Review 审查

Engineering next-generation oxygen-generating scaffolds to enhance bone regeneration
设计下一代产氧支架以增强骨再生

Hypoxia promotes angiogenesis through multifaceted signaling involving a combination of angiogenic factors and inflammatory cytokines. Although acute hypoxia appears to be essential for stimulating wound healing, prolonged or chronic hypoxia lasting for >7 days has been shown to delay the tissue repair process [16]. Despite vascular infiltration, low local O₂ tension persists longer with increasing defect size, extending from 2 weeks [17,18] to 10 weeks post-injury [19]. Since angiogenesis is crucial for bone healing and regeneration, a major concern with O₂-releasing scaffolds was that they might hinder vascular infiltration and subsequent tissue regeneration. However, O₂-generating calcium peroxide (CaO₂)/gelatin microspheres developed for treating osteonecrosis demonstrated enhanced angiogenesis, and CD31⁺ vessels were prevalent in the defect area 4 weeks post-surgery [20]. Similarly, injectable sodium alginate/carboxymethyl chitosan (CMC)/CaO₂ hydrogel led to ectopic osteogenesis in rat subcutaneous defects [21], and significantly greater blood vessel volumes were observed in scaffolds with 25% CaO₂ compared with control poly(ε-caprolactone) (PCL) scaffolds [22]. Although not statistically significant, a higher density of 'type H' vessels was observed in CaO_2–PCL scaffold groups, suggesting that these proregenerative vessel subtypes may be characteristic of the oxygenated microenvironment [22].
缺氧通过涉及血管生成因子和炎症细胞因子组合的多方面信号传导促进血管生成。尽管急性缺氧似乎对于刺激伤口愈合至关重要，但持续 >7 天的长期或慢性缺氧已被证明会延迟组织修复过程 [ 16 ]。尽管有血管浸润，但随着缺损尺寸的增加，局部低O ₂张力持续时间更长，从受伤后2周[ 17 , 18 ]延长到10周[ 19 ]。由于血管生成对于骨愈合和再生至关重要，因此释放O ₂的支架的一个主要问题是它们可能阻碍血管浸润和随后的组织再生。然而，为治疗骨坏死而开发的产生O ₂的过氧化钙(CaO ₂ )/明胶微球显示出增强的血管生成，并且术后4周缺损区域普遍存在CD31 ⁺血管[ 20 ]。同样，可注射的海藻酸钠/羧甲基壳聚糖（CMC）/CaO ₂水凝胶导致大鼠皮下缺损中的异位成骨[ 21 ]，并且与对照聚（ε-己内酯）相比，在含有25% CaO ₂的支架中观察到显着更大的血管体积）（PCL）支架[ 22 ]。 虽然没有统计学意义，但在 CaO _{2 –} PCL 支架组中观察到较高密度的“H 型”血管，表明这些再生血管亚型可能是含氧微环境的特征 [ 22 ]。

Hypoxia elevates hypoxia-inducible factor 1α (HIF-1α) expression and induces the recruitment of neutrophils, macrophages, endothelial cells, and subsequently fibroblasts to the wound site [23]. In hypoxic environments, macrophages switch their metabolism from oxidative phosphorylation towards glycolysis, and this biases them toward the M1 phenotype [24,25]. Although the prolonged presence of M1 macrophages causes inflammation and hinders healing, they are crucial for the initiation of bone healing through clearing cell debris and pathogens, and the recruitment of more immune cells [26]. Thus, ideally, acute hypoxia for up to 48 h might benefit bone healing. Subsequent angiogenesis alleviates hypoxia and facilitates the transition of macrophages from M1 to M2, thus promoting tissue repair [27,28]. M2 macrophages continue to be active during the ossification phase and support bone remodeling (Figure 1A) [29]. Oxygenated biomaterials with a transient O2 release profile provide an opportunity to alter immune cell behavior from proinflammatory to proregenerative within a few days post-implantation. Efforts have been directed towards investigating the immunomodulatory potential of ROS-scavenging bone scaffolds which can influence M1 to M2 macrophage polarization [27,30]. O₂ release from OGS could facilitate such a transition and stabilize bone repair through M2 polarization.
