8, Supporting Information Fig. 6), Protease Inhibitor Library datasheet which was consistent with previous observations in HBV patients.26 As reported in WT mice, the naturally activated NKT cells have a protective effect on acute liver fibrosis, although no function in long-term liver fibrosis,22 which is contrary to our conclusion from HBV-tg mice in this study. We think this discrepancy may support the common idea that the NKT cell is a double-sword cell type.23, 43-45 We think this may be due to the different subsets of NKT cells in different disease models, with a different cell-differentiating environment (such as absence or presence of HBV). For example,

in WT mice, although the naturally activated NKT cells could suppress stellate cell activation after CCl4 injection, the NKT cells stimulated with α-GalCer could activate stellate cells.22 In our study, we found that blockade of CD1d in HBV-tg mice may alleviate liver fibrosis (Fig. 7E), although we do not know which antigen (possibly a glycolipid which is hard to examine) was presented by CD1d molecules. The ongoing progress in CD1d signaling biology and NKT cell differentiation PARP inhibitor will help to resolve the basic questions. Previously, we and others reported that NK cells are antifibrotic by both direct

killing and the secreting of the antifibrosis cytokine interferon-γ in CCl4-treated WT mice.20, 21 Interestingly, in this study we found that NK cells MCE公司 sustained an inactive status with a lower level of CD69, even though the number of NK cells increased after CCl4 treatment in HBV-tg mice (Fig. 7A,B). This suggested that the inactivation of NK cells may cause the HBV-tg mice to lose the inhibitory function on HSCs, which is at least another explanation for the

overactivation of HSCs in HBV-tg mice. Considering the positive regulation of NKT cells on activation of HSCs, the losing of inhibitory function of NK cells on HSCs may possibly also play an important role in liver fibrosis in HBV-tg mice, although we do not know how the NK cells become inactive. In conclusion, the spontaneously developed liver fibrosis and aggravated CCl4-induced liver fibrosis in HBV-tg mice suggests the HBV-tg mice as a mouse model to investigate HBV-related liver fibrosis. From our findings, NKT cells exerted a positive role in HSCs activation, which implicates the inhibition of NKT cell activation (such as CD1d) or function (such as cytokine neutralization) that may attenuate HBV-related liver fibrosis. Additional supporting information may be found in the online version of this article. “
“Aim:  The usefulness of transient elastography remains to be validated in chronic hepatitis B, particularly as a tool for monitoring the degree of liver fibrosis during treatment. Methods:  The subjects were 50 patients with chronic hepatitis B virus infection.

003 to 0.04. Methylation levels of the individual 26,486 autosomal CpG sites as well as the overall means were compared between the 62 pairs of tissues. There were 2,324 CpG sites that significantly differed in methylation level between tumor and nontumor tissues after Bonferroni’s adjustment (for a complete list, see Supporting Tables Proteases inhibitor 2 and 3). Among all significant CpG sites,

684 were significantly hypermethylated (covering 548 genes) and 1,640 were significantly hypomethylated (covering 1,290 genes) in tumor, compared to nontumor, tissues. Figure 1 displays mean DNA-methylation differences between the 62 paired tumor/adjacent tissues at all 26,486 CpG sites using a volcano plot. Both hyper- and hypomethylation alterations are common events in HCC tumor tissues. The top 20 hyper- or hypomethylated sites ranked by statistical significance are given in Table 2. Regardless of whether they were hypo- or hypermethylated, all significant CpG sites had similar mean methylation levels in tumor tissues

