A novel minimally invasive OFM technique with orthotopic transplantation of hUC-MSCs and in vivo monitoring of liver metabolic microenvironment in liver fibrosis treatment

Background Mesenchymal stromal cells (MSCs) transplantation showed promising therapeutic results in liver fibrosis. However, efficient cell delivery method is urgently needed and the therapeutic mechanism remains unclear. This study focused on developing a minimally invasive open-flow microperfusion (OFM) technique, which combined orthotopic transplantation of human umbilical cord-derived (hUC)-MSCs to liver and in vivo monitoring of liver microenvironment in mice with CCl4-induced liver fibrosis. Methods The therapeutic potential of OFM route was evaluated by comparing OFM with intravenous (IV) injection route in terms of hUC-MSCs engraftment at the fibrosis liver, liver histopathological features, liver function and fibrotic markers expression after hUC-MSCs administration. OFM was also applied to sample liver interstitial fluid in vivo, and subsequent metabolomic analysis was performed to investigate metabolic changes in liver microenvironment. Results Compared with IV route, OFM route caused more hUC-MSCs accumulation in the liver and was more effective in improving the remodeling of liver structure and reducing collagen deposition in fibrotic liver. OFM transplantation of hUC-MSCs reduced blood ALT, AST, ALP and TBIL levels and increased ALB levels, to a greater extent than IV route. And OFM route appeared to have a more pronounced effect on ameliorating the CCl4-induced up-regulation of the fibrotic markers, such as α-SMA, collagen I and TGF-β. In vivo monitoring of liver microenvironment demonstrated the metabolic perturbations induced by pathological condition and treatment intervention. Two metabolites and eight metabolic pathways, which were most likely to be associated with the liver fibrosis progression, were regulated by hUC-MSCs administration. Conclusion The results demonstrated that the novel OFM technique would be useful for hUC-MSCs transplantation in liver fibrosis treatment and for monitoring of the liver metabolic microenvironment to explore the underlying therapeutic mechanisms.

Comparison and Prediction of Hyperthyroidism Accuracy Rate Using Novel Deep Learning Technology and Vivo Monitoring

Aim: The aim of this research work is to determine the presence of hyperthyroidism using modern algorithms, and comparing the accuracy rate between deep learning algorithms and vivo monitoring. Materials and methods: Data collection containing ultrasound images from kaggle's website was used in this research. Samples were considered as (N=23) for Deep learning algorithm and (N=23) for vivo monitoring in accordance to total sample size calculated using clinical.com. The accuracy was calculated by using DPLA with a standard data set. Results: Comparison of accuracy rate is done by independent sample test using SPSS software. There is a statistically indifference between Deep learning algorithm and in vivo monitoring. Deep learning algorithm (87.89%) showed better results in comparison to vivo monitoring (83.32%). Conclusion: Deep learning algorithms appear to give better accuracy than in vivo monitoring to predict hyperthyroidism.