Chronic hepatitis B patients with high viral loads are at increased risk of cirrhosis and hepatocellular carcinoma (HCC). In patients with low viral loads, higher hepatitis B surface antigen (HBsAg) levels have been shown to predict HCC development. However, little is known about the difference in risk for other hepatitis B virus (HBV)-related adverse outcomes with varying HBsAg levels. A total of 1,068 Taiwanese hepatitis B e antigen (HBeAg)-negative HBV carriers with serum HBV DNA level <2,000 IU/mL at baseline were followed for a mean duration of 13.0 years. Patients were categorized based on their HBsAg levels, and the relationships between HBsAg level and development of HBeAg-negative hepatitis, hepatitis flare, and cirrhosis were investigated. Of the 1068 patients with low viral loads, 280 developed HBeAg-negative hepatitis, with an annual incidence rate of 2.0%. HBsAg level, but not HBV DNA level, was found to be a risk factor for HBeAg-negative hepatitis. Multivariate analysis showed that the adjusted hazard ratio in patients with an HBsAg level !1,000 versus <1000 IU/mL was 1.5 (95% confidence interval, 1.2-1.9). The positive correlation was present when evaluating other endpoints, including hepatitis flare and cirrhosis, and remained consistent when the study population was restricted to those with normal alanine aminotransferase (ALT) level at baseline. The annual incidence rate of HBeAg-negative hepatitis was lowered to 1.1% in patients with low levels of HBV DNA, HBsAg, and ALT. Conclusion: In HBeAg-negative patients with low viral loads and genotype B or C virus infection, a higher HBsAg level can predict disease progression. HBsAg <1,000 IU/mL in combination with low levels of HBV DNA and ALT help define minimal-risk HBV carriers. (HEPATOLOGY 2013;57:441-450) H epatitis B virus (HBV) infection is a global health problem, resulting in more than 1 million deaths per year.1 Patients with chronic HBV infection are at risk of developing cirrhosis, hepatic decompensation, and hepatocellular carcinoma (HCC), with an estimated lifetime risk of 25%-40% in carriers who acquire the virus early in life.

Background: Aspirin is the most commonly used antiplatelet agent for the prevention of cardiovascular diseases. However, a certain proportion of patients do not respond to aspirin therapy. The mechanisms of aspirin non-response remain unknown. The unique metabolomes in platelets of patients with coronary artery disease (CAD) with aspirin non-response may be one of the causes of aspirin resistance.Materials and Methods: We enrolled 29 patients with CAD who were aspirin non-responders, defined as a study subject who were taking aspirin with a platelet aggregation time less than 193 s by PFA-100, and 31 age- and sex-matched patients with CAD who were responders. All subjects had been taking 100 mg of aspirin per day for more than 1 month. Hydrophilic metabolites from the platelet samples were extracted and analyzed by nuclear magnetic resonance (NMR). Both 1D 1H and 2D J-resolved NMR spectra were obtained followed by spectral processing and multivariate statistical analysis, such as partial least squares discriminant analysis (PLS-DA).Results: Eleven metabolites were identified. The PLS-DA model could not distinguish aspirin non-responders from responders. Those with low serum glycine level had significantly shorter platelet aggregation time (mean, 175.0 s) compared with those with high serum glycine level (259.5 s). However, this association became non-significant after correction for multiple tests.Conclusions: The hydrophilic metabolic profile of platelets was not different between aspirin non-responders and responders. An association between lower glycine levels and higher platelet activity in patients younger than 65 years suggests an important role of glycine in the pathophysiology of aspirin non-response.

