All 22 samples with positive results in one to three PCR reactions were retested, and only samples with positive results in at least six of eight PCR reactions (i.e., positive results with both sets of primers) were considered positive. Descriptive statistics

were reported as number and percent or mean and standard deviation. Baseline characteristics and HBV markers of HCC cases and matched controls were compared with conditional logistic regression for matched pairs. Controls were excluded if data were not available for their case. All analyses were performed at the data coordinating center (New England Research Institutes, Watertown, MA) using SAS statistical software version 9.2 (SAS Institute, Cary, NC). A two-sided MG132 significance level of 5% was used for all analyses. Ninety-one HCC cases (definite and presumed) PD0332991 order and 182 matched controls were included in this study. Among the HCC cases, three were diagnosed during the lead-in phase of HALT-C, one after achieving a sustained virologic response, and the remaining 87 were nonresponders. In the latter group, HCC was diagnosed a median of 4.2 years (range, 0.2-8.5 years) after randomization. In comparing HCC cases and controls, those with HCC were older and more likely to have characteristics of more advanced liver disease, including lower platelet counts, lower serum albumin levels, and higher bilirubin

levels, and more frequently had varices (Table 1). Risk factors for HCV infection, estimated duration of HCV infection, alcohol consumption, and history of smoking were not different between HCC cases

and controls. Frozen liver samples were available from 28 of 91 (30.8%) HCC cases and from 55 of 182 (30.2%) controls. Patients (HCC cases and controls) with liver samples were similar to those without liver samples regarding demographics, severity of liver disease, fibrosis stage, and treatment assignment. Among those with liver samples, the baseline characteristics of the cases and controls learn more were similar except for lower serum albumin levels in the HCC cases. Almost half of the patients had evidence of previous HBV infection; thus 38 of 91 (41.8%) HCC cases and 83 of 182 (45.6%) controls had anti-HBc detectable in their serum (Fig. 1, Table 2). Of these, 15 (16.5%) HCC cases and 45 (24.7%) controls had isolated anti-HBc (without anti-HBs). Compared with patients who were seronegative for anti-HBc, the odds ratio (OR) and 95% confidence interval (CI) for HCC development was OR 0.85 (95% CI 0.51-1.43) for those who were anti-HBc positive (with or without anti-HBs) and OR 0.63 (95% CI 0.33-1.12) for those who were anti-HBc–alone positive. The OR was similar when only patients with definite HCC were evaluated. HBV DNA was detected in the serum of only one (0.4%) patient. This was a control subject with isolated anti-HBc. Serum HBV DNA level was very low (<30 IU/mL), but HBV DNA was detected in the liver Three of 28 (10.7%) HCC cases and 13 of 55 (23.

We think that an explanatory strategy for building the Cox model, using time-dependent covariates and a propensity score to adjust for the potential confounding factors, would have enriched the study.3–5 In this way, instead of being PI3K inhibitor driven by significance tests, covariates would have entered and remained in the explanatory model as a result of their modification effect on the association of therapy and mortality.3 Moreover, they could have checked for confounding and likely interactions to explore whether the observed effect was the same in different subsets of patients, as the editorialists claimed. Besides, the use of time-dependent covariates would have allowed fine-tuning of the

beta-blocker therapy duration and would have better addressed its influence on outcomes.4 Finally, a propensity score, which defines the probability that an individual will receive a specific treatment based on his or her pretreatment characteristics, is useful for overcoming the imbalance

between Selleck Daporinad groups when treatment assignment is not random.5 Specifically, in Serstè et al.’s study, the propensity score would have corrected the effect of beta-blockers for patient characteristics such as the presence of varices, which heavily conditions their prescription. With such an analysis, the focus of the model would have been the influence of beta-blockers on survival rather than the identification of factors influencing survival; hence, it would have offered more clues to the causal effect. The proposed approach would add robustness to the interesting results provided by Serstè et al. Agustín Albillos M.D., Ph.D.* ‡, Javier