缺氧会提高缺氧诱导因子 1α (HIF-1α) 的表达，并诱导中性粒细胞、巨噬细胞、内皮细胞以及随后的成纤维细胞募集到伤口部位 [ 23 ]。在缺氧环境中，巨噬细胞将其代谢从氧化磷酸化转变为糖酵解，这使它们偏向于 M1 表型 [ 24 , 25 ]。尽管M1巨噬细胞的长期存在会引起炎症并阻碍愈合，但它们通过清除细胞碎片和病原体以及招募更多免疫细胞，对于启动骨愈合至关重要[ 26 ]。因此，理想情况下，急性缺氧长达 48 小时可能有利于骨愈合。随后的血管生成缓解缺氧并促进巨噬细胞从M1向M2的转变，从而促进组织修复[ 27 , 28 ]。 M2 巨噬细胞在骨化阶段继续活跃并支持骨重塑（图 1 A）[ 29 ]。具有瞬时氧气释放特性的氧化生物材料提供了在植入后几天内将免疫细胞行为从促炎性改变为促再生性的机会。人们一直致力于研究 ROS 清除骨支架的免疫调节潜力，它可以影响 M1 到 M2 巨噬细胞的极化 [ 27 , 30 ]。 OGS 释放的 O ₂可以促进这种转变并通过 M2 极化稳定骨修复。

In summary, O₂ generation and/or release from an implant should maintain O₂ tension between 6.6 and 8.6%, a level that is crucial for bone formation and homeostasis, until blood vessel infiltration is stabilized – a process that typically takes at least 2 weeks (Figure 1B) [17]. An acute moderately hypoxic environment (1–5% O₂ for 24–48 h) immediately post-implantation could enhance immune response and angiogenesis [26,31,32], although this is highly dependent on the cell type and might require further consideration regarding the specific experimental conditions. As the BTE construct size increases to a clinically relevant scale, O₂ deficiency would progressively become a problem, necessitating adjustments in the O₂ delivery profile to ensure immediate O₂ availability. Consequently, the release rate of oxygenated scaffolds designed for bone repair should match the physiological O₂ demand within the defect site.
总之，植入物产生和/或释放的 O ₂应将 O ₂张力维持在 6.6% 至 8.6% 之间，这一水平对于骨形成和体内平衡至关重要，直到血管浸润稳定为止——这一过程通常至少需要 2周（图 1 B）[ 17 ]。植入后立即处于急性中度缺氧环境（1-5% O ₂ ，​​持续 24-48 小时）可以增强免疫反应和血管生成 [26,31,32 ] ，尽管这高度依赖于细胞类型，可能需要进一步考虑关于具体的实验条件。随着 BTE 构造尺寸增加到临床相关规模，O ₂缺乏将逐渐成为一个问题，需要调整 O ₂输送曲线以确保立即获得 O ₂可用性。因此，设计用于骨修复的含氧支架的释放速率应与缺损部位内的生理O ₂需求相匹配。

OCS do not generate O₂ on their own but serve as vehicles for transporting and delivering O₂ at a desired dose to target sites. Examples include hemoglobin-based oxygen carriers (HBOCs), lipid-based O₂ microbubbles, O₂-laden nanosponges, polymeric microtanks, and perfluorocarbons (PFC). O₂ release by these systems is rate-limited by the diffusion of O₂ through the polymeric or lipid membrane. For example, O₂ was hyperbarically loaded into hollow biodegradable microtanks and incorporated into PCL for subsequent 3D printing into scaffolds. The scaffolds released O₂ for up to 8 h – a relatively short period – but enhanced the deposition of extracellular matrix (ECM) in murine calvarial and subcutaneous defects [55], suggesting that even short O₂ release at the earliest stages of healing can be beneficial. Owing to their excellent O₂ solubility, PFC-based emulsion-loaded hollow microparticles enabled controlled release of O₂ for 10 days [56]. They maintained the survival and osteogenic differentiation potential of human periosteal-derived cells under hypoxic conditions and provided a conducive environment for enhanced bone regeneration in miniature pig mandibular defects. However, for PFCs, long-term stability remains a significant concern, as the emulsions often decay during the freeze–thaw storage process or destabilize due to Ostwald ripening [57]. Oxygent, an FDA-approved PFC product, has a half-life of only 12–48 h, which limits its use primarily to temporary blood substitution during surgery [58]. These issues pose significant challenges for the clinical translation of OCS in BTE applications.
