Experimental Study on Vacuum Distillation Separation Characteristics of Tar Containing Solid Particles: Distributions of Light Components.

The impacts of the composition and properties of tar products on their utilization are of great importance, while the consequences of varying tar separation conditions on distillation fractions remain underexplored. Solid impurities in special tar products (e.g., subsurface in situ pyrolysis-derived tar-like substances) can contribute to the separation as well. In the present study, low-temperature coal tar (LTCT) was used as an analogue to pyrolysis product, mixed with semi-coke and coal dust, representing pyrolytic byproducts and nonpyrolytic substances, respectively. The LTCT mixtures were tested with vacuum distillation at various pressures and temperatures. The results revealed the role of pressure in fraction distribution across temperatures, with higher pressure concentrating fractions at lower temperatures. The impact of solid impurities on distillation primarily stemmed from adsorption. Minimal concentrations of solid impurities carried coal dust/semi-coke into the distillation, but higher levels retained them in the residue. The adsorption of coal dust was quite high at lower temperatures and waned as temperature increased, unlike semi-coke, which had consistent adsorption throughout the distillation. The present study can advance the understanding of vacuum distillation for tar products in the presence of solid impurities, offering a framework for the effective distillation/utilization of coal tar. By probing separation conditions, tar properties, and solid impurity effects, the present research will refine strategies for optimizing coal tar use, crucial for enhancing energy security and sustainable progress in China.

A robust web-based tool to predict viral shedding in patients with Omicron SARS-CoV-2 variants.

Background: Data on viral kinetics and variants affecting the duration of viral shedding were limited. Our objective was to determine viral shedding in distinct severe acute respiratory syndrome coronavirus 2 variants, including Omicron BA.4/5 and BF.7, and to identify the relevant influencing factors. Methods: We carried out a longitudinal cohort study at Beijing Xiaotangshan Fangcang shelter hospital from May to June 2022 (Omicron BA.4/5) and from November to December 2022 (Omicron BF.7). Nucleocapsid protein (N) and open reading frame (ORF) genes were considered as the target genes of the reverse transcription PCR. The daily results of cycle threshold (CT), including lowest ORF1ab-CT values for days 1-3 post-hospitalisation and lowest N-CT values for days 1-3 post-hospitalisation (CT3minN) and demographic and clinical characteristics were collected. Results: 1433 patients with coronavirus disease 2019 (COVID-19) were recruited from the Fangcang shelter hospital, in which 278 patients were diagnosed with Omicron BA.4/5 and 1155 patients with Omicron BF.7. Patients with BF.7 infection showed a longer duration of viral shedding. The duration of viral shedding was associated with the variants age, alcohol use, the severity of COVID-19 and CT3minN. Moreover, the nomogram had excellent accuracy in predicting viral shedding. Conclusions: Our results indicated that patients with Omicron BF.7 had a longer period of contagiousness than those with BA.4/5. The duration of viral shedding was affected by a variety of factors and the nomogram may become an applicable clinical instrument to predict viral shedding. Furthermore, we developed a new COVID-19 viral shedding predicting model that can accurately predict the duration of viral shedding for COVID-19, and created a user-friendly website to apply this prediction model (https://puh3.shinyapps.io/CVSP_Model/).

Impact of Lianhua Qingwen on viral shedding in omicron mild/asymtomatic patients: a real-world study.

Background: Lianhuaqingwen (LHQW), a traditional Chinese medicine comprised of 13 herbal extracts renowned for their robust heat-clearing and detoxifying properties, has gained widespread utilization in China but has yet to garner similar recognition abroad. It is believed to exhibit efficacy in ameliorating symptoms in individuals afflicted with coronavirus disease 2019 (COVID-19). However, the precise impact of LHQW on viral shedding (VS), particularly in the context of mild or asymptomatic infections caused by the Omicron BF.4/5 or BF.7 variants of COVID-19, remained inadequately elucidated. Consequently, a real-world study was conducted, involving patients diagnosed with COVID-19, with the primary objective of ascertaining the effectiveness of LHQW in this specific clinical context. Methods: We conducted an investigation on Omicron-infected patients through a single-center, propensity score-matched real-world study conducted at Xiaotangshan Fangcang Hospital from May to November 2022. A total of 3,368 COVID-19 patients were enrolled in the study, all of whom presented mild or asymptomatic infections caused by either BF.4/5 or BF.7 strains of the virus. Demographic and clinical data were systematically collected from medical records. Patients were allocated to receive treatment with LHQW (designated as the treatment group) or received no LHQW treatment (designated as the not-treated/no-treatment group). Viral load was quantified utilizing quantitative real-time PCR (qPCR), and the duration of VS was defined as the time interval between the initial negative test result and the date of COVID-19 diagnosis or symptom onset. Results: The study encompassed a cohort of 3,368 patients, and following propensity score matching, a subset of 296 patients was meticulously chosen for subsequent analysis. Notably, baseline characteristics exhibited disparities between the treatment and not-treated/no-treatment groups. However, post-matching, these characteristics achieved a commendable level of comparability. Our findings unequivocally demonstrated that there existed no statistically significant disparity in VS. This holds true when comparing patients subjected to LHQW treatment against those not administered LHQW, as well as when contrasting individuals presenting asymptomatic and mild COVID-19 manifestations. Conclusion: No statistically significant difference in VS was observed between patients who underwent LHQW treatment and those who did not. Additional investigations are imperative to provide a comprehensive assessment of LHQW's efficacy, particularly in patients afflicted with severe COVID-19 or those infected with viral strains distinct from BF.4/5 or BF.7.

