骨代谢或修复有负面影响,在开发镁基植入物时不应忽视这一点。因此,仍然有必要评估镁植入物在与临床治疗方式相关的动物模型中的生物学效应。本研究的主要目的是在改良的大鼠模型中验证髓内镁植入物对股骨骨折愈合结果的有益影响。此外,还评估了多个解剖部位的矿化参数,以研究它们与愈合结果和潜在临床应用的关联。与未植入镁的对照组相比,术后第 12 周的影像学显示,镁组的愈合效果更好,3D 分析中的结合更稳定,2D 评估中的高矿化桥接更稳定。镁组非手术股骨和腰椎的骨组织矿物质密度(TMD)较高,而肱骨骨组织体积(TV)、TMD和骨矿物质含量(BMC)组间没有差异。在手术股骨中,Mg 组的 TMD 较高,但远端干骺端区域的 TV 和 BMC 较低,截骨部位的 BMC 较低。基于主成分分析 (PCA) 的机器学习表明,通过选择临床相关参数,可以构建放射标记来区分愈合结果,其性能优于 2D 评分。该研究为生物活性材料的合理研究、潜在不良反应的识别以及骨折愈合诊断能力的提升提供了见解和临床前证据。
更新日期:2024-01-29
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