Zamora M.D., Ph.D.† §, * Departments of Gastroenterology and Hepatology, Ramón y Cajal Institute of Health Research, University of Alcalá, Madrid, Spain, † Clinical Biostatistics, Ramón selleck chemicals y Cajal University Hospital, Ramón y Cajal Institute of Health Research, University of Alcalá, Madrid, Spain, ‡ Network Centers for Biomedical Research in Hepatic and Digestive Diseases Carlos III Institute of Health, Madrid, Spain, § Epidemiology and Public Health, Carlos III Institute of Health, Madrid, Spain. “
“The hepatitis C virus (HCV) is a small, parenterally transmitted RNA virus that is acquired today almost exclusively by the use of unsterile needles. It occurs in sixdistinct genotypes, genotype 1 being the most prevalent in North America, Europe, and Japan. HCV causes an acute hepatitis that is clinically silent in most cases and persists in the majority (80%) of patients leading to chronic hepatitis. Chronic hepatitis C remains clinically silent, and progresses to cirrhosis in about 10% of patients within 20 years. Once cirrhosis is established, morbidity and mortality from hepatic decompensation and hepatocellular carcinoma ensues. Concomitant alcohol consumption, male gender, co-infection with HIV or HBV, and older age at time of infection accelerates the progression to cirrhosis.

We think that an explanatory strategy for building the Cox model, using time-dependent covariates and a propensity score to adjust for the potential confounding factors, would have enriched the study.3–5 In this way, instead of being C646 price driven by significance tests, covariates would have entered and remained in the explanatory model as a result of their modification effect on the association of therapy and mortality.3 Moreover, they could have checked for confounding and likely interactions to explore whether the observed effect was the same in different subsets of patients, as the editorialists claimed. Besides, the use of time-dependent covariates would have allowed fine-tuning of the

beta-blocker therapy duration and would have better addressed its influence on outcomes.4 Finally, a propensity score, which defines the probability that an individual will receive a specific treatment based on his or her pretreatment characteristics, is useful for overcoming the imbalance

between learn more groups when treatment assignment is not random.5 Specifically, in Serstè et al.’s study, the propensity score would have corrected the effect of beta-blockers for patient characteristics such as the presence of varices, which heavily conditions their prescription. With such an analysis, the focus of the model would have been the influence of beta-blockers on survival rather than the identification of factors influencing survival; hence, it would have offered more clues to the causal effect. The proposed approach would add robustness to the interesting results provided by Serstè et al. Agustín Albillos M.D., Ph.D.* ‡, Javier

Zamora M.D., Ph.D.† §, * Departments of Gastroenterology and Hepatology, Ramón y Cajal Institute of Health Research, University of Alcalá, Madrid, Spain, † Clinical Biostatistics, Ramón this website y Cajal University Hospital, Ramón y Cajal Institute of Health Research, University of Alcalá, Madrid, Spain, ‡ Network Centers for Biomedical Research in Hepatic and Digestive Diseases Carlos III Institute of Health, Madrid, Spain, § Epidemiology and Public Health, Carlos III Institute of Health, Madrid, Spain. “
“The hepatitis C virus (HCV) is a small, parenterally transmitted RNA virus that is acquired today almost exclusively by the use of unsterile needles. It occurs in sixdistinct genotypes, genotype 1 being the most prevalent in North America, Europe, and Japan. HCV causes an acute hepatitis that is clinically silent in most cases and persists in the majority (80%) of patients leading to chronic hepatitis. Chronic hepatitis C remains clinically silent, and progresses to cirrhosis in about 10% of patients within 20 years. Once cirrhosis is established, morbidity and mortality from hepatic decompensation and hepatocellular carcinoma ensues. Concomitant alcohol consumption, male gender, co-infection with HIV or HBV, and older age at time of infection accelerates the progression to cirrhosis.

Nuclear receptors are popular drug targets, and drugs that modulate nuclear receptor activity are among the most prescribed CP-868596 supplier pharmaceuticals on the market.27 Nuclear receptors may have the potential to be therapeutic

targets to control the HCV-associated disease process.15, 16 Among the nuclear receptor genes studied, RXRα, PPARα and β, SHP, and CAR showed significant changes in their expression levels in HCV-infected livers. These nuclear receptors play important roles in lipid metabolism or related pathways (Fig. 4). Our published data show that serum cholesterol and triglyceride levels are increased due to hepatocyte RXRα deficiency.28, 29 PPARα modulates genes encoding lipid metabolism enzymes, lipid transporters, and apolipoproteins, and PPARα mRNA level is reduced in HCV-infected livers. An interesting finding is the concomitant down-regulation of RXRα and PPARβ in HCV-infected patients. It has been shown that the expression of RXRα is not www.selleckchem.com/products/pexidartinib-plx3397.html altered in HCV-associated cirrhotic livers.30 The difference may be due to lack of advanced cirrhosis in most patients included in the current study. PPARβ has recently received a great deal of attention in research on metabolic diseases. Activation of mouse PPARβ increases fatty acid β-oxidation, which is accompanied by marked reduction of lipid droplets in skeletal muscle.31 Hepatic-restricted PPARβ activation

