设计下一代产氧支架以促进骨再生。
Engineering next-generation oxygen-generating scaffolds to enhance bone regeneration.
作者信息
Zhao Jingtong, Sarkar Naboneeta, Ren Yunke, Pathak Arvind P, Grayson Warren L
机构信息
Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD, USA.
Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD, USA; Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD, USA.
出版信息
Trends Biotechnol. 2025 Mar;43(3):540-554. doi: 10.1016/j.tibtech.2024.09.006. Epub 2024 Sep 28.
In bone, an adequate oxygen (O) supply is crucial during development, homeostasis, and healing. Oxygen-generating scaffolds (OGS) have demonstrated significant potential to enhance bone regeneration. However, the complexity of O delivery and signaling in vivo makes it challenging to tailor the design of OGS to precisely meet this biological requirement. We review recent advances in OGS and analyze persisting engineering and translational hurdles. We also discuss the potential of computational and machine learning (ML) models to facilitate the integration of novel imaging data with biological readouts and advanced biomanufacturing technologies. By elucidating how to tackle current challenges using cutting-edge technologies, we provide insights for transitioning from traditional to next-generation OGS to improve bone regeneration in patients.
在骨骼中，充足的氧气供应在发育、稳态维持和愈合过程中至关重要。产氧支架（OGS）已显示出增强骨再生的巨大潜力。然而，体内氧气输送和信号传导的复杂性使得定制OGS的设计以精确满足这一生物学需求具有挑战性。我们回顾了OGS的最新进展，并分析了持续存在的工程和转化障碍。我们还讨论了计算和机器学习（ML）模型在促进将新型成像数据与生物学读数及先进生物制造技术相结合方面的潜力。通过阐明如何利用前沿技术应对当前挑战，我们为从传统OGS向新一代OGS转变以改善患者骨再生提供了见解。
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