The clonal nature of most malignant tumors is well established. Experiments spanning several decades have shown, however, that as many as one million murine or human tumor cells are required to transplant a new tumor from an existing one. Two theories have been developed to account for the observation that apparently not every tumor cell is a tumor initiating cell (T-IC). The stochastic theory predicts that every tumor cell can form an entirely new tumor; however, entry into the cell cycle is a stochastic event with low probability. Alternatively, tumor cells may exist in a hierarchical state in which only a small number of cells possess tumor initiating potential. If the stochastic model is correct, then tumor cells are biologically homogeneous and genetic or epigenetic programs that allow for tumorigenesis are operative in the majority of cells that comprise a tumor. The hierarchical model, however, predicts the tumor cells possess a functional heterogeneity and that quantitatively the cells capable of tumorigenesis are a relatively minor population among the bulk of tumor cells.Recent data from both hematologic malignancies and solid tumors have suggested that there are only minor populations of cells in each malignancy that are capable of tumor initiation. These T-ICs have the functional properties of tumor stem cells. They appear to be capable of asymmetric division and self renewal, are only a minor faction among the bulk of more differentiated cells in the tumor, and can reconstitute all the cell types in the tumor of origin.in vitro clonogenic potential. These cells also engrafted successfully into Non-obese Diabetic/Severe Combined Immunodeficient (NOD/SCID mice), whereas CD138 positive cells did not engraft.Currently, tumor stem cells have been isolated and characterized in several hematologic malignancies and some solid tumors. One of the first tumors in which a stem cell was identified was acute myeloid leukemia (AML). In this disease, the frequency of the leukemic stem cell (LSC) was approximately 1 per million AML blasts, establishing that not every AML cell had T-IC capacity. A CD34 positive/CD38 negative cell fraction representing 0.1 to 1 percent of the tumor cells possessed all the leukemia initiating activity in the NOD/SCID transplantation model. By contrast, the major fraction of the CD34 positive/ CD38 positive cells and the majority of CD34 negative cells, which comprise most of the cells in the tumor, could not initiate leukemia. A multiple myeloma stem cell has also been characterized. Multiple myeloma cell lines and primary patient derived cells express the cell surface marker syndecan-1 (CD138). A population of cells representing less than 5 percent of the cells in the bulk population of multiple myeloma cells was found to be CD138 negative, and possessed in vivo NOD/SCID engraftment assay. The mammary tumor stem cells represented only 2 percent of the unfractionated bulk tumor cells.Recently, a mammary carcinoma stem cell has been isolated from primary mammary carcinomas using four cell surface markers . The tumor initiating capacity of the cells was again verified in an Finally, a putative brain tumor stem cell has also been isolated. These cells appear to be between 0.3 to 25 percent of the cells in the brain tumors examined. They are positive for the neural stem cell marker CD133 and have a marked capacity for self renewal and differentiation. Transplantation of these putative neural tumor stem cells into the forebrains of NOD/SCID mice yields tumors phenotypically resembling the tumors from which the stem cells were isolated.Isolation of tumor stem cells from a larger spectrum of solid tumors and characterization of markers for such cells will be important in understanding how general the role of tumor stem cells are in the pathogenesis of cancer.In addition to isolating and characterizing tumor stem cells themselves, it is also important to identify the genes and proteins that facilitate the self renewal phenotype that characterize all stem cells. The proteins involved in self renewal in embryonic and adult stem cells appear to be subverted in tumorigenesis to allow the tumor-initiating cells to maintain self renewal capacity. Two families of proteins related to self renewal, the polycomb gene Bmi-1 and the Wnt signaling pathway proteins, have been related to the maintenance of the tumor stem cell phenotype.The polycomb genes have an essential role in embryogenesis, regulation of the cell cycle, and lymphopoieisis. These genes are transcriptional repressors that are essential for the silencing of other families of genes. Deletion of polycomb gene Bmi-1 in mice results in a progressive loss of all hematopoietic lineages. This loss results from the inability of the Bmi-1(−/ −) stem cells to self renew. Introducing genes known to produce AML into Bmi-1(−/ −) hematopoeitic stem cells , induced AML with normal kinetics; however, the Bmi-1(−/ −) leukemic stem cells from primary recipients were unable to produce AML in secondary recipients. These results demonstrate that Bmi-1 is also required for self renewal of leukemic stem cells in the murine AML model.in vitro and reduce hematopoietic reconstitution in vitro. Activation of Wnt signaling in hematopoeitic stem cells leads to increased expression of Hox B4 and Notch-1 genes previously implicated in self renewal of hematopoietic stem cells. The Wnt signaling pathway is involved in both hematopoietic malignancy and colon carcinoma. Although the Wnt ligands themselves are only rarely involved in tumorigenesis, mutations mimicking Wnt receptor (Frizzled) activation induce a set of genes associated with repression of differentiation and potentiation of self-renewal. In general, these mutations involve Wnt signal transduction proteins including the activation of beta-catenin and inactivation of the (APC) adenomatosis polyposis coli protein. In myeloid leukemia, nonphosphorylated beta-catenin accumulates in granulocyte-macrophage progenitors as they progress toward leukemia. These normally more committed progenitors can thus acquire self-renewal properties. A similar accumulation of unphosphorylated beta-catenin has also been observed in multiple myeloma cells. In colon cancer, the APC gene is mutated early in the development of about 90 percent of the colon carcinomas. Similarity in gene expression patterns between populations of colon cancer cells and colon epithelial stem cells has also been observed by DNA microarray analysis. It is possible that mutations in the Wnt signaling pathway maintain the program of stem cell genes in the transcriptionally active state.Another group of genes involved in self renewal are those involved in the Wnt signal transduction cascade. The Wnt protein binds to a receptor called Frizzled and activates cell fate decisions during tissue development. Inhibitors of Wnt signaling produce inhibition of hematopoietic stem cell growth Additional research on the important genes and proteins that function to maintain the stem cell phenotype could contribute to increasing the number of specific targets for which cancer therapeutic agents can be designed.Recent studies of Helicobacter infection in mice have suggested that bone marrow hematopoietic stem cells can contribute to repopulating the gastric mucosal epithelium. These cells can progress, with time, to metaplasia, dysplasia, and cancer. The possibility of stable fusion between the bone marrow derived cells and the gastric mucosa was eliminated, suggesting the possibility of transdifferentiation of hematopoietic stem cells. Research aimed at establishing whether or not stem cells have the capacity to transdifferentiate is also encouraged.in vivo assays for the functional identification of such cells. Studies of the genes regulating self renewal, and studies of regulation of stem cell division by the stem cell niche and/or microenvironment are also encouraged. Investigators working on the cell and molecular biology of embryonic stem cells, adult stem cells, and tumor stem cells are encouraged to apply for support under this funding opportunity. The following questions illustrate areas of high interest, but other relevant innovative projects are also encouraged: 1) What governs the proliferation rate of normal and tumor stem cells? 2) Can oncogenes and their associated mutations affect asymmetric versus symmetric divisions in stem cells? 3) Stem cell quiescence versus growth must ultimately be understood in terms of progression through the cell cycle. Which stem cell-specific genes alter the cell cycle pathway proteins? 4) Do tumor stromal cells constitute a unique tumor stem cell niche? Does the tumor stromal niche act as a constituent of a feedback mechanism with tumor stem cells to control their growth? 5) Are the phenotypes of invasion and metastasis uniquely connected to the tumor stem cell phenotype? 6) Are normal resident adult tissue stem cells a special target for carcinogenic insults? 7) Can new and/or better markers and assays for the isolation and enrichment of tumor stem cells be developed? 8) Can new and/or better in vivo functional assays to identify tumor initiating cells  be developed? 9) How do changes to stem cells or their environment due to aging affect formation of tumor stem cells or alter their properties?This funding opportunity is intended to promote research on all aspects of tumor stem cell biology, and on the genes and proteins responsible for the tumor stem cell phenotype. Research studies on the characterization of tumor stem cells from the broad spectrum of solid and liquid tumors not already examined, on markers potentially shared by tumor stem cells and normal stem cells, and on the biochemical and molecular regulation of normal and tumor stem cell function are encouraged. Such research can and should include research on This funding opportunity will use the R01 and R21 award mechanisms. As an applicant, you will be solely responsible for planning, directing, and executing the proposed project.http://grants.nih.gov/grants/funding/modular/modular.htm). Specifically, if you are submitting an application with direct costs in each year of $250,000 or less, use the modular budget format described in the PHS 398 application instructions. Otherwise, follow the instructions for non-modular research grant applications.This funding opportunity uses just-in-time concepts. It also uses the modular as well as the non-modular budget formats  or by e-mail: GrantsInfo@nih.gov.The PHS 398 application instructions are available at http://www.dnb.com/us/. The D&B number should be entered on line 11 of the face page of the PHS 398 form. The complete version of this PA is available at http://grants/guide/pa-files/PAR-05-086.html.Applications must be prepared using the most current PHS 398 research grant application instructions and forms. Applications must have a D&B Data Universal Numbering System (DUNS) number as the universal identifier when applying for Federal grants or cooperative agreements. The D&B number can be obtained by calling 866-705-5711 or through the web site at am214t@nih.gov; Jill Carrington, Systems Branch, Musculoskeletal Biology, Biology of Aging Program, National Institute on Aging, National Institutes of Health, 7201 Wisconsin Ave, Suite 2C231, MSC 9205, Bethesda, MD 20892 USA, 301-496-6402, fax: 301-402-0010, e-mail:carringtonj@nia.nih.gov. Reference: PA No. PA-05-086Contact: R. Allan Mufson, Cancer Immunology Hematology Branch, Division of Cancer Biology, National Cancer Institute, 6130 Executive Blvd, EPN Rm 5062, Bethesda, MD 20892 USA, Rockville, MD 20852 USA (for express/courier service) 301-496-7815, Fax: 301-480-2844, E-mail:The specific objectives of this announcement on Secondary Analyses In Obesity, Diabetes, Digestive And Kidney Diseases are to support the following: (a) research on secondary analyses of data related to the epidemiology of disease areas of NIDDK; (b) preliminary projects using secondary analysis that could lead to subsequent applications for individual research awards; (c) rapid analyses of new databases and experimental modules to inform the design and content