Background: Embryonic stem cells (ES) have a great potential for cell-based therapies in a regenerative medicine. However, the ethical and safety issues limit its clinical application. ES-derived extracellular vesicles (ES-EVs) have been reported suppress cellular senescence. Mesenchymal stem cells (MSCs) are widely used for clinical cell therapy. In this study, we investigated the beneficial effects of ES-EVs on aging MSCs to further enhancing their therapeutic effects.Methods: In vitro, we explored the rejuvenating effects of ES-EVs on senescent MSCs by senescence-associated β-gal (SA-β-gal) staining, immunostaining, and DNA damage foci analysis. The therapeutic effect of senescent MSC pre-treated with ES-EVs was also evaluated by using mouse cutaneous wound model.Results: We found that ES-EVs significantly rejuvenated the senescent MSCs in vitro and improve the therapeutic effects of MSCs in a mouse cutaneous wound model. In addition, we also identified that the IGF1/PI3K/AKT pathway mediated the antisenescence effects of ES-EVs on MSCs.Conclusions: Our results suggested that ES cells derived-extracellular vesicles possess the antisenescence properties, which significantly rejuvenate the senescent MSCs and enhance the therapeutic effects of MSCs. This strategy might emerge as a novel therapeutic strategy for MSCs clinical application.

Background: Histopathological grading is a significant risk factor for postsurgical recurrence in hepatocellular carcinoma (HCC). Preoperative knowledge of histopathological grading could provide instructive guidance for individualized treatment decision-making in HCC management. Purpose: This study aims to develop and validate a newly proposed deep learning model to predict histopathological grading in HCC with improved accuracy. Methods: In this dual-centre study, we retrospectively enrolled 384 HCC patients with complete clinical, pathological and radiological data. Aiming to synthesize radiological information derived from both tumour parenchyma and peritumoral microenvironment regions, a modelling strategy based on a multi-scale and multi-region dense connected convolutional neural network (MSMR-DenseCNNs) was proposed to predict histopathological grading using preoperative contrast enhanced computed tomography (CT) images. Multiscale inputs were defined as three-scale enlargement of an original minimum bounding box in width and height by given pixels, which correspondingly contained more peritumoral analysis areas with the enlargement.Multi-region inputs were defined as three regions of interest (ROIs) including a squared ROI, a precisely delineated tumour ROI, and a peritumoral tissue ROI. The DenseCNN structure was designed to consist of a shallow feature extraction layer, dense block module, and transition and attention module. The proposed MSMR-DenseCNN was pretrained by the ImageNet dataset to capture basic graphic characteristics from the images and was retrained by the collected retrospective CT images. The predictive ability of the MSMR-DenseCNN models on triphasic

BackgroundWe aimed to explore the risk factors for hemorrhage of esophagogastric varices (EGVs) in patients with hepatitis B cirrhosis and to construct a novel nomogram model based on the spleen volume expansion rate to predict the risk of esophagogastric varices bleeding.MethodsUnivariate and multivariate logistic regression analysis was used to analyze the risk factors for EGVs bleeding. Nomograms were established based on the multivariate analysis results. The predictive accuracy of the nomograms was assessed using the area under the curve (AUC or C-index) of the receiver operating characteristic (ROC) and calibration curves. Decision curve analysis was used to determine the clinical benefit of the nomogram. We created a nomogram of the best predictive models.ResultsA total of 142 patients' hepatitis B cirrhosis with esophagogastric varices were included in this study, of whom 85 (59.9%) had a history of EGVs bleeding and 57 (40.1%) had no EGVs bleeding. The spleen volume expansion rate, serum sodium levels (mmol/L), hemoglobin levels (g/L), and prothrombin time (s) were independent predictors for EGVs bleeding in patients with hepatitis B liver cirrhosis (P < 0.05). The above predictors were included in the nomogram prediction model. The area under the ROC curve (AUROC) of the nomogram was 0.781, the C-index obtained by internal validation was 0.757, and the calibration prediction curve fit well with the ideal curve. The AUROCs of the PLT-MELD and APRI were 0.648 and 0.548, respectively.ConclusionIn this study, a novel nomogram for predicting the risk of EGVs bleeding in patients with hepatitis B cirrhosis was successfully constructed by combining the spleen volume expansion rate, serum sodium levels, hemoglobin levels, and prothrombin time. The predictive model can provide clinicians with a reference to help them make clinical decisions.