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A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-based therapies. Nevertheless, high-throughput investigation of the cell-type-specific biophysical cues associated with stem cell-derived neural interfaces continues to be a significant obstacle to overcome. To this end, we developed a combinatorial nanoarray-based method for high-throughput investigation of neural interface micro-/nanostructures (physical cues comprising geometrical, topographical, and mechanical aspects) and the effects of these complex physical cues on stem cell fate decisions. Furthermore, by applying a machine learning (ML)-based analytical approach to a large number of stem cell-derived neural interfaces, we comprehensively mapped stem cell adhesion, differentiation, and proliferation, which allowed for the cell-type-specific design of biomaterials for neural interfacing, including both adult and human-induced pluripotent stem cells (hiPSCs) with varying genetic backgrounds. In short, we successfully demonstrated how an innovative combinatorial nanoarray and ML-based platform technology can aid with the rational design of stem cell-derived neural interfaces, potentially facilitating precision, and personalized tissue engineering applications. 

Abstract Extracellular vesicles (e.g., exosomes) carrying various biomolecules (e.g., proteins, lipids, and nucleic acids) have rapidly emerged as promising platforms for many biomedical applications. Despite their enormous potential, their heterogeneity in surfaces and sizes, the high complexity of cargo biomolecules, and the inefficient uptake by recipient cells remain critical barriers for their theranostic applications. To address these critical issues, multifunctional nanomaterials, such as magnetic nanomaterials, with their tunable physical, chemical, and biological properties, may play crucial roles in next‐generation extracellular vesicles (EV)‐based disease diagnosis, drug delivery, tissue engineering, and regenerative medicine. As such, one aims to provide cutting‐edge knowledge pertaining to magnetic nanomaterials‐facilitated isolation, detection, and delivery of extracellular vesicles and their associated biomolecules. By engaging the fields of extracellular vesicles and magnetic nanomaterials, it is envisioned that their properties can be effectively combined for optimal outcomes in biomedical applications. 

Abstract Nanoparticle‐based nucleic acid conjugates (NP‐NACs) hold great promise for theragnostic applications. However, several limitations have hindered the realization of their full potential in the clinical treatment of cancer and other diseases. In diagnoses, NP‐NACs suffer from low signal‐to‐noise ratios, while the efficiency of NP‐NACs‐mediated cancer therapies has been limited by the adaptation of alternative prosurvival pathways in cancer cells. The recent emergence of personalized and precision medicine has outlined the importance of having both accurate diagnosis and efficient therapeutics in a single platform. As such, the controlled assembly of hybrid graphene oxide/gold nanoparticle (Au@GO NP)‐based cancer‐specific NACs (Au@GO NP‐NACs) for multimodal imaging and combined therapeutics is reported. The developed Au@GO NP‐NACs show excellent surface‐enhanced Raman scattering (SERS)‐mediated live‐cell cancer detection and multimodal synergistic cancer therapy through the use of photothermal, genetic, and chemotherapeutic strategies. Synergistic and selective killing of cancer cells are then demonstrated using in vitro microfluidic models. Moreover, with the distinctive advantages of the Au@GO NP‐NACs for cancer theragnostics, precision cancer treatment through the detection of cancer cells in vivo using SERS followed by efficient ablation of tumors is shown. Therefore, the Au@GO NP‐NACs can pave a new road for advanced disease theragnostics.