To study the relationship between vascular endothelial growth factor (VEGF) and formation and repair of engineering bone, second-generation bone marrow stromal cells (BMSCs) of New Zealand white rabbits that were separated in vitro were transfected with VEGF 165 gene vectors by adenovirus to detect gene expressions. Transfected BMSCs and β-tricalcium phosphate material were complexed and implanted at the femoral injury sites of the study group (n = 12), and the control group (n = 12) were implanted with engineering bones that were not transfected with VEGF. Femoral recoveries of the two groups were observed on the 15th, 30th, 45th and 60th days, and their vascularization and ossification statuses were observed by immunohistochemical methods. The BMSCs transfected with VEGF highly expressed VEGF genes and excreted VEGF. The two groups both experienced increased vascularization and bone volume after implantation (t = 7.92, P<0.05), and the increases of the study group were significantly higher than those of the control group (t = 6.92, P<0.05). VEGF is clinically applicable because it can accelerate the formation and repair of engineering bone by promoting vascularization and ossification.
Deep learning analysis of images and text unfolds new horizons in medicine. However, analysis of transcriptomic data, the cause of biological and pathological changes, is hampered by structural complexity distinctive from images and text. Here we conduct unsupervised training on more than 20,000 human normal and tumor transcriptomic data and show that the resulting Deep-Autoencoder, DeepT2Vec, has successfully extracted informative features and embedded transcriptomes into 30-dimensional Transcriptomic Feature Vectors (TFVs). We demonstrate that the TFVs could recapitulate expression patterns and be used to track tissue origins. Trained on these extracted features only, a supervised classifier, DeepC, can effectively distinguish tumors from normal samples with an accuracy of 90% for Pan-Cancer and reach an average 94% for specific cancers. Training on a connected network, the accuracy is further increased to 96% for Pan-Cancer. Together, our study shows that deep learning with autoencoder is suitable for transcriptomic analysis, and DeepT2Vec could be successfully applied to distinguish cancers, normal tissues, and other potential traits with limited samples.
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