Recently, a review article by the research teams led by Professor Chen Shiyi from Huashan Hospital, Professor Dai Li Hua from the Affiliated City Dong Hospital, and Director Wu Guanghui from the Affiliated Ningde Hospital was published in the renowned journal Research. The study emphasizes how robotic exoskeletons can significantly improve cancer patients' mobility, muscle strength, and gait training, while also contributing to psychological recovery through personalized support and real-time feedback. This innovation is poised to reshape the future of cancer rehabilitation.

Recently, research teams led by Professor Cai Songhua from the Shenzhen Branch of the Cancer Hospital of the Chinese Academy of Medical Sciences, Professor Mao Wenjun from Wuxi People's Hospital, and Professor Tang Bufu from Zhongshan Hospital affiliated to Fudan University have focused on the immune microenvironment and lung cancer radiotherapy. They demonstrated a combined therapeutic approach targeting SPP1⁺ macrophages to enhance radiotherapy sensitivity, with the findings published under the title“ SPP1⁺ TAM: CD8⁺ T cell crosstalk associates with blocking radiotherapy efficacy in lung cancer” on Research.

Recently, Professor Jie Kong, Associate Professor Jin Liang, and their research team from Northwestern Polytechnical University have systematically reviewed the design strategies, printing processes, and application progress of magnetically responsive materials in 4D printing. This study thoroughly analyzes the working principles and performance optimization methods of magnetically responsive shape memory materials, and demonstrates their innovative applications in fields such as biomedical tissue engineering, robotics, and intelligent devices. The relevant work was published in Research under the title "4D Printing of Magnetically Responsive Materials and Their Applications" (Research, 2025, DOI: 10.34133/research.0847).

To address this challenge, Professor Liu Rui's team from South China University of Technology introduces a novel quantitative method, cell-specific causal network entropy (CCNE), which analyzes gene-level causal relationships to infer distinct causal networks for individual cells and identify pre-transition states in complex biological systems.