Prevalence and clinical impact of sarcopenia in liver transplant recipients: A meta-analysis.

BACKGROUND: The prevalence of sarcopenia in patients undergoing liver transplantation (LT) remains to be determined partly because of different diagnostic criteria. Sarcopenia has recently been recognized as a new prognostic factor for predicting outcomes in LT candidates. AIM: To estimate the prevalence of sarcopenia and evaluate its clinical effect on LT candidates. METHODS: This systematic search was conducted in PubMed, Web of Science, Embase, and Cochrane Library for original English-language articles that investigated the prevalence and influence of sarcopenia in patients undergoing LT from database inception to November 30, 2022. Cohort studies of the definition of sarcopenia that estimate sarcopenia prevalence and evaluate its effect on clinical outcomes and the risk of mortality were included. RESULTS: Twenty-five studies involving 7760 patients undergoing LT were included. The pooled prevalence of sarcopenia in patients undergoing LT was 40.7% [95% confidence intervals (95%CI): 32.1-49.6]. The 1-, 3-, and 5-year cumulative probabilities of post-LT survival in patients with preoperative sarcopenia were all lower than those without sarcopenia (P < 0.05). Sarcopenia was associated with an increased risk of post-LT mortality in patients undergoing LT (adjusted hazard ratio: 1.58; 95%CI: 1.21-2.07). Patients with preoperative sarcopenia had a longer intensive care unit stay, a high risk ratio of sepsis, and serious post-LT complications than those without sarcopenia. CONCLUSION: Sarcopenia is prevalent in a substantial proportion of patients undergoing LT and is strongly and independently associated with higher a risk of mortality risk.

Thermodynamic Analysis on In Situ Underground Pyrolysis of Tar-Rich Coal: Secondary Reactions.

To develop the in situ underground pyrolysis process of tar-rich coal more scientifically, the effect of temperature and pressure on the distribution of pyrolysis products should be clarified. This paper selected the typical components in five distillates of light tar, phenol tar, naphthalene tar, washing tar, and anthracene tar as the main reaction products. 32 typical secondary reactions were constructed. Based on the thermodynamic analysis strategy, the variation of the Gibbs free energy and equilibrium constant of secondary reactions was investigated. The results showed that pressure mainly affected the reaction characteristics of molecule-increasing reactions. The Gibbs free energy value of the molecule-increasing reactions increased with increasing pressure. The trend that the reaction could proceed spontaneously gradually weakened. The initial temperature of some reactions that could proceed spontaneously would need to increase by dozens or even hundreds of degrees. Due to the influence of formation pressure, the generation of related components of light tar, naphthalene tar, washing tar, and anthracene tar would be inhibited to varying degrees in the in situ underground pyrolysis process. The secondary reactions related to phenol tar were equimolecular reactions, which were almost unaffected by stratal pressure. Axial pressure and confining pressure of different coal seam depths should be considered in the process of in situ underground pyrolysis.

An Integrated Approach of Learning Genetic Networks From Genome-Wide Gene Expression Data Using Gaussian Graphical Model and Monte Carlo Method.

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets.

Investigation of Pyrolysis and Mild Oxidation Characteristics of Tar-Rich Coal via Thermogravimetric Experiments.

