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Journal of Zhejiang University SCIENCE B
ISSN 1673-1581(Print), 1862-1783(Online), Monthly
2025 Vol.26 No.9 P.863-880
Three-dimensional (3D) printing-assisted freeze-casting of processed pyritum-doped β-tricalcium phosphate biomimetic scaffold with angiogenesis and bone regeneration capability
Abstract: Bone repair remains an important target in tissue engineering, making the development of bioactive scaffolds for effective bone defect repair a critical objective. In this study, β-tricalcium phosphate (β-TCP) scaffolds incorporated with processed pyritum decoction (PPD) were fabricated using three-dimensional (3D) printing-assisted freeze-casting. The produced composite scaffolds were evaluated for their mechanical strength, physicochemical properties, biocompatibility, in vitro pro-angiogenic activity, and in vivo efficacy in repairing rabbit femoral defects. They not only demonstrated excellent physicochemical properties, enhanced mechanical strength, and good biosafety but also significantly promoted the proliferation, migration, and aggregation of pro-angiogenic human umbilical vein endothelial cells (HUVECs). In vivo studies revealed that all scaffold groups facilitated osteogenesis at the bone defect site, with the β-TCP scaffolds loaded with PPD markedly enhancing the expression of neurogenic locus Notch homolog protein 1 (Notch1), vascular endothelial growth factor (VEGF), bone morphogenetic protein-2 (BMP-2), and osteopontin (OPN). Overall, the scaffolds developed in this study exhibited strong angiogenic and osteogenic capabilities both in vitro and in vivo. The incorporation of PPD notably promoted the angiogenic-osteogenic coupling, thereby accelerating bone repair, which suggests that PPD is a promising material for bone repair and that the PPD/β-TCP scaffolds hold great potential as a bone graft alternative.
Key words: Bone defect; Processed pyritum; Three-dimensional (3D) printing-assisted freeze-casting; Angiogenesis; Bone regeneration; VEGF-Notch1-BMP-2-OPN coupling
海军航空大学信息融合研究所,中国烟台市,264001
摘要:以统一航迹批号和减少航迹冗余为目标的航迹-航迹关联(track-to-track association, T2TA)是实现航迹融合和态势感知的前提和基础。目前,T2TA主要面临两方面问题:航迹数据和关联方法。普遍存在的问题表现为,航迹数据中的误差和不一致的更新周期,以及关联方法中的次优关联结果和对先验信息和假定运动模型的依赖。为此,提出一种基于智能航迹评分的多传感器航迹关联多假设算法(MH-T2TA)。设计了一个基于自注意力和对比学习的时空配准模块,从而消除误差并统一异步航迹分布。将多假设算法与深度学习结合,在不依赖先验信息和假定运动模型的情况下,估计一对航迹的关联评分,从而获得最优关联对。在3种损失函数的约束下,来自相同目标的航迹相互靠近,来自不同目标的航迹相互远离,并且估计的航迹评分与真实的航迹评分非常相似。实验结果表明,MH-T2TA能够在复杂场景下实现航迹关联,并且关联效果优于其他T2TA方法。
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DOI:
10.1631/jzus.B2400340
CLC number:
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On-line Access:
2025-06-23
Received:
2024-07-07
Revision Accepted:
2024-09-01
Crosschecked:
2025-09-23