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.

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.

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.

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.

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.

Fold change and p-value cutoffs significantly alter microarray interpretations.

BACKGROUND: As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of <0.02 lead data collection to look only at genes which vary wildly amongst other genes. Therefore, questions arise as to whether the biology or the statistical cutoff are more important within the interpretation. In this paper, we reanalyzed a zebrafish (D. rerio) microarray data set using GeneSpring and different differential gene expression cut-offs and found the data interpretation was drastically different. Furthermore, despite the advances in microarray technology, the array captures a large portion of genes known but yet still leaving large voids in the number of genes assayed, such as leptin a pleiotropic hormone directly related to hypoxia-induced angiogenesis. RESULTS: The data strongly suggests that the number of differentially expressed genes is more up-regulated than down-regulated, with many genes indicating conserved signalling to previously known functions. Recapitulated data from Marques et al. (2008) was similar but surprisingly different with some genes showing unexpected signalling which may be a product of tissue (heart) or that the intended response was transient. CONCLUSIONS: Our analyses suggest that based on the chosen statistical or fold change cut-off; microarray analysis can provide essentially more than one answer, implying data interpretation as more of an art than a science, with follow up gene expression studies a must. Furthermore, gene chip annotation and development needs to maintain pace with not only new genomes being sequenced but also novel genes that are crucial to the overall gene chips interpretation.

Amino acid function and docking site prediction through combining disease variants, structure alignments, sequence alignments, and molecular dynamics: a study of the HMG domain.

BACKGROUND: The DNA binding domain of HMG proteins is known to be important in many diseases, with the Sox sub-family of HMG proteins of particular significance. Numerous natural variants in HMG proteins are associated with disease phenotypes. Integrating these natural variants, molecular dynamic simulations of DNA interaction and sequence and structure alignments give detailed molecular knowledge of potential amino acid function such as DNA or protein interaction. RESULTS: A total of 33 amino acids in HMG proteins are known to have natural variants in diseases. Eight of these amino acids are normally conserved in human HMG proteins and 27 are conserved in the human Sox sub-family. Among the six non-Sox conserved amino acids, amino acids 16 and 45 are likely targets for interaction with other proteins. Docking studies between the androgen receptor and Sry/Sox9 reveals a stable amino acid specific interaction involving several Sox conserved residues. CONCLUSION: The HMG box has structural conservation between the first two of the three helixes in the domain as well as some DNA contact points. Individual sub-groups of the HMG family have specificity in the location of the third helix, DNA specific contact points (such as amino acids 4 and 29), and conserved amino acids interacting with other proteins such as androgen receptor. Studies such as this help to distinguish individual members of a much larger family of proteins and can be applied to any protein family of interest.

Aberrant signaling pathways in squamous cell lung carcinoma.

Lung cancer is the second most commonly occurring non-cutaneous cancer in the United States with the highest mortality rate among both men and women. In this study, we utilized three lung cancer microarray datasets generated by previous researchers to identify differentially expressed genes, altered signaling pathways, and assess the involvement of Hedgehog (Hh) pathway. The three datasets contain the expression levels of tens of thousands genes in normal lung tissues and squamous cell lung carcinoma. The datasets were combined and analyzed. The dysregulated genes and altered signaling pathways were identified using statistical methods. We then performed Fisher's exact test on the significance of the association of Hh pathway downstream genes and squamous cell lung carcinoma.395 genes were found commonly differentially expressed in squamous cell lung carcinoma. The genes encoding fibrous structural protein keratins and cell cycle dependent genes encoding cyclin-dependent kinases were significantly up-regulated while the ones encoding LIM domains were down. Over 100 signaling pathways were implicated in squamous cell lung carcinoma, including cell cycle regulation pathway, p53 tumor-suppressor pathway, IL-8 signaling, Wnt-ß-catenin pathway, mTOR signaling and EGF signaling. In addition, 37 out of 223 downstream molecules of Hh pathway were altered. The P-value from the Fisher's exact test indicates that Hh signaling is implicated in squamous cell lung carcinoma.Numerous genes were altered and multiple pathways were dysfunctional in squamous cell lung carcinoma. Many of the altered genes have been implicated in different types of carcinoma while some are organ-specific. Hh signaling is implicated in squamous cell lung cancer, opening the door for exploring new cancer therapeutic treatment using GLI antagonist GANT 61.

cDNA microarray gene expression profiling of hedgehog signaling pathway inhibition in human colon cancer cells.