of future studies; (d) the archiving of data sets to be made publicly available for research purposes related to disease areas of NIDDK, including both epidemiological studies and multi-center clinical trials.Research that employs analytic techniques that demonstrate or promote methodological advances in patient-oriented and epidemiologic research is also of interest. International comparative analyses are encouraged. Applications that are innovative and high risk with the likelihood for high impact would be especially encouraged.Patient-oriented and epidemiologic research projects, particularly multicenter projects, typically generate data with potential utility beyond the specific hypotheses and questions for which they were designed. Often data are not fully analyzed, especially when unexpected research questions emerge after the end of the project's funding period. Analyzing such existing data sets can therefore provide a cost-effective means to test specific hypotheses that have not been adequately examined. The further analysis of existing research data may also be prompted by a need to confirm new findings or to aid in the development of new research questions.Applicants may conduct secondary analyses using data from a variety of sources. These would include investigator-initiated research activities, cooperative agreements, and contracts or from other public or private sources. Sources may be large, nationally representative data sets such as those of the National Center for Health Statistics or smaller, regional or locally based data sets. Also appropriate for secondary analyses are relevant cross-sectional and longitudinal survey data collected by federal, state, and local government agencies. Secondary data analyses of these data may serve as an economical alternative to expensive and time-consuming new data collection projects. Applicants may also secure access to other data sets that may or may not be in the public domain, such as those collected under research grant funds, sponsored by private entities, or originally collected for purposes other than research, such as health care administrative data sets.In addition to the examination of specific research hypotheses, existing data sets may also be used to cross-validate exploratory analyses in ongoing studies, to test complex statistical models, and in special circumstances to provide comparison groups for experimental studies. Moreover, secondary analysis is appropriate for many types of data, including qualitative information, and may also cover the integration of quantitative and qualitative data.http://pubnts06.rti.org/niddk/home.do). Applicants are encouraged to consider the research opportunities available in this NIDDK resource.NIDDK has established a repository for the archiving of data sets, as well as genetics and tissues from NIDDK sponsored clinical trials and epidemiological studies , specifically in the areas of fatty liver disease, viral hepatitis, drug- and toxicant-induced liver disease, autoimmune liver disease, pediatric liver disease, liver transplantation, complications of liver disease, and gallbladder and biliary disease.This program announcement addresses several areas considered to be high priority for liver disease research as delineated in the recently published Trans-NIH Action Plan for Liver Disease Research  award mechanism. As an applicant, you will be solely responsible for planning, directing, and executing the proposed project.http://grants.nih.gov/grants/funding/modular/modular.htm).This funding opportunity uses just-in-time concepts. It also uses the modular budget format described in the PHS 398 application instructions (see http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. Applicants must use the currently approved version of the PHS 398. For further assistance contact GrantsInfo at 301-435-0714 (telecommunications for the hearing impaired: TTY 301-451-0088) or by e-mail: GrantsInfo@nih.gov.The PHS 398 application instructions are available at http://www.dnb.com/us/. The D&B number should be entered on line 11 of the face page of the PHS 398 form. The complete version of this PA is available at http://grants.nih.gov/grants/guide/pa-files/PA-05-091.html.Applications must be prepared using the most current PHS 398 research grant application instructions and forms. Applications must have a D&B Data Universal Numbering System (DUNS) number as the universal identifier when applying for Federal grants or cooperative agreements. The D&B number can be obtained by calling 866-705-5711 or through the web site at pe39h@nih.gov. Reference: PA No. PA-05-091Contact: James E. Everhart, Epidemiology and Clinical Trials Branch, Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, 6707 Democracy Blvd, Rm 655, Bethesda MD 20892-5450 USA, 301-594-8878, fax: 301-480-8300, e-mail: je17g@nih.gov; Catherine C. Cowie, Type 1 Diabetes Clinical Trials Program, Division of Diabetes, Endocrinology, and Metabolism, National Institute of Diabetes and Digestive and Kidney Diseases, 6707 Democracy Blvd, Rm 691, Bethesda, MD 20892-5460 USA, 301-594-8804, fax: 301-480-3503, e-mail: cc68v@nih.gov; Paul W. Eggers, Epidemiology and U.S. Renal Data System, Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, 6707 Democracy Blvd, Rm 615, Bethesda, MD 20892-5458 USA, 301 594-8305, fax: 301-480-3510, e-mail:


High-throughput genotyping generates vast amounts of data for analysis; results can be difficult to summarize succinctly. A single project may involve genotyping many genes with multiple variants per gene and analyzing each variant in relation to numerous phenotypes, using several genetic models and population subgroups. Hundreds of statistical tests may be performed for a single SNP, thereby complicating interpretation of results and inhibiting identification of patterns of association.To facilitate visual display and summary of large numbers of association tests of genetic loci with multiple phenotypes, we developed a Phenotype-Genotype Association (PGA) grid display. A database-backed web server was used to create PGA grids from phenotypic and genotypic data . HTML pages were generated using Tcl scripts on an AOLserver platform, using an Oracle database, and the ArsDigita Community System web toolkit. The grids are interactive and permit display of summary data for individual cells by a mouse click . PGA grids can be used to visually summarize results of individual SNP associations, gene-environment associations, or haplotype associations.The PGA grid, which permits interactive exploration of large numbers of association test results, can serve as an easily adapted common and useful display format for large-scale genetic studies. Doing so would reduce the problem of publication bias, and would simplify the task of summarizing large-scale association studies. The advent of high-throughput technology is generating unprecedented amounts of genotypic data that are being used in association analyses for multiple phenotypes. A single project may involve genotyping many genes with several variants (such as single nucleotide polymorphisms [SNPs]) per gene and analyzing each variant in relation to numerous phenotypes. In turn, each phenotype-SNP pair may be subjected to multiple genetic models and subgroup analyses. Hundreds of statistical tests may be performed for a single SNP, thereby complicating interpretation of results and inhibiting identification of patterns of association within a vast sea of data. Ultra-dense genome scans using 300,000 to 1,000,000 SNPs [We are currently studying common SNPs in 200 candidate genes to test associations with alterations in echocardiographic phenotypes in participants from NHLBI's Framingham Heart Study. For each SNP, 144 statistical tests are performed: genotypes are analyzed with regard to six phenotypes  through four genetic models , with two levels of covariate adjustment  in three samples . Planned analyses of 1500 SNPs will generate nearly one quarter of a million statistical tests. Further details can be found on the CardioGenomics website [As analyses commenced, it became obvious that we needed summary methods of data distillation and presentation to highlight findings of potential importance and to identify patterns of association, such as associations limited to one of multiple phenotypes, or associations limited to one sex. Therefore, we developed an approach that displays strengths of statistical associations at a glance, and that makes supporting data available easily via graphs accessed by a mouse click.Figure The PGA grid is interactive. Clicking on a specific cell generates a plot of adjusted least squares means for the trait of interest by genotype for the corresponding genetic model. Figure The software to create PGA grids from user-supplied data  utilizes a database-backed web server. We generate HTML pages using Tcl scripts on an AOLserver platform  using anThe PGA grid was developed to summarize large numbers of phenotype-genotype association tests in a visually useful manner to facilitate interactive exploration of results. This approach could serve as a common format for large-scale association studies. Due to the large number of association tests performed, there will be many nominally significant results. One approach to multiple testing is to indicate P-values deemed statistically significant based on consideration of false discovery rates ,10. MostThe PGA grid provides a simple visual method for displaying a large number of results, potentially reduces the problem of publication bias, and simplifies the task of summarizing large-scale association studies.LVM = left ventricular mass; LVID = left ventricular internal diameter at end diastole; LVWT = sum of septal and left ventricular posterior wall thickness; FS = left ventricular fractional shortening; AoR = aortic root diameter; LA = left atrial anteroposterior dimension. M = men only; F = women only; MF = men and women.Daniel Levy: Conception of the display grid, drafting of paper, revisions to manuscriptSteven R. DePalma: Development of source code for display grid, revisions to manuscriptEmelia J. Benjamin: Conception of the display grid, revisions to manuscriptChristopher J. O'Donnell: Conception of the display grid, revisions to manuscriptHelen Parise: Statistical analyses for incorporation into display gridJoel N. Hirschhorn: Conception of the display grid, revisions to manuscriptRamachandran S. Vasan: Conception of the display grid, revisions to manuscriptSeigo Izumo: Principal investigator of CardioGenomics, funding of the projectMartin G. Larson: Conception of the display grid, development of statistical methods, revisions to manuscript."pga-grid-v1.01-src.zip" is the source code for PGA Grid, version 1.01, as a .zip archive containing 31 text files  for use with a Linux/AOLserver/Oracle/ACS web server platform. File descriptions are available in Additional File 2, pga-grid-v1.01-readme.htm. The most recent version of this software is available from Click here for file"pga-grid-v1.01-readme.htm" is an HTML-format file that lists and describes each of the files contained in Additional File 1, pga-grid-v1.01-src.zip.Click here for file


We investigated the presence of colony-stimulating factor (CSF) in supernatants obtained from TS/A, a new metastatic murine cell line, and from its high-and low-metastatic clonal derivatives . TS/A cells produced a CSF in vitro that induced proliferation and differentiation of murine monocytic and granulocytic progenitors in agar cultures. In TS/A-bearing mice remarkable splenomegaly, blood granulocytosis and thymus depletion were observed along with a stimulatory activity in serum and a strong proliferative activity both in spleen and in bone marrow populations. Conditioned media from E clones showed an in vitro colony-stimulating activity greater than those of F clones. Mice injected subcutaneously with cells of all clones studied showed granulocytosis, splenomegaly and thymus depletion, although to varying degrees. However, no direct correlation between granulocytosis-splenomegaly and the number of spontaneous lung metastases was observed.


