Pancreatic adenocarcinoma is the most lethal of the solid tumors and the fourth-leading cause of cancer-related death in North America. Matrix metalloproteinases (MMPs) have long been targeted as a potential anticancer therapy because of their seminal role in angiogenesis and extracellular matrix (ECM) degradation of tumor survival and invasion. However, the inhibition specificity to MMPs and the molecular-level understanding of the inhibition mechanism remain largely unresolved. Here, we found that endohedral metallofullerenol Gd@C ₈₂ (OH) ₂₂ can successfully inhibit the neoplastic activity with experiments at animal, tissue, and cellular levels. Gd@C ₈₂ (OH) ₂₂ effectively blocks tumor growth in human pancreatic cancer xenografts in a nude mouse model. Enzyme activity assays also show Gd@C ₈₂ (OH) ₂₂ not only suppresses the expression of MMPs but also significantly reduces their activities. We then applied large-scale molecular-dynamics simulations to illustrate the molecular mechanism by studying the Gd@C ₈₂ (OH) ₂₂ –MMP-9 interactions in atomic detail. Our data demonstrated that Gd@C ₈₂ (OH) ₂₂ inhibits MMP-9 mainly via an exocite interaction, whereas the well-known zinc catalytic site only plays a minimal role. Steered by nonspecific electrostatic, hydrophobic, and specific hydrogen-bonding interactions, Gd@C ₈₂ (OH) ₂₂ exhibits specific binding modes near the ligand-specificity loop S1′, thereby inhibiting MMP-9 activity. Both the suppression of MMP expression and specific binding mode make Gd@C ₈₂ (OH) ₂₂ a potentially more effective nanomedicine for pancreatic cancer than traditional medicines, which usually target the proteolytic sites directly but fail in selective inhibition. Our findings provide insights for de novo design of nanomedicines for fatal diseases such as pancreatic cancer.

Purpose: Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models.Methods: This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results.Results: To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described.Conclusion: Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators.