While conventionally manufactured metallic biomaterials can hardly meet all the requirements for bone implants including complex geometry, exact dimensions, adequate biodegradability, bone-matching mechanical properties, and biological function, two additional tools have recently appeared in the arsenal of biomaterials scientists which promise to deliver the desired combination of properties. First, the unique mechanical, electrical, and biological properties of graphene (Gr) and its derivatives (GDs), e.g., a Young's modulus up to 1 TPa, can be utilized to create metal matrix composites in which GDs of varied contents (typically not more than 2 wt%), sizes (lateral sizes from a few nanometers to several micrometers), surface areas (up to the theoretical value of 2630 m²/g), and layer numbers (typically up to 10) are embedded in the biodegradable metal matrix, thereby endowing the composite implants with extraordinary properties. Second, the distinct advantages of additive manufacturing (AM) make it possible for GD-containing composite materials to precisely mimic the complex shapes and structures of bones at multiple length scales. Here, a comprehensive review of the recent advances in the development of GD-containing biodegradable metal matrix composites (GBMMCs), ranging from composite fabrication, including composite powder preparation, and AM processes, to the evaluation of AM composites in terms of their mechanical and biological properties, is presented. Furthermore, the constraints in processing composite powders, the advantages and disadvantages of applicable AM techniques, and the mechanisms of mechanical reinforcement, biodegradation modulation, osteogenesis improvement, and cytotoxicity/antibacterial balance are critically analyzed. Thereafter, the foreseeable challenges faced in the development of the next-generation of bone implants based on GBMMCs are presented and some future directions of research are identified.

Medical image analysis often involves time-consuming annotation processes. Pediatric image analysis introduces additional complexity due to the scarcity of data, noise, and growth-related anatomical variations, particularly in bone analysis, where bone structures evolve more slowly compared to other organs. This study aims to develop a segmentation model that scales across different age groups, reduces annotation effort, and ensures high accuracy, particularly in low-quality images. To address these challenges, we propose a segmentation pipeline (PedVision) that first uses a Region of Interest (ROI) network to identify relevant regions, followed by a foundation model that translates each region into meaningful instances. These instances are then mapped to segmentation classes through an instance classifier (IC) network. To initiate rounds of the training of ROI and IC networks, we developed a fast, semi-automated annotation framework that leverages foundation models to annotate a subset of images using an object-level approach. In subsequent rounds, a human discriminator selects promising predictions from the last round, which are fed by unseen data, progressively enriching the model's training dataset for further fine-tuning of the networks. The networks are expanded from low-parameter to high-parameter models across rounds, incorporating a curriculum learning approach to capture increasingly complex features. We evaluated PedVision on 552 hand X-ray images of children, retrieved from the publicly available Radiological Society of North America (RSNA) and Digital Hand Atlas (DHA) datasets, which represent a diverse range of ages and racial backgrounds. PedVision performed segmentation of 19 hand bones, grouped in five classes, and was compared against U-Net and DeepLabV3+ models using ResNet34 and ResNet101 backbones, as well as the SegFormer model with four different encoder variants. For pediatric cases (i.e., 0–7 years), the PedVision pipeline outperforms the best-performing models, achieving an 11.08 % improvement in Dice score over U-Net in the RSNA dataset and a 7.68 % improvement in the DHA dataset. When compared to DeepLabV3+, the improvements are even more substantial, with gains of 14.43 % in RSNA and 14.78 % in DHA. Additionally, PedVision shows notable advantages over the best SegFormer model, with improvements of 8.16 % in RSNA and 1.91 % in DHA. The project is open source at github.com/mohofar/PedVision.

