Nerve guidance conduits (NGCs) have become a promising alternative for peripheral nerve regeneration; however, the outcome of nerve regeneration and functional recovery is greatly affected by the physical, chemical, and electrical properties of NGCs. In this study, a conductive multiscale filled NGC (MF‐NGC) consisting of electrospun poly(lactide‐co‐caprolactone) (PCL)/collagen nanofibers as the sheath, reduced graphene oxide /PCL microfibers as the backbone, and PCL microfibers as the internal structure for peripheral nerve regeneration is developed. The printed MF‐NGCs presented good permeability, mechanical stability, and electrical conductivity, which further promoted the elongation and growth of Schwann cells and neurite outgrowth of PC12 neuronal cells. Animal studies using a rat sciatic nerve injury model reveal that the MF‐NGCs promote neovascularization and M2 transition through the rapid recruitment of vascular cells and macrophages. Histological and functional assessments of the regenerated nerves confirm that the conductive MF‐NGCs significantly enhance peripheral nerve regeneration, as indicated by improved axon myelination, muscle weight increase, and sciatic nerve function index. This study demonstrates the feasibility of using 3D‐printed conductive MF‐NGCs with hierarchically oriented fibers as functional conduits that can significantly enhance peripheral nerve regeneration.

In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.

Cancer models play critical roles in basic cancer research and precision medicine. However, current in vitro cancer models are limited by their inability to mimic the three-dimensional architecture and heterogeneous tumor microenvironments (TME) of in vivo tumors. Here, we develop an innovative patient-specific lung cancer assembloid (LCA) model by using droplet microfluidic technology based on a microinjection strategy. This method enables precise manipulation of clinical microsamples and rapid generation of LCAs with good intra-batch consistency in size and cell composition by evenly encapsulating patient tumor-derived TME cells and lung cancer organoids inside microgels. LCAs recapitulate the inter- and intratumoral heterogeneity, TME cellular diversity, and genomic and transcriptomic landscape of their parental tumors. LCA model could reconstruct the functional heterogeneity of cancer-associated fibroblasts and reflect the influence of TME on drug responses compared to cancer organoids. Notably, LCAs accurately replicate the clinical outcomes of patients, suggesting the potential of the LCA model to predict personalized treatments. Collectively, our studies provide a valuable method for precisely fabricating cancer assembloids and a promising LCA model for cancer research and personalized medicine.