Exosomes, abundant in blood, deliver various molecules to recipient cells. Endothelial cells are directly exposed to circulating substances. However, how endothelial cells respond to serum exosomes (SExos) and the implications in diabetes-associated vasculopathy have never been explored. In the present study, we showed that SExos from diabetic mice ( SExos) were taken up by aortic endothelial cells, which severely impaired endothelial function in nondiabetic mice. The exosomal proteins, rather than RNAs, mostly account for SExos-induced endothelial dysfunction. Comparative proteomics analysis showed significant increase of arginase 1 in SExos. Silence or overexpression of arginase 1 confirmed its essential role in SExos-induced endothelial dysfunction. This study is a demonstration that SExos deliver arginase 1 protein to endothelial cells, representing a cellular mechanism during development of diabetic endothelial dysfunction. The results expand the scope of blood-borne substances that monitor vascular homeostasis.

This study shows that laparoscopic hepatectomy for liver malignancy in selected patients is a safe, effective, and oncologically efficient procedure with better short-term results and similar survival outcomes to open hepatectomy for liver malignancy after midterm follow-up.

Background: Early-stage diagnosis and treatment can improve survival rates of liver cancer patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for differential diagnosis of liver tumors. However, MRI diagnosis is affected by subjective experience, so deep learning may supply a new diagnostic strategy. We used convolutional neural networks (CNNs) to develop a deep learning system (DLS) to classify liver tumors based on enhanced MR images, unenhanced MR images, and clinical data including text and laboratory test results. Methods: Using data from 1,210 patients with liver tumors (N = 31,608 images), we trained CNNs to get seven-way classifiers, binary classifiers, and three-way malignancyclassifiers (Model A-Model G). Models were validated in an external independent extended cohort of 201 patients (N = 6,816 images). The area under receiver operating characteristic (ROC) curve (AUC) were compared across different models. We also compared the sensitivity and specificity of models with the performance of three experienced radiologists. Results: Deep learning achieves a performance on par with three experienced radiologists on classifying liver tumors in seven categories. Using only unenhanced images, CNN performs well in distinguishing malignant from benign liver tumors (AUC, 0.946; 95% CI 0.914-0.979 vs. 0.951; 0.919-0.982, P = 0.664). New CNN combining unenhanced images with clinical data greatly improved the performance of classifying malignancies as hepatocellular carcinoma (AUC, 0.985; 95% CI 0.960-1.000), metastatic tumors (0.998; 0.989-1.000), and other primary malignancies (0.963; 0.896-1.000), and the agreement with pathology was 91.9%.These models mined diagnostic information in unenhanced images and clinical data by deepneural-network, which were different to previous methods that utilized enhanced images. The sensitivity and specificity of almost every category in these models reached the same high level compared to three experienced radiologists. Zhen et al. Deep Learning for Liver Tumor Diagnosis Conclusion: Trained with data in various acquisition conditions, DLS that integrated these models could be used as an accurate and time-saving assisted-diagnostic strategy for liver tumors in clinical settings, even in the absence of contrast agents. DLS therefore has the potential to avoid contrast-related side effects and reduce economic costs associated with current standard MRI inspection practices for liver tumor patients.