The development of high-fidelity three-dimensional (3D) tissue models can minimize the need for animal models in clinical medicine and drug development. However, physical limitations regarding the distances within which diffusion processes are effective impose limitations on the size of such constructs. That is because larger-size constructs experience necrosis, especially in their centers, due to the cells residing deep inside such constructs not receiving enough oxygen and nutrients. This hampers the sustained in vitro growth of the tissues which is required for achieving functional microtissues. To address this challenge, we used three types of 3D printing technologies to create perfusable networks at different length scales and integrate them into such constructs. Toward this aim, networks incorporating porous conduits with increasingly complex configurations were designed and fabricated using fused deposition modeling, stereolithography, and two-photon polymerization while optimizing the printing conditions for each of these technologies. Furthermore, following network embedding in hydrogels, contrast agent-enhanced micro-computed tomography and confocal fluorescence microscopy were employed to characterize one of the essential network functionalities, namely the diffusion function. The investigations revealed the effects of various design parameters on the diffusion behavior of the porous conduits over 24 h. We found that the number of pores exerts the most significant influence on the diffusion behavior of the contrast agent, followed by variations in the pore size and hydrogel concentration. The analytical approach and the findings of this study establish a solid base for a new technological platform to fabricate perfusable multiscale 3D porous networks with complex designs while enabling the customization of diffusion characteristics to meet specific requirements for sustained in vitro tissue growth. Statement of significance: This study addresses an essential limitation of current 3D tissue engineering, namely, sustaining tissue viability in larger constructs through optimized nutrient and oxygen delivery. By utilizing advanced 3D printing techniques this research proposes the fabrication of perfusable, multiscale and customizable networks that enhance diffusion and enable cell access to essential nutrients throughout the construct. The findings highlighted the role of network characteristics on the diffusion of a model compound within a hydrogel matrix. This work represents a promising technological platform for creating advanced in vitro 3D tissue models that can reduce the use of animal models in research involving tissue regeneration, disease models and drug development.

Osteoimmunomodulation (OIM) is emerging as a key biofunctionality of orthopedic implants. Biomaterial surface geometries can modulate the interactions between immune cells and osteoprogenitors at the bone-implant interface, positively affecting osteogenic differentiation and implant osseointegration. This review highlights the recent advancements in geometry-induced OIM (G-OIM) across multiple length scales (nano to mesoscale, including multiscale topographies and 3D scaffolds), identifying relations between specific geometries and subsequent mechanisms of OIM, as provided by the coculture model used. Our review reveals surface geometries with OIM potential at each length scale. These effects can be mediated by both M1 and M2 macrophages, wherein the pathway depends on the shape and length scale of the geometrical cues provided (e.g., integrin-mediated mechanotransduction for nanoscale topographies and macrophage contact inhibition for micropatterns). Most studies assess G-OIM predominantly based on geometry-induced macrophage polarization and its paracrine effect on osteoprogenitors. However, few studies utilizing direct coculture reveal the key role of the direct interplay between macrophages, osteoprogenitors, and biomaterial for OIM. The novel field of G-OIM is advancing at a high pace. It could benefit from improved, clinically relevant coculture models involving human-derived cells and technological developments in biomaterial design and fabrication. Such advances could establish (G-)OIM as a transformative approach for regenerative immunoengineering of orthopedic implants. Statement of significance: Osteoimmunomodulation, the ability of biomaterials to modulate the interactions between immune cells and skeletal cells to enhance osteogenesis, is increasingly recognized as a crucial biofunctionality for orthopedic biomaterials. Various biomaterial surface geometries can be used to target osteoimmune pathways. Given the complexity of these interactions, suitable coculture models are essential for studying the underlying cellular mechanisms. This review reveals the state-of-the-art results on geometry-induced osteoimmunomodulation. Not only does this review discuss approaches that have been taken thus far in terms of biomaterial geometry design at various length scales, but it also highlights the role of the coculture model in osteoimmunomodulation and the importance of advances in these in vitro models to improve the translation of research to clinical practice.

