Osteochondral tissue engineering remains a significant challenge due to the complex biochemical and mechanical gradients between cartilage and subchondral bone. In this study, we present the development of a 3D-printed, multi-material magnetic hydrogel scaffold with tunable stiffness. To achieve this, we formulated a gelatin-alginate hydrogel matrix with various levels of embedded iron oxide magnetic particles (MPs) to create controlled hard-soft interfacial regions. The optimal composition (i.e . , 2.5% gelatin, 5% alginate, and 10% (w/v) MPs) demonstrated magnetorheological behavior, including increased effective Young’s modulus from 159 to 172 kPa and decreased viscosity from 175 to 145 kPa·s under a static magnetic field. Later, we evaluated scaffold printability through filament collapse, fusion, and porous scaffold tests, identifying a Gel:Alg ratio of 1:2 as optimal for structural fidelity. Mechanical and rheological characterizations confirmed that MPs significantly enhanced stiffness and responsiveness to magnetic fields. A checkered scaffold design enabled the fabrication of alternating hard and soft regions, and a bi-layered scaffold demonstrated improved interfacial adhesion. Micro-computed tomography provided quantitative evidence of magnetic field-induced particle redistribution within the hydrogel, confirming internal reorganization beyond bulk mechanical response. Importantly, in vitro live/dead assays confirmed that scaffold fabrication and magnetic functionality did not adversely affect cell viability. This platform offers a tunable, bioactive, and magneto-responsive scaffold architecture with potential for osteochondral repair or other applications requiring dynamic interface tissue engineering.

Real-time mapping of small forces with micrometer resolution is essential for studying soft and biological matter. However, existing techniques are slow, limited in spatial sampling or require non-planar substrates that can perturb cell behavior. Here we present silicon sensor arrays for rapid surface force mapping that operate using the elasto-optically induced wavelength shift in thin polymer-cladded optical ring resonators. Using a nano-indenter, we demonstrate that the sensor array reaches a force resolution down to 12 µN and shows a linear response. We present both a five-ring linear array and a 10×5 two-dimensional array at 15 µm pitch, and demonstrate the feasibility of localization and force mapping of a spherical nanoindentation tip. Combined measurement of forces by nano-indenter and the optical ring resonator sensor presents a methodology for calibrating this type of photonic force sensor. Moreover, good correspondence between measurements and finite element simulations provides evidence for the proposed operation mechanism. The shown combination of biocompatible claddings, strong opto-mechanical coupling, and foundry-ready photonics, presents a route towards scalable, real-time force mapping for soft-matter metrology, tactile interfaces, and in vitro mechanobiology.

A bstract This study was the first attempt to design, fabricate, and evaluate multi-material bone scaffolds composed of Ti6Al4V and akermanite (Ca₂Mg(Si₂O₇), Ak), produced via direct ink writing (DIW), followed by sintering. Two scaffold architectures were developed (i.e., monolithic and core-shell) aimed at combining the mechanical strength of the Ti alloy with the osteoinductive properties of Ak. Uniaxial compression testing demonstrated that the core-shell scaffolds exhibited higher relative-density-normalized elastic moduli (up to 7.65 ± 0.35 GPa) and yield strengths (up to 444.7 ± 8.1 MPa) than the monolithic designs, namely Ti6Al4V-only scaffolds (elastic modulus: 4.29 ± 0.18 GPa; yield strength: 230.9 ± 1.7 MPa) and 90% Ti6Al4V/10% Ak composite scaffolds (elastic modulus: 3.05 ± 0.08 GPa; yield strength: 24.7 ± 1.4 MPa).The enhanced mechanical performance was attributed to interfacial reinforcement and optimized material distribution. Bioactivity assays in r-SBF revealed surface Ca–P deposition on akermanite-containing scaffolds by SEM and EDS, a response not observed on Ti6Al4V only specimens. Complementary ICP-OES showed marked depletion of phosphate and calcium ions, consistent with rapid HAp nucleation and growth, and substantial silicon release in composite samples, a known pro-osteogenic stimulus. Cell culture assays further confirmed the cytocompatibility of the Ti6Al4V, composite and core-shell scaffolds for preosteoblasts. Furthermore, SEM imaging showed that all the scaffolds supported cell attachment and evidenced a distinct cell spatial distribution depending on scaffold composition and architecture. These results contribute to advancing the scaffold design for bone repair and regeneration by proposing DIW-fabricated Ti6Al7V/Ak core-shell scaffolds that show potential as customizable, load-bearing implants with improved mechanical properties and surface bioactivity relative to the Ti6Al4V scaffolds.