(42.2% versus 42.9%), whereas the mean methylation levels in nontumor tissues were dramatically different (26.0% for hypermethylated versus 58.4% for hypomethylated sites). Figure 2 shows selleck chemicals llc the heatmap of the top 1,000 CpG sites (based on statistical significance) distinguishing tumor from adjacent tissues. In general, good separation of tumor and adjacent tissues was observed, with a small amount of misclassification. A Manhattan plot was used MCE to display the −log10 (adjusted P value) for the differences in methylation by chromosome (Supporting Fig. 2) and indicates that aberrant methylation is spread across all chromosomes. Among the 2,324 significantly differentially methylated CpG sites, >80% (82.3% and 85.8% for hyper- and hypomethylated sites, respectively)

had a >10% absolute tumor/nontumor difference in percent methylation, and >50% had a >15% difference (Supporting Table 4). These data indicate that the methylation changes occurring during HCC development are robust and may provide useful biomarkers. The majority of the significantly differentially methylated CpG sites are located within the proximal promoter regions. Among the 2,324 significant CpG sites, the distances to the transcription start site (TSS) ranged from 0 to 1,498 bp (base pairs), with an average of 407 bp and an SD of 362 bp. Hypermethylated CpG sites are more common within a short distance of TSS (50.7% within 250 bp and 26.9% between 250 and 500 bp), compared to hypomethylated sites (41.6% and 23.3%, respectively) (Supporting Fig. 3). The average distance to the TSS was significantly shorter for hypermethylated (mean = 332 bp; SD = 312 bp), compared with hypomethylated, sites (mean = 437 bp; SD = 377 bp; P = 3.95 × 10−10). Within CpG islands, more sites were significantly hypermethylated in tumors, whereas within non-CpG island regions, more sites were significantly hypomethylated in tumors (Supporting Table 5; Supporting Fig. 4).

003 to 0.04. Methylation levels of the individual 26,486 autosomal CpG sites as well as the overall means were compared between the 62 pairs of tissues. There were 2,324 CpG sites that significantly differed in methylation level between tumor and nontumor tissues after Bonferroni’s adjustment (for a complete list, see Supporting Tables MLN0128 2 and 3). Among all significant CpG sites,

684 were significantly hypermethylated (covering 548 genes) and 1,640 were significantly hypomethylated (covering 1,290 genes) in tumor, compared to nontumor, tissues. Figure 1 displays mean DNA-methylation differences between the 62 paired tumor/adjacent tissues at all 26,486 CpG sites using a volcano plot. Both hyper- and hypomethylation alterations are common events in HCC tumor tissues. The top 20 hyper- or hypomethylated sites ranked by statistical significance are given in Table 2. Regardless of whether they were hypo- or hypermethylated, all significant CpG sites had similar mean methylation levels in tumor tissues

(42.2% versus 42.9%), whereas the mean methylation levels in nontumor tissues were dramatically different (26.0% for hypermethylated versus 58.4% for hypomethylated sites). Figure 2 shows CDK inhibitor the heatmap of the top 1,000 CpG sites (based on statistical significance) distinguishing tumor from adjacent tissues. In general, good separation of tumor and adjacent tissues was observed, with a small amount of misclassification. A Manhattan plot was used MCE to display the −log10 (adjusted P value) for the differences in methylation by chromosome (Supporting Fig. 2) and indicates that aberrant methylation is spread across all chromosomes. Among the 2,324 significantly differentially methylated CpG sites, >80% (82.3% and 85.8% for hyper- and hypomethylated sites, respectively)

had a >10% absolute tumor/nontumor difference in percent methylation, and >50% had a >15% difference (Supporting Table 4). These data indicate that the methylation changes occurring during HCC development are robust and may provide useful biomarkers. The majority of the significantly differentially methylated CpG sites are located within the proximal promoter regions. Among the 2,324 significant CpG sites, the distances to the transcription start site (TSS) ranged from 0 to 1,498 bp (base pairs), with an average of 407 bp and an SD of 362 bp. Hypermethylated CpG sites are more common within a short distance of TSS (50.7% within 250 bp and 26.9% between 250 and 500 bp), compared to hypomethylated sites (41.6% and 23.3%, respectively) (Supporting Fig. 3). The average distance to the TSS was significantly shorter for hypermethylated (mean = 332 bp; SD = 312 bp), compared with hypomethylated, sites (mean = 437 bp; SD = 377 bp; P = 3.95 × 10−10). Within CpG islands, more sites were significantly hypermethylated in tumors, whereas within non-CpG island regions, more sites were significantly hypomethylated in tumors (Supporting Table 5; Supporting Fig. 4).