High-performance soft–hard interfaces are inherently difficult to fabricate due to the dissimilar mechanical properties of both materials, especially when connecting extremely soft biomaterials, such as hydrogels, to much harder biomaterials, such as rigid polymers. Nevertheless, there is significant clinical demand for synthetic soft–hard interfaces. Here, soft–hard interface geometries are proposed, designed with the aid of computational analyses and fabricated as 3D-printed hydrogel-to-polylactide (PLA) structures. Two primary interlocking geometries (i.e., anti-trapezoidal (AT) and double-hook (DH)) are used to study the envelope of 2.5D geometric interlocking designs, fabricated through hybrid 3D printing, combining pneumatic extrusion with fused deposition modeling. Finite-element analysis, uniaxial tensile tests, and digital image correlation (DIC) are used to characterize the geometries and identify parameters that significantly influence their mechanical performance. These findings reveal significant differences between geometric designs, where DH geometries performed significantly better than AT geometries, exhibiting a 190% increase in the maximum force, F_max, and a 340% increase in the fracture toughness, W. Compared to the control groups (i.e., flat, inset, and 90° interfaces), F_max and W values increased by 500%–990% and 350%–1200%, respectively. The findings of this study can serve as a guideline for the design and fabrication of efficient soft–hard interfaces with performances close to predicted values.

Biocompatible and shape-morphing metallic structures have been proposed for musculoskeletal applications to provide structural support to bony tissues. However, fabricating these structures to conform to a wide range of curvatures, including both single and double curvatures, remains a significant challenge. In this study, we present and analyze structures featuring a regular tiling network connected by spherical joints, forming a chain mail-like mechanism capable of adapting to complex geometries with clay-like flexibility. Simulations using a multibody kinematics model show that parameters such as unit cell shape, dimension ratios, and substrate curvature affect the shape-matching abilities of the structure. Experimental validation using specimens additively manufactured through laser-based powder bed fusion (from Ti6Al4V) and full-field strain measurements performed through digital image correlation confirms the simulation results, demonstrating that reducing structural density (i.e., fewer bodies, struts, and joints per unit area) improves shape adaptability. However, the improved shape morphing capability often comes at the expense of mechanical strength under uni-axial tensile loads. These findings provide a framework for optimizing structures designed to achieve efficient surface conformance and adaptability in load-bearing applications.

Incorporating shape-morphing capability into 3D microprinting enables the fabrication of 4D-printed microarchitectures as proof-of-concept actuators for potential use in soft robotics and microfluidic systems. The ability of these 3D microstructures to actuate rapidly and reversibly enables precise, non-invasive, and controllable deformation. In this study, we investigated the programmable shape-morphing behavior of 3D microarchitectures fabricated using two-photon polymerization (2PP) of a well-established temperature-responsive hydrogel, poly(N-isopropylacrylamide) (pNIPAM). We first systematically studied how 2PP 3D printing parameters (e.g., laser power, scanning speed) and the chemical composition of pNIPAM, including monomer and crosslinker, influence the shape morphing of bilayer microstructures within a temperature range of ~ 32 °C to 60 °C. The (thermo)mechanical properties of the hydrogels, including the Young’s modulus, thermal expansion coefficients, and angular deflection, were also measured at different laser doses and temperatures. Based on these experimental measurements, we calibrated a thermomechanical model capable of predicting the shape morphing of 4D-printed microarchitectures. These microarchitectures served as proof-of-concept actuators, demonstrating the potential of programmable microscale soft robotics and microfluidic systems. The findings provide design guidelines for engineering stimuli-responsive 3D microstructures, highlighting limitations and opportunities for future integration into functional soft robotic or microfluidic systems made of a single material.

Mechanical characterization of three-dimensional (3D) printed meta-biomaterials is rapidly becoming a crucial step in the development of novel medical device concepts, including those used in functionally graded implants for orthopedic applications. Finite element simulations are a valid, FDA-acknowledged alternative to experimental tests, which are time-consuming, expensive, and labor-intensive. However, when applied to 3D-printed meta-biomaterials, state-of-the-art finite element modeling approaches are becoming increasingly complex, while their accuracy remains limited. A critical condition for accurate simulation results is the identification of correct modelling parameters. This study proposes a machine learning-based strategy for identifying model parameters, including material properties and model boundary conditions, to enable accurate simulations of macro-scale mechanical behavior. To achieve this goal, a physics-informed artificial neural network model (PIANN) was developed and trained using data generated through a fully automated finite element modeling workflow. Subsequently, the PIANN model was then tested using real experimental force-displacement data as its input. The experimental data from 3D-printed structures were used to predict the associated parameters for finite element modeling. Finally, the workflow was validated by qualitatively and quantitatively comparing simulation results to the experimental data. Based on these results, we concluded that the proposed workflow could identify model parameters such that the predictions of associated finite element simulations are in agreement with experimental observations. Furthermore, resulting finite element models were found to outperform state-of-the-art models in terms of both quantitative and qualitative accuracy. Therefore, the proposed strategy has the potential to facilitate the broader application of finite element simulations in evaluating 3D-printed parts, in general, and 3D-printed meta-biomaterials, in particular.