The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types.SBEAMS-Microarray, a module of the open-source Systems Biology Experiment Analysis Management System (SBEAMS), enables MIAME-compliant storage, management, analysis, and integration of high-throughput genomic expression data. It is interoperable with the Cytoscape network integration, visualization, analysis, and modeling software platform.SBEAMS-Microarray provides end-to-end support for genomic expression analyses for network-based systems biology research. The extraction of biological information from high throughput genomic expression data is a fundamentally network-based systems biology problem . ComplexSystems biology research is characterized by the development and application of technologies enabling quantitative measurements that are genomic in scale. Data coSBEAMS-Microarray, existing microarray data-analysis software packages were evaluated based on desired attributes for the support of microarray-based systems biology research. These attributes included:Before the development of 1. Free open-source availability2. Compliance with the Minimum Information About a Microarray Experiment (MIAME) data content specification .3. Flexible application of multiple microarray-data analysis methods4. Strong support for large-scale datasets derived from the high-probe-density Affymetrix microarray platform5. Integration of genomic expression data with other data types such as proteomics dataCytoscape platform [6. Interoperability with network visualization, analysis, and modeling software, for example, the platform .Microarray module of SBEAMS, the Systems Biology Experiment Analysis Management System [SBEAMS-Microarray combines all of the above attributes, making SBEAMS-Microarray uniquely advantageous for network-based systems biology research.However no single package had all of these attributes. Several packages -8 were dt System . SBEAMS-SBEAMS-Microarray module is built using SBEAMS, a software and database framework for collecting, storing, accessing and analyzing data from different types of experimental data [SBEAMS combines a relational database management system (RDBMS) back end, a collection of tools to store, manage, and query experiment information and results in the RDBMS, a web front end for querying the database and providing integrated access to remote data sources, and an interface to other data processing and analysis programs  commands  for the schema for several different RDBMSs is also provided. Its use is described in the installation instructions. A schema diagram , developed at The Institute for Genomic Research. MeV provides numerous statistical tests, classification methods, and clustering algorithms [SBEAMS-Microarray. From the SBEAMS-Microarray web interface, MeV is launched using Java Web Start, so that users do not need to install MeV, and are immediately presented with their data in the MeV environment.gorithms  that extSBEAMS is modular in design to allow the integrated storage and access of disparate types of experiments and data, for example, microarray and proteomics experiments, molecular interaction data, and gene annotations. This integrated system is a consistent framework that combines a RDBMS back end, and a web front end providing integrated access to the data. For example, from the SBEAMS-Microarray GetExpression interface, queries can be made for gene annotations of interest or by defining threshold levels of metrics for statistical significance of expression change across one or more user-specified microarray experiments. The results can be viewed within the SBEAMS web interface, or exported in Excel, CSV, TSV, or XML formats, or accessed programmatically via HTTPS. Data sets may originate in-house or be imported from external sources. Currently, development of SBEAMS is driven mainly by the SBEAMS-Microarray and SBEAMS-Proteomics projects, with multiple modules in early stages of development.SBEAMS-Microarray is interoperable with Cytoscape software. The results of data analyses can be loaded directly to the Cytoscape environment, launched from the SBEAMS environment. Cytoscape is an open-source bioinformatics software platform for visualizing molecular interaction networks and integrating these networks with gene expression data, proteomics data, gene annotations, and other data [Cytoscape plugins. Plugins implement integrated network analyses, connection with outside databases and tools, and modeling capabilities. In SBEAMS-Microarray, query results from GetExpression can be loaded directly into Cytoscape via Java Web Start, as described above for MeV. With the data in Cytoscape, users can load molecular interaction data, annotation data, and a wide variety of other data, to generate integrated networks. Data types can be loaded directly as files, or imported from outside databases using plugins like InteractionFetcher [her data . A wide nFetcher .Cytoscape enables automated data integration and subsequent network-based analyses to extract information that is not present in any one data type. For example, the Biomodules plugin [Interoperability with s plugin  implemens plugin  applied SBEAMS-Microarray enables investigators to store, manage, analyze, and integrate genomic expression data for systems biology research projects. Investigators begin by performing a microarray experiment to answer questions about their biological system of interest. Once the primary data have been obtained, investigators log in through the SBEAMS-Microarray web interface to see their data that has been automatically loaded into the database. Before beginning analysis, it is advisable to ensure that the data are acceptable by viewing various quality control metrics and diagnostic plots provided by the software. Investigators may choose to annotate their data further, by providing greater details on the biological samples hybridized to their microarrays to aid others involved with analysis of their data and in compliance with MIAME standards [MultiExperiment Viewer [Cytoscape, is differential expression analysis. Investigators choose parameters for their analysis, including a statistical method, groups of biological replicates to be compared, and thresholds for statistically significant differential expression. After analysis, result tables show genes with the greatest and most significant expression differences. Users have the option to store the results in the database. These tables of differentially expressed genes are themselves informative, but will provide further system-level insights when integrated with other biological data. From stored analyses, sets of genes with their differential expression values can be loaded directly into Cytoscape launched from SBEAMS. Expression data can be mapped to interpolated colors of nodes representing differentially expressed genes. Investigators then may begin to use Cytoscape to explore their expression data in the context of biological network information. One method is to employ the InteractionFetcher [SBEAMS-Microarray User Guide  allowing for incorporation of disparate data types into a single framework. In combination with network-analysis tools like Cytoscape, it provides end-to-end support for systems biology research projects involving high-throughput genomic expression analysis.Project name: SBEAMS-Microarray• Project home page: • Operating system(s):• ○ Application server: Linux/UNIX○ RDBMS: Windows or Linux/UNIXProgramming languages: Perl, SQL, PHP, JavaScript• Other requirements:• ○ Apache web server○ R 2.1.0 through 2.3.0 with Bioconductor 1.6 through 1.8○ Microsoft SQL Server, Sybase Adaptive Server Enterprise or MySQL○ FreeTDS 0.63○ libgd 2.0.33License: GNU General Public License version 2• Restrictions to use by non-academics: None• BM contributed to software design, did software testing and debugging, and drafted the manuscript. EWD conceived the software project, designed and coded software, and drafted the manuscript. PM designed and coded software. DC coded and debugged software. MHJ coded and debugged software. TG supervised the project, participated in software design, and revised the manuscript. All authors read and approved the final manuscript.SBEAMS-MicroarraySchema of . A GIF image representation of the SBEAMS-Microarray database schema. The information used to generate this diagram, including detailed column definitions and relationships, is in the SBEAMS distribution which can be obtained at .Click here for fileSBEAMS-Microarray User Guide. SBEAMS-Microarray_UserGuide.pdf provides instructions on using the various functions available in SBEAMS-Microarray.Click here for file


Single nucleotide polymorphisms (SNPs) and genes that exhibit presence/absence variation have provided informative marker sets for bacterial and viral genotyping. Identification of marker sets optimised for these purposes has been based on maximal generalized discriminatory power as measured by Simpson's Index of Diversity, or on the ability to identify specific variants. Here we describe the Not-N algorithm, which is designed to identify small sets of genetic markers diagnostic for user-specified subsets of known genetic variants. The algorithm does not treat the user-specified subset and the remaining genetic variants equally. Rather Not-N analysis is designed to underpin assays that provide 0% false negatives, which is very important for e.g. diagnostic procedures for clinically significant subgroups within microbial species.Campylobacter jejuni, Yersinia enterocolitica and Clostridium difficile.The Not-N algorithm has been incorporated into the "Minimum SNPs" computer program and used to derive genetic markers diagnostic for multilocus sequence typing-defined clonal complexes, hepatitis C virus (HCV) subtypes, and phylogenetic clades defined by comparative genome hybridization (CGH) data for Not-N analysis is effective for identifying small sets of genetic markers diagnostic for microbial sub-groups. The best results to date have been obtained with CGH data from several bacterial species, and HCV sequence data. The last two decades have seen an exponential increase in the generation of comparative genetic data from within bacterial and viral species. Many of the bacterial data sets are derived from electrophoresis-based genotyping methods, such as pulsed-field gel electrophoresis, which has been used to develop the inter-laboratory PulseNet system for real-time monitoring of foodborne bacterial pathogens . More reThe extensive knowledge base of comparative genetic information can be exploited to develop rationally-designed genotyping methods for examining epidemiology, or inferring virulence potential, vaccine susceptibility or antimicrobial-antiviral resistance. One approach to discriminating known genotypes within a species is to interrogate every known genetic polymorphism. However, this approach is inefficient due to linkage of alleles, and may also provide more resolving power than is required . DespiteD), and are therefore optimized with respect to discriminating all variants from each other [Neisseria meningitidis and Staphylococcus aureus variants [D module has been used to extract D-maximized SNP sets from S. aureus and Campylobacter jejuni MLST databases [D-maximized binary gene sets (sets of genes that are present in some isolates but not others) from C. jejuni CGH data [The considerable volume of comparative