produces hepatic glycogen and an increase in monounsaturated fatty acid production as well as up-regulation of glucose utilization.32 Our results showed that hepatic PPARβ mRNA was decreased and GLUT2 mRNA was increased in hepatitis C patients, suggesting an imbalance in using fat and sugar as energy sources in HCV-infected livers. Coregulator NCOA3 mRNA level was increased in HCV-infected livers,

which is consistent with the finding that deletion of NCOA3 in mice prevents high-fat-diet-induced steatosis and inflammation.33 However, whether selleck compound the modest changes in NCOA3 mRNA level found in patients are biologically significant remains to be validated. In vitro, HCV core proteins NS2 and NS5 induce hepatic lipid accumulation by activating SREBP-1c and PPARγ.34–36 However, SREBP-1c mRNA level was reduced in HCV-infected livers. Our unpublished data also show reduced expression of SREBP-1c in HCV core protein transgenic mice. The expression of SREBP-1c is negatively regulated by SHP and CAR and positively regulated by PPARβ (Fig. 4). The up-regulated SHP and CAR as well as the down-regulated PPARβ found in our HCV-infected patients could explain reduced SREBP-1c expression. Reduced expression of SREBP-1c could also be due to overloading of fat in the hepatocyte caused by up-regulated expression of FATP2 and FATP5, and thus potentially is an adaptive response.

These patients were given a standard ECF neo-adjuvant chemotherapy. All these patients who receive neo-adjuvant chemotherapy were examined the TN stage by EUS again after 1 cycle, 2 cycles, 3 cycles till the results showed down-stage or 3 cycles were finished. After that, surgery were performed. All EUS examinations were performed at the day before next cycle. In addition to TN stage, the tumor size was recorded with 3 parameters – the maximum thickness, longitude of tumor and width by EUS. The OTX015 purchase surgical

TN stage and pathological complete response (No residual carcinoma in the primary), pathological partial response (<10% residual carcinoma in the primary) were compared with EUS stage and tumor size change. Results: After neo-adjuvant chemotherapy, EUS correctly identified 53.8% (21/39) T stage and 46.1% (18/39) N stage of patients, respectively, in line with their histological staging. Whereas, 78.6% patients who EUS showed down-stage learn more (11/14) were confirmed by pathology as partial response. In the same time, the tumor size changes were found correlated with pathological response. Conclusion: Although EUS had relatively low accuracy of TN stage

after neo-adjuvant chemotherapy comparing with preoperative EUS staging, the study showed a good correlation between down-stage or dramatic decreased tumor size checked by EUS and pathological response of tumor. Conclusion: EUS could be an effective method to evaluate the treatment response and decide best time for operation of gastric cancer patients who receive neo-adjuvant chemotherapy. Key Word(s): 1. EUS; 2. gastric cancer;

3. neo-adjuvant; 4. chemotherapy; Presenting Author: ROMAN KUVAEV Additional Authors: EVGENY NIKONOV, SERGEY KASHIN, ALEXANDER NADEZHIN, HERBERT EDELSBRUNNER, MICHAEL MACHIN, OLGA DUNAEVA Corresponding Author: ROMAN KUVAEV Affiliations: Yaroslavl Regional check details Cancer Hospital; Polyclinic №1 of the Business Administration for the President of the Russian Federation; Institute of Science and Technology Austria; P.G. Demidov Yaroslavl State University Objective: High-magnification endoscopy with narrow-band imaging is currently applied for differentiation of gastric lesions and required training and experience. Newly developed computer-aided decision support systems in endoscopy aim to prediction of pathologies and thus to assist an experts. The algorithm based on effective and suitable classification system is needed for functioning of such systems. The aim of the study was to assess a cancer risk in gastric lesions with different types of vascular and surface patterns and create an algorithm for computer-aided diagnostic system in patients with chronic Helicobacter pylori associated gastritis. Methods: 148 gastric lesions in 134 patients (mean age 58.9 years, SD = 13.4) were observed with NBI-HME (GIF-Q260Z, Olympus Lucera, GIF-Q160Z, Olympus Exera). V- and S-patterns were assessed independently according to the most useful criteria of known classifications.