OCS 本身并不产生O ₂ ，​​而是作为将所需剂量的O ₂运输和递送到目标位置的载体。例子包括基于血红蛋白的氧载体(HBOC)、基于脂质的O ₂微泡、载有O ₂的纳米海绵、聚合物微罐和全氟化碳(PFC)。这些系统释放的O ₂受到O ₂通过聚合物或脂质膜的扩散的速率限制。例如，O ₂被高压加载到中空的可生物降解微型罐中，并掺入 PCL 中，以便随后 3D 打印到支架中。支架释放 O _{2 的}时间长达 8 小时（相对较短的时间），但增强了小鼠颅骨和皮下缺损中细胞外基质 (ECM) 的沉积 [ 55 ]，这表明即使在愈合的最初阶段短暂的 O ₂释放也可以是有益的。由于其优异的 O ₂溶解度，基于 PFC 的乳液负载中空微粒能够控制 O ₂释放 10 天[ 56 ]。它们在缺氧条件下维持了人骨膜来源细胞的存活和成骨分化潜力，并为增强小型猪下颌骨缺损的骨再生提供了有利的环境。然而，对于 PFC 来说，长期稳定性仍然是一个重要问题，因为乳液经常在冻融储存过程中腐烂或由于奥斯特瓦尔德熟化而不稳定[ 57 ]。 Oxygent 是 FDA 批准的 PFC 产品，其半衰期仅为 12-48 小时，这限制了其主要用于手术期间的临时血液替代[ 58 ]。这些问题给 OCS 在 BTE 应用中的临床转化带来了重大挑战。

Compared with OCS, the maximum O₂ load in OGS may be two to four orders of magnitude higher, allowing O₂ release lasting from several days to a month. This longer release period potentially provides an advantage in the context of treating critical-sized bone defects. Examples include solid peroxides (sodium percarbonates, manganese oxide, CaO₂, etc.), liquid peroxides (hydrogen peroxide, H₂O₂), and photosynthetic algae [59], of which the solid peroxides are the most promising for BTE applications Sustained O₂ release from solid peroxides is achieved by their chemical reaction with the aqueous environment. O₂-generating particles are usually encapsulated within micro/nanocarriers such as liposomes, dendrimers, exosomes, nanospheres, nanocapsules, solid–lipid nanoparticles, nanofibers, or polymeric micelles [60], and O₂ release from these systems depends on water penetration and polymeric matrix biodegradation.
与OCS相比，OGS中的最大O ₂负荷可能高出两到四个数量级，允许O ₂释放持续几天到一个月。这种较长的释放期可能在治疗临界尺寸的骨缺损方面提供优势。例子包括固体过氧化物（过碳酸钠、氧化锰、CaO ₂等）、液体过氧化物（过氧化氢、H ₂ O ₂ ）和光合藻类[ 59 ]，其中固体过氧化物最有希望用于BTE应用。固体过氧化物中O _{2 的}释放是通过它们与水环境的化学反应来实现的。 O ₂生成颗粒通常封装在微/纳米载体中，如脂质体、树枝状聚合物、外泌体、纳米球、纳米胶囊、固体脂质纳米颗粒、纳米纤维或聚合物胶束[ 60 ]，这些系统中 O _{2 的}释放取决于水的渗透和聚合物基质生物降解。

Among solid peroxides, CaO₂ is the most prevalently researched in BTE and has been employed in different formulations such as coatings [4,61], composite microspheres and filaments [62., 63., 64.], electrospun nanofibers [60,65,66], and hydrogel systems [3,20,21,67,68]. Used in hydrogel systems [3,67], near-complete bone regeneration in critical-sized calvaria defects was observed after 12 weeks [3], but hydrogels are not suitable for load-bearing applications. Alternatively, CaO₂ has been coated onto robocasted biphasic calcium phosphate (BCP) scaffolds [61]. When implanted into a 15 mm segmental radial defect in rabbits, a nearly twofold increase in bone formation was observed 6 months after surgery compared with uncoated scaffolds [4]. In polymer–CaO₂ composites, the polymer matrix serves as a hydrophobic barrier that slows O₂ release by limiting CaO₂–water interactions. However, in electrospinning systems [60,65,66], sustained O₂ release does not exceed 2 weeks, probably due to their high surface area. Nevertheless, CaO₂-based OGS have shown efficacy in promoting bone growth. PCL–CaO₂ microparticles developed by Suvarnapathaki and coworkers demonstrated high viability of preosteoblasts and reduced apoptosis, as confirmed by minimal in vitro caspase 3/7 activity. In vivo results from the same study also revealed that their scaffolds accelerated new bone formation, with less fibrous tissue and better tissue integration over 12 weeks, suggesting long-term safety and efficacy [3]. Gelatin–CaO₂ microparticles gave similar in vivo results over 10 weeks, and showed low immunogenicity and a significant reduction in inflammatory cells from the first to the third week post-implantation [69]. These studies collectively highlight the promise of CaO₂-based scaffolds in promoting bone regeneration for future clinical applications, while maintaining biocompatibility and reducing adverse immune responses.