genetic information now available renders computerized data-mining the only practical means of identifying sets of polymorphisms optimized for particular genotyping tasks. Our research group has previously developed and described the "Minimum SNPs" computer program, which extracts resolution-optimized single-nucleotide polymorphism (SNP) sets from complex DNA sequence alignments . Previouch other ,20. The variants , while tatabases -23, and,CGH data .D maximization module [Other bioinformatics programs that carry out similar functions to "Minimum SNPs" include a linkage disequilibrium-selection algorithm, which identifies SNPs diagnostic for haplotype blocks in mammalian genomes , and SNPn module . One funE. coli, two independent MLST schemes were examined. Using scheme 1, Not-N analysis successfully identified 15 SNPs that completely differentiated the 10 major CCs .The clonal complexes (CCs) defined by MLST data are emerging as important epidemiological or taxonomic units ,26. TherH. influenzae and E. coli, Not-N analysis was unable to identify high-confidence SNPs (≥ 98%) for four S. aureus and five C. jejuni CCs. These CCs were the largest in their respective databases. The difficulty in identifying SNPs diagnostic for the larger CCs was investigated further and was found to be due to CC members that have diverged from the CC founders by recombination rather than mutation. S. aureus exhibits a low recombination frequency, with approximately 10% of CC members arising by recombination [S. aureus SNPs are dimorphic, coupled with the effects of recombination and the small pool of available SNPs in S. aureus sequence data, there exist few SNPs unique to all members of a CC. Therefore, the probability of finding highly discriminatory SNP sets is low. In support of this, the Not-N algorithm was used to find sets of SNPs diagnostic for the 212 methicillin-resistant S. aureus (MRSA) STs in the MLST database [S. aureus MLST database, and therefore the CCs contain correspondingly fewer recombinants. This analysis yielded 22 SNPs that delineated the ten major MRSA CCs with 100% confidence (results not shown).In contrast to bination . While mdatabase . The MRSC. jejuni, the influence of recombination on Not-N performance is similar to S. aureus but is more extreme. The majority of STs in C. jejuni arise by recombination, at an estimated frequency of 50 times the rate of mutation [S. aureus CCs are more efficiently derived using the D maximization algorithm of "Minimum SNPs", while in C. jejuni, the high recombination rate renders the identification of small numbers of CC-specific SNPs with high discriminatory power and a low false-negative rate potentially impossible by any means. Other researchers have identified small numbers of C. jejuni CC SNPs characteristic of six major CCs of C. jejuni; ST-21, ST-45, ST-48, ST-61, ST-206 and ST-257 [In mutation , resultid ST-257 . HoweverD-optimized (diversity maximizing) SNP sets from the S. aureus and C. jejuni databases [S. aureus, the genotypes defined by approximately eight D-optimized SNPs correspond closely to the population structure as defined by eBURST analysis. Thus, the D-optimized SNP sets appear superior to the Not-N derived SNP sets for assigning an S. aureus isolate to a CC. In the case of C. jejuni, the correspondence between D-optimized SNP genotypes and population structure is less than in S. aureus because of the higher recombination frequency. However, adding the interrogation of more loci such as a hypervariable region (sequencing of the flagellin A short variable region) to the SNP-based genotyping reduced to insignificance the incidence of unrelated isolates failing to be discriminated, thus demonstrating the value of the D-optimized SNP set. In summary, for both species, Not-N-derived SNPs are generally not optimal for assigning isolates to larger CCs, but are highly effective for identifying the smaller CCs.The "Minimum SNPs" software has previously been used to derive atabases -23. In tC. jejuni, Y. enterocolitica and C. difficile enabled the identification of phylogenetic clades that can predict infection source or pathogenicity traits. Champion et al. [C. jejuni predictive of infection source, with one clade containing predominantly livestock isolates and the other non-livestock  isolates. Isolates identified from human infection were roughly evenly distributed between the two clades. In Y. enterocolitica, three clades corresponding to non-pathogenic (biotype 1A), low-pathogenicity (biotypes 2–5) and highly pathogenic (biotype 1B) were identified by the comparative phylogenomics approach [C. difficile, CGH phylogeny identified four clades comprising a hypervirulent clade, a toxin A-B+ clade, and two clades containing human and animal isolates [CGH allows the large-scale identification of genetic differences across a number of strains. Bayesian-based algorithms applied to the CGH data of n et al.  identifiapproach . For C. isolates .C. jejuni that were characteristic of the livestock clade, including the gene cluster Cj1321 to Cj1326 within the O-linked flagellin glycosylation locus. However, none of the 33 CDS identified by MacClade 4 analysis were 100% specific to the livestock clade [C. jejuni CGH data that separated, with 100% confidence, isolates from the livestock clade from those in the nonlivestock clade  and HIV-1 (1507 genomes) 8 genomes, providiCurrent HCV genotyping methods such as the line probe assay (INNO-LiPA) , the reaTo our knowledge, this is the first set of genotyping targets that enables the specific and accurate discrimination of the 13 major subtypes of HCV. Real-time PCR-based methods, such as allele-specific real-time PCR  or high-Bacillus anthracis [This study has shown that the Not-N algorithm provides a practical tool for identifying diagnostic polymorphisms that discriminate bacterial or viral populations of interest. Not-N analysis was particularly valuable with bacterial CGH and HCV genome sequence data, where the software identified genetic markers with superior performance to polymorphisms in current use. The ability of the algorithm to select SNPs diagnostic for MLST-defined CCs was dependent on CC size, with large numbers of SNPs required to delineate the larger CCs that have undergone extensive recombination. The purpose of the Not-N algorithm is conceptually similar to the identification of canonical phylogenetic SNPs, such as those previously described for nthracis , and indC. jejuni, S. aureus, and Haemophilus influenzae were downloaded from the MLST databases for these organisms [Escherichia coli, data was obtained from two MLST schemes using seven loci [S. aureus and C. jejuni, CCs were defined as STs sharing 6/7 loci with the ancestral clone, whereas with H. influenzae and E. coli, this parameter was set to 5/7 loci and 4/7 loci, respectively.The sequence type (ST) and allele files for rganisms . For Escven loci -46, eBURven loci . For S. Sequence data for HIV-1 and HCV were downloaded from the respective databases ,10. The C. jejuni, Y. enterocolitica and C. difficile was downloaded from BμG@s [C. jejuni, 93 Y. enterocolitica and 74 C. difficile strains [Y. enterocolitica strain 237_02, which was shown to group with the non-pathogenic clade following ClustalX phylogenetic analysis [CGH array data for om BμG@s  accessio strains  were ana strains . Isolateanalysis  and visuanalysis  of the fThe Not-N algorithm is designed to derive, from sequence alignments, sets of SNPs or binary genes that discriminate a user-defined subset of the isolates (the group of interest) from all the other genotypes in the alignment (the out-group). The fundamental principle of this algorithm is that it does not treat the group of interest and the out-group equally. A position in the alignment is only considered informative if one or more bases that are present at that position in the out-group are not present in any of the sequences in the group of interest. The resolving power of a position is the proportion of out-group sequences that contain the base(s) in common with the group of interest. The rationale for the algorithm design is twofold. Firstly, the derived SNP sets cannot give rise to false-negatives since SNPs are specifically selected to identify all members of a group of interest. This lack of false-negatives is important if the SNP sets are to form the bases of e.g. diagnostic procedures for identifying virulent subgroups within a species. Secondly, Not-N can accommodate polymorphisms within the group of interest; that is, the algorithm is not reliant on identifying only invariant sites within the group of interest. Therefore, Not-N efficiently uses the available sequence data. The algorithm is demonstrated in Table D, or maximization of the power to discriminate one user-selected sequence from all known sequences. The Not-N algorithm represents a third user-selectable performance criterion for SNP set assembly. The SNP sets are assembled one SNP at a time, with the SNP giving the highest informative power identified first, followed by the SNP that gives the highest informative power in combination with the previous SNP, and so forth. Where different SNPs have identical informative power, multiple SNP combinations are assembled, until either a pre-set level of discrimination, a pre-set number of SNPs, or 100% discrimination is reached. The software incorporates 'include' and 'exclude' functions that allow the operator to force the inclusion of one or more SNPs in the output SNP set, or to remove one or more SNPs from the analysis. This provides considerable flexibility in SNP set assembly, which can be of benefit when optimizing actual assays, and provides a means of protecting against "local minima" . In the example shown in Table The Not-N function has been incorporated into the "Minimum SNPs" version 2.043 software ,23. Prev. There is a requirement to agree to a click-wrap license that is applicable to non-commercial use only. The software is written using the Java Runtime Environment which makes is essentially platform independent. Users need to have the Java Runtime Environment installed on their computer. This is freeware that can be obtained from . Downloading of this also requires agreeing to a license."Minimum SNPs" version 2.043, together with documentation, may be obtained from The authors on this manuscript are inventors on patent applications describing this algorithm and its applications. They may in consequence be eligible for financial benefit if these patent applications are commercialised.EPP carried out most of the data analysis and drafted the manuscript. JI-B and FH carried out additional data analysis. VT wrote the source code of the Not-N algorithm for integration into the "Minimum SNPs" software. PG conceived of the study, and participated in its design and coordination and helped draft the manuscript. All authors read and approved the final manuscript.Single-nucleotide polymorphisms extracted from multilocus sequence typing data using the Not-N module of "Minimum SNPs".Click here for file


Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations.We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer.Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%.A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques. This paper presents an information recognition system for gene-disease association mentioned in literature. Such systems are receiving increased attention, particularly from medical doctors and pharmacists, as they have the potential of reducing the burden on researchers to explore the extensive pool of literature.disease and treatment using several machine learning techniques, including hidden Markov models and neural networks. The relations were cure, only disease, only treatment, prevent, vague, side effect, and no cure. Some of these semantic relations described binary relations between diseases and treatments. Using 3,662 labeled sentences in MEDLINE abstracts and dynamic hidden Markov models, the authors achieved an F-measure of 0.71.Similar works include those by Rosario and Hearst  in whichIn our previous work , we extrtopic-classified relation recognition.We aim to recognize relations between prostate cancer terms and relevant gene terms from MEDLINE abstracts. To determine the utility of this approach, we identified prostate cancer and gene terms with ID tags that are used in six publicly available biological databases. Moreover, to enable human genetics experts and oncologists to use our results, we classified them and their relations based on six topics. We call this approach Our system first collects sentences that contain at least one pair of gene and prostate cancer terms, using dictionary-based longest matching. Dictionary-matching results contained numerous false positive gene and prostate cancer terms and their relations, so we used machine learning (ML)-based named entity recognition (NER) and topic classified relation recognition to solve this problem. Our system outputs topic-classified relations.To link each output gene or prostate cancer term to publicly available biomedical databases, we created human gene and disease dictionaries by merging the entries of numerous public biomedical databases. These dictionaries provide gene- and disease-related terms and cross-references between the original databases.LocusLink identifier for genetic loci is assigned to each entry in the human gene dictionary, which enabled us to consistently merge gene information contained in different databases. Each entry in the merged gene dictionary holds all relevant literature information associated with a given gene. We used five public databases to build the gene dictionary: HUGO, LocusLink, SwissProt, RefSeq, and DDBJ (July 2004). Each entry in the merged gene dictionary consists of five attributes: gene name, gene symbol, gene product, chromosomal band, and PubMed ID tags. The current version of the gene dictionary contains a total of 34,959 entries with 19,815 HUGO-approved gene symbols, 19,788 HUGO-approved gene names, and 29,470 gene products. Note that there are numerous alias gene symbols and gene names in these entries.A unique We used the Unified Medical Language System (UMLS) to collect disease-related vocabulary. From the 2003AC edition of the UMLS Metathesaurus, we selected 12 unique identifiers of semantic types (TUIs) that correspond to disease names, abnormal phenomena, or symptoms , modality, genetic variation, epigenetics, gene expression, gene produces variation, molecular function, sub-cellular localization, pharmacology, clinical marker, risk factor, tumor biology, and remarks.We classified gene and prostate cancer terms and their relations based on 13 topics: • PMID:Our corpus provides a PMID for each co-occurrence.For the annotation of binary relations between gene and prostate cancer terms, the biologists considered three aspects.1. Pathophysiology, mechanisms of prostate cancer, including etiology, causes of prostate cancer.2. Therapeutic significance of genes or gene products; specifically, classification of genes or gene products based on their current therapeutic use and their potential as therapeutic targets.3. Use of genes and gene products as markers for prostate cancer risk, diagnosis, and prognosis.binary relation between gene and prostate cancer terms, we classified prostate cancer and gene terms and their relations based on 13 topics. All topics are mutually independent, so certain co-occurrence can be classified by more than one topic. We selected the following six topics based on the inter-annotator agreement rates that had over 70% F-measure. To calculate the inter-annotator agreement rates for the four annotators, we randomly selected 40 co-occurrences and annotated them.In addition to the G and P, respectively, with square brackets.Examples of topics contain gene and prostate cancer terms that are represented by 1. Study description (method)Methods section of papers do not give specific results or conclusions. However, those sentences might still contain allusive gene-prostate cancer term relations.Sentences in the Example 1 Thereafter plasma S, cortisol (F) and [adrenocorticotropic hormone]G (ACTH) responses to metyrapone were investigated in 13 normal adult males and 39 patients with [prostatic cancer]P.2. Genetic variationThere are genotypic differences among individuals in a population. For example, mutation (including germ line and somatic), polymorphism , and LOH.Example 2 A polymorphism in [endostatin]G, an angiogenesis inhibitor, predisposes for the development of [prostatic adenocarcinoma]P.3. Gene expressionGene expression is the phenotypic manifestation of a gene by the processes of genetic transcription and translation. Its profiling is also included.Example 3 The expression of [HNK-l]G antigen on [prostatic cancer]P was investigated immunohisto-chemically using the avidin-biotin-peroxidase complex (ABC) method with the anti-HNK-1 monoclonal antibody.4. EpigeneticsChemical mutations to DNA or histones alter the structure of a chromatin without changing the nucleotide sequence of the DNA.Example 4 Hypermethylation of the 5' promoter region of the [glutathione S-transferase pi]G gene (GSTP1) occurs at a very high frequency in [prostate adenocarcinoma]P.5. PharmacologyPharmacology is the science of drugs, including their compositions, uses, and effects.Example 5 OBJECTIVES: To assess the involvement of calcitonin gene-related peptide ([CGRP]G) in the occurrence of hot flashes in men after castration for treatment of [prostate cancer]P, we investigated the effects of CGRP on skin temperature in surgically and medically castrated male rats.6. Clinical markerMeasurable and quantifiable gene products are used as biological parameters to assess health- and physiology-related factors, such as prostate cancer risk, prostate cancer diagnosis, cell line development, and epidemiologic studies.Example 6 The use of [prostate specific antigen (PSA)]G and digital rectal examination (DRE) results in a three fold increase in [prostatic carcinoma]P detection.We used ML-based NER for two purposes: to provide a feature for each candidate relation in an ML-based topic-classified relation recognition method and to filter out numerous false positive gene and prostate cancer terms from the dictionary matching results before performing topic-classified relation recognition. Maximum entropy (ME) models  have beeThe following features were used in the NER.• Bag of words:All contextual terms in a co-occurrence.• Candidate entities:Candidate gene and prostate cancer terms that were recognized using dictionary matching.• Unigram and bigram words of candidate entities:Unigram words refer to the word before and after the candidate term; bigram words refer to the two adjacent words before and after the candidate term.• Use of capital letters in the candidate term:We determined whether the given entities consisted entirely of upper or lowercase letters or a combination of them.• Use of numbers in the candidate term:We determined whether the given entities contained numbers.• Affixes of the candidate term:cin, ~ mide, ~ zole, ~ lipid, ~ rogen, ~ vitamin, ~ blast, ~ cyte, ~ peptide, ~ ma, and ~ virus.We considered whether the given entities include the 11 biomedical suffixes: ~ • Greek letters in the candidate term:alpha, beta, α, and β).We determined whether the given entities contained Greek letters . The next two most important features were the bag of words and the unigram words. We achieved 93.5 and 93.1% precisions, respectively, using these features. For the task of prostate cancer term recognition, dictionary matching generated very high performance. Thus, it slightly improved the precision.Gene and prostate cancer term pairs co-occurring in a sentence have some potential relations. However, these co-occurring pairs also have numerous false positive relations. We developed an ME-based relation recognizer to filter out false positives.The following features were used in the topic-classified relation recognizer.• Bag of words:All contextual terms in a co-occurrence.• Candidate gene and prostate cancer entities:Entities that were recognized using dictionary matching.• Unigram and bigram words of candidate gene and prostate cancer entities:We determined unigram words of candidate gene and prostate cancer entities simultaneously. For bigram words, we followed the same procedure as that for unigram words.• Order of candidate entities:We accounted for the order of candidate gene and prostate cancer terms in each co-occurrence. In other words, we determined whether a candidate gene term appeared before a candidate prostate cancer term in each co-occurrence.bag of words: omitting it, we achieved only 89.6, 59.7, and 73.7% precisions and 97.5, 29.5, and 46.4% relative recalls, respectively, in topic-classified relation recognition .Table order of candidate entities seemed to be the most important feature. Leaving out the order of candidate entities lead to most significant decrease: decreases of 1.4, 2.1, 3.1, and 0.7% from 73.4, 85.4, 65.7, and 77.4% precisions, respectively.For recognition of gene expression, epigenetics, pharmacology, and clinical marker, the Table automatic NER, and the third, sixth, and eighth experiments used human-generated NER annotation results on both training and testing procedures (a gold standard), which we call manual NER. A series of experimental results showed that automatic NER is comparable to manual NER.We conducted eight experiments for topic-classified relation recognition. The inputs of the experiments were co-occurrences that contained at least one pair of gene and prostate cancer terms recognized by dictionary-based longest matching. The first experiment used only gene and disease dictionary-based longest matching. The second and third experiments used dictionary matching and ME-based NE filtering. The next five experiments used ME-based topic-classified relation recognition. The fourth experiment used only ME-based topic-classified relation recognition and did not use NER results. The fifth and sixth experiments used ME-based NER results as features for topic-classified relation recognition. The seventh and eighth experiments used ME-based NER results as a filtering measure. We compared the ME-based NER results with human-generated NER annotation results. Thus, the second, fifth, and seventh experiments used ME-based NER results on both training and testing procedures, which we call The baseline experiment is very simple. We assumed that all gene-prostate cancer pairs recognized by dictionary-based longest matching had a relation.We applied NER to filter out false positive gene and prostate cancer terms generated by dictionary matching, and we assumed that all the remaining gene-prostate cancer pairs had a relation. NE filtering improved the precision of all topic-classified relation recognitions at the cost of a small reduction in recall. We used the best combination of features based on the F-measure that had been obtained empirically for NER.