1C). On MRI, FNH may have subtle, low signal intensity on T1-weighted images and minimal, high signal

intensity on T2-weighted images. A central scar is usually present; however, central scars can also be seen in other tumors.1 The scar in FNH usually has high signal intensity on T2-weighted images secondary to the presence of vessels and bile ducts within the scar. Delayed scans may show enhancement of the scar. This appearance may help to differentiate the more fibrotic scar of FL-HCC, which typically is hypointense and has less HDAC assay enhancement.1 The visualization of a central feeding artery or draining vein can improve diagnostic specificity. On ultrasound, FNH can have variable echogenicity. FNH lesions are usually isoechoic to the normal liver and have been termed stealth lesions. Color and power Doppler may show increased central stellate vascularity. The appearance of HAs varies according to the size and complexity of the lesions. On CT and MRI, smaller lesions typically show nearly homogeneous hyperenhancement. Larger lesions may appear more heterogeneous and may contain areas of fat, hemorrhaging, necrosis, and rarely calcification.

A fibrous capsule may be present in one-third of an HA. On ultrasound, the echogenicity depends on the presence of fat, hemorrhaging, or calcification.7 The detection of HCC in a cirrhotic liver is often challenging, and differentiation from regenerative nodules and perfusion abnormalities can be difficult. Multiphase imaging with CT and MRI is important for optimizing Tamoxifen research buy the detection and characterization of lesions. The presence of an arterial hyperenhancing mass that shows washout (low attenuation on CT or low signal intensity on MRI with respect to the normal parenchyma) on portal venous phase or delayed images is considered diagnostic. HCC may also demonstrate some peripheral delayed enhancement

secondary to a pseudocapsule. FL-HCCs are hyperenhancing masses that may have a central fibrotic scar with a low density on CT and a low signal intensity on MRI. The scar usually is not enhanced on delayed images and may have areas of calcification Although the classic appearance of the aforementioned hepatic masses is well known, atypical appearances MCE are not uncommon and can lead to uncertainty in diagnosis. Atypical findings may occur in 10% to 50% of FNH cases.2 Several atypical findings have been reported; they include a high T1 signal (fat, hemorrhaging, or copper), a low T2 signal (iron), less intense arterial enhancement, tumor heterogeneity, an unusual appearance of the central scar such as no enhancement or an absence (up to 50%), and the presence of a pseudocapsule (10%-37%).1, 2 In such situations, it may be difficult to differentiate FNH from adenoma, HCC, or metastases. Therefore, in these inconclusive cases, further imaging or biopsy is usually performed.

Expression of PFKP was the highest among PFK isoforms in NCI-60 cell lines (Supporting Fig. 6B), further supporting that cancer-specific expression of PFKP is regulated by miR-520a/b/e and TARDBP. We next assessed the clinical relevance of TARDBP in HCC. Expression of TARDBP is significantly associated with prognosis when estimated by receiver operating characteristic (ROC) analysis. Areas under the curve (AUCs) of TARDBP expression over 3-year overall survival (OS) were 0.6 (95% confidence interval [CI]: 0.53-0.66; P = 0.007) (Fig. 6A). When patients were stratified according to expression level of TARDBP, patients with high TARDBP expression

showed significantly shorter survival (P = 3.8 × 10−4; Fig. 6B). Association of TARDBP with prognosis is further supported by its significant correlation with the 65-gene risk score (r = Palbociclib order 0.5; P = 2.2 × 10−16) (Fig. 6C) that was previously developed for prediction of recurrence.31 Significant positive correlation between expression of TARDBP and PFKP in HCC patients is also concordant with their roles as positive regulators for cell growth (Fig. 6D). The critical roles of TARDBP and its downstream targets, the miR-520