Tar-rich coal has the potential to substitute the supply of oil-gas resources, which is abundant in China. The effective conversion of tar-rich coal into oil-gas products can promote coal utilization, reduce resource wastage, alleviate environmental pollution, and benefit carbon neutrality. Nevertheless, less work, if any, has been performed on the pyrolysis and mild oxidation behaviors of tar-rich coal in Northwestern China. The influences of limited oxygen addition and an extremely low heating rate on the micromorphology of the residual semi-coke are yet to be fully understood. Here, an experimental study on the pyrolysis and mild oxidation characteristics of tar-rich coal was conducted by the thermogravimetric analysis method, with further elucidation of the physical-chemical properties of the residual semi-coke. Experimental results show that an increase in the ultimate temperature of pyrolysis leads to a decline in the residue mass, while the mass loss from 500 to 550 °C presents the maximum elevation. Volatile matter is inclined to discharge from a certain direction, and the pores formed in various directions hold different possibilities. The organic components undergo both pyrolysis and slow oxidation with limited oxygen in the heating medium. Compared with an inert atmosphere, the mass loss under conditions of a small amount of O2 is brought forward but prolonged. Compared with a N2 atmosphere, the oxidation reactions of tar-rich coal are weakened in the presence of CO2. A large decrease in the heating rate exerts an unfavorable effect on the production of total volatiles. An extremely low heating rate possibly brings about a change in the mechanism of chemical bond cracking during pyrolysis. More pores can be yielded in tar-rich coal with an increase in the heating rate, and the morphology of the residual semi-coke after pyrolysis is susceptible to the heating rate. The present study offers an improved understanding of the pyrolysis characteristics of tar-rich coal as well as insights into the efficient utilization of tar-rich coal.

Urinary Neutrophil Gelatinase-Associated Lipocalin Can Predict the Efficacy of Volume Expansion Therapy in Patients With Hepatitis B Cirrhosis and AKI.

Backgrounds: Kidney biomarkers in urine appear to be useful in differential diagnosis between acute tubular necrosis and other types of acute kidney injury (AKI) in cirrhosis. In clinical practice, prerenal azotemia (PRA) is often distinguished from other types of AKI by volume expansion therapy. The aim of the current study was to investigate the accuracy of urinary biomarkers in the differential diagnosis between PRA and other types of AKI. Methods: A total of 65 patients with hepatitis B cirrhosis were prospectively included and divided into AKI and non-AKI groups. Patients with hepatitis B cirrhosis and AKI discontinue diuretics, vasodilators, and nephrotoxic drugs and give volume expansion therapy. The efficacy was judged after 48 h of treatment. Urinary biomarkers were measured at the time of diagnosis of AKI and 48 h after volume expansion therapy. Univariate and multivariate analyses were used to identify independent risk factors for nonresponse to volume expansion therapy. Results: Of the 65 patients, 49 patients with newly diagnosed AKI were screened in the study, and 16 hospitalized patients with hepatitis B cirrhosis without AKI at the same period were screened as the control group. In patients with cirrhosis and AKI, 29 (59.18%) patients were in the response group and 20 (40.81%) patients were in the nonresponse group. The mortality rate in the nonresponse group was significantly higher than that in the response group (75% vs. 13.8% p < 0.001). After logistic regression analysis, urinary neutrophil gelatinase-associated lipocalin (NGAL) and serum creatinine (SCr) at diagnosis of AKI showed significant association with nonresponse to volume expansion therapy. The cutoff values for SCr and urinary NGAL were 128.50 µmol/L and 90.75 ng/ml, respectively. The area under the receiver operating curve (AUC) for SCr and urinary NGAL was 0.815 and 0.831. Conclusion: Elevated urinary NGAL can reflect the degree of kidney injury and is an independent risk factor for nonresponse to volume expansion therapy in patients with hepatitis B cirrhosis and AKI.

Duration of SARS-CoV-2 RNA shedding and factors associated with prolonged viral shedding in patients with COVID-19.

To investigate the factors associated with the duration of severe acute respiratory syndrome coronavirus 2 RNA shedding in patients with coronavirus disease 2019 (COVID-19). A retrospective cohort of COVID-19 patients admitted to a designated hospital in Beijing was analyzed to study the factors affecting the duration of viral shedding. The median duration of viral shedding was 11 days (IQR, 8-14.3 days) as measured from illness onset. Univariate regression analysis showed that disease severity, corticosteroid therapy, fever (temperature>38.5°C), and time from onset to hospitalization were associated with prolonged duration of viral shedding (P < .05). Multivariate regression analysis showed that fever (temperature>38.5°C) (OR, 5.1, 95%CI: 1.5-18.1), corticosteroid therapy (OR, 6.3, 95%CI: 1.5-27.8), and time from onset to hospitalization (OR, 1.8, 95%CI: 1.19-2.7) were associated with increased odds of prolonged duration of viral shedding. Corticosteroid treatment, fever (temperature>38.5°C), and longer time from onset to hospitalization were associated with prolonged viral shedding in COVID-19 patients.