• Recognition of gene names:Candidate names, unigram words, and presence of capital letters in the candidate term.• Recognition of prostate cancer names:Candidate names, unigram words, and presence of capital letters or Greek letters in the candidate term.relations in the cells in the first and second rows, and fourth column of Table The performance of recognizing general We used ML for topic-classified relation recognition with the best combination of features based on the F-measure.• Study description, genetic variation, gene expression, and pharmacology:Bag of words, candidate gene and prostate cancer terms, unigram and bigram words, and order of candidate terms.• Relation:Bag of words, candidate gene and prostate cancer terms, unigram words, and order of candidate terms.• Epigenetics:Bag of words, unigram and bigram words and order of candidate terms.• Clinical marker:Candidate gene and prostate cancer terms, unigram and bigram words, and order of candidate terms.Although the experiment did not consider NER results, the precision of ML-based topic-classified relation recognition was much better than that in the baseline experiment.We used NER results as features in addition to the contextual features that we used in the fourth experiment. Experimental results showed that using NER results as features for topic-classified relation recognition improved the precision in the relation and four topics. We can thus infer that NER information is a cogent feature. For recognition of epigenetics, the performance in the automatic NER experiment was higher than that in the manual NER experiment. These results are statistically not significant because the number of correct epigenetics relations is only 53.NER results were used to filter out gene-prostate cancer pairs over-generated by dictionary matching. Topic-classified relation recognition modules were given only co-occurrences that remained after filtering. We used the same combination of features as those in the fourth experiment. Filtering with NER results improved the performance of topic-classified relation recognition more than using them as features for ML-based topic-classified relation recognition. Recognition of epigenetics led to the most significant increase in precision (2.4%) and recognition of genetic variation led to the next most significant increase in precision (0.8%).We have developed ML-based topic-classified relation recognizers between prostate cancer and gene terms. Six topics were used to classify prostate cancer and gene terms, and their relations. Simple dictionary-based longest matching was tested, which produced numerous false positive results. Annotated abstracts were then input to an ME-based ML module to train NER and relation recognizers. A comprehensive series of experiments revealed that the ML-based approach that used rich contextual features have the potential to improve the performance of topic-classified relation recognition. The effect of combining the recognizers was also investigated. The results were encouraging, and we are planning several extensions that include incorporating disambiguation  and deep


The aim of this study was to evaluate new and previously hypothesised non-genetic risk factors for serologic subtypes of rheumatoid arthritis (RA) defined by the presence or absence of auto-antibodies to cyclic citrullinated peptides (CCP). In a national case-control study, we included 515 patients recently diagnosed with RA according to the American College of Rheumatology 1987 classification criteria and 769 gender- and age-matched population controls. Telephone interviews provided information about non-genetic exposures, and serum samples for patients were tested for anti-CCP-antibodies. Associations between exposure variables and risk of anti-CCP-positive and anti-CCP-negative RA were evaluated using logistic regression. A series of RA subtype-specific risk factors were identified. Tobacco smoking  was selectively associated with risk of anti-CCP-positive RA, whereas alcohol consumption exhibited an inverse dose-response association with this RA subtype . Furthermore, coffee consumption , ever use of oral contraceptives  and having a first-degree relative with schizophrenia  appeared more strongly associated with risk of anti-CCP-positive RA. Obesity was selectively associated with risk of anti-CCP-negative RA, with obese individuals being at more than 3-fold increased risk of this subtype compared with normal-weight individuals . Age at menarche was the only examined factor that was significantly associated with both serologic subtypes of RA (p-trends = 0.01); women with menarche at age ≥ 15 years had about twice the risk of either RA subtype compared with women with menarche at age ≤ 12 years. Major differences in risk factor profiles suggest distinct etiologies for anti-CCP-positive and anti-CCP-negative RA. A number of genetic and environmental factors have been implicated in the etiology of rheumatoid arthritis (RA). The only well-established environmental risk factor is tobacco smoking, which has been shown in a number of studies to be associated with increased RA risk -4. AssocOne possible explanation for conflicting results in etiologic studies might be that risk factors differ between subtypes of RA. It was recently demonstrated that smoking is selectively associated with rheumatoid factor (RF)-positive RA  or with The study was conducted as a frequency-matched case-control study. Patients with RA diagnosed within the previous 5 years were identified in rheumatology and internal medicine departments throughout Denmark, which has a predominantly Caucasian population of approximately 5.2 million inhabitants. To be included, patients had to be diagnosed with RA between ages 18 and 65 years and fulfill the American College of Rheumatology (ACR) 1987 classification criteria for RA  between Controls who were frequency-matched by gender and birth year were randomly selected from the Danish population by means of the Civil Registration System, a national database that keeps track of all demographic changes in Denmark . Using iThe questionnaire was tested in a pilot experiment comprising 50 patients with RA and 50 controls whose data are not included in this report. Three trained female medical students carried out all interviews between September 2002 and February 2004. Bimonthly meetings were held to ensure that all interviews were conducted in a uniform manner. Interviews were conducted as computer-assisted telephone interviews, and answers were entered directly into a database. Logical tests were built into the program to keep data entry errors at a minimum. Each telephone interview took approximately half an hour and included questions about a wide range of exposure and confounder variables, including level of education, age at menarche, parity, spontaneous abortions, breastfeeding, age at menopause, use of oral contraceptive pills and hormone replacement therapy, marital status, lifetime number of sexual partners of opposite sex, age at first sexual intercourse, lifetime number of anal-intercourse partners of opposite sex, lifetime number of same-sex sexual partners , lifetime number of prostitute visits , histories of venereal diseases , smoker status, pack-years smoked , coffee, alcohol, and wine consumption 10 years before interview, frequency of fish intake as a hot meal or on bread  10 years before interview, intake of fish oil (ever/never), vegetarian diet (ever/never), body mass index at age 20 years and 10 years before interview, level of physical activity at work and during leisure time 10 years before interview, pets in childhood and in adulthood, histories of mononucleosis, hay fever, atopic dermatitis, asthma before age 45 years, stomach or duodenal ulcer, heavy diarrhea of at least 4 days' duration, type I diabetes, thyroid disease, periodontal disease, urinary tract infection, cancer, blood transfusion, tonsillectomy, adenoidectomy, appendectomy, splenectomy, and schizophrenia among first-degree relatives.Blood samples were collected at rheumatology departments (patients) or by general practitioners (patients and controls), and serum was stored at -20°C. Anti-CCP immunoglobulin (Ig) G antibodies were determined by a second-generation enzyme-linked immunosorbent assay (ELISA) using the Immunoscan RA kit , and parvovirus B19 IgG antibodies were determined by ELISA using the Biotrin Parvovirus B19 IgG kit  according to instructions provided by the manufacturers. Levels of IgG antibodies to Epstein-Barr viral capsid antigen (VCA) were determined in arbitrary units by ELISA using the Biotest anti-EBV VCA IgG kit . A standard pool of serum was given the value of 100 arbitrary units. All samples were diluted 1:40 and tested together with dilutions  of the standard. Samples that were negative at 1:40 were retested undiluted together with dilutions of the standard. This enabled us to convert optical density values for the study samples into arbitrary units.To make exposure information comparable for patients and controls, a pseudo-year of diagnosis was attributed to controls according to the frequency distribution of year of RA diagnosis in patients of the same sex. Throughout, we disregarded information about exposures after the year (patients) or pseudo-year (controls) of diagnosis. We performed logistic regression analyses for men and women separately to study associations of exposure variables with overall RA risk regardless of RA subtype. For each exposure variable, we also combined information for women and men when there was no significant interaction with gender . In these analyses, we adjusted only for birth year, for year or pseudo-year of diagnosis, and  for gender.Risk factors for anti-CCP-positive and anti-CCP-negative RA were identified in a polytomous logistic regression model using these two groups of patients and the control group as the dependent variable. This model included gender, birth year, year or pseudo-year of diagnosis, place of residence (five categories), educational status (four categories), and exposure variables identified in a three-step selection process. (a) In the first step, exposure variables with a p value less than 0.05 in the gender-combined analyses described above were included in the final polytomous logistic regression model. (b) In the second step, we performed polytomous logistic regression to study the associations with risk of anti-CCP-positive and anti-CCP-negative RA for exposure variables with a p value less than 0.20 in the gender-combined analyses from the first step. The rather broad screening criterion of p less than 0.20 was used to ensure inclusion of potential RA subtype-specific risk factors for which associations might be blurred in subtype-unrestricted analyses. Adjustment was made for gender, birth year, and year or pseudo-year of RA diagnosis. Exposure variables with a p value less than 0.05 for at least one of the RA subtypes were included in the final multifactorial model. (c) In the third step, we considered a polytomous logistic regression model that included gender, birth year, year or pseudo-year of diagnosis, place of residence, educational status, and the exposure variables