在固体过氧化物中，CaO ₂在 BTE 中研究最为普遍，并已应用于不同的配方中，例如涂料 [ 4 , 61 ]、复合微球和长丝 [ 62. , 63. , 64. ]、电纺纳米纤维 [ 60 , 65 ] , 66 ] 和水凝胶系统 [ 3 , 20 , 21 , 67 , 68 ]。用于水凝胶系统[ 3 , 67 ]，12周后观察到临界大小的颅骨缺损几乎完全再生[ 3 ]，但水凝胶不适合承重应用。或者，CaO ₂已被涂覆到机器人铸造的双相磷酸钙（BCP）支架上[ 61 ]。当植入兔子的 15 毫米节段性径向缺损时，与未涂层支架相比，术后 6 个月观察到骨形成增加了近两倍 [ 4 ]。 在聚合物-CaO ₂复合材料中，聚合物基质充当疏水屏障，通过限制CaO ₂ -水相互作用来减缓O ₂释放。然而，在静电纺丝系统中[60,65,66 ] ，O _{2 的}持续释放不会超过2周，这可能是由于它们的高表面积。然而，基于CaO ₂的OGS已显示出促进骨生长的功效。 Suvarnapathaki 及其同事开发的 PCL–CaO ₂微粒表现出前成骨细胞的高活力并减少了细胞凋亡，这一点通过最小的体外caspase 3/7 活性得到证实。同一项研究的体内结果还显示，他们的支架在 12 周内加速了新骨形成，纤维组织更少，组织整合更好，这表明长期安全性和有效性 [ 3 ]。明胶-CaO ₂微粒在 10 周内给出了类似的体内结果，并且显示出较低的免疫原性，并且从植入后第一周到第三周炎症细胞显着减少[ 69 ]。这些研究共同强调了基于CaO ₂的支架在促进未来临床应用的骨再生、同时保持生物相容性和减少不良免疫反应方面的前景。

Magnesium peroxide (MgO₂), another solid peroxide, exhibits the slowest and most persistent release kinetics of O₂ owing to its low decomposition rate [70]. A 3D-printed scaffold composed of PCL, β-tricalcium phosphate (β-TCP), and MgO₂ created by Peng and colleagues [70] released O₂ over 21 days and facilitated the formation of new bone in a femoral condyle defect model. Mg²⁺ ions also directly aid bone formation and impede the growth of various tumors through oxidative damage to DNA, making it particularly effective for repairing bone defects after osteosarcoma resection [71]. Similarly, strontium peroxide (SrO₂)-based OGS also promoted new bone formation and inhibited bone resorption, leveraging the dual role of strontium in bone metabolism by stimulating osteoblasts and inhibiting osteoclasts [72]. Of note, strontium is recognized not only as a bioactive trace element that supports bone health but also as a component of strontium ranelate, a well-known treatment for postmenopausal osteoporosis.
