Advanced Healthcare Materials最新文献

Absolute uterine factor infertility (AUFI) remains untreatable with current assisted reproduction techniques. Although decellularized uterine extracellular matrix (dUECM) holds promise for uterine reconstruction, its clinical translation is limited by rapid degradation and poor angiogenic capacity. Herein, a dual-component system was developed by combining oligomeric proanthocyanidin (OPC)-crosslinked dUECM with an injectable VEGF-loaded nanocomposite hydrogel. The OPC-dUECM scaffold (1 mg/mL OPC solution) restored native-like tensile strength, reduced collagenase-induced degradation to 52%, and facilitated cellular infiltration. The VEGF-EPL-HP nanocomposite hydrogel was fabricated by encapsulating VEGF (350 ng/mL) in ε-polylysine/heparin-poloxamer (EPL-HP) nanoparticles dispersed within a thermosensitive hydrogel. The hydrogel exhibited a gelation temperature of 23.2°C, provided sustained VEGF release over 7 days, and showed negligible hemolytic toxicity. In vitro, this hydrogel significantly enhanced the migration and tube formation of human umbilical vein endothelial cells. At 90 days post-surgery in a rat uterine defect model, the combined OPC-dUECM + VEGF-EPL-HP group achieved full endometrial and myometrial regeneration, accompanied by elevated microvessel density, attenuated inflammation, and negligible fibrotic lesions. Collectively, this study validated a synergistic scaffold-hydrogel platform enabling concurrent structural reconstruction and vascularization of the injured uterus, offering a clinically translatable strategy for AUFI patients.

In 2022, a global Mpox outbreak occurred, and the World Health Organization (WHO) declared it a Public Health Emergency of International Concern (PHEIC). Although the outbreak was quickly constrained, new cases of infection associated with mutated virions continue to be documented worldwide. In recent global health crises, there is an urgent need to develop rapid, cost-effective diagnostic assays. While nucleic acid assays (e.g., PCR and CRISPR-based methods) represent the current gold standard for Mpox virus (MPXV) detection, point-of-care (POC) assays for Mpox remain limited. Here, we developed a molecularly imprinted polymer (MIP)-based MPXV sensor targeting A29L, a surface protein involved in host cell binding. The MIP sensor exhibits a remarkable detection limit of 7.7 pg/mL (0.54 pM). Moreover, we demonstrated that the MIP sensor detected A29L in artificial interstitial fluid (AIF) with a detection limit of 12.4 pg/mL (0.86 pM), supporting sensitive detection in a lesion-relevant model matrix. The MIP sensor preferentially responded to A29L over MPXV B6R, vaccinia virus A27L, and biointerferents and retained its response after 30 days. The results indicate that the MIP-based MPXV sensor has strong potential for clinical Mpox detection and could serve as a valuable diagnostic platform in future pandemics.

Severe endometrial injury is a leading cause of female infertility, driven by the convergence of inadequate vascularization, persistent inflammation, infection, and excessive fibrosis. Existing therapies address these pathologies in isolation, falling short of the coordinated remodeling essential for functional regeneration. Here, we report an injectable, thermally responsive composite hydrogel that embeds copper-based metal-organic framework (HKUST-1) nanoparticles within a porcine acellular dermal matrix (PADM) network, coupling biomimetic extracellular matrix support with sustained Cu^{2 +} delivery. In vitro, the hydrogel promotes endothelial migration and tubular network formation, shifts macrophage polarization toward the reparative M2 phenotype, and exhibits broad-spectrum bactericidal activity. In a rat endometrial injury model, it increases gland density and CD31⁺ neovascularization while suppressing fibrotic collagen deposition. Crucially, it restores hormonal responsiveness by upregulating estrogen and progesterone receptor expression. These coordinated improvements translate into meaningful fertility restoration, with implanted embryos increasing from 8 ± 2 to 14 ± 1. Transcriptomic profiling indicates coordinated changes in extracellular matrix remodeling and ion transport programs. The PADM@HKUST-1 hydrogel demonstrates that integrating biomimetic scaffolding with sustained bioactive ion delivery achieves comprehensive uterine microenvironment remodeling, offering a translatable approach for clinical management of severe endometrial injury.

The clinical success of immunotherapies is hindered by the absence of robust methodologies for the early and accurate assessment of antitumor immune responses. Integrating engineered biomaterials with artificial intelligence (AI)-driven molecular imaging technology offers a powerful approach for predicting immune efficacy. However, its clinical translation is significantly hindered by several critical limitations, including the complex and costly synthesis of multifunctional imaging probes, insufficient deep-tissue penetration, suboptimal in vivo signal-to-noise ratios, potential immunogenicity and off-target toxicity, and the limited generalizability of deep learning models across heterogeneous patient populations. In view of these pressing scientific challenges, this review focuses on the application of chemical and biomolecular strategies-enhanced engineered biomaterials in promoting the sensitivity and specificity of advanced imaging modalities for visualizing signaling molecules and immune cells in the tumor microenvironment (TME). Meanwhile, recent progress in deep learning-driven image analysis has enabled strong predictive capabilities and automated the understanding of complex immune system dynamics. Together, these technologies provide a transformative platform for non-invasive monitoring, personalized treatment, and response prediction, with the potential to advance precision immuno-oncology and significantly impact both basic research and clinical translation.