Medical devices contribute to the carbon footprint generated by the healthcare sector. The development of implants and biomaterials using recycled waste materials promotes sustainable advances in tissue engineering. Additively manufactured (AM) bone-substituting biomaterials with multifunctional properties, e.g., biodegradability, antibacterial and osteogenic potential, can contribute to sustainable healthcare. Biodegradable biomaterials eliminate secondary surgeries to remove implants, reduce post-surgical complications, and enhance patient recovery, thus decreasing the energy usage and waste associated with medical treatments. Herein, we present porous iron (Fe) scaffolds incorporating 20 vol% waste-derived eggshell particles for bone substitution. The Fe-eggshell scaffolds were fabricated using direct ink writing (DIW) technique and underwent post-AM heat treatment. During sintering, the eggshell's main component – CaCO₃, transformed into CaO. Atomic diffusion between α-Fe and CaO phases resulted in the formation of Ca₂Fe₂O₅ phase at the interface. The scaffolds were 70 % porous and displayed a biodegradation rate of 0.11 mm/year. The mechanical properties were comparable to trabecular bone and the scaffolds endured 3 million loading cycles at 0.7σ_y in r-SBF. The scaffolds showed apatite-forming ability, evidenced by the formation of (carbonaceous) hydroxyapatite, which are conducive to preosteoblast adhesion, proliferation, and differentiation. RT-qPCR analysis confirmed the osteogenic potential of the specimens as evidenced by the upregulated expression of osteopontin and osteocalcin as compared to Ti6Al4V controls. Furthermore, the scaffolds exhibited bactericidal activity (>3.9-log CFU reduction) against methicillin-sensitive and multidrug-resistant strains of Staphylococcus aureus and delayed their biofilm formation. Our research showcases the exceptional multifunctionality of DIW Fe-eggshell composite scaffolds for the sustainable development of orthopedic biomaterials. Statement of significance: We aim to improve the biofunctionalities and sustainability of biodegradable bone substitutes, by developing the extrusion-based 3D printed porous Fe composite scaffolds containing eggshell-derived CaO bioceramics. Our results demonstrated that Fe-eggshell scaffolds exhibited hydroxyapatite-forming ability in simulated body fluid, having mechanical properties in the range of trabecular bone even after 4 weeks biodegradation, supported the proliferation of preosteoblasts and upregulated the expression of osteogenic genes. Moreover, the scaffolds were bactericidal against methicillin-sensitive and multi-drug resistant strains Staphylococcus aureus and delayed their biofilm formation.

Organoids are innovative three-dimensional and self-organizing cell cultures of various lineages that can be used to study diverse tissues and organs. Human organoids have dramatically increased our understanding of developmental and disease biology. They provide a patient-specific model to study known diseases, with advantages over animal models, and can also provide insights into emerging and future health threats related to climate change, zoonotic infections, environmental pollutants or even microgravity during space exploration. Furthermore, organoids show potential for regenerative cell therapies and organ transplantation. Still, several challenges for broad clinical application remain, including inefficiencies in initiation and expansion, increasing model complexity and difficulties with upscaling clinical-grade cultures and developing more organ-specific human tissue microenvironments. To achieve the full potential of organoid technology, interdisciplinary efforts are needed, integrating advances from biology, bioengineering, computational science, ethics and clinical research. In this Review, we showcase pivotal achievements in epithelial organoid research and technologies and provide an outlook for the future of organoids in advancing human health and medicine.

