No description available.

This thesis sought to investigate novel methods for protecting the liver from ischaemia reperfusion injury in the context of liver transplantation. Research in the heart, brain and kidneys has suggested that hypoxia inducible factor (HIF) may play a key role in the delayed phase of ischaemic preconditioning and can protect organs for up to 3 days. However, although there is good evidence for the potential of HIF to protect organs from ischaemia, the HIF pathway still presents some what of a paradox because it targets both pro-death (e.g. BNIP3,NIX) as well as pro-survival genes (e.g. HO-I, EPO). HIF is primarily controlled by 3 oxygen dependent prolyl hydroxylases (PHD 1 , PHD2, PHD3), and inhibition of these prolyl hydroxylases leads to HIF activation. It was hypothesised that differential inhibition of PHD 1,2 or 3 may result in selective gene regulation and may confer greater or less protection against ischaemia reperfusion injury. To investigate this hypothesis mouse embryonic fibroblasts (MEFs) were isolated from PHDl, 2, and 3 knock-out (KO) embryos and compared to MEFs derived from WT littermate controls. In these MEFs, cell growth and proliferation, as well as cell survival following exposure to anoxia and inducers of apoptosis was studied. The principal findings were that PHD2 is the dominant regulator of HIF in normoxia. PHD2 knock-out MEFs exhibited glycolytic metabolism and had a lower oxygen consumption compared to wild-type MEFs. Gene array studies confirmed the dominant role of PHD2 but also demonstrated that PHD 1 upregulates a number of HIF target genes, albeit to a lesser extent than PHD2. There were no differences, however, in susceptibility to hypoxic injury in the PHDl, 2, and 3 knock-out MEFs compared to wild-type controls. A further aim of the study was to investigate whether prolyl hydroxylase inhibition using dimethyloxalyglycerine (DMOG) may protect the liver in a rodent model of ischaemia reperfusion injury. DMOG effectively upregulated HIF and IllF target genes. Serum transaminases (AST and AL T) were significantly lower in the DMOG treated animals compared to the normal saline treated controls 24 hours following ischaemia. This protection was similar to the protection conferred by surgically induced ischaemic preconditioning. This thesis provides important insights into the individual function of the prolyl hydroxylases and provides preliminary evidence that prolyl hydroxylase inhibitors may be useful in the treatment of ischaemia reperfusion injury in liver transplantation.

University of Macau / Institute of Chinese Medical Sciences

Non-alcoholic steatohepatitis (NASH) is a chronic liver disease associated with the worldwide spread of obesity. NASH predisposes development of fibrosis and hepatocellular carcinoma (HCC), but has no approved therapy due to incomplete understanding of the pathogenesis. Notch signaling normally specifies cell fate during development, but here we investigate how this pathway becomes dysregulated in NASH and contributes to fibrosis and cancer. In the first study, we show that hepatocyte Notch activity tracks with disease severity and treatment response in NASH patients, and is similarly increased in a mouse model of diet-induced NASH and liver fibrosis. Different genetic models demonstrate causatively that hepatocyte Notch induces liver fibrosis via secretion of the fibrogenic factor Osteopontin that activates hepatic stellate cells (HSCs), while pharmacologic inhibition of hepatocyte Notch could ameliorate NASH-associated fibrosis. In the second study, we research how hepatocyte Notch activation leads to HCC in mice on NASH diet. Transcriptomic analysis reveals nerve growth factor (NGF) as a Notch target gene in hepatocytes, and the abundance of hepatocyte NGF precursor protein (proNGF) is uniquely associated with HCC. We provide evidence that proNGF may facilitate HCC growth and expansion in a non-cell autonomous manner by inducing HSC deactivation and fibrosis remodeling. In summary, hepatocyte Notch maladaptively contributes to fibrogenesis and possibly HCC expansion by directly signaling to HSCs at different stages of NASH progression, and could be an accessible target for treatment of NASH-associated liver pathologies.

