Therapeutic potential of menstrual blood stem cells in treating acute liver failure.

BACKGROUND: Acute liver failure (ALF) is a significant and complex hepatic insult that may rapidly progress to life-threatening conditions. Recently, menstrual blood stem cells (MenSCs) have been identified as a group of easily accessible mesenchymal stem cells with the advantages of non-invasive acquisition, low immunogenicity, a greater capacity of self-renewal and multi-lineage differentiation, making them promising candidates for stem cell-based therapy to revolutionize the treatment strategies for liver failure. AIM: To investigate the therapeutic potential of MenSCs for treating ALF in pigs and to dynamically trace the biodistribution of transplanted cells. METHODS: MenSCs were labeled in vitro with PKH26, a lipophilic fluorescent dye. The treatment group received immediate transplantation of PKH26-labelled MenSCs (2.5 × 106/kg) via the portal vein after D-galactosamine injection, and the control group underwent sham operation. The survival time, liver function, and hepatic pathological changes were compared between the two groups. Three major organs (liver, lungs and spleen) were extracted from animals and imaged directly with the In vivo Imaging System (IVIS) at the predetermined time points. The regions of interest were drawn to quantify the cell uptake in different organs. RESULTS: The labelling procedure did not affect the morphology, viability or multipotential differentiation of MenSCs. Biochemical analysis showed that the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL) and prothrombin time (PT) measured at selected time points 24 h after transplantation were significantly decreased in the treatment group (P < 0.05). The survival time of ALF animals was prolonged in the treatment group compared with the control group (75.75 ± 5.11 h vs 53.75 ± 2.37 h, log rank, P < 0.001). The liver pathological tissue in the MenSC treatment group showed obviously increased numbers of remaining hepatocytes and a comparatively slight necrotic degree and area. In addition, the IVIS imaging revealed that PKH26-positive MenSCs were clearly retained in the liver initially and then diffused through the systemic circulation. Interestingly, the signal intensity in the liver increased obviously at 36 h, which corresponded to the biochemical result that liver function deteriorated most rapidly at 24 - 36 h. CONCLUSION: Our study demonstrates the therapeutic efficacy and homing ability of transplanted MenSCs in a large animal model of ALF and suggests that MenSC transplantation could be a promising strategy for treating ALF.

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.