上海中医药大学发表最新Cell子刊论文

Core Viewpoint - The integration of artificial intelligence (AI) with biomaterials and biofabrication is revolutionizing the simulation of tumor extracellular matrix (ECM), enhancing physiological relevance and establishing patient-specific drug testing platforms [30]. Group 1: AI in Tumor ECM Modeling - AI methods are incorporated into three key stages of tumor ECM modeling: material formulation, optimization of biofabrication processes, and post-manufacturing analysis [4]. - AI enables the rational development of bioinks with tunable mechanical, chemical, and biological properties, improving printing accuracy and consistency [4]. - AI-enhanced in vitro tumor modeling aids in the rational design and real-time optimization of engineered tumor models, providing powerful tools for drug discovery and cancer mechanism research [4]. Group 2: Limitations of Current Methods - Existing in vitro models struggle to replicate the biochemical complexity and dynamic physical properties of the ECM, limiting their effectiveness [2][7]. - Advances in biomaterials and biofabrication technologies have allowed for the simulation of certain ECM features, but challenges remain in capturing the inherent complexity and dynamic behavior of ECM [7]. Group 3: AI's Role in Biofabrication - AI improves precision and adaptability in the three stages of ECM modeling: pre-process, in-process, and post-process [7]. - In the pre-process stage, AI facilitates the design of biomaterials through predictive modeling and exploration of initial design options [7]. - During the in-process stage, AI enables real-time monitoring and optimization of biofabrication methods, ensuring accurate replication of tumor ECM structures and properties [7]. - In the post-process stage, AI assists in high-throughput analysis of ECM datasets, linking biophysical properties to tumor behavior [7]. Group 4: Future Directions - Establishing standardized datasets, improving model interpretability, and incorporating clinical validation are crucial for bridging the gap between AI-driven ECM modeling and real-world translational impact [4]. - The framework developed for tumor ECM modeling can be extended to other diseases involving ECM dysfunction, such as fibrosis, neurodegenerative diseases, and inflammatory bowel disease [4].