The efficacy and safety of methylprednisolone in hepatitis B virus-related acute-on-chronic liver failure: a prospective multi-center clinical trial.

BACKGROUND: Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a severe condition with high mortality due to lack of efficient therapy. Until now, the use of methylprednisolone (MP) in HBV-ACLF is still controversial. We aimed to evaluate the efficacy and safety of MP in HBV-ACLF. METHODS: Totally 171 HBV-ACLF patients from three medical centers were randomly allocated into MP group (83 patients treated with MP intravenously guttae for 7 days plus standard treatment: 1.5 mg/kg/day [day 1-3], 1 mg/kg/day [day 4-5], and 0.5 mg/kg/day [day 6-7]) and control group (88 patients treated with standard treatment). The primary endpoints were 6-month mortality and prognostic factors for 6-month survival. The survival time, cause of death, adverse events, liver function, and HBV DNA replication were analyzed. RESULTS: The 6-month mortality was significantly lower in MP group than control group [32.4% vs. 42.5%, P = 0.0037]. MP treatment was an independent prognostic factor for 6-month survival [HR (95% CI) 0.547(0.308-0.973); P = 0.040]. Factors associated with reduced 6-month mortality in MP group included HBV DNA and lymphocyte/monocyte ratio (LMR) (P < 0.05). Based on ROC curve, LMR+MELD had a better predictive value for prognosis of HBV-ACLF under MP treatment. No significant difference in HBV DNA replication was observed between groups (P > 0.05). CONCLUSIONS: MP therapy is an effective and safe clinical strategy in HBV-ACLF, increasing the 6-month survival rate. Clinical trials registered at http://www.chictr.org.cn as ChiCTR-TRC-13003113 registered on 16 March 2013.

Cancer Genetic Network Inference Using Gaussian Graphical Models.

The Cancer Genome Atlas (TCGA) provides a rich resource that can be used to understand how genes interact in cancer cells and has collected RNA-Seq gene expression data for many types of human cancer. However, mining the data to uncover the hidden gene-interaction patterns remains a challenge. Gaussian graphical model (GGM) is often used to learn genetic networks because it defines an undirected graphical structure, revealing the conditional dependences of genes. In this study, we focus on inferring gene interactions in 15 specific types of human cancer using RNA-Seq expression data and GGM with graphical lasso. We take advantage of the corresponding Kyoto Encyclopedia of Genes and Genomes pathway maps to define the subsets of related genes. RNA-Seq expression levels of the subsets of genes in solid cancerous tumor and normal tissues were extracted from TCGA. The gene expression data sets were cleaned and formatted, and the genetic network corresponding to each cancer type was then inferred using GGM with graphical lasso. The inferred networks reveal stable conditional dependences among the genes at the expression level and confirm the essential roles played by the genes that encode proteins involved in the two key signaling pathway phosphoinositide 3-kinase (PI3K)/AKT/mTOR and Ras/Raf/MEK/ERK in human carcinogenesis. These stable dependences elucidate the expression level interactions among the genes that are implicated in many different human cancers. The inferred genetic networks were examined to further identify and characterize a collection of gene interactions that are unique to cancer. The cross-cancer genetic interactions revealed from our study provide another set of knowledge for cancer biologists to propose strong hypotheses, so further biological investigations can be conducted effectively.

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

A Novel Role for CAMKK1 in the Regulation of the Mesenchymal Stem Cell Secretome.

Transplantation of adult stem cells into myocardial tissue after acute myocardial infarction (AMI), has been shown to improve tissue recovery and prevent progression to ischemic cardiomyopathy. Studies suggest that the effects of mesenchymal stem cells (MSC) are due to paracrine factors released by MSC, as the benefits of MSC can be achieved through delivery of conditioned media (CM) alone. We previously demonstrated that downregulation of Dab2 enhances MSC cardiac protein expression and improves cardiac function after AMI following MSC engraftment. In order to define the molecular mechanisms that regulate MSC secretome, we analyzed gene arrays in MSC following downregulation of Dab2 via TGFß1 pretreatment or transfection with Dab2:siRNA or miR-145. We identified 23 genes whose expressions were significantly changed in all three conditions. Among these genes, we have initially focused our validation and functional work on calcium/calmodulin-dependent protein kinase kinase-1 (CAMKK1). We quantified the effects of CAMKK1 overexpression in MSC following injection of CM after AMI. Injections of CM from MSC with CAMKK1 over-expression correlated with an increase in vascular density (CAMKK1 CM: 2,794.95 ± 44.2 versus Control: 1,290.69 ± 2.8 vessels/mm2 ) and decreased scar formation (CAMKK1 CM 50% ± 3.2% versus Control: 28% ± 1.4%), as well as improved cardiac function. Direct overexpression of CAMKK1 in infarcted tissue using a CAMKK1-encoding plasmid significantly improved ejection fraction (CAMKK1: 83.2% ± 5.4% versus saline: 51.7% ± 5.8%. Baseline: 91.3% ± 4.3%) and decreased infarct size after AMI. Our data identify a novel role for CAMKK1 as regulator of the MSC secretome and demonstrate that direct overexpression of CAMKK1 in infarcted cardiac tissue, results in therapeutic beneficial effects. Stem Cells Translational Medicine 2017;6:1759-1766.

Prognostic value of immunoscore to identify mortality outcomes in adults with HBV-related primary hepatocellular carcinoma.

This study aimed to determine if the immunoscore (IS) staging system would be a potential prognostic factor in hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) in China.IS was performed in a consecutive cohort of HBV-HCC patients (n= 92). CD3+, CD8+, and CD45RO+ T cells were quantified by immunohistochemical analyses. The patients were stratified into 5 IS groups: I0, I1, I2, I3, I4 for every 2 cell phenotypes (IS1 (CD8/CD45RO, IS2 (CD3/CD8), and IS3 (CD3/CD45RO), respectively. ImagePro Plus software was used in the calculation of the paraffin-embedded tumor sections.The staining of CD3+, CD8+, and CD45RO+ cells in the HBV-HCC tissue demonstrated that there were higher density and larger area of lymphocytes in the invasive margins (IM) region than in the center (CT). Univariate analysis showed that preoperative TNM staging (P = .01), serum gamma-glutamyl transpeptidase (GGT) level (P = .03), vascular invasion (P = .00), and density of CD3+T (CT) (P = 0.01) were correlated significantly with disease-free survival (DFS); serum alpha-fetoprotein (AFP) level (P = .02), tumor size (P = .00), serum cholinesterase (CHE) (P = .04), and GGT level (P = .01), density of CD3+T(CT) (P = .00), CD8+T(CT)(P = .00), CD45RO+T(CT) (P = .00), and CD45RO+T (IM) (P = .02) were correlated with overall survival (OS). Multivariate analysis showed that TNM staging was not an independent prognostic factor of DFS and OS. Our results showed ISs did not have a significantly correlation with DFS (P = .35, .19, and .07, respectively), but it was correlated significantly with OS (P = .00, .00, and .00, respectively). There were statistical differences among the OS of every ISs subgroup except I0 and I1 by the Cox regressions analysis.The IS staging was closely related to the outcome of patients. It can compensate the TNM tumor classification system in predicting the prognosis of HBV-HCC patients.

A unique microRNA profile in end-stage heart failure indicates alterations in specific cardiovascular signaling networks.

It is well established that the gene expression patterns are substantially altered in cardiac hypertrophy and heart failure, however, less is known about the reasons behind such global differences. MicroRNAs (miRNAs) are short non-coding RNAs that can target multiple molecules to regulate wide array of proteins in diverse pathways. The goal of the study was to profile alterations in miRNA expression using end-stage human heart failure samples with an aim to build signaling network pathways using predicted targets for the altered miRNA and to determine nodal molecules regulating individual networks. Profiling of miRNAs using custom designed microarray and validation with an independent set of samples identified eight miRNAs that are altered in human heart failure including one novel miRNA yet to be implicated in cardiac pathology. To gain an unbiased perspective on global regulation by top eight altered miRNAs, functional relationship of predicted targets for these eight miRNAs were examined by network analysis. Ingenuity Pathways Analysis network algorithm was used to build global signaling networks based on the targets of altered miRNAs which allowed us to identify participating networks and nodal molecules that could contribute to cardiac pathophysiology. Majority of the nodal molecules identified in our analysis are targets of altered miRNAs and known regulators of cardiovascular signaling. Cardio-genomics heart failure gene expression public data base was used to analyze trends in expression pattern for target nodal molecules and indeed changes in expression of nodal molecules inversely correlated to miRNA alterations. We have used NF kappa B network as an example to show that targeting other molecules in the network could alter the nodal NF kappa B despite not being a miRNA target suggesting an integrated network response. Thus, using network analysis we show that altering key functional target proteins may regulate expression of the myriad signaling pathways underlying the cardiac pathology.