This Roadmap surveys the diversity of different approaches for characterising, modelling and designing metamaterials. It contains articles covering the wide range of physical settings in which metamaterials have been realised, from acoustics and electromagnetics to water waves and mechanical systems. In doing so, we highlight synergies between the many different physical domains and identify commonality between the main challenges. The articles also survey a variety of different strategies and philosophies, from analytic methods such as classical homogenisation to numerical optimisation and data-driven approaches. We highlight how the challenging and many-degree-of-freedom nature of metamaterial design problems call for techniques to be used in partnership, such that physical modelling and intuition can be combined with the computational might of modern optimisation and machine learning to facilitate future breakthroughs in the field.

Existing deep learning (DL) networks are primarily trained on adult datasets and may not always generalize to pediatric populations, where growth plays a major role. Here, we investigated improving semantic segmentation outcomes of pediatric hand phalanges from radiographs without relying on fully pediatric training datasets, which are scarce. First, alternative DL networks (FCN-8, FCN-32, U-Net, Inception U-Net, and DeepLabv3+) were trained with manually segmented radiographs of near-skeletally-mature (NSM) subjects and their performances were evaluated using mean intersection-over-union (Mean IoU) and multiclass Dice scores. DeepLabv3+ and Inception U-Net performed the best for NSM segmentation, with Mean IoU scores of 0.899 ± 0.035 and 0.887 ± 0.062, respectively. These networks were then used to investigate zero pediatric data (scaling-based data augmentation) and minimal pediatric data (incremental pediatric data substitution) approaches to improve age-domain generalizability. The minimal pediatric data approach proved effective, with a 20 % pediatric data inclusion leading to an up to 21.1 % increase in Mean IoU for pediatric subjects compared to networks trained exclusively on NSM subjects. Furthermore, no adverse effects of this approach were found when tested on NSM subjects, and there were even improvements in performance for Inception U-Net. To conclude, we highlight that networks utilizing multi-scale filters perform best for the semantic segmentation of hand phalanges. We further demonstrate that a minimal inclusion of pediatric training data can markedly improve age-domain generalizability for semantic segmentation tasks. This removes the difficult task of gathering large training datasets of pediatric subjects, which is often impractical, if not impossible.

Additively manufactured (AM) biodegradable zinc alloys hold huge potential as promising candidates for bone defect and fracture repair, thanks to their suitable biodegradation rates and acceptable biocompatibility. However, the mechanical properties of AM zinc alloys developed so far, ductility in particular, fall short of the requirements for bone substitution. Here, we present Zn-1Mn and Zn-1Mn-0.4Mg alloy implants with unique microstructures, fabricated using laser powder bed fusion (LPBF). Notably, the LPBF Zn-Mn-Mg alloy exhibited an extraordinary balance of strength and ductility, with an ultimate tensile strength of 289 MPa, yield strength of 213.5 MPa, and elongation over 20 %, outperforming all previously reported AM zinc alloys. The simultaneously enhanced strength and ductility of the ternary alloy were attributed to the strong grain-refining effect of the Mg₂Zn₁₁ second phase and the synthetic strengthening caused by the dispersion of the MnZn₁₃ and Mg₂Zn₁₁ second phases inside the grains and at the grain boundaries. In addition, both alloys had similar rates of in vitro biodegradation (∼0.15 mm/year), properly aligned with the bone remodeling process, while also demonstrating favorable biocompatibility and upregulating multiple osteogenic markers. The Zn-Mn-Mg alloy showed even better osteogenic potential than the Zn-Mn alloy, owing to the addition of Mg. The combined attributes of the LPBF Zn-Mn-Mg ternary alloy indicated huge potential for its use as a bone repair material, especially for load-bearing bone fixation. Statement of significance: The mechanical properties of previously developed additively manufactured biodegradable zinc alloys, especially ductility, have not met the requirements for bone repair. Using laser powder bed fusion (LPBF), we fabricated Zn-1Mn and Zn-1Mn-0.4Mg alloy implants with unique microstructures. The LPBF Zn-Mn-Mg alloy demonstrated an exceptional balance of strength and ductility, achieving a tensile strength of 289 MPa, yield strength of 213.5 MPa, and elongation over 20 %, surpassing all reported AM zinc alloys. This study is the first to produce a directly printed biodegradable alloy meeting the mechanical requirements for bone fixation devices without post-processing. Additionally, the alloy exhibited moderate a biodegradation rate and excellent biocompatibility, underscoring its potential for load-bearing bone repair applications.