过氧化镁（MgO ₂ ）是另一种固体过氧化物，由于其分解速率低，因此表现出最慢且最持久的 O ₂释放动力学[ 70 ]。 Peng 及其同事创建的由 PCL、β-磷酸三钙 (β-TCP) 和 MgO ₂组成的 3D 打印支架 [ 70 ] 在 21 天内释放 O ₂ ，​​并促进股骨髁缺损模型中新骨的形成。 Mg ²⁺离子还直接帮助骨形成，并通过对 DNA 的氧化损伤阻止各种肿瘤的生长，使其对于修复骨肉瘤切除术后的骨缺损特别有效[ 71 ]。同样，基于过氧化锶（SrO ₂ ）的OGS也通过刺激成骨细胞和抑制破骨细胞，利用锶在骨代谢中的双重作用，促进新骨形成并抑制骨吸收[ 72 ]。值得注意的是，锶不仅被认为是一种支持骨骼健康的生物活性微量元素，而且还是雷尼酸锶的成分，雷尼酸锶是一种众所周知的绝经后骨质疏松症治疗方法。

ROS-responsive scaffolds have been utilized in drug delivery. Lee and colleagues developed poly(D,L-lactide-co-glycolic acid) (PLGA)-based nanoparticles that managed ischemia/reperfusion injuries via the controlled release of heparin and glutathione in response to H₂O₂, thereby enhancing bone healing [90]. BTE scaffold coatings using ROS-responsive thioketal-based polymers enabled tunable release of BMP-2 and increased bone regeneration by 50% in rat calvarial defect models [91]. Similarly, a titanium implant coated with a hydrogel responsive to ROS in femoral bone defects released Tβ4 to promote macrophage transformation and osteogenic differentiation, and improve bone healing [92]. Aleemardani and colleagues integrated ROS-mitigating quercetin, a plant flavanol, into silk fibroin–CaO₂ hydrogel–electrospun fiber–hydrogel constructs [68]. This novel construct released O₂ for 12 days while simultaneously performing ROS scavenging, and increased cell viability by 30% under normoxia and by 10% under hypoxia compared with its quercetin-lacking counterpart. Overall, harnessing and modulating ROS within OGS is a promising strategy for overcoming the challenges associated with balancing O₂ supply and demand. Continued developments will include fine-tuning their sensitivity to ROS levels. Future research could explore the incorporation of novel O₂-sensing elements, chemical bonds, or O₂ carriers within the scaffolds. In addition, the long-term stability and biocompatibility of redox-active materials and their byproducts will necessitate further research to fully understand their interactions with the biological environment [93].
ROS 响应支架已用于药物输送。 Lee及其同事开发了基于聚(D,L-丙交酯-乙醇酸) (PLGA) 的纳米颗粒，该纳米颗粒通过响应 H ₂ O ₂控制释放肝素和谷胱甘肽来控制缺血/再灌注损伤，从而增强骨愈合。 90 ]。使用 ROS 响应性硫缩酮基聚合物的 BTE 支架涂层能够调节 BMP-2 的释放，并将大鼠颅骨缺损模型中的骨再生增加 50% [ 91 ]。同样，股骨缺损中涂有水凝胶的钛植入物会释放 Tβ4，促进巨噬细胞转化和成骨分化，并改善骨愈合[ 92 ]。 Aleemardani 及其同事将减少 ROS 的槲皮素（一种植物黄烷醇）整合到丝素蛋白 - CaO ₂水凝胶 - 电纺纤维 - 水凝胶结构中 [ 68 ]。这种新型构建体可释放 O ₂ 12 天，同时进行 ROS 清除，与不含槲皮素的对应物相比，常氧条件下细胞活力提高了 30%，缺氧条件下细胞活力提高了 10%。总体而言，在 OGS 中利用和调节 ROS 是克服与平衡 O ₂供需相关的挑战的一种有前景的策略。持续的开发将包括微调它们对 ROS 水平的敏感性。未来的研究可以探索新型O ₂传感元件、化学键或O ₂载体在支架内的结合。 此外，氧化还原活性材料及其副产物的长期稳定性和生物相容性需要进一步研究，以充分了解它们与生物环境的相互作用[ 93 ]。

Biophysical models applied to cell-seeded PLGA–sodium percarbonate OGS could predict dynamic spatiotemporal O₂ interactions during bone healing by accounting for scaffold size, O₂ release, and cellular consumption rates in avascular bone defects [94]. Finite element modeling (FEM) has been used to explore how local O₂ tension, coupled with the mechanical environment within an implanted osteochondral scaffold, impacted on tissue differentiation and repair outcomes of mesenchymal stem cells (MSCs) [95]. FEM also simulated cellular-level vascular development by mathematically linking O₂ sources and Dll4-Notch signaling in endothelial cells [96,97]. At the tissue level, FEM-based approaches have been used to simulate bone regeneration by integrating mechanical stimuli, scaffold degradation, biological responses to VEGF supplementation, vascularization, and O₂ delivery [98]. In critical-sized bone defects, spatial variations in these parameters occur during bone regeneration. For accurate FEM simulations, thousands of representative volume elements are needed, resulting in high computational costs. However, the use of neural networks to predict bone regeneration in a sheep tibia model achieved results as accurate as FEM but at one-fifth of the computational cost. The ML model was validated by longitudinal in vivo X-ray images, and showed consistent bone regeneration at 12 month based on remodeling parameters established from the first 9 months of data [99]. Similarly, Ghosh and colleagues used neural networks to substitute tedious FEM analyses in predicting bone growth over macro-textured bone implant surfaces, further demonstrating the efficiency improvements provided by ML compared with traditional FEM methods [100]. Biological parameters for both FEM and ML models are usually sourced from the literature, in vitro/in vivo data, and empirical estimation, which can be updated via iterations and new experimental data [95,99]. ML tools integrate data from in vitro and in vivo experiments, thereby providing a comprehensive framework to elucidate the underlying mechanisms by which spatiotemporal O₂ gradients and bone scaffolds enhance osteogenesis, angiogenesis, and overall bone healing. ML approaches can also guide on-demand OGS design to match a desired optimal cellular response. Integrating noninvasive imaging data with ML techniques has the potential to further enhance the simulation of biological processes such as bone regeneration and revascularization. ML models trained on extensive imaging datasets can provide more accurate and robust predictions of the in vivo O₂ distribution. Ultimately, these models would be used clinically to facilitate customized interventions tailored to the patient and their defect based on initial imaging data.
应用于细胞接种的 PLGA-过碳酸钠 OGS 的生物物理模型可以通过考虑无血管骨缺损中的支架尺寸、O ₂释放和细胞消耗率来预测骨愈合过程中动态时空 O ₂相互作用[ 94 ]。有限元模型（FEM）已被用来探索局部O ₂张力与植入骨软骨支架内的机械环境如何影响间充质干细胞（MSC）的组织分化和修复结果[ 95 ]。 FEM 还通过数学方式连接内皮细胞中的 O ₂源和 Dll4-Notch 信号传导来模拟细胞水平血管发育 [ 96 , 97 ]。在组织水平上，基于 FEM 的方法已被用于通过整合机械刺激、支架降解、对 VEGF 补充的生物反应、血管化和 O ₂输送来模拟骨再生 [ 98 ]。在临界尺寸的骨缺损中，这些参数在骨再生过程中会发生空间变化。为了进行精确的有限元模拟，需要数千个有代表性的体积单元，从而导致计算成本很高。然而，使用神经网络来预测绵羊胫骨模型中的骨再生，获得了与有限元法一样准确的结果，但计算成本仅为有限元法的五分之一。 ML 模型通过纵向体内X 射线图像进行验证，并根据前 9 个月数据建立的重塑参数在 12 个月时显示出一致的骨再生 [ 99 ]。同样，Ghosh 及其同事使用神经网络代替繁琐的 FEM 分析来预测宏观纹理骨植入物表面的骨生长，进一步证明了 ML 与传统 FEM 方法相比所提供的效率改进 [ 100 ]。 FEM 和 ML 模型的生物参数通常来源于文献、体外/体内数据和经验估计，可以通过迭代和新的实验数据进行更新 [ 95 , 99 ]。 ML 工具集成了体外和体内实验的数据，从而提供了一个全面的框架来阐明时空 O ₂梯度和骨支架增强成骨、血管生成和整体骨愈合的潜在机制。机器学习方法还可以指导按需 OGS 设计，以匹配所需的最佳细胞反应。将无创成像数据与机器学习技术相结合有可能进一步增强骨再生和血运重建等生物过程的模拟。在广泛的成像数据集上训练的 ML 模型可以提供更准确、更稳健的体内O ₂分布预测。 最终，这些模型将在临床上使用，以促进根据初始成像数据针对患者及其缺陷进行定制干预措施。

Biophysical simulations and ML also offer powerful solutions to address the technical limitations of OGS designs by predicting the effects of different materials on O₂ generation, diffusion, and distribution. For example, O₂ gradients within CaO₂-based scaffolds supporting the high O₂ demand of islet cells were simulated using FEM [101., 102., 103.]