Ischemic heart disease is a highly prevalent and debilitating condition throughout the world. Following an ischemic injury, the immune response can become overly active and spread damage to nearby healthy tissue. Because the heart lacks regenerative capacity, preventing excessive damage is critical. Existing immunosuppressive therapeutics provide indiscriminate suppression of inflammatory and repair functions of infiltrating immune cells. Regulatory T cells (Treg) are the body's highly sophisticated system for regulating immunologic activity. These cells can provide powerful immune suppression while amplifying repair pathways through direct stimulation of local cells. Our research group has previously developed a polymeric delivery system comprised of poly(lactic-co-glycolic acid) (PLGA) degradable microparticles (MP) loaded with the Treg-attracting protein, CCL22. In our previous work, we demonstrated CCL22MP attracts Treg to the injection site and can suppress disadvantageous inflammation. Here we combine our Treg-attracting MP technology with Hydrogels to Enhance or Attract Regulatory T cells (HEART). Topically applied HEART significantly reduced infarct size and improved ejection fraction after permanent-ligation MI, and in a cardiac transplant ischemia model reduced graft fibrosis while significantly increasing intragraft Foxp3⁺ Treg at 30 days. Efficacy tracked with injury severity, identifying severe ischemic injury as a setting where this therapeutic strategy may be most beneficial.

Chronic diabetic glucolipotoxicity is associated with progressive skeletal muscle atrophy, bone loss, cellular senescence, and ferroptotic injury. Because ferroptosis inhibition alone may not restore compromised myogenic capacity, we developed a combined stem cell-conditioning strategy integrating fullerol priming with the stem cell screen-enrich-combine(-biomaterials) circulating system (SECCS). This integrated three-dimensional culture platform uses porous β-TCP granules and intermittent medium circulation to generate BMSCs-derived extracellular vesicles (EVs). Among 2D-EVs, F-2D-EVs, SECCS-EVs, and F-SECCS-EVs, F-SECCS-EVs exhibited the strongest anti-ferroptotic and pro-regenerative effects. In glucolipotoxicity-challenged myoblasts, F-SECCS-EVs attenuated ferroptotic injury and enhanced proliferation and myogenic differentiation. Transcriptomic and functional experiments identified FOXO3 as an important mediator linking antioxidant defense with myogenic programs. In diabetic mice, F-SECCS-EVs improved motor function, muscle strength, myofiber cross-sectional area, and myogenic marker expression while modulating macrophage polarization. Free fullerol, 2D-EVs, and F-2D-EVs produced limited recovery, whereas SECCS-EVs were less effective than F-SECCS-EVs. These muscle benefits were accompanied by favorable changes in tibial bone structure. Collectively, these findings support fullerol-primed SECCS culture as a strategy for coordinating ferroptosis control with myogenic recovery in diabetes-associated musculoskeletal degeneration.

Bone regeneration in orthopedics is a great challenge, as efficient recovery affects patient's quality of life. Common methods encounter different limitations regarding diagnosis, implantation, and bone health monitoring. Recent advances in artificial intelligence (AI), specifically machine learning (ML) algorithms, have presented opportunities to enhance these aspects by accurately analyzing imaging data. This provides detailed evaluations that help to determine bone condition and guide treatment strategies. ML models can facilitate a patient-specific approach to select and design intelligent implants by examining extensive biomaterial datasets to identify the best match for each patient's biomechanical and biological needs. Furthermore, ML models can analyze data to accurately predict bone-healing timelines, allowing clinicians to track recovery trends, and make timely interventions in potential complications. In clinical settings, ML tools assist with preoperative planning, postoperative follow-up, and the design of intelligent implants, offering more effective, data-driven decision-making and favorable outcomes in bone regeneration. Looking ahead, while ML advancements show promise for effective orthopedic therapies, challenges persist. ML could revolutionize bone regeneration in various ways and offer predictive insights that promote patient-centered orthopedic care. Altogether, considering the continued evolution of ML, its integration into clinical applications will be crucial for developing truly intelligent bone regeneration therapies.

The intrinsic heterogeneity of solid tumors remains a crucial barrier to understanding cancer biology and predicting therapeutic outcomes. Here, we introduce a scalable and versatile biofabrication platform that enables the controlled assembly of multitype spheroid arrays (MSA) within standard culture microplates. By performing programmable bioprinting within soft, microstructured culture devices, we achieve the precise spatial deposition of multiple cell types into defined microwells, yielding reproducible arrays of heterogeneous 3D tumor spheroids that collectively recapitulate the macro- and micro-heterogeneity of solid cancers. We demonstrate that this approach permits the fine-tuning of spheroid size, composition, and arrangement. It also supports the delayed secondary programmed seeding of cells on pre-grown spheroids, mimicking biological processes such as the invasion of immune cells. These methods enable quantitative drug-response profiling within a single assay. They overcome some critical limitations of current 3D cancer models, introducing the concept of the “cancer-in-a-well” technology. This technology provides a simple-to-use, cost-effective, high-throughput, and highly reproducible platform to recreate the complexity of tumors in vitro using methods and instruments that can be potentially used by any researcher in any cell culture lab. We can foresee a positive impact on cancer biology studies, drug screening, and personalized oncology.

Intelligent Orthopedics

Machine learning arrives in the orthopaedic clinic. The cover shows imaging, patient data and biomaterial design converging on a single question: how a given bone will heal. Algorithms trained on that convergence can flag disease sooner, shape an implant to one skeleton rather than an average, and follow regeneration as it proceeds. More details can be found in the Review Article by Nasim Golafshan, Gorka Orive, and co-workers (DOI: 10.1002/adhm.71447).