identified in steps one and two. Using the method of forward inclusion, we introduced in this model, one by one, all variables with a p value between 0.05 and 0.20 in at least one of the initial subtype-specific analyses in step two or the gender-combined analyses in step one. Introduced variables with a p value less than 0.05 for at least one RA subtype were included in the final model.Tests for difference between exposure categories were evaluated by either trend tests or tests for homogeneity, as relevant. Trend tests were performed by treating categorical variables as continuous variables in the regression analyses. In trend tests for ordered categorical variables, representing an underlying continuous variable , categories were assigned the category median of the underlying variable. In trend tests for ordered categorical variables with no well-defined underlying continuous variable , the categories were assigned consecutive numbers starting with one. Tests for difference between risk factor associations with anti-CCP-positive and anti-CCP-negative RA were evaluated by polytomous logistic regression by comparing either subtype-specific trends or subtype-specific categorical variables, as relevant. All tests were likelihood-ratio-based. All analyses were carried out using SAS software . Throughout, we considered two-sided p values < 0.05 as statistically significant.The study was approved by the Scientific Ethical Committees for Copenhagen and Frederiksberg (J. no. KF 01-039/01) and the Danish Data Protection Agency (2001-41-0658).The study population consisted of 515 patients with RA (participation rate 83%) and 769 population controls (participation rate 64%). Selected socio-demographic characteristics are shown in Table Reproductive factors were studied in women only Table . Late agSignificantly more patients with RA than controls had remained unmarried . Associations were not statistically significant for the lifetime number of sexual partners of opposite sex, age at first sexual intercourse, lifetime number of anal-intercourse partners of opposite sex, or history of venereal disease Table . Also, aBoth former and current smokers were at significantly increased RA risk compared with never-smokers, and their risk increased proportionally with the number of pack-years smoked Table . Coffee Seropositivity for parvovirus B19 antibodies  and above-median levels  of IgG antibodies to viral capsid antigens of the Epstein-Barr virus  were both non-significantly associated with RA risk. Physician-verified asthma before age 45 years and a history of urinary tract infection were both associated with significantly reduced risk of RA. No statistically significant associations were seen with a range of other self-reported health conditions (Table 2) individuals at more than threefold increased risk compared with normal-weight (body mass index 18.5 to <25 kg/m2) individuals . Although other homogeneity tests for difference between risk associations with anti-CCP-positive and anti-CCP-negative RA were not statistically significant, ever use of oral contraceptives, a high intake of coffee, being unmarried or unemployed, and having a first-degree relative with schizophrenia were each more strongly associated with increased risk of anti-CCP-positive RA, whereas physician-verified asthma before age 45 years appeared to be more strongly inversely associated with anti-CCP-negative RA. Age at menarche was the only risk factor that was statistically significantly associated with both serologic subtypes of RA. Being 15 years or older at menarche was associated with double risk for both RA subtypes compared with having menarche at age 12 or younger (p-trend = 0.01 for both RA subtypes). Furthermore, having pets in adulthood was associated with decreased risk of both subtypes, although this association reached statistical significance for anti-CCP-positive RA only.Serum samples were successfully analysed for anti-CCP antibodies for 445 86%) of interviewed patients with RA. Of these, 309 (69%) were positive and 136 (31%) were negative for anti-CCP antibodies. Several statistically significant differences were seen between anti-CCP-positive and anti-CCP-negative RA  among RA cases, invited population controls were slightly more reluctant to participate (64%). Theoretically, such a difference might lead to biased associations for some of the studied risk factors, to the extent these factors were also associated with the decision to accept or decline our invitation to participate. If invited subjects who did not want to participate comprised more tobacco smokers than those who actually participated, such non-random self-selection might have contributed to the observed positive dose-response association with tobacco consumption. However, the supporting evidence for a genuine RA subtype-specific effect of tobacco smoking in anti-CCP-positive RA which has been described by other researchers ,23 and tWe assessed risk factors retrospectively by means of telephone interviews, so the possible influence of recall problems among study participants needs consideration. Because patients with RA are unlikely to be aware of their anti-CCP antibody status and because exposures covered by our questionnaire are not broadly recognised as RA risk factors, we believe that misclassification arising from recall problems would most likely have been non-differential and tended to produce conservative ORs and blur risk factor differences between the two RA subtypes. The RA subtype-specific risk factor associations we observed are therefore unlikely to be the result of recall problems among our study participants.Our national case-control study addressed a large number of environmental factors potentially involved in the etiology of RA. Upon dichotomisation of patients with RA according to the presence or absence of anti-CCP-antibodies, we show that environmental risk factors differ considerably between anti-CCP-positive and anti-CCP-negative RA.ACR = American College of Rheumatology; CCP = cyclic citrullinated peptide; CI = confidence interval; ELISA = enzyme-linked immunosorbent assay; Ig = immunoglobulin; OR = odds ratio; RA = rheumatoid arthritis; RF = rheumatoid factor; VCA = viral capsid antigen.The authors declare that they have no competing interests.MP participated in the study design, data collection, and drafting of the manuscript and performed statistical analyses. SJ and MF participated in the study design and drafting of the manuscript. MK participated in the data collection and drafting of the manuscript. BVP participated in the statistical analyses and drafting of the manuscript. AW participated in the study design and serum analyses and edited the manuscript. JW participated in study design, the statistical analyses, and the drafting of the manuscript. All authors read and approved the final version.


Cardiac biomarkers are routinely obtained in the setting of suspected myocardial ischemia and infarction. Evidence suggests these markers may correlate with functional and clinical outcomes, but the strength of this correlation is unclear. The relationship between enzyme measures of myocardial necrosis and left ventricular performance and adverse clinical outcomes were explored.Creatine kinase (CK) and CK-MB data were analyzed, as were left ventricular ejection fraction (LVEF) by angiogram, and infarct size by single-photon emission computed tomography (SPECT) imaging in patients in 2 trials: Prompt Reperfusion In Myocardial-infarction Evolution (PRIME), and Efegatran and Streptokinase to Canalize Arteries Like Accelerated Tissue plasminogen activator (ESCALAT). Both trials evaluated efegatran combined with thrombolysis for treating acute ST-segment elevation myocardial infarction (STEMI).Peak CK and CK area-under-the-curve (AUC) correlated significantly with SPECT-determined infarct size 5 to 10 days after enrollment. Peak CK had a statistically significant correlation with LVEF, but CK-AUC and LVEF correlation were less robust. Statistically significant correlations exist between SPECT-determined infarct size and peak CK-MB and CK-MB AUC. However, there was no correlation with LVEF for peak CK-MB and CK-MB AUC. The combined outcome of congestive heart failure and death were significantly associated with CK AUC, CK-MB AUC, peak CK, and peak CK-MB measurements.Peak CK and CK-MB values and AUC calculations have significant correlation with functional outcomes (LVEF- and SPECT-determined infarct size) and death or CHF outcomes in the setting of STEMI. Cardiac biomarkers provide prognostic information and may serve as valid endpoint measurements for phase II clinical trials. There has been considerable interest in validating established cardiac biomarkers as a mechanism for assessing infarct size in clinical practice and as an endpoint in clinical trials. Since assays for creatine kinase (CK) and CK-MB became widely available in the 1970s, attempts have been made to correlate the levels of these biomarkers with outcomes and infarct size using kinetic models and curve-fitting techniques . DespiteStudies in animals ,6,9,10 aLittle has been done to determine how the different methods used to assess infarct size in acute MI patients treated with reperfusion therapy correlate. Improving our understanding of the methods used to measure infarct size and clarifying the association between infarct size and outcomes can be of tremendous value to clinical trials, particularly in the early evaluation of new therapies or interventions.The aims of the current analyses were to 1) determine whether enzyme-determined infarct size would correlate to the degree of infarction measured by single-photon emission computed tomography (SPECT) imaging and to left ventricular ejection fraction (LVEF) measured by LV angiography, and 2) assess whether the amount of enzyme released could be used to predict clinical outcomes in patients treated with thrombolysis. For this analysis, data from the Efegatran and Streptokinase to Canalize Arteries Like Accelerated Tissue plasminogen activator (ESCALAT)  and PromThe ESCALAT trial has been published . In brieIn the PRIME trial , patientSPECT quantification of infarct size was performed 5 to 10 days after randomization per the ESCALAT protocol. SPECT images were evaluated in blinded fashion by a central core laboratory  using previously described methodologies .In the PRIME trial, patients who did not have percutaneous coronary intervention or bypass surgery were to have repeat coronary angiography 5 to 7 days after enrollment or prior to hospital discharge, whichever occurred first. All angiograms were reviewed by an independent evaluator, blinded to treatment assignment, at a central core laboratory . Angiograms were assessed for vessel patency, Thrombolysis In Myocardial Infarction (TIMI) flow grades, and LV function.In both the ESCALAT and PRIME trials, CK and CK-MB levels were to be drawn at baseline (time of enrollment) and at 6, 12, 24, 36, and 72 hours after enrollment. All samples were analyzed at a central core laboratory , and the evaluator was blinded to patient information. Cardiac biomarker curves were then plot-fitted by the method proposed by Vollmer and colleagues . Area-unClinical outcomes of death or congestive heart failure (CHF) that occurred during the index hospitalization were reported by the investigator with standard data collection tools without