These patients were given a standard ECF neo-adjuvant chemotherapy. All these patients who receive neo-adjuvant chemotherapy were examined the TN stage by EUS again after 1 cycle, 2 cycles, 3 cycles till the results showed down-stage or 3 cycles were finished. After that, surgery were performed. All EUS examinations were performed at the day before next cycle. In addition to TN stage, the tumor size was recorded with 3 parameters – the maximum thickness, longitude of tumor and width by EUS. The Cetuximab in vitro surgical

TN stage and pathological complete response (No residual carcinoma in the primary), pathological partial response (<10% residual carcinoma in the primary) were compared with EUS stage and tumor size change. Results: After neo-adjuvant chemotherapy, EUS correctly identified 53.8% (21/39) T stage and 46.1% (18/39) N stage of patients, respectively, in line with their histological staging. Whereas, 78.6% patients who EUS showed down-stage buy Cabozantinib (11/14) were confirmed by pathology as partial response. In the same time, the tumor size changes were found correlated with pathological response. Conclusion: Although EUS had relatively low accuracy of TN stage

after neo-adjuvant chemotherapy comparing with preoperative EUS staging, the study showed a good correlation between down-stage or dramatic decreased tumor size checked by EUS and pathological response of tumor. Conclusion: EUS could be an effective method to evaluate the treatment response and decide best time for operation of gastric cancer patients who receive neo-adjuvant chemotherapy. Key Word(s): 1. EUS; 2. gastric cancer;

3. neo-adjuvant; 4. chemotherapy; Presenting Author: ROMAN KUVAEV Additional Authors: EVGENY NIKONOV, SERGEY KASHIN, ALEXANDER NADEZHIN, HERBERT EDELSBRUNNER, MICHAEL MACHIN, OLGA DUNAEVA Corresponding Author: ROMAN KUVAEV Affiliations: Yaroslavl Regional click here Cancer Hospital; Polyclinic №1 of the Business Administration for the President of the Russian Federation; Institute of Science and Technology Austria; P.G. Demidov Yaroslavl State University Objective: High-magnification endoscopy with narrow-band imaging is currently applied for differentiation of gastric lesions and required training and experience. Newly developed computer-aided decision support systems in endoscopy aim to prediction of pathologies and thus to assist an experts. The algorithm based on effective and suitable classification system is needed for functioning of such systems. The aim of the study was to assess a cancer risk in gastric lesions with different types of vascular and surface patterns and create an algorithm for computer-aided diagnostic system in patients with chronic Helicobacter pylori associated gastritis. Methods: 148 gastric lesions in 134 patients (mean age 58.9 years, SD = 13.4) were observed with NBI-HME (GIF-Q260Z, Olympus Lucera, GIF-Q160Z, Olympus Exera). V- and S-patterns were assessed independently according to the most useful criteria of known classifications.

Mineralized tissues such as bone, tooth enamel, and tooth dentin are more commonly used in historical, archaeological or paleontological studies. These

tissues are composites of mineral, protein, and lipid. The mineral is Vemurafenib a highly substituted form of hydroxyapatite (Ca10[PO4]6[OH]2) that we will call bioapatite. Bioapatite has a few weight percent carbonate substituting for OH and PO4 and various cations (e.g., Sr, Pb) substituting for Ca. Bone is composed of tiny bioapatite crystals intergrown with an organic matrix (chiefly made of the protein collagen) that is approximately 30% of its dry weight. Enamel is much less porous than bone. It contains <5 weight% organic matter (chiefly noncollagenous proteins) and has much larger crystals with fewer substitutions. The crystal size, organic content, and organic composition of tooth dentin resemble bone, whereas its porosity is intermediate between enamel and bone. These differences in crystal size and porosity lead to large differences in the ability of bioapatite from these tissues to retain isotopic

values during burial and fossilization. In general, only tooth enamel bioapatite is highly retentive and useful for studies of paleontological materials (>10,000-yr-old), whereas bone and dentin are reliable in historical (<500-yr-old) specimens. Samples of intermediate age (10,000–500-yr-old) must be screened carefully. Much more information can be obtained if isotopic analysis Sirolimus can be conducted at the