. These models, based on oxygenated reaction, diffusion, and consumption kinetics, successfully guided the optimization of cell loading [103]. There are limited studies highlighting the potential of ML for advancing biomaterial designs for BTE applications. In the Polymer Genome Project, computational methods were used to predict polymer properties, such as glass transition temperature and solubility parameters, thereby enhancing material selection and design [104]. ML accelerated peptide discovery for self-assembling hydrogels by combining Monte Carlo tree search and random forest algorithms with molecular dynamics simulations, and efficiently identified sequences with high self-assembly potential [105]. In addition, ML could predict the material properties and bone-forming ability of metals using artificial neural networks (ANNs) [106] or in ceramics [106,107]. ML has been used to predict scaffold biodegradability, which affects O₂ release kinetics, tissue integration, and scaffold longevity [108]. Bioceramics and natural substances (e.g., collagen, fibrin), commonly used as OGS binding materials, exhibit complex degradation patterns owing to their inherent heterogeneity and bioactivity. Predicting these patterns using traditional mathematical principles is challenging. However, random forest algorithms have been trained on histological images to predict collagen-based scaffold biodegradation [109]. In addition, neural networks trained on the relationships between mechanical properties, crosslinking, and swelling characteristics and degradation rates have predicted gelatin-based bone scaffold degradation [110].
生物物理模拟和机器学习还提供了强大的解决方案，通过预测不同材料对 O ₂生成、扩散和分布的影响来解决 OGS 设计的技术限制。例如，使用FEM模拟支持胰岛细胞高O ₂需求的基于CaO ₂的支架内的O ₂梯度[ 101. 、 102. 、 103 .]。这些模型基于氧化反应、扩散和消耗动力学，成功指导了细胞负载的优化[ 103 ]。很少有研究强调机器学习在推进 BTE 应用生物材料设计方面的潜力。在聚合物基因组计划中，计算方法用于预测聚合物特性，例如玻璃化转变温度和溶解度参数，从而增强材料的选择和设计[ 104 ]。机器学习通过将蒙特卡罗树搜索和随机森林算法与分子动力学模拟相结合，加速了自组装水凝胶的肽发现，并有效地识别了具有高自组装潜力的序列[ 105 ]。此外，机器学习还可以使用人工神经网络 (ANN) [ 106 ] 或陶瓷 [ 106 , 107 ] 来预测金属的材料特性和成骨能力。 ML已被用来预测支架的生物降解性，这会影响O ₂释放动力学、组织整合和支架寿命[ 108 ]。生物陶瓷和天然物质（例如胶原蛋白、纤维蛋白）通常用作 OGS 结合材料，由于其固有的异质性和生物活性，表现出复杂的降解模式。使用传统数学原理预测这些模式具有挑战性。然而，随机森林算法已经在组织学图像上进行了训练，以预测基于胶原蛋白的支架生物降解[ 109 ]。此外，根据机械性能、交联、膨胀特性和降解率之间的关系训练的神经网络已经预测了基于明胶的骨支架的降解[ 110 ]。

Finally, ML has been used to create bone scaffolds with controlled porosity, Young's modulus, and compression strength, and achieved <5% error from design targets [111,112]. Although integrating FEM and computational fluid dynamics (CFD) helps to simulate mechanical properties and O₂ permeability concurrently to iteratively adjust scaffold design [113,114], recent advances have utilized a more efficient ML approach for inverse design, such as supervised learning to generate Voronoi lattices with targeted mechanical properties that were validated through compression tests and simulations [112]. Another study used FEM simulations to create a neural network dataset linking microstructural parameters to the elastic matrix of bone [111]. ML also provides significant benefits to scaffold accuracy and consistency in image-based computer-aided design (CAD) and additive manufacturing (AM)-based biomanufacturing. Creating patient-specific bone scaffolds relies on accurate bone segmentation from computerized tomography (CT) scans. Minnema and colleagues improved this step by using automated convolutional neural networks (CNNs), and achieved a 92% similarity to gold standard segmentation tools in shorter times [115]. During the subsequent bioprinting process, ML models analyzed data signatures from in situ IR sensors in a feedforward process to predict print quality metrics and adjust process parameters in real time, thereby preventing manufacturing flaws and improving print quality [116].
