Adipose-derived stromal cells resemble bone marrow stromal cells in hepatocyte differentiation potential in vitro and in vivo

AIM To investigate whether mesenchymal stem cells(MSCs) from adipose-derived stromal cells(ADSCs) and bone marrow stromal cells(BMSCs) have similar hepatic differentiation potential.METHODS Mouse ADSCs and BMSCs were isolated and cultured. Their morphological and phenotypic characteristics, as well as their multiple differentiation capacity were compared. A new culture system was established to induce ADSCs and BMSCs into functional hepatocytes. Reverse transcription polymerase chain reaction, Western blot, and immunofluorescence analyses were performed to identify the induced hepatocytelike cells. CM-Dil-labeled ADSCs and BMSCs were then transplanted into a mouse model of CCl4-induced acute liver failure. fluorescence microscopy was used to track the transplanted MSCs. Liver function was tested by an automatic biochemistry analyzer, and liver tissue histology was observed by hematoxylin and eosin(HE) staining.RESULTS ADSCs and BMSCs shared a similar morphology and multiple differentiation capacity, as well as a similar phenotype(with expression of CD29 and CD90 and no expression of CD11 b or CD45). Morphologically, ADSCs and BMSCs became round and epithelioid following hepatic induction. These two cell types differentiated into hepatocyte-like cells with similar expression of albumin, cytokeratin 18, cytokeratin 19, alpha fetoprotein, and cytochrome P450. fluorescence microscopy revealed that both ADSCs and BMSCs were observed in the mouse liver at different time points. Compared to the control group, both the function of the injured livers and HE staining showed significant improvement in the ADSC-and BMSC-transplanted mice. There was no significant difference between the two MSC groups.CONCLUSION ADSCs share a similar hepatic differentiation capacity and therapeutic effect with BMSCs in an acute liver failure model. ADSCs may represent an ideal seed cell type for cell transplantation or a bio-artificial liver support system.

Intelligent prediction of RBC demand in trauma patients using decision tree methods

Background:The vital signs of trauma patients are complex and changeable,and the prediction of blood transfusion demand mainly depends on doctors'experience and trauma scoring system;therefore,it cannot be accurately predicted.In this study,a machine learning decision tree algorithm[classification and regression tree(CRT)and eXtreme gradient boosting(XGBoost)]was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.Methods:A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database.The vital signs,laboratory examination parameters and blood transfusion volume were used as variables,and the non-invasive parameters and all(non-invasive+invasive)parameters were used to construct an intelligent prediction model for red blood cell(RBC)demand by logistic regression(LR),CRT and XGBoost.The prediction accuracy of the model was compared with the area under curve(AUC).Results:For non-invasive parameters,the LR method was the best,with an AUC of 0.72[95%confidence interval(CI)0.657–0.775],which was higher than the CRT(AUC 0.69,95%CI 0.633–0.751)and the XGBoost(AUC 0.71,95%CI 0.654–0.756)(P<0.05).The trauma location and shock index are important prediction parameters.For all the prediction parameters,XGBoost was the best,with an AUC of 0.94(95%CI 0.893–0.981),which was higher than the LR(AUC 0.80,95%CI 0.744–0.850)and the CRT(AUC 0.82,95%CI 0.779–0.853)(P<0.05).Haematocrit(Hct)is an important prediction parameter.Conclusions:The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method.It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment,so as to improve the success rate of patient treatment.

Underwater video transceiver designs based on channel state information and video content

Underwater hostile channel conditions challenge video transmission designs. The current designs often treat video coding and transmission schemes as individual modules. In this study, we develop an adaptive transceiver with channel state information(CSI) by taking into account the importance of video components and channel conditions. The design is more effective than the traditional ones. However, in practical systems, perfect CSI may not be available. Therefore, we compare the imperfect CSI case with existing schemes, and validate the effectiveness of our design through simulations and measured channels in terms of a better peak signal-to-noise ratio and a higher video structural similarity index.