Non-alcoholic fatty liver disease (NAFLD) is a worldwide health problem which includes steatosis (triglyceride accumulation alone), non-alcoholic steatohepatitis (NASH, with liver inflammation), fibrosis, cirrhosis and hepatocellular carcinoma. Liver fibrosis, which is a reversible response, is the final phase of most chronic liver disease and is characterized by accumulation of extracellular matrix (ECM) from activated hepatic stellate cells (HSCs). Glucocorticoids (GCs) regulate many aspects of metabolism involved in NAFLD. Also, GCs limit HSC activation in vitro. Tissue GC levels are regulated by 11β- hydroxysteroid dehydrogenase-1 (11β-HSD1) which converts inactive 11- dehydrocorticosterone (DHC) into active corticosterone. Previous studies demonstrate that 11β-HSD1 deficiency improves fatty liver in obesity models, but the role of 11β-HSD1 in mechanisms involved in the progression and/or resolution of hepatic injury is largely unknown. I hypothesized that 11β-HSD1 modulates fibrotic and inflammatory responses during hepatic injury and/or the resolution phase. First I sought to address if the levels of 11β-HSD1 during different models of liver injury are dysregulated. In mice, 11β-HSD1 was down-regulated in choline deficient diet (CDD) induced steatosis, methionine and choline deficient diet (MCDD) induced NASH, carbon tetrachloride (CCL4) induced liver fibrosis and thioacetamide (TAA) induced liver fibrosis. In CCL4 injured livers, the down regulation of 11β- HSD1 was observed around the scar area. To test if 11β-HSD1 plays a key role in modulating liver inflammation and fibrosis responses in NAFLD and liver fibrosis I used initially11β-HSD1 knockout (KO) mice. 11β-HSD1 KO showed higher HSC activation only in the High fat feeding model but not in CDD and MCDD models. In the CCL4 injury model, despite reduced hepatocellular injury, 11β-HSD1 KO mice showed enhanced collagen deposition during peak injury and increased fibrotic gene expression during the early resolution phase although unaltered inflammatory markers during both peak injury and resolution. To further dissect cell-specificity on the effect of 11β-HSD1, I repeated the CCL4-injury model using the hepatocyte-specific 11β-HSD1 KO (Alb-HSD1). Alb-HSD1 mice did not show increased susceptibility to fibrosis compared to control littermates suggesting that the 11β- HSD1 possibly modulates fibrotic response by affecting HSC function. To mechanistically address how GCs inhibit HSC activation in vitro I studied the effects of 11β-HSD1 on HSC in vitro. 11β-HSD1 expression was down-regulated during ‘spontaneous’ HSC activation, and 11β-HSD1 deficiency enhanced susceptibility to activation. The GC (11-DHC)’s inhibitory effect on HSC activation was reversed by 11β-HSD1 inhibition. Finally, to address the clinical relevance of 11β-HSD1 in hepatic injury and/or resolution a selective 11β-HSD1 inhibitor, UE2316, was used. UE2316 induced a pro-fibrotic phenotype in ob/ob mice and CCL4-treated C57BL/6 mice, but had no effect when administered only during injury resolution. In conclusion, 11β-HSD1 deficiency causes increased activation of HSCs following diet and chemical injury and promotes liver fibrosis. Effects of 11β-HSD1 inhibitors, which are a potential treatment for metabolic syndrome, are perhaps offset by adverse outcomes in liver.