family, in cell growth and the significant correlation of TARDBP with patient survival strongly suggested that TARDBP and its downstream targets would be potential therapeutic targets for cancer treatment. To test this, we carried out a mouse xenograft experiment with Selleck CHIR-99021 SK-Hep1 cells and siRNA specific to TARDBP. Compared to treatment with control siRNA, treatment with siTARDBP resulted in a significant reduction medchemexpress in tumor weight (Fig. 7A), recapitulating the effects of silencing TARDBP in vitro. Efficient silencing of TARDBP by siRNA was confirmed by immunostaining of TARDBP and its downstream target, PFKP, and further validated by qRT-PCR (Fig. 7B,C and Supporting Fig. 7). As expected, cell proliferation, as examined by Ki67 immunostaining, was significantly decreased in tumors

treated with siTARDBP (Fig. 7B). In addition, lactate and ATP levels were also significantly decreased (Fig. 7C) and expression of miR-520b and miR-520e (Fig. 7D) was significantly increased in siTARDBP-treated mice, compared to control. These results clearly demonstrate the importance of TARDBP in tumor growth and the potential of TARDBP as a therapeutic target. In the current work, we have presented a mechanistic link from TARDBP to PFKP, the rate-limiting enzyme of glycolysis, and we also have provided evidence suggesting that this pathway is associated with poor prognosis of HCC. A notable finding was the identification of the miR-520 family as an intermediary regulator of this pathway. Although TARDBP was originally identified as a transcription repressor binding to the human immunodeficiency virus transactivation response region,1 downstream targets and molecular mechanisms related to its transcription repressor activity have not been properly explored.

It has been proved that the transcription of MnSOD is regulated by STAT319, 29 depending on the JAK2/STAT3 protein complex

that is formed by the interaction of Hes proteins with STAT3.30 We found that Hes5 was the major downstream effector of Notch signaling in hepatocytes during I/R injury. Thus, disruption of Notch signaling resulted in decreased Hes5-STAT3 complex, and overexpressing constitutively active STAT3 or Hes5 rescued MnSOD expression, leading to reduced ROS levels and hepatocyte apoptosis subjected to I/R in the absence of Notch signaling. In summary, the data presented in this study OSI-906 in vitro establish a signal axis by which canonical Notch signaling regulates hepatic I/R injury: the activated Notch receptors up-regulate Hes5 through transcription factor RBP-J, and Hes5 facilitates STAT3 activation through the formation of a Hes5-JAK2/STAT3 complex,30 which in turn activates the transcription of MnSOD gene to scavenge ROS and restricts I/R injury (Supporting Fig. 12, left). The JAK/STAT pathway mediates cytokine signaling and participates

in the initiation, propagation, and resolution of inflammation.32 The basic selleck kinase inhibitor players in this pathway include four JAK kinases and seven STAT members, with other modifiers such as SOCSs. The JAK2/STAT3-SOCS3 module mediates proinflammation or anti-inflammation signaling, depending on cell types and other environmental cues, and is involved in both hepatic and myocardial I/R injury.33, 34 We have shown recently that Notch signaling down-regulates JAK2/STAT3 signaling through the up-regulation of SOCS3 in macrophages.23 This signal could be enhanced through the auto-amplification of Notch signaling by TLR-induced and RBP-J–dependent induction of Notch ligands,35 likely to result in down-regulation of MnSOD and increased ROS levels, to facilitate the destruction of ingested pathogens in macrophages MCE公司 (Supporting Fig. 12, right). In tissue parenchymal cells such as hepatocytes, as shown in the current study, I/R also up-regulates

