Liver cancer is a malignant tumor with high morbidity and mortality, which has a tremendous negative impact on human survival. However, it is a challenging task to recognize tens of thousands of histopathological images of liver cancer by naked eye, which poses numerous challenges to inexperienced clinicians. In addition, factors such as long time-consuming, tedious work and huge number of images impose a great burden on clinical diagnosis. Therefore, our study combines convolutional neural networks with histopathology images and adopts a feature fusion approach to help clinicians efficiently discriminate the differentiation types of primary hepatocellular carcinoma histopathology images, thus improving their diagnostic efficiency and relieving their work pressure. In this study, for the first time, 73 patients with different differentiation types of primary liver cancer tumors were classified. We performed an adequate classification evaluation of liver cancer differentiation types using four pre-trained deep convolutional neural networks and nine different machine learning (ML) classifiers on a dataset of liver cancer histopathology images with multiple differentiation types. And the test set accuracy, validation set accuracy, running time with different strategies, precision, recall and F1 value were used for adequate comparative evaluation. Proved by experimental results, fusion networks (FuNet) structure is a good choice, which covers both channel attention and spatial attention, and suppresses channel interference with less information. Meanwhile, it can clarify the importance of each spatial location by learning the weights of different locations in space, then apply it to the study of classification of multi-differentiated types of liver cancer. In addition, in most cases, the Stacking-based integrated learning classifier outperforms other ML classifiers in the classification task of multi-differentiation types of liver cancer with the FuNet fusion strategy after dimensionality reduction of the fused features by principle component analysis (PCA) features, and a satisfactory result of 72.46% is achieved in the test set, which has certain practicality.

Exosomes serve as intercellular communication vectors and are involved in a broad range of physiological functions. Although exosome‐based therapies have demonstrated diverse functional potential, the regulatory mechanisms underlying their biogenesis and secretion remain poorly understood. Here, we report that TEAD1 functions as a molecular switch, dramatically enhancing the synthesis and secretion of exosomes. Mechanistically, TEAD1 enhances exosome secretion by promoting the expression of exosome secretion‐associated proteins RAB11, CD9, and SNAP23. We found that TEAD1 enhances exosome secretion from adipose‐derived mesenchymal stem cells, thereby promoting skin wound healing in diabetic mice. Similarly, TEAD1 promotes the release of exosomes from bone marrow‐derived mesenchymal stem cells, thereby facilitating spinal cord injury (SCI) repair. Our study elucidates a novel role for TEAD1 in driving exosome secretion in different cell types, highlighting the therapeutic potential of TEAD1 in enhancing tissue regeneration, particularly in diabetic wound healing and SCI repair.