A novel dynamic model for predicting outcome in patients with hepatitis B virus related acute-on-chronic liver failure.

AIM: It is challenging to predict the outcome of patients with hepatitis B virus related acute-on-chronic liver failure (HBV-ACLF) through existing prognostic models. Our aim was to establish a novel dynamic model to improve the predictive efficiency of 30-day mortality in HBV-ACLF patients. METHODS: 305 patients who were diagnosed as HBV-ACLF (derivation cohort, n=211; validation cohort, n=94) were included in this study. The HBV-ACLF dynamic (HBV-ACLFD) model was constructed based on the daily levels of predictive variables in 7 days after diagnosis combined with baseline risk factors by multivariate logistic regression analysis. The HBV-ACLFD model was compared with the Child-Turcotte-Pugh (CTP) score, end-stage liver disease (MELD) score, and MELD within corporation of serum sodium (MELD-Na) score by the area under the receiver-operating characteristic curves (AUROC). RESULTS: The HBV-ACLFD model demonstrated excellent discrimination with AUROC of 0.848 in the derivation cohort and of 0.813 in the validation cohort (p=0.620). The performance of the HBV-ACLFD model appeared to be superior to MELD score, MELD-Na score and CTP score (P<0.0001). CONCLUSION: The HBV-ACLFD model can accurately predict 30-day mortality in patients with HBV-ACLF, which is helpful to select appropriate clinical procedures, so as to relieve the social and economic burden.

Correction: Interaction of G-Protein ßγ Complex with Chromatin Modulates GPCR-Dependent Gene Regulation.

[This corrects the article DOI: 10.1371/journal.pone.0052689.].

miRNA-548c: a specific signature in circulating PBMCs from dilated cardiomyopathy patients.

High fidelity genome-wide expression analysis has strengthened the idea that microRNA (miRNA) signatures in peripheral blood mononuclear cells (PBMCs) can be potentially used to predict the pathology when anatomical samples are inaccessible like the heart. PBMCs from 48 non-failing controls and 44 patients with relatively stable chronic heart failure (ejection fraction of ≤ 40%) associated with dilated cardiomyopathy (DCM) were used for miRNA analysis. Genome-wide miRNA-microarray on PBMCs from chronic heart failure patients identified miRNA signature uniquely characterized by the downregulation of miRNA-548 family members. We have also independently validated downregulation of miRNA-548 family members (miRNA-548c & 548i) using real time-PCR in a large cohort of independent patient samples. Independent in silico Ingenuity Pathway Analysis (IPA) of miRNA-548 targets shows unique enrichment of signaling molecules and pathways associated with cardiovascular disease and hypertrophy. Consistent with specificity of miRNA changes with pathology, PBMCs from breast cancer patients showed no alterations in miRNA-548c expression compared to healthy controls. These studies suggest that miRNA-548 family signature in PBMCs can therefore be used to detect early heart failure. Our studies show that cognate networking of predicted miRNA-548 targets in heart failure can be used as a powerful ancillary tool to predict the ongoing pathology.

Interaction of G-protein ßγ complex with chromatin modulates GPCR-dependent gene regulation.

Heterotrimeric G-protein signal transduction initiated by G-protein-coupled receptors (GPCRs) in the plasma membrane is thought to propagate through protein-protein interactions of subunits, Gα and Gßγ in the cytosol. In this study, we show novel nuclear functions of Gßγ through demonstrating interaction of Gß(2) with integral components of chromatin and effects of Gß(2) depletion on global gene expression. Agonist activation of several GPCRs including the angiotensin II type 1 receptor specifically augmented Gß(2) levels in the nucleus and Gß(2) interacted with specific nucleosome core histones and transcriptional modulators. Depletion of Gß(2) repressed the basal and angiotensin II-dependent transcriptional activities of myocyte enhancer factor 2. Gß(2) interacted with a sequence motif that was present in several transcription factors, whose genome-wide binding accounted for the Gß(2)-dependent regulation of approximately 2% genes. These findings suggest a wide-ranging mechanism by which direct interaction of Gßγ with specific chromatin bound transcription factors regulates functional gene networks in response to GPCR activation in cells.