Notch signal activation, but it assists JAK2/STAT3 signaling without the activation of SOCS3 expression, resulting in the up-regulation of MnSOD and increased scavenging of ROS, restricting the extent of tissue damage. Therefore, it appears that, as one of the early response signals, the Notch pathway plays differential roles through JAK2/STAT3-MnSOD in macrophages and hepatocytes—namely, increasing ROS in macrophages to destroy pathogens but reducing ROS in hepatocytes to protect cells. This scenario might also be an explanation of the contradictory observations about the roles of Notch signaling in myocardial and brain I/R injuries.12, 14 However, mechanisms such as epigenetic elements by which Notch signal differentially regulates SOCS3 expression between macrophages and hepatocytes should be determined by further studies. We thank Klaus Rajewski for Mx-Cre transgenic mice, J. C. Zúñiga-Pflücke for OP9 derivatives, and Yongzhan Nie for plasmids expressing STAT3 mutants.

28 Three different constructs were selected, each carrying the mutant (mt) viral isolate representative of the dominant HBV population infecting patients 14, 4, and 8 (pHBV-mtpreS1, pHBV-mtpreS2, and pHBV-mtS, respectively) (Fig. 1A). Linear HBV monomers were released from pHBV-mtpreS1, pHBV-mtpreS2, and pHBV-mtS constructs and from plasmid pUC-HBV (genotype D), used as a WT control, by way of cleavage with the restriction enzyme

SapI (New England Biolabs, Ipswich, MA). After digestion, linear HBV genomes were gel-purified and Palbociclib in vivo transiently transfected into HepG2 cells using the FuGENE transfection reagent (Roche Applied Science). Briefly, HepG2 cells were seeded at a density of 1 × 106 cells in 100-mm-diameter Petri dishes and transfected 24 hours later with 2 μg of SapI-digested HBV DNA. Culture medium was changed 1 day after transfection, and cells harvested 1 day later. All transfections included 1 μg of reporter plasmid expressing enhanced green fluorescence protein to assess transfection efficiency. All transfection experiments were done at least three times, each time using independently prepared HBV DNA (Qiagen Maxi Preparation Kit). Statistical analysis was performed HKI-272 in vitro by SPSS version

11.0 software package (SPSS Inc, Chicago, IL). A nonparametric approach was used to examine variables showing absence of a normal distribution, as verified by the Kolmogorov-Smirnov test. The interdependence between numerical variables was performed by the use of the Spearman

rank correlation test, whereas the Mann-Whitney test was applied to perform comparisons of continuously distributed variables between two independent groups. To evaluate the association between categorical variables, the log-likelihood ratio test was applied. P < 0.05 were considered as statistically significant. Quantification analyses showed a significant positive correlation MCE公司 between HBsAg (median, 2.3 × 103 IU/mL; range, 56-9.4 × 104 IU/mL) and HBV DNA (median, 2.5 × 105 IU/mL; range, 482-2.4 × 108 IU/mL) serum levels (r = 0.416; P = 0.008) in the study population (Fig. 2A). However, when HBeAg-positive and HBeAg-negative subgroups of patients were separately examined, no correlation was found between HBsAg and HBV DNA levels in either subgroup (Fig. 2B,C), likely because of the limited number of individuals included in each of them. HBeAg-positive patients had significantly higher serum HBV DNA levels (median, 1 × 108 IU/mL; range, 2.1 × 104-1.1 × 108; P = 0.017) compared to HBeAg-negative cases (median, 2.3 × 106 IU/mL; range, 482-2.3 × 108), whereas the median HBsAg titer did not differ significantly between the two subgroups (4.9 × 103 IU/mL versus 2 × 103 IU/mL, P = 0.27).