Additively manufactured (AM) iron (Fe)-based scaffolds have been developed as promising biodegradable bone-substituting biomaterials. Multi-material extrusion-based 3D printing has recently yielded Fe-manganese (Mn) alloy-based scaffolds that can resolve ferromagnetism and cytotoxicity associated with Fe-based biomaterials. Herein, we, for the first time, present the findings from in vivo study on extrusion-based AM FeMn-akermanite (Ak) scaffolds for critical-size bone defect repair. The scaffolds comprised Fe, 35 wt% Mn, and 20 or 30 vol% Ak, with microporous struts and 61–63 % porosity. Both scaffolds exhibited mechanical properties within the range of trabecular bone and provided suitable sites for Ca/P deposition during in vitro biodegradation. In vitro cell cultures demonstrated favorable cell responses without negating the osteogenic potential of cells. An in vivo study was conducted in a murine semi-orthotopic subcutaneous model. With this model, 4 bovine bone plugs were implanted subcutaneously with critical-size defects created at their cores. Scaffolds were placed into these critical-size defects to assess biodegradation and bone formation. After 16 weeks, the volume of scaffolds decreased by 6–8 %. The FeMn-20Ak scaffolds retained their yield strength and elastic modulus during the 16 weeks in vivo, whereas the mechanical integrity of FeMn-30Ak scaffolds deteriorated after mechanical push-out tests. Excellent osseointegration of both scaffold groups was apparent. 3D reconstruction of CT images revealed that FeMn-30Ak scaffolds had more newly formed tissue in the macro-pores than FeMn-20Ak. Altogether, our findings demonstrate the potential of AM FeMn-Ak scaffolds as biodegradable bone substitutes, encouraging further in vivo research in a large animal model.

Recent developments in metal additive manufacturing (AM) processes have resulted in the fabrication of functional parts with reliable, reproducible, and predictable properties. Powder bed fusion (PBF) (e.g., selective laser melting and electron beam melting) and directed energy deposition (DED) techniques are the most common categories of AM technologies used for processing metals and their alloys. While the PBF processes can manufacture complex parts with structural gradations, the DED process offers advantages toward multi-material structures. Solid-state AM processes are also being developed for specific applications where sheet metals are readily available. This chapter addresses some critical aspects of various AM techniques, including processing parameters, material types, and geometrical design effects on the final productʼs quality, functionality, and properties. Finally, we discuss the challenges, limitations, and future outlooks for AM of metallic materials.

The Poisson's ratio and elastic modulus are two parameters determining the elastic behavior of biomaterials. While the effects of elastic modulus on the cell response is widely studied, very little is known regarding the effects of the Poisson's ratio. The micro-architecture of meta-biomaterials determines not only the Poisson's ratio but also several other parameters that also influence cell response, such as porosity, pore size, and effective elastic modulus. It is, therefore, very challenging to isolate the effects of the Poisson's ratio from those of other micro-architectural parameters. Here, we computationally design meta-biomaterials with controlled Poisson's ratios, ranging between -0.74 and +0.74, while maintaining consistent porosity, pore size, and effective elastic modulus. The 3D meta-biomaterials were additively manufactured at the micro-scale using two-photon polymerization (2PP), and were mechanically evaluated at the meso‑scale. The response of murine preosteoblasts to these meta-biomaterials was then studied using in vitro cell culture models. Meta-biomaterials with positive Poisson's ratios resulted in higher metabolic activity than those with negative values. The cells could attach and infiltrate all meta-biomaterials from the bottom to the top, fully covering the scaffolds after 17 days of culture. Interestingly, the meta-biomaterials exhibited different cell-induced deformations (e.g., shrinkage or local bending) as observed via scanning electron microscopy. The outcomes of osteogenic differentiation (i.e., Runx2 immunofluorescent staining) and matrix mineralization (i.e., Alizarin red staining) assays indicated the significant potential impact of these meta-biomaterials in the field of bone tissue engineering, paving the way for the development of advanced bone meta-implants. Statement of significance: We studied the influence of Poisson's ratio on bone cell response in meta-biomaterials. While elastic modulus effects are well-studied, the impact of Poisson's ratio, especially negative values found in architected biomaterials, remains largely unexplored. The complexity arises from intertwined micro-architectural parameters, such as porosity and elastic modulus, making it challenging to isolate the Poisson's ratio. To overcome this limitation, this study employed rational computational design to create meta-biomaterials with controlled Poisson's ratios, alongside consistent effective elastic modulus, porosity, and pore size. The study reveals that two-photon polymerized 3D meta-biomaterials with positive Poisson's ratios displayed higher metabolic activity, while all the developed meta-biomaterials supported osteogenic differentiation of preosteoblasts as well as matrix mineralization. The outcomes pave the way for the development of advanced 3D bone tissue models and meta-implants.