BACKGROUND: Hedgehog (HH) signaling plays a critical role in normal cellular processes, in normal mammalian gastrointestinal development and differentiation, and in oncogenesis and maintenance of the malignant phenotype in a variety of human cancers. Increasing evidence further implicates the involvement of HH signaling in oncogenesis and metastatic behavior of colon cancers. However, genomic approaches to elucidate the role of HH signaling in cancers in general are lacking, and data derived on HH signaling in colon cancer is extremely limited. METHODOLOGY/PRINCIPAL FINDINGS: To identify unique downstream targets of the GLI genes, the transcriptional regulators of HH signaling, in the context of colon carcinoma, we employed a small molecule inhibitor of both GLI1 and GLI2, GANT61, in two human colon cancer cell lines, HT29 and GC3/c1. Cell cycle analysis demonstrated accumulation of GANT61-treated cells at the G1/S boundary. cDNA microarray gene expression profiling of 18,401 genes identified Differentially Expressed Genes (DEGs) both common and unique to HT29 and GC3/c1. Analyses using GenomeStudio (statistics), Matlab (heat map), Ingenuity (canonical pathway analysis), or by qRT-PCR, identified p21(Cip1) (CDKN1A) and p15(Ink4b) (CDKN2B), which play a role in the G1/S checkpoint, as up-regulated genes at the G1/S boundary. Genes that determine further cell cycle progression at G1/S including E2F2, CYCLIN E2 (CCNE2), CDC25A and CDK2, and genes that regulate passage of cells through G2/M (CYCLIN A2 [CCNA2], CDC25C, CYCLIN B2 [CCNB2], CDC20 and CDC2 [CDK1], were down-regulated. In addition, novel genes involved in stress response, DNA damage response, DNA replication and DNA repair were identified following inhibition of HH signaling. CONCLUSIONS/SIGNIFICANCE: This study identifies genes that are involved in HH-dependent cellular proliferation in colon cancer cells, and following its inhibition, genes that regulate cell cycle progression and events downstream of the G1/S boundary.

Application of committee kNN classifiers for gene expression profile classification.

In this study, we develop a two-class classification system based on a committee of k-Nearest Neighbour (kNN) classifiers. The system includes a sequence of simple data preprocessing steps. Each committee consists of 5 kNN classifiers of different architectures. Each classifier on the committee takes in a different set of features. The classification system is then applied to a set of microarray gene expression profiles from leukaemia patients. We show that the system can be effectively used for classifying microarray gene expression data. The results demonstrate the committee approach consistently outperforms individual kNN classifiers in terms of both classification accuracy and stability.

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

It is well established that gene expression patterns are substantially altered in cardiac hypertrophy and heart failure, but the reasons for such differences are not clear. MicroRNAs (miRNAs) are short noncoding RNAs that provide a novel mechanism for gene regulation. The goal of this study was to comprehensively test for alterations in miRNA expression using human heart failure samples with an aim to build signaling pathway networks using predicted targets for the miRNAs and to identify nodal molecules that control these networks. Genome-wide profiling of miRNAs was performed using custom-designed miRNA microarray followed by validation on an independent set of samples. Eight miRNAs are significantly altered in heart failure of which we have identified two novel miRNAs that are yet to be implicated in cardiac pathophysiology. To gain an unbiased global perspective on regulation by altered miRNAs, predicted targets of eight miRNAs were analyzed using the Ingenuity Pathways Analysis network algorithm to build signaling networks and identify nodal molecules. The majority of nodal molecules identified in our analysis are targets of altered miRNAs and are known regulators of cardiovascular signaling. A heart failure gene expression data base was used to analyze changes in expression patterns for these target nodal molecules. Indeed, expression of nodal molecules was altered in heart failure and inversely correlated to miRNA changes validating our analysis. Importantly, using network analysis we have identified a limited number of key functional targets that may regulate expression of the myriad proteins in heart failure and could be potential therapeutic targets.