28 Three different constructs were selected, each carrying the mutant (mt) viral isolate representative of the dominant HBV population infecting patients 14, 4, and 8 (pHBV-mtpreS1, pHBV-mtpreS2, and pHBV-mtS, respectively) (Fig. 1A). Linear HBV monomers were released from pHBV-mtpreS1, pHBV-mtpreS2, and pHBV-mtS constructs and from plasmid pUC-HBV (genotype D), used as a WT control, by way of cleavage with the restriction enzyme

SapI (New England Biolabs, Ipswich, MA). After digestion, linear HBV genomes were gel-purified and PD-0332991 solubility dmso transiently transfected into HepG2 cells using the FuGENE transfection reagent (Roche Applied Science). Briefly, HepG2 cells were seeded at a density of 1 × 106 cells in 100-mm-diameter Petri dishes and transfected 24 hours later with 2 μg of SapI-digested HBV DNA. Culture medium was changed 1 day after transfection, and cells harvested 1 day later. All transfections included 1 μg of reporter plasmid expressing enhanced green fluorescence protein to assess transfection efficiency. All transfection experiments were done at least three times, each time using independently prepared HBV DNA (Qiagen Maxi Preparation Kit). Statistical analysis was performed I-BET-762 clinical trial by SPSS version

11.0 software package (SPSS Inc, Chicago, IL). A nonparametric approach was used to examine variables showing absence of a normal distribution, as verified by the Kolmogorov-Smirnov test. The interdependence between numerical variables was performed by the use of the Spearman

rank correlation test, whereas the Mann-Whitney test was applied to perform comparisons of continuously distributed variables between two independent groups. To evaluate the association between categorical variables, the log-likelihood ratio test was applied. P < 0.05 were considered as statistically significant. Quantification analyses showed a significant positive correlation medchemexpress between HBsAg (median, 2.3 × 103 IU/mL; range, 56-9.4 × 104 IU/mL) and HBV DNA (median, 2.5 × 105 IU/mL; range, 482-2.4 × 108 IU/mL) serum levels (r = 0.416; P = 0.008) in the study population (Fig. 2A). However, when HBeAg-positive and HBeAg-negative subgroups of patients were separately examined, no correlation was found between HBsAg and HBV DNA levels in either subgroup (Fig. 2B,C), likely because of the limited number of individuals included in each of them. HBeAg-positive patients had significantly higher serum HBV DNA levels (median, 1 × 108 IU/mL; range, 2.1 × 104-1.1 × 108; P = 0.017) compared to HBeAg-negative cases (median, 2.3 × 106 IU/mL; range, 482-2.3 × 108), whereas the median HBsAg titer did not differ significantly between the two subgroups (4.9 × 103 IU/mL versus 2 × 103 IU/mL, P = 0.27).

28 Three different constructs were selected, each carrying the mutant (mt) viral isolate representative of the dominant HBV population infecting patients 14, 4, and 8 (pHBV-mtpreS1, pHBV-mtpreS2, and pHBV-mtS, respectively) (Fig. 1A). Linear HBV monomers were released from pHBV-mtpreS1, pHBV-mtpreS2, and pHBV-mtS constructs and from plasmid pUC-HBV (genotype D), used as a WT control, by way of cleavage with the restriction enzyme

SapI (New England Biolabs, Ipswich, MA). After digestion, linear HBV genomes were gel-purified and Small molecule library datasheet transiently transfected into HepG2 cells using the FuGENE transfection reagent (Roche Applied Science). Briefly, HepG2 cells were seeded at a density of 1 × 106 cells in 100-mm-diameter Petri dishes and transfected 24 hours later with 2 μg of SapI-digested HBV DNA. Culture medium was changed 1 day after transfection, and cells harvested 1 day later. All transfections included 1 μg of reporter plasmid expressing enhanced green fluorescence protein to assess transfection efficiency. All transfection experiments were done at least three times, each time using independently prepared HBV DNA (Qiagen Maxi Preparation Kit). Statistical analysis was performed Tofacitinib purchase by SPSS version

