Anye Zhang
Zhejiang University
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Featured researches published by Anye Zhang.
Biotechnology and Bioengineering | 2011
Guoliang Lv; Lifu Zhao; Anye Zhang; Weibo Du; Yu Chen; Chengbo Yu; Xiaoping Pan; Yimin Zhang; Tao Song; Jiansheng Xu; Lanjuan Li
Bioartificial liver (BAL) support system has been proposed as potential treatment method for end‐stage liver diseases. We described an improved BAL system based on a choanoid fluidized bed bioreactor containing alginate–chitosan encapsulated primary porcine hepatocytes. The feasibility, safety, and efficiency of this device were estimated using an allogeneic fulminant hepatic failure (FHF) model. FHF was induced with intravenous administration of D‐galactosamine. Thirty FHF pigs were divided into three groups: (1) an FHF group which was only given intensive care; (2) a sham BAL group which was treated with the BAL system with empty encapsulation, and (3) a BAL group which was treated with the BAL system containing encapsulated freshly isolated primary porcine hepatocytes. The survival times and biochemical parameters of these animals were measured, and properties of the encapsulations and hepatocytes before and after perfusion were also evaluated. Compared to the two control groups, the BAL‐treated group had prolonged the survival time and decreased the blood lactate levels, blood glucose, and amino acids remained stable. No obvious ruptured beads or statistical decline in viability or function of encapsulated hepatocytes were observed. This new fluidized bed BAL system is safe and efficient. It may represent a feasible alternative in the treatment of liver failure. Biotechnol. Bioeng. 2011;108:2229–2236.
Digestive and Liver Disease | 2013
Haijun Huang; Anye Zhang; Hongcui Cao; Haifeng Lu; Baohong Wang; Qing Xie; Wei Xu; Lanjuan Li
BACKGROUND The study of faeces offers a unique opportunity to observe cooperation between the microbiome and the metabolism of mammalian hosts, an essential element in the study of the human metabolome. In the present study, a global metabolomics approach was used to identify metabolites differentially excreted in the faeces of cirrhotic patients compared to controls. METHODS Seventeen cirrhotic patients and 24 healthy individuals were recruited. Faecal metabolites were detected through non-targeted reversed-phase ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry. RESULTS A total of 9215 peaks were detected. Using unequal variance t-tests, 2393 peaks were observed with P≤0.05, approximately 74.0% of which were due to decreased faecal metabolite concentrations in liver cirrhosis vs. healthy controls. Integrating multivariate data analyses, we identified six major groups of metabolites. Relative levels of identified metabolites were as follows: strong increase in lysophosphatidylcholines, aromatic amino acids, fatty acids, and acylcarnitines, and a dramatic decrease in bile acids and bile pigments. CONCLUSION With severe hepatic injury in patients with liver cirrhosis, malabsorption occurs along with disorders of fatty acid metabolism, potentially due to changes in gut microflora.
Scientific Reports | 2016
Pengcheng Zhou; Li Shao; Lifu Zhao; Guoliang Lv; Xiaoping Pan; Anye Zhang; J. Li; Ning Zhou; Deying Chen; Lanjuan Li
Bioartificial livers may act as a promising therapy for fulminant hepatic failure (FHF) with better accessibility and less injury compared to orthotopic liver transplantation. This study aims to evaluate the efficacy and safety of a fluidized bed bioartificial liver (FBBAL) and to explore its therapeutic mechanisms based on metabolomics. FHF was induced by D-galactosamine. Eighteen hours later, pigs were treated with an FBBAL containing encapsulated primary porcine hepatocytes (B group), with a sham FBBAL (containing cell-free capsules, S group) or with only intensive care (C group) for 6 h. Serum samples were assayed using ultra-performance liquid chromatography-mass spectrometry. The difference in survival time (51.6 ± 7.9 h vs. 49.3 ± 6.6 h) and serum metabolome was negligible between the S and C groups, whereas FBBAL treatment significantly prolonged survival time (70.4 ± 11.5h, P < 0.01) and perturbed the serum metabolome, resulting in a marked decrease in phosphatidylcholines, lysophosphatidylcholines, sphingomyelinase, and fatty acids and an increase in conjugated bile acids. The FBBAL exhibits some liver functions and may exert its therapeutic effect by altering the serum metabolome of FHF pigs. Moreover, alginate–chitosan capsules have less influence on serum metabolites. Nevertheless, the alterations were not universally beneficial, revealing that much should be done to improve the FBBAL.