The European LeukemiaNet (ELN) recently proposed a revised recommendation for the diagnosis and management of acute myeloid leukemia (AML) in adults, recognized as ELN‐2022. However, validation in a large real‐world cohort remains lacking. In this study, we aimed to validate the prognostic relevance of the ELN‐2022 in a cohort of 809 de novo, non‐M3, younger (ages 18–65 years) AML patients receiving standard chemotherapy. The risk categories of 106 (13.1%) patients were reclassified from that determined using ELN‐2017 to that determined using ELN‐2022. The ELN‐2022 effectively helped distinguish patients as favorable, intermediate, and adverse risk groups in terms of remission rates and survival. Among patients who achieved first complete remission (CR1), allogeneic transplantation was beneficial for those in the intermediate risk group, but not for those in the favorable or adverse risk groups. We further refined the ELN‐2022 system by re‐categorizing AML patients with t(8;21)(q22;q22.1)/RUNX1::RUNX1T1 with KIThigh, JAK2 or FLT3‐ITDhigh mutations into the intermediate risk subset, AML patients with t(7;11)(p15;p15)/NUP98::HOXA9 and AML patients with co‐mutated DNMT3A and FLT3‐ITD into the adverse risk subsets, and AML patients with complex or monosomal karyotypes, inv (3)(q21.3q26.2) or t(3;3)(q21.3;q26.2)/GATA2,MECOM(EVI1) or TP53 mutation into the very adverse risk subset. The refined ELN‐2022 system performed effectively to distinguish patients as favorable, intermediate, adverse, and very adverse risk groups. In conclusion, the ELN‐2022 helped distinguish younger, intensively treated patients into three groups with distinct outcomes; the proposed refinement of ELN‐2022 may further improve risk stratification among AML patients. Prospective validation of the new predictive model is necessary.

Heparin-binding protein (HBP) has been shown to be a robust predictor of the progression to organ dysfunction from sepsis, and we hypothesized that dynamic changes in HBP may reflect the severity of sepsis. We therefore aim to investigate the predictive value of baseline HBP, 24-h, and 48-h HBP change for prediction of 30-day mortality in adult patients with sepsis. This is a prospective observational study in an intensive care unit of a tertiary center. Patients aged 20 years or older who met SEPSIS-3 criteria were prospectively enrolled from August 2019 to January 2020. Plasma levels of HBP were measured at admission, 24 h, and 48 h and dynamic changes in HBP were calculated. The Primary endpoint was 30-day mortality. We tested whether the biomarkers could enhance the predictive accuracy of a multivariable predictive model. A total of 206 patients were included in the final analysis. 48-h HBP change (HBPc-48 h) had greater predictive accuracy of area under the curve (AUC: 0.82), followed by baseline HBP (0.79), PCT (0.72), lactate (0.71), and CRP (0.65), and HBPc-24 h (0.62). Incorporation of HBPc-48 h into a clinical prediction model significantly improved the AUC from 0.85 to 0.93. HBPc-48 h may assist clinicians with clinical outcome prediction in critically ill patients with sepsis and can improve the performance of a prediction model including age, SOFA score and Charlson comorbidity index.

Coronary artery ectasia (CAE) is a disease characterized by abnormally dilated coronary arteries. The mechanism of CAE remains unclear, and its treatment is limited. Previous studies have shown that risk factors for CAE were related to changes in DNA methylation. However, no systematic investigation of methylation profiles has been performed. Therefore, we compared methylation profiles between 12 CAE patients and 12 propensity-matched individuals with normal coronary arteries using microarrays. Wilcoxon's rank sum tests revealed 89 genes with significantly different methylation levels (<0.05 and Δβ > |0.1|). Functional characterization using the DAVID database and gene set enrichment analysis indicated that these genes were involved in immune and inflammatory responses. Of these genes 6 were validated in 29 CAE patients and 87 matched individuals with CAE, using pyro-sequencing. and showed significant differences in methylation between the two groups, and lower protein levels of toll-like receptor 6 (TLR6) were detected in CAE patients. In conclusion, this genome-wide analysis of methylation profiles in CAE patients showed that significant changes in both methylation and expression of deserve further study to elucidate their roles in CAE.