11.0 software package (SPSS Inc, Chicago, IL). A nonparametric approach was used to examine variables showing absence of a normal distribution, as verified by the Kolmogorov-Smirnov test. The interdependence between numerical variables was performed by the use of the Spearman

rank correlation test, whereas the Mann-Whitney test was applied to perform comparisons of continuously distributed variables between two independent groups. To evaluate the association between categorical variables, the log-likelihood ratio test was applied. P < 0.05 were considered as statistically significant. Quantification analyses showed a significant positive correlation 上海皓元 between HBsAg (median, 2.3 × 103 IU/mL; range, 56-9.4 × 104 IU/mL) and HBV DNA (median, 2.5 × 105 IU/mL; range, 482-2.4 × 108 IU/mL) serum levels (r = 0.416; P = 0.008) in the study population (Fig. 2A). However, when HBeAg-positive and HBeAg-negative subgroups of patients were separately examined, no correlation was found between HBsAg and HBV DNA levels in either subgroup (Fig. 2B,C), likely because of the limited number of individuals included in each of them. HBeAg-positive patients had significantly higher serum HBV DNA levels (median, 1 × 108 IU/mL; range, 2.1 × 104-1.1 × 108; P = 0.017) compared to HBeAg-negative cases (median, 2.3 × 106 IU/mL; range, 482-2.3 × 108), whereas the median HBsAg titer did not differ significantly between the two subgroups (4.9 × 103 IU/mL versus 2 × 103 IU/mL, P = 0.27).

We have recently highlighted cases of ALF that have occurred Fulvestrant as a result of administration of APAP at the maximum recommended daily dose in adults with malnutrition and/or low body weight. We also demonstrated through an internal audit at our center that most practitioners are unaware that these patients have increased susceptibility to APAP toxicity and that biochemical evidence of subclinical liver injury is not infrequent.2 This has led us to suspect that APAP toxicity following “therapeutic”

doses in high-risk patients contributes to the number of cases labeled as indeterminate ALF. Although previous studies have shown that adduct levels are low in subjects receiving therapeutic doses,3 these were carried out in healthy subjects. Adducts are likely to be significantly elevated in those with low body mass whose peak plasma concentration MI-503 purchase of APAP reaches toxic levels, and in malnourished individuals with glutathione deficiency and diminished capacity

to neutralize N-acetyl-p-benzoquinone imine. Furthermore, in the United States, up to 50% of APAP-induced ALF is thought to occur as a result of unintentional overdose,4 leading to the recent decision by the U.S. Food and Drug Administration to limit the dosage unit of APAP to 325 mg in combination prescription products. Thus, unless there is a clear psychiatric history, transplantation must not be precluded on the basis of positive acetaminophen–cysteine adducts. The use of these adducts may help confirm APAP toxicity as the cause of ALF, providing

more accurate epidemiological data. Yet, unless the levels can be correlated with prognosis, it is difficult to see how they will change clinical practice. There is already evidence for the efficacy of N-acetylcysteine (NAC) in non-APAP-induced ALF,5 and it is therefore surprising that only 40% of adduct-positive and 17.8% of adduct-negative patients with indeterminate ALF received NAC. The most important message we should take from this study is that all patients with indeterminate ALF should be treated with NAC. Lee C. Claridge Ph.D.*, * Centre for Liver Research, University of Birmingham, Birmingham, United Kingdom. “
“I can hardly share the passionate enthusiasm of Breuhahn et al. for the “dramatic” 上海皓元 improvements in understanding of molecular pathogenesis of hepatocellular carcinoma (HCC) and the claim for “further rationally designed clinical trials based on molecular evidence”.1 Among the causes of HCC, they cite aflatoxins and hemochromatosis but failed, as too many do, to cite tobacco, which represents the cause of one-third of the cases.2 Despite the success of the hepatitis B vaccine and the cure for hepatitis C, HCC remains a growing epidemic due to alcohol, tobacco, and processed foods (obesity and diabetes).3 Here are the three agents of the modern epidemics.