Additively manufactured (AM) biodegradable zinc (Zn) alloys have recently emerged as promising porous bone-substituting materials, due to their moderate degradation rates, good biocompatibility, geometrically ordered microarchitectures, and bone-mimicking mechanical properties. While AM Zn alloy porous scaffolds mimicking the mechanical properties of trabecular bone have been previously reported, mimicking the mechanical properties of cortical bone remains a formidable challenge. To overcome this challenge, we developed the AM Zn-3Mg alloy. We used laser powder bed fusion to process Zn-3Mg and compared it with pure Zn. The AM Zn-3Mg alloy exhibited significantly refined grains and a unique microstructure with interlaced α-Zn/Mg₂Zn₁₁ phases. The compressive properties of the solid Zn-3Mg specimens greatly exceeded their tensile properties, with a compressive yield strength of up to 601 MPa and an ultimate strain of >60 %. We then designed and fabricated functionally graded porous structures with a solid core and achieved cortical bone-mimicking mechanical properties, including a compressive yield strength of >120 MPa and an elastic modulus of ≈20 GPa. The biodegradation rates of the Zn-3Mg specimens were lower than those of pure Zn and could be adjusted by tuning the AM process parameters. The Zn-3Mg specimens also exhibited improved biocompatibility as compared to pure Zn, including higher metabolic activity and enhanced osteogenic behavior of MC3T3 cells cultured with the extracts from the Zn-3Mg alloy specimens. Altogether, these results marked major progress in developing AM porous biodegradable metallic bone substitutes, which paved the way toward clinical adoption of Zn-based scaffolds for the treatment of load-bearing bony defects. Statement of significance: Our study presents a significant advancement in the realm of biodegradable metallic bone substitutes through the development of an additively manufactured Zn-3Mg alloy. This novel alloy showcases refined grains and a distinctive microstructure, enabling the fabrication of functionally graded porous structures with mechanical properties resembling cortical bone. The achieved compressive yield strength and elastic modulus signify a critical leap toward mimicking the mechanical behavior of load-bearing bone. Moreover, our findings reveal tunable biodegradation rates and enhanced biocompatibility compared to pure Zn, emphasizing the potential clinical utility of Zn-based scaffolds for treating load-bearing bony defects. This breakthrough opens doors for the wider adoption of zinc-based materials in regenerative orthopedics.

4D (bio-)printing endows 3D printed (bio-)materials with multiple functionalities and dynamic properties. 4D printed materials have been recently used in biomedical engineering for the design and fabrication of biomedical devices, such as stents, occluders, microneedles, smart 3D-cell engineered microenvironments, drug delivery systems, wound closures, and implantable medical devices. However, the success of 4D printing relies on the rational design of 4D printed objects, the selection of smart materials, and the availability of appropriate types of external (multi-)stimuli. Here, this work first highlights the different types of smart materials, external stimuli, and design strategies used in 4D (bio-)printing. Then, it presents a critical review of the biomedical applications of 4D printing and discusses the future directions of biomedical research in this exciting area, including in vivo tissue regeneration studies, the implementation of multiple materials with reversible shape memory behaviors, the creation of fast shape-transformation responses, the ability to operate at the microscale, untethered activation and control, and the application of (machine learning-based) modeling approaches to predict the structure–property and design–shape transformation relationships of 4D (bio)printed constructs.