level of individual organic molecules, rather than bulk tissue (see review by Evershed et al. 2007). Because the different amino or fatty acids in proteins this website or lipids have different biosynthetic pathways, they provide a finer probe of animal ecology and physiology. At the most basic level, by isolating and analyzing indispensable amino and fatty acids, which must be incorporated from the same compound in diet, we have very direct access to information on dietary sources. For dispensable amino and fatty acids, the extent to which they resemble “bulk” diet versus dietary protein or lipid may provide useful information on animal physiology and perhaps trophic level. This is a rich area that has received little attention in studies of marine mammal ecology, but has been applied to studies of other marine consumers (Popp et al. 2007). An added benefit of the compound-specific approach is that even fossils that have suffered breakdown of biological macromolecules may retain characteristic amino or fatty acids that can provide isotopic information (Fogel and Tuross 2003, Evershed et al. 2007).

Mineralized tissues such as bone, tooth enamel, and tooth dentin are more commonly used in historical, archaeological or paleontological studies. These

tissues are composites of mineral, protein, and lipid. The mineral is SAHA HDAC supplier a highly substituted form of hydroxyapatite (Ca10[PO4]6[OH]2) that we will call bioapatite. Bioapatite has a few weight percent carbonate substituting for OH and PO4 and various cations (e.g., Sr, Pb) substituting for Ca. Bone is composed of tiny bioapatite crystals intergrown with an organic matrix (chiefly made of the protein collagen) that is approximately 30% of its dry weight. Enamel is much less porous than bone. It contains <5 weight% organic matter (chiefly noncollagenous proteins) and has much larger crystals with fewer substitutions. The crystal size, organic content, and organic composition of tooth dentin resemble bone, whereas its porosity is intermediate between enamel and bone. These differences in crystal size and porosity lead to large differences in the ability of bioapatite from these tissues to retain isotopic

values during burial and fossilization. In general, only tooth enamel bioapatite is highly retentive and useful for studies of paleontological materials (>10,000-yr-old), whereas bone and dentin are reliable in historical (<500-yr-old) specimens. Samples of intermediate age (10,000–500-yr-old) must be screened carefully. Much more information can be obtained if isotopic analysis H 89 can be conducted at the

level of individual organic molecules, rather than bulk tissue (see review by Evershed et al. 2007). Because the different amino or fatty acids in proteins selleck compound or lipids have different biosynthetic pathways, they provide a finer probe of animal ecology and physiology. At the most basic level, by isolating and analyzing indispensable amino and fatty acids, which must be incorporated from the same compound in diet, we have very direct access to information on dietary sources. For dispensable amino and fatty acids, the extent to which they resemble “bulk” diet versus dietary protein or lipid may provide useful information on animal physiology and perhaps trophic level. This is a rich area that has received little attention in studies of marine mammal ecology, but has been applied to studies of other marine consumers (Popp et al. 2007). An added benefit of the compound-specific approach is that even fossils that have suffered breakdown of biological macromolecules may retain characteristic amino or fatty acids that can provide isotopic information (Fogel and Tuross 2003, Evershed et al. 2007).

To address the Neratinib mechanisms underlying increased fibrosis and aberrant tissue remodeling in c-Met deleted livers, we examined the levels of MMPs, the primary proteolytic enzymes involved in the breakdown of ECM. A time course of MMP9 activation showed that in control mice, the proteolytic activity of MMP9 was progressively increasing, along with the expansion of oval cells, whereas c-Met-deficient mice displayed a decrease in MMP9 (Fig. 7A,B). This was consistent with the results of in situ zymography combined

with A6 staining, which showed a close proximity of MMP9 activity to oval cell reaction (Fig. 7C; Supporting Fig. 3). Levels of MMP9 were reduced in both models of liver- and epithelial-specific c-Met deletion