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 techniques to culture and differentiate adult and pluripotent stem cells into diverse cell types over the past decades has sparked an increasing interest in the use of cells for organ regeneration. Such therapies aim to replace lost or damaged cells with functional ones. This can be achieved either through tissue engraftment of therapeutic cells or via their paracrine effects on resident cells, thereby offering a potential cure for debilitating degenerative diseases. The development of regenerative cell therapies, however, is ultimately complex. Effective cell therapy requires the delivery and successful engraftment of therapeutic cells to the correct location or sufficient paracrine activity, while ensuring safety is key to gaining support from funders, caregivers, and patients. A wide variety of cell sources has been used for the development of regenerative cell therapies, ranging from mesenchymal stromal cells (MSC) that act to stimulate local progenitor cells through their secretome to tissue-specific cell types differentiated from adult or pluripotent stem cells and organoids that engraft in tissues. While cell administration to patients is challenging based on both practical and ethical perspectives, the development of techniques to preserve transplant organs ex situ on machine perfusion devices offers a unique opportunity for studying regenerative cell therapy for organ repair in a safe and controllable environment. The present review addresses the current progress of cell therapy for organ regeneration of the intestine, kidney, liver, lung, and heart and discusses the challenges and opportunities of this potentially curing therapeutic approach.

Zinc (Zn) has emerged as a promising biodegradable metal for bone tissue engineering, yet fabricating porous scaffolds via laser-based additive manufacturing (AM) remains challenging due to Zn evaporation. This study presents the successful fabrication of porous Zn scaffolds via extrusion-based AM through systematic ink formulation and sintering optimization. Printability was optimized through rheological analysis of 50–56 vol % Zn-loaded inks, while sintering conditions were refined within a precise temperature window. SEM and micro-CT characterized sintering quality and quantified pore defects. Optimal scaffolds, printed with 53 vol % ink and sintered at 415 °C for 5 h, achieved 40 ± 3% absolute porosity with minimal evaporation, attributed to a hybrid solid-liquid phase sintering mechanism. The scaffolds exhibited trabecular bone-matching mechanical properties with compressive yield strength of 16.1 ± 1.3 MPa and elastic modulus of 1.4 ± 0.1 GPa. In vitro biodegradation in r-SBF showed a corrosion rate of 0.03 ± 0.01 mm/year after 28 days, with biodegradation products including ZnO, Ca₃(PO₄)₂, and Zn-phosphate/chloride hydrates. Electrochemical tests demonstrated increasing polarization resistance (21.1 ± 3.8 kΩ·cm²) and passivation behavior. Indirect cytocompatibility assays showed > 90% metabolic activity for MC3T3-E1 cells in ≤ 50% Zn extracts, while direct seeding confirmed cell adhesion. These results establish extrusion-based AM as a viable route for fabricating Zn scaffolds with tailored porosity, controlled biodegradation, bone-like properties, and acceptable cytocompatibility, advancing the development of Zn-based biodegradable implants. Statement of significance Although laser-based additive manufacturing of pure zinc and its alloys is becoming increasingly mature, its inherent drawbacks, such as evaporation-driven composition loss and melt-pool instabilities, remain non-negligible and underscore the need to develop and apply alternative AM strategies for Zn-based bone scaffolds. We presented an extrusion-based route to fabricate porous Zn bone scaffolds and establish an end-to-end workflow spanning ink formulation, debinding, sintering, and multi-scale characterization. By tailoring the binder system and defining a robust thermal window, we achieved high-fidelity architectures with densified struts. The resulting scaffolds displayed bone-mimicking mechanical behavior together with predictable in-vitro degradation and cytocompatibility. Our work positions extrusion-based 3D printing as a practical manufacturing platform for Zn-based biodegradable bone substitutes.

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.

Acetabular defects pose significant challenges in orthopedic surgery, particularly in revision total hip arthroplasty (THA). Here, we design, additively manufacture, and evaluate shape-morphing porous implants with kinematic structures to address these defects. Three defect types were examined using synthetic hemipelvis models: posterior wall, cranial-posterior combination, and central-posterior defects. The implants were secured with screws and bone cement, and their surface conformity was assessed through micro computed tomography (µCT). Biomechanical performance was evaluated under quasi-static compression and cyclic loading conditions. Results demonstrated high surface conformity of the flexible mesh across all defect types, with minimal differences from healthy acetabula (< 10 mm). The mesh implants exhibited strong load-bearing capacity, with failures occurring only in the pubic region of the hemipelvis, while both the implants and mesh-cement interfaces remained intact. The implants withstood cyclic loading simulating half the body weight of a 80 kg patient for >1000,000 loading cycles with no evidence of fatigue failure, further confirming their durability. These findings suggest that the flexible mesh implant provides a potential solution for complex acetabular defects, offering anatomical conformity and mechanical stability, even in cases where conventional mesh grafts might be inadequate. Future studies, including cadaveric testing and clinical trials, are necessary to further validate these results in (pre-)clinical settings. Statement of significance: This study addresses the need for adaptable solutions to complex acetabular defects in revision total hip arthroplasty (THA). Traditional implants struggle to conform to severe bone loss and irregular geometries, risking suboptimal fit, and implant migration. We introduce a 3D-printed, shape-morphing porous implant with kinematic structures, offering high anatomical conformity, mechanical robustness, and support for bone graft integration. Combining the adaptability of patient-specific implants with the efficiency of standard designs, this implant reduces lead times while enabling a tailored fit. This innovative approach provides a reliable solution for managing complex defects, addressing limitations of conventional implants, and improving outcomes in orthopedic reconstruction.