Serum level of adiponectin correlated with gender and genotype in patients with chronic hepatitis C.

Adiponectin is well recognized as plasma physiologically active polypeptide hormone exclusively derived from human and animal mature adipocytes, with vigorous property in antidiabetic, antiobesity, antiatherogenic, and anti-inflammatory processes. In this study, we investigated the correlation between serum adiponectin level and clinical and pathological parameters in patients with chronic hepatitis C (CHC). The study included 127 patients with CHC and 42 healthy volunteers as controls whose laboratory parameters and serum adiponectin and tumor necrosis factor-alpha (TNF-alpha) were assessed using enzyme-linked immunosorbent assay (ELISA). We demonstrated that a lower serum adiponectin level was associated with male gender, higher gamma-glutamyltransferase (gamma-GGT), higher albumin, higher TNF-alpha, and steatosis grade. The higher level of serum adiponectin in patients with genotype 2a was demonstrated when compared with that in the patients with genotype 1b. Furthermore, of great interest, results suggested that the significant differences regarding viral genotype seemed to occur only in male patients with CHC but not in female patients. In conclusion, serum adiponectin was associated with gender, genotype, liver steatosis, and TNF-alpha in a Chinese population with CHC.

[Relationship between the level of serum TNF-alpha of patients with chronic hepatitis C and treatment with interferon-alpha and the influencing factors].

OBJECTIVE: To assess the relationship between the level of serum TNF-alpha of chronic hepatitis C with severity of disease and curative effect of anti-virus therapy with interferon alpha. METHODS: Thirty healthy controls and 102 patients with chronic hepatitis C were recruited into this study. The level of serum TNF-alpha was determined by ELISA in both groups. Then the 102 patients with chronic hepatitis C were evaluated after being classified into mild (44 cases), moderate (34 cases), and severe types (24 cases) based on the results of liver function tests. Liver functions, viral load and genotype of HCV RNA were measured. RESULTS: Before anti-virus treatment, the level of fasting serum TNF-alpha in the patients with chronic hepatitis C (1) was higher than that of the normal controls; (2) in the cases with mild type was lower than that in the moderate and severe groups; (3) serum TNF-alpha was not associated with the HCV load; (4) serum TNF-alpha was not significantly different between HCV subtypes 1b and 2a; (5) serum TNF-alpha was not significantly different between the patients responsive and non-responsive to the anti-virus treatment; (6) serum TNF-alpha was positively correlated with the level of serum direct bilirubin, negatively correlated with cholinesterase (CHE). CONCLUSIONS: The level of fasting serum TNF-alpha in the patients with chronic hepatitis was higher than that in the normal controls, and was positively correlated with the severity of the disease but not correlated with the therapeutic effect of interferon-alpha.

A gene selection method for classifying cancer samples using 1D discrete wavelet transform.

Selecting a set of discriminant genes for biological samples is an important task for designing highly efficient classifiers using DNA microarray data. The wavelet transform is a very common tool in signal-processing applications, but its potential in the analysis of microarray gene expression data is yet to be explored fully. In this paper, we present a wavelet-based feature selection method that assigns scores to genes for differentiating samples between two classes. The gene expression signal is decomposed using several levels of the wavelet transform. The genes with the highest scores are selected to form a feature set for sample classification. In this study, the feature sets were coupled with k-nearest neighbour (kNN) classifiers. The classification accuracies were assessed using several real data sets. Their performances were compared with several commonly used feature selection methods. The results demonstrate that 1D wavelet analysis is a valuable tool for studying gene expression patterns.