医学
肝细胞癌
移植
肝硬化
胃肠病学
内科学
肝移植
外科
乙型肝炎
肝炎
肝病
凝血酶原时间
骨髓
作者
Liang Peng,Dong‐Ying Xie,Bingliang Lin,Jing Liu,Haipeng Zhu,Chan Xie,Yubao Zheng,Zhiliang Gao
出处
期刊:Hepatology
[Wiley]
日期:2011-05-23
卷期号:54 (3): 820-828
被引量:344
摘要
Our study aimed to investigate the short-term efficacy and long-term prognosis of liver failure patients caused by hepatitis B after a single transplantation with autologous marrow mesenchymal stem cells (MMSCs). A total of 527 inpatients with liver failure caused by hepatitis B were recruited and received the same medical treatments, among whom 53 patients underwent a single transplantation with autologous MMSCs. A total of 105 patients matched for age, sex, and biochemical indexes, including alanine aminotransferase (ALT), albumin, total bilirubin (TBIL), prothrombin time (PT), and Model for End-Stage Liver Disease (MELD), comprised the control group. A total of 120 mL of bone marrow was obtained from each patient and then diluted and separated. Then, the MMSC suspension was slowly transfused into the liver through the proper hepatic artery. The success rate of transplantation was 100%, without serious side effects or complications. Levels of ALB, TBIL, and PT and MELD score of patients in the transplantation group were markedly improved from 2-3 weeks after transplantation, compared with those in the control group. At 192 weeks of follow-up, there were no dramatic differences in incidence of hepatocellular carcinoma (HCC) or mortality between the two groups. Additionally, there were no significant differences in the incidence of HCC or mortality between patients with and without cirrhosis in the transplantation group.Autologous MMSC transplantation is safe for liver failure patients caused by chronic hepatitis B. Short-term efficacy was favorable, but long-term outcomes were not markedly improved. In respect to several parameters, this method is preferable for patients with liver cirrhosis and may have potential for reducing their incidence of HCC and mortality.
科研通智能强力驱动
Strongly Powered by AbleSci AI