Miniaturized optomechanical devices are well-suited for applications in the automotive, aerospace, and biomedical sectors due to their compact size and lightweight design, which make them ideal for measuring small forces [1]. The significant refractive index contrast between the silicon waveguide core and the silicon dioxide cladding in silicon-on-insulator (SOI) structures enables submicron core dimensions. This design supports single-mode propagation at a wavelength of 1.55 µm, with strong optical confinement that allows for sharp bends with radii as small as a few micrometers [2]. Micro-optical-electromechanical systems (MOEMS) offer several advantages over traditional micro-electromechanical systems (MEMS), including higher optical sensitivity, simplicity, cost-effectiveness, and suitability for use in electromagnetically active environments and ultra-high vacuum conditions [3].

OBJECTIVE: The treatment of mature biofilm in implant-associated infections (IAI) has become increasingly challenging, mainly due to the rise of antibiotic-resistant bacteria. While many antibacterial biomaterials harness their functionality through their surface properties, alternating magnetic field (AMF)-induced hyperthermia offers an approach from a fundamentally different angle. METHOD: To summarize and compare the practice of assessing AMF-induced hyperthermia in vitro and in vivo as treatment for implant-associated infections and the efficacy of this therapy, a literature search was conducted and 18 articles were selected based on relevance. RESULTS AND CONCLUSION: The studies have demonstrated that AMF-induced hyperthermia can effectively eliminate biofilms as a standalone treatment or in combination with antimicrobials. Although thermal tissue damage is an inherent concern, it can be controlled and reduced by implementing short intermittent heating patterns around 65-75ºC while still preserving antibacterial efficacy. However, clear guidelines for evaluating safety, particularly regarding thermal injury, are still lacking and should be a key focus of future work.

Living biological tissue is a complex system, constantly growing and changing in response to external and internal stimuli. These processes lead to remarkable and intricate changes in shape. Modeling and understanding both natural and pathological (or abnormal) changes in the shape of anatomical structures is highly relevant, with applications in diagnostic, prognostic, and therapeutic healthcare. Nevertheless, modeling the longitudinal shape change of biological tissue is a non-trivial task due to its inherent nonlinear nature. In this review, we highlight several existing methodologies and tools for modeling longitudinal shape change (i.e., spatiotemporal shape modeling). These methods range from diffeomorphic metric mapping to deep-learning based approaches (e.g., autoencoders, generative networks, recurrent neural networks, etc.). We discuss the synergistic combinations of existing technologies and potential directions for future research, underscoring key deficiencies in the current research landscape.

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 levels of physical activity or high BMI during puberty could negatively influence bone and cartilage development. Little is known about the effects of loading on patellar and femoral bone shape in a young population. Therefore, we aim to identify the association between 3D patella and femur shape and biomechanical loading in a young adolescent population. Participants were selected from an ongoing cohort study (Generation-R study). Participants that underwent knee-MRI at 13 years-old follow-up were included. Patellae and femora were segmented from these MRIs and using these 3D models, statistical shape modeling was performed. Generalized estimating equations were used to analyze the association between loading (BMI, physical activity and sports participation) and shape variation. Bonferroni correction was used to correct for multiple testing. 1912 participants underwent MRI of which 3638 patellae and 3355 femora were included in the statistical shape models. Nine patellar (modes 1–7, 10 and 11) and nine femoral (modes 1–3, 6–10 and 14) shape modes were associated with BMI. Sports participation at thirteen years old was associated with one patellar (mode 1) and two femoral (modes 1 and 6) shape modes. One shape mode (mode 12) was associated with sports participation at 9 and 13 years old. Sports participation and BMI were significantly associated with bone shape variations. BMI was associated with most shape variations found in our statistical shape models, emphasizing the significant impact of BMI on bone morphology during adolescence with implications for musculoskeletal health and injury prevention.

Magnetic particles (MPs), due to their unique physical and chemical properties, have emerged as promising tools in bone tissue engineering. Their incorporation into scaffolds or uptake by bone cells, combined with exposure to external magnetic fields, has been shown in various studies to enhance cell adhesion, proliferation, and osteogenic differentiation. In this review, the state-of-the-art is presented on the synthesis processes of magnetized cells (MCs) and magnetized scaffolds (MSs), as well as the biological and mechanical effects of scaffold-free MCs, cell-seeded MSs, and MC-seeded MSs under externally applied magnetic fields on bone tissue engineering. Furthermore, the specific applications of these systems is highlighted, such as non-contact mechanical stimulation, and discuss their application to advance bone tissue engineering strategies.

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.

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.

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.