最后，机器学习已用于创建具有受控孔隙率、杨氏模量和压缩强度的骨支架，并实现了设计目标的 <5% 误差 [ 111 , 112 ]。尽管集成 FEM 和计算流体动力学 (CFD)有助于同时模拟机械性能和 O ₂渗透性，以迭代调整支架设计 [ 113 , 114 ]，但最近的进展利用了更有效的 ML 方法进行逆向设计，例如监督学习来生成Voronoi 晶格具有通过压缩测试和模拟进行验证的目标机械性能 [ 112 ]。另一项研究使用 FEM 模拟创建一个神经网络数据集，将微观结构参数与骨弹性矩阵联系起来 [ 111 ]。机器学习还为基于图像的计算机辅助设计 (CAD)和基于增材制造 (AM)的生物制造中的支架准确性和一致性提供了显着的优势。创建患者特异性骨支架依赖于计算机断层扫描 (CT) 扫描的准确骨分割。 Minnema 和同事通过使用自动卷积神经网络 (CNN) 改进了这一步骤，并在更短的时间内实现了与黄金标准分割工具 92% 的相似度 [ 115 ]。 在随后的生物打印过程中，机器学习模型在前馈过程中分析来自原位红外传感器的数据签名，以预测打印质量指标并实时调整过程参数，从而防止制造缺陷并提高打印质量[ 116 ]。

Challenges to ML for OGS include the need for extensive, high-quality data for model training. The variability in the experimental conditions and requirements for preclinical versus clinical applications require collaborative efforts to build comprehensive datasets for OGS materials. Currently, there are limited publicly available biomaterial databases, and most groups still rely on extracting descriptors from multiple sources, which reduces scalability and increases computing power requirements [120]. High-quality, standardized data collection protocols will be needed in the future to ensure accurate and reproducible experimental data. In addition, the 'black-box' nature of many ML models limits the interpretability of ML-generated designs for in vivo applications. Therefore, ML should be considered as an aid, and not as a replacement for empirical trials, to be accompanied by rigorous experimental validation of OGS performance. Enhancing the transparency of ML algorithms through explainable artificial intelligence (AI) techniques will increase their clinical acceptance among medical professionals [121]. Creating effective ML models for OGS requires interdisciplinary expertise in materials science, tissue engineering, and computer science, which would further advance bone scaffold and next-generation OGS design.
OGS 的 ML 面临的挑战包括需要广泛的高质量数据来进行模型训练。临床前与临床应用的实验条件和要求的可变性需要协作努力来构建 OGS 材料的综合数据集。目前，公开可用的生物材料数据库有限，大多数小组仍然依赖从多个来源提取描述符，这降低了可扩展性并增加了计算能力要求[ 120 ]。未来将需要高质量、标准化的数据收集协议，以确保准确且可重复的实验数据。此外，许多机器学习模型的“黑盒”性质限制了机器学习生成的体内应用设计的可解释性。因此，ML 应被视为一种辅助手段，而不是经验试验的替代品，并伴有对 OGS 性能的严格实验验证。通过可解释的人工智能（AI）技术增强机器学习算法的透明度将提高其在医疗专业人员中的临床接受度[ 121 ]。为 OGS 创建有效的 ML 模型需要材料科学、组织工程和计算机科学方面的跨学科专业知识，这将进一步推进骨支架和下一代 OGS 设计。