Although some progress has been made in the treatment of cancer, challenges remain. In recent years, advancements in nanotechnology and stem cell therapy have provided new approaches for use in regenerative medicine and cancer treatment. Among them, magnetic nanomaterials have attracted widespread attention in the field of regenerative medicine and cancer; this is because they have high levels of safety and low levels of invasibility, promote stem cell differentiation, and affect biological nerve signals. In contrast to pure magnetic stimulation, magnetic nanomaterials can act as amplifiers of an applied electromagnetic field in vivo, and by generating different effects (thermal, electrical, magnetic, mechanical, etc.), the corresponding ion channels are activated, thus enabling the modulation of neuronal activity with higher levels of precision and local modulation. In this review, first, we focused on the relationship between biological nerve signals and stem cell differentiation, and tumor development. In addition, the effects of magnetic nanomaterials on biological neural signals and the tumor environment were discussed. Finally, we introduced the application of magnetic-nanomaterial-mediated electromagnetic stimulation in regenerative medicine and its potential in the field of cancer therapy.

Recent years have seen significant progress in imaging technology, which has deepened our understanding of the brain. However, challenges remain in detecting nanoscale structures like the extracellular space and mapping complex neural networks. Various chemical probes have been developed to improve the imaging precision of microstructures and functional imaging. Yet, the integration of imaging technologies and probes stays underdeveloped. Differences in disciplinary paradigms, unclear quantitative and qualitative relationships, and a lack of optimized cross-functional orchestration are the main causes of this problem. In this paper, we propose potential strategies to achieve deep integration of probes and equipment for high-quality imaging. Specifically, we advocate for three main approaches: (1) designing imaging sequences driven by probe properties, (2) constructing quantitative dose-effect models, and (3) leveraging artificial intelligence for system optimization. Importantly, these strategies establish an adaptable framework that will facilitate the seamless integration of emerging probe and equipment technologies in future brain science research. Ultimately, this enhanced integration promises to advance our knowledge of brain function and structure.