Metabolomics | 2012
Pengcheng Zhou; J. Li; Li Shao; Guoliang Lv; Lifu Zhao; Haijun Huang; Anye Zhang; Xiaoping Pan; Wei Liu; Qing Xie; Deying Chen; Yongzheng Guo; Shaorui Hao; Wei Xu; Lanjuan Li
Fulminant hepatic failure (FHF) is still an intractable disease associated with serious metabolic disorder. Investigating the dynamic changes of serum metabolites during the development of FHF would facilitate revealing the pathogenesis and also promote its treatment. Therefore, this study characterized the dynamic metabonome of serum from FHF Pigs using ultra performance liquid chromatography–mass spectrometry. Based on multiple statistical analysis of the resulting dataset, three types of up-regulated and one type of down-regulated patterns were delineated. Each pattern demonstrated distinct trends at different stages during the whole process of FHF, implying the differential clinical significance of them. Specifically, aromatic amino acids (Pattern 1) and lysophosphatidylcholines (LPCs) (Pattern 4) might be good markers for evaluating the severity of FHF, while some conjugated bile acids, long chain acylcarnitines (Pattern 2) and Glycocholic acid (Pattern 3) could indicate liver injury in the early stage. Inspired from the PCA plot that the pathogenetic condition of FHF aggravated with sampling time, a linear discriminant analysis (LDA) model based on phenylalanine and LPC 18:1 were further constructed for evaluating the severity of FHF. The leave-one-out cross-validation accuracy of 91.67% for the training set and the prediction accuracy of 92.31% for the external validation set confirmed its excellent performance. In conclusion, findings obtained from the present study, including four types of Dynamic Patterns of serum metabolites during FHF development and an LDA model for evaluating the severity of FHF, will be of great help to the research and management of FHF in the future.
biomedical engineering and informatics | 2011
Lifu Zhao; Guoliang Lv; Yimin Zhang; Anye Zhang; J. Li; Pengcheng Zhou; Xiaoping Pan; Yuemei Chen; Xiaopeng Yu; Chengbo Yu; Wei Xu; Ying Yang; Lanjuan Li; Jiansheng Xu; Tao Song; Yang Yu
Background/introduction: Artificial liver support treatment is a promising alternative to liver transplantation. An ideal artificial liver support system (ALSS) should be a combination of a nonbiological liver (NBL) device and a bioreactor based bioartificial liver (BAL). Material and methods: A novel ALSS which can not only fulfill toxin-removal functions of NBL but also provide biotransformation and synthetic functions of BAL is constructed. The unique dual-chamber reservoir can improve the efficiency of material exchange. The funnel-shaped fluidized bed bioreactor can provide an ideal physiological environment for hepatocytes. Quick bubble handling function improves the security during treatment. The software design provides error correction function. Our control center is an industrial personal computer and most components are integrated via the RS485 buses. The whole control system consists of three parts: a pump drive module, a sensor network and a human-machine communication interface. To verify our design, we test the system on miniature pigs. Results: The system runs normally in all treatment modes and meets the clinical requirements. Functions of all components are verified. Conclusions: The system provides a reliable research platform for artificial liver support treatment.
Archive | 2012
Jiajia Chen; Lanjuan Li; Guoliang Lv; Xiaoping Pan; Chengbo Yu; Anye Zhang; Yimin Zhang; Lifu Zhao; Chunxia Zhu
Archive | 2011
Lanjuan Li; Lifu Zhao; Anye Zhang; Guoliang Lv; Xiaoping Pan; Yimin Zhang; Pengcheng Zhou; J. Li; Xiaopeng Yu; Ying Yang; Yuemei Chen; Jiajia Chen
Archive | 2010
Jing Guo; Lanjuan Li; Guoliang Lu; Xiaoping Pan; Anye Zhang; Lifu Zhao; Chunxia Zhu
Archive | 2012
Lanjuan Li; Pengcheng Zhou; J. Li; Lifu Zhao; Guoliang Lv; Anye Zhang; Chengbo Yu; Xiaoping Pan; Yimin Zhang; Xiaopeng Yu
Archive | 2010
Jiajia Chen; Lanjuan Li; Guoliang Lu; Xiaoping Pan; Chengbo Yu; Anye Zhang; Yimin Zhang; Lifu Zhao; Chunxia Zhu