(Fig. 7D,E). There was no difference in MMP2 activity, regardless of genotype. These data link the aberrant tissue remodeling in c-Met-deficient livers with a reduction in stem cell niche component MMP9. Finally, we determined the cell source of MMP9 in DDC-treated livers. For this, we carried out gelatin zymography on isolated hepatocytes, nonparenchymal cell (NPC) fraction, and FACS-sorted F4/80-positive macrophages. Quantification of the intensity of active MMP9 band showed that the main source of active MMP9 was NPC cells, and that monocytes/macrophages accounted for approximately 80% of this activity (Fig. 8A,B). Confirming the zymography results, http://www.selleckchem.com/products/H-89-dihydrochloride.html dual immunofluorescence staining for MMP9 and markers for oval (A6), Kupffer (F4/80), and stellate (alpha smooth muscle actin; αSMA) cells revealed colocalization at the interface with Kupffer and oval cells,

but not with stellate cells (Fig. 8C). These data show that macrophage is the primary cell source of active MMP9 in this model. To provide additional evidence that the absence of c-Met creates a defective stem cell microenvironment, we examined the expression of chemokine stromal-cell–derived factor 1 (SDF1), known as a powerful chemoattractant for bone-marrow–derived monocytes. SDF1 protein levels were considerably decreased in both Met mutant models as well as the number of A6+/SDF1+ oval cells (Supporting Fig. 4). The aim of this study was to define the role of c-Met-signaling pathway in different phases selleck chemicals of adult hepatic stem cell activation by utilizing mice harboring c-met floxed alleles and Alb-Cre or Mx1-Cre transgenes. Using conditional mouse genetics and a DDC toxic liver injury model, we demonstrate that the lack of c-Met signals impaired both hepatocyte- and stem-cell–mediated liver regeneration, leading to the death of mice. Genetic loss of c-Met function has profound effects on tissue remodeling and overall composition of the HSC niche microenvironment concomitant with a failure of HSCs to expand and differentiate into hepatocytes.

To address the selleck inhibitor mechanisms underlying increased fibrosis and aberrant tissue remodeling in c-Met deleted livers, we examined the levels of MMPs, the primary proteolytic enzymes involved in the breakdown of ECM. A time course of MMP9 activation showed that in control mice, the proteolytic activity of MMP9 was progressively increasing, along with the expansion of oval cells, whereas c-Met-deficient mice displayed a decrease in MMP9 (Fig. 7A,B). This was consistent with the results of in situ zymography combined

with A6 staining, which showed a close proximity of MMP9 activity to oval cell reaction (Fig. 7C; Supporting Fig. 3). Levels of MMP9 were reduced in both models of liver- and epithelial-specific c-Met deletion

(Fig. 7D,E). There was no difference in MMP2 activity, regardless of genotype. These data link the aberrant tissue remodeling in c-Met-deficient livers with a reduction in stem cell niche component MMP9. Finally, we determined the cell source of MMP9 in DDC-treated livers. For this, we carried out gelatin zymography on isolated hepatocytes, nonparenchymal cell (NPC) fraction, and FACS-sorted F4/80-positive macrophages. Quantification of the intensity of active MMP9 band showed that the main source of active MMP9 was NPC cells, and that monocytes/macrophages accounted for approximately 80% of this activity (Fig. 8A,B). Confirming the zymography results, FDA-approved Drug Library solubility dmso dual immunofluorescence staining for MMP9 and markers for oval (A6), Kupffer (F4/80), and stellate (alpha smooth muscle actin; αSMA) cells revealed colocalization at the interface with Kupffer and oval cells,

but not with stellate cells (Fig. 8C). These data show that macrophage is the primary cell source of active MMP9 in this model. To provide additional evidence that the absence of c-Met creates a defective stem cell microenvironment, we examined the expression of chemokine stromal-cell–derived factor 1 (SDF1), known as a powerful chemoattractant for bone-marrow–derived monocytes. SDF1 protein levels were considerably decreased in both Met mutant models as well as the number of A6+/SDF1+ oval cells (Supporting Fig. 4). The aim of this study was to define the role of c-Met-signaling pathway in different phases selleck chemicals of adult hepatic stem cell activation by utilizing mice harboring c-met floxed alleles and Alb-Cre or Mx1-Cre transgenes. Using conditional mouse genetics and a DDC toxic liver injury model, we demonstrate that the lack of c-Met signals impaired both hepatocyte- and stem-cell–mediated liver regeneration, leading to the death of mice. Genetic loss of c-Met function has profound effects on tissue remodeling and overall composition of the HSC niche microenvironment concomitant with a failure of HSCs to expand and differentiate into hepatocytes.