by Wun Yuk Tsan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 121-129). / ABSTRACT --- p.i / ACKNOWLEDGMENT --- p.iii / TABLE OF CONTENTS --- p.iv / TABLE OF FIGURES AND TABLES --- p.vi / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Ultrasonography in Clinical Medicine --- p.1 / Chapter 1.1.1 --- Ultrasonic features of the liver --- p.1 / Chapter 1.1.2 --- Image artifacts in liver ultrasonograms --- p.4 / Chapter 1.1.3 --- Characteristics of liver ultrasonic image --- p.6 / Chapter 1.2 --- Liver Carcinoma in Hong Kong --- p.9 / Chapter 1.2.1 --- Morphological features of liver carcinoma --- p.10 / Chapter 1.2.2 --- Ultrasonographic features of liver carcinoma --- p.11 / Chapter 1.3 --- Ultrasonography and Computer --- p.12 / Chapter 1.4 --- Objectives of Thesis --- p.14 / Chapter 1.4.1 --- Hypothesis of the thesis --- p.15 / Chapter 1.4.2 --- Methods of experiment --- p.15 / Chapter 1.5 --- Organization of this Thesis --- p.17 / Chapter CHAPTER 2: --- COMPUTERIZED MEDICAL IMAGING: A REVIEW --- p.19 / Chapter 2.1 --- Computer Vision and Medical Imaging --- p.19 / Chapter 2.1.1 --- Artificial intelligence --- p.21 / Chapter 2.1.2 --- Mathematics models --- p.23 / Chapter 2.2 --- Computer Vision and Ultrasonic Images of Liver --- p.25 / Chapter 2.2.1 --- Studies on radiofrequency (RF) --- p.25 / Chapter 2.2.2 --- Studies on amplitude derived data --- p.26 / Chapter 2.3 --- Implications of Previous Work --- p.28 / Chapter 2.4 --- Limitations of Previous Work --- p.30 / Chapter CHAPTER 3: --- STATISTICAL TEXTURE --- p.32 / Chapter 3.1 --- Statistical Textural Analysis --- p.32 / Chapter 3.2 --- Statistical Texture for Segmentation --- p.34 / Chapter 3.3 --- Statistical Features Studied in This Research --- p.35 / Chapter 3.3.1 --- First-order statistics --- p.35 / Chapter 3.3.2 --- Second-order statistics --- p.36 / Chapter 3.3.3 --- Higher-order statistics --- p.41 / Chapter 3.4 --- Novel Statistical Texture Features --- p.42 / Chapter 3.5 --- Stable Statistical Textures: A New Hypothesis --- p.43 / Chapter 3.6 --- Centroids of Statistical Texture Descriptors --- p.45 / Chapter CHAPTER 4: --- NORMAL LIVER IMAGES --- p.48 / Chapter 4.1 --- Further Description of Normal Liver USG --- p.48 / Chapter 4.1.1. --- Equalized images --- p.50 / Chapter 4.2 --- Stable Statistical Descriptors in Normal Liver Images --- p.50 / Chapter 4.3 --- Clustering Algorithm --- p.53 / Chapter 4.3.1. --- Accuracy of the algorithm --- p.58 / Chapter 4.3.2 --- The algorithm and ultrasound artifacts --- p.60 / Chapter 4.3.3 --- Fuzzy algorithm for clustering --- p.62 / Chapter 4.4 --- Evaluation of the Algorithm --- p.63 / Chapter CHAPTER 5: --- IMAGES OF LIVER CARCINOMA --- p.64 / Chapter 5.1 --- Characteristics of Liver Carcinoma --- p.64 / Chapter 5.2 --- Algorithm for Tumour Detection --- p.65 / Chapter 5.2.1 --- Which statistical descriptors to use? --- p.66 / Chapter 5.2.2 --- How to isolate the capsules subimages? --- p.68 / Chapter 5.2.3 --- How to estimate the position of the tumour cells in the descriptor curve? --- p.72 / Chapter 5.2.4 --- Refinements of the algorithm --- p.73 / Chapter 5.3 --- Results of the Algorithm --- p.75 / Chapter 5.4 --- Further Examples --- p.80 / Chapter 5.5 --- Evaluation of the Algorithm --- p.87 / Chapter 5.5.1 --- Time required by the algorithm --- p.87 / Chapter 5.5.2 --- Sensitivity --- p.88 / Chapter 5.5.3 --- False positives and negatives --- p.88 / Chapter CHAPTER 6: --- REVIEW AND PROSPECTS --- p.90 / Chapter 6.1 --- Conclusions --- p.91 / Chapter 6.1.1. --- The objectives --- p.91 / Chapter 6.1.2 --- Hypotheses --- p.91 / Chapter 6.1.3. --- Statistical features --- p.92 / Chapter 6.2 --- Evaluation --- p.93 / Chapter 6.2.1 --- Noises --- p.93 / Chapter 6.2.2 --- Statistical features --- p.94 / Chapter 6.2.3 --- Methodology --- p.96 / Chapter 6.3 --- Future Work and Research --- p.98 / Chapter 6.3.1 --- Implementation and further development of the system --- p.98 / Chapter 6.3.2 --- Future research of the system --- p.99 / Chapter 6.3.3 --- Fuzzy algorithm --- p.100 / Chapter 6.3.4 --- Further work on statistical texture features --- p.100 / Chapter 6.3.5 --- The commercial potential of the system --- p.100 / Chapter 6.4 --- Final Conclusion --- p.101 / APPENDICES --- p.102 / Appendix A: Program Listings --- p.102 / Listing 1: pcx.c --- p.103 / Listing 2: feature.c --- p.108 / "Listing 3: detect, c" --- p.108 / Listing 4: centroid. c --- p.117 / AppendexB: Further Readings --- p.120 / Chapter I. --- Textbooks on Computer Vision or Images --- p.120 / Chapter II. --- Reference Books on Processing Algorithms in C Language --- p.120 / REFERENCES --- p.121

No description available.

Thesis (Ph. D.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 129-153). Also available in print.

Thesis (M.D.)--University of Hong Kong, 1984.

published_or_final_version / Medicine / Master / Doctor of Medicine