adjudication using standard definitions.Statistical analyses were performed with SAS software  on the UNIX system. Continuous variables were summarized using medians and interquartile ranges whereas categorical variables were summarized as frequencies and percentages.Patients from both trials were excluded from analyses if they had a history of MI or no elevation of CK or CK-MB due to confounding. Within the ESCALAT trial, after these exclusions, the study size was 136 patients, representing 56% of the original study population. From the PRIME trial, 165 patients were included in the analysis after exclusions, accounting for 49% of the original trial population Figure .Spearman rank correlations were used to determine associations between the cardiac biomarker data and size of infarct as measured by SPECT for the ESCALAT trial and LVEF for the PRIME trial. Wilcoxon rank sum tests were used to determine differences in continuous variables between PRIME and ESCALAT.The baseline demographics were similar for the ESCALAT and PRIME trial populations with respect to age, sex, race, and history of smoking. None of the patients had a history of MI as part of the inclusion criteria for this analysis.There were no significant differences in death or CHF during the index hospitalization between the treatment groups in either trial. There were also no statistical differences between the trials with respect to CK and CK-MB data Table ; therefoIn ESCALAT, no differences in infarct size by SPECT imaging, CK-AUC, or CK-MB AUC were observed between the efegatran-plus-streptokinase- and heparin plus t-PA-treated groups. Therefore, all treatment groups were combined for these analyses. The median 25th, 75th) time to SPECT was 6 days th, 75th .r = 0.65, P < 0.0001) and CK-MB  were observed. SPECT infarct size also correlated with the AUC measurements for both CK  and CK-MB   Table .r = -0.30, P = 0.035). Borderline significant correlations were observed between LVEF and CK-AUC . The negative correlation between LVEF and peak CK-MB  as well as between LVEF and CK-MB AUC  did not achieve statistical significance  of 5 days ,6 from t5th, 75thP = 0.007) and AUC (P = 0.001) measurements; there was also an association between death/CHF and peak CK-MB (P = 0.021) and AUC (P = 0.005) measurements.The total number of events in each trial is shown in Table This study showed a significant correlation between infarct size as assessed by SPECT and infarct size as determined by CK and CK-MB release using AUC or peak values. Weaker correlations were observed between infarct size measured by cardiac biomarker assessments and LVEF determined by angiography. Patients with larger release of CK or CK-MB had an increased risk of death and/or CHF.These findings are consistent with earlier studies ,24-33 thAttempts have been made to correlate CK- and CK-MB-based infarct measurements with LV function, with conflicting results. EF has been variably correlated to CK-MB AUC ,34, peakThe relatively poor correlation noted here between angiographic and enzymatic methods of infarct sizing might be due to post-infarct stunning, leading to an artifactually low LVEF; earlier clinically or electrocardiographically silent MIs contributing to LV dysfunction attributed to the index event; site of infarct (inferior vs. anterior); and the degree of right ventricular involvement. There also appears to be wide variability in the published Spearman coefficients on enzyme/LVEF correlations, with the current data falling at the lower end of the previously published ranges. These discrepant results might be explained by a number of factors, including variations in enzyme curve measurements; inter- and intra-study variability of LVEF measurements; timing of catheterization relative to MI; differences in treatment allotments; the year of study, which has implications for the availability of reperfusion and improvements in coronary care; and publication bias, with nonsignificant or weak association preferentially not being reported in the literature.The ability of cardiac enzymes to predict clinical outcomes would enhance confidence in the use of this assessment in phase II clinical trials but would not exclude the need to conduct definitive trials designed to assess clinical outcomes directly. An early study of patients presenting with acute MI prior to the era of widespread revascularization showed a correlation between peak CK and CK-AUC and arrhythmias, pulmonary edema, and higher pulmonary artery wedge pressure . SubsequThe strengths of the present study are that the data were collected in the context of 2 randomized, controlled trials with strict adherence to serial biomarker assessments, analyses by a central core laboratory, and sophisticated curve-fitting techniques. Most of the noted previous studies used the modified formula proposed by Roberts et al . This meSeveral weaknesses should be acknowledged. Sample sizes from the 2 trials were relatively small. The ventriculographic and scintigraphic data were incomplete, which may have implications if the missing data were significantly different from that reported here. Also, to more accurately assess the damage done by the index infarction, this analysis included only patients presenting with their first MI. This factor may affect the generalizability of the results. Although plot-fitting appears to be the most accurate method of determining enzyme peaks and integrating areas given current routine practices in measuring cardiac markers, it is possible that the sampling frequency used misrepresented the true nature of the enzyme curves. Finally, cardiac biomarkers, though useful, still represent surrogate measurements of clinical outcomes and need to be used with caution in the evaluation of new therapies, because a new treatment that has had no effect on a surrogate measurement (such as CK or CK-MB) might still have an important treatment effect.Given the small sample size, we were not able to rigorously assess the potential impact of infarct location on the relationships observed. The contribution of right ventricular infarct to CK-MB release and the correlation of this release to assessment of left ventricular function could confound these findings.Given their affordability and routine availability, cardiac biomarkers remain an attractive tool for measuring myocardial damage and determining prognosis. Cardiac enzyme markers continue to be used as endpoints in clinical trials, such as the Complement And ReDuction of INfarct size after Angioplasty or Lytics (CARDINAL) program ,41 and tAn attractive alternative in early drug development is to continue to combine a variety of measures of efficacy, such as cardiac enzymes, ECG analysis, SPECT, and angiography, to develop a "biomarker array." If multiple validated and significant prespecified outcomes are analyzed in the context of phase II clinical research, fewer patients would potentially need to be enrolled to discern differences in treatment effects between trial arms. This analysis substantiates the concept that routine cardiac markers can predict infarct size and clinical outcomes and may therefore act as a valid endpoint in phase II trials of acute MI therapy.Serial cardiac biomarker assessment and curve-fitting techniques can be used to determine infarct size with statistically significant correlation to SPECT imaging. Weaker correlations were observed with LVEF by angiography. Death and CHF outcomes were associated with larger infarct size as determined by biomarkers. These data support the use of biomarker-determined infarct size as a potential endpoint in phase II clinical trials.AUC, area-under-the-curveCHF, congestive heart failureCK, creatine kinaseLVEF, left ventricular functionMI, myocardial infarctionNSTEMI non-ST-segment myocardial infarctionSPECT, single-photon emission computed tomographySTEMI, ST-segment myocardial infarctiont-PA, tissue plasminogen activatorThe authors have no competing interests to declare.The PRIME trial was funded by grants from Centocor, Inc., Malvern, Pennsylvania, and Eli Lilly and Company, Indianapolis, Indiana. The ESCALAT trial was sponsored by Lilly Research Laboratories, Indianapolis, Indiana; Eli Lilly Canada, Inc., Scarborough, Ontario, Canada; and LillyResearch Center Ltd., Surrey, United Kingdom.ATT served as a primary author. KWM served as a primary author and provided critical review of manuscript. DG provided statistical analyses. WDW provided critical review of manuscript. RHC provided critical review of manuscript. NRE provided critical review of manuscript. EMO provided critical review of manuscript. All authors have given final approval of the version to be published.


Yucca schidigera is a medicinal plant native to Mexico. According to folk medicine, yucca extracts have anti-arthritic and anti-inflammatory effects. The plant contains several physiologically active phytochemicals. It is a rich source of steroidal saponins, and is used commercially as a saponin source. Saponins have diverse biological effects, including anti-protozoal activity. It has been postulated that saponins may have anti-arthritic properties by suppressing intestinal protozoa which may have a role in joint inflammation. Yucca is also a rich source of polyphenolics, including resveratrol and a number of other stilbenes . These phenolics have anti-inflammatory activity. They are inhibitors of the nuclear transcription factor NFkappaB. NFkB stimulates synthesis of inducible nitric oxide synthase (iNOS), which causes formation of the inflammatory agent nitric oxide. Yucca phenolics are also anti-oxidants and free-radical scavengers, which may aid in suppressing reactive oxygen species that stimulate inflammatory responses. Based on these findings, further studies on the anti-arthritic effects of Yucca schidigera are warranted. Yucca schidigera is an herbaceous plant of the lily family, native to the deserts of the south-western United States and northern Mexico. This plant was used in traditional medicine by Native Americans to treat a variety of ailments including arthritis. Yucca products are currently used in a number of applications. Yucca powder and yucca extract are used as animal feed additives, as discussed in detail by Cheeke and Otero [nd Otero . BeneficYucca contains a number of phytochemicals which contribute to these effects. The best known are the steroidal saponins. Saponins are natural detergents  that forin vivo [in vitro [Interactions of saponins with cholesterol and other sterols account for many of their biological effects, particularly those involving membrane activity. It was demonstrated more than 45 years ago that dietary saponin reduces blood cholesterol levels . This efin vivo -11; and in vitro ,13. The Giardia lamblia in the intestine [Yucca schidigera contains as much as 10% of steroidal saponins in its stem dry matter, making this plant one of the richest commercial sources of saponins. Acid hydrolysed fractions of yucca contain both furostanol and spirostanol aglycones. These include sarsapogenin, markogenin, smilagenin, samogenin, gitogenin and neogitogenin [O- and bidesmosides with two sugar chains at 3-O- and 26-O- positions  were identified in yucca bark. Also, some unique compounds with aols A-E ,19  is an important factor in the development and maintenance of rheumatoid arthritis in humans and animal models . One soutrans-3,3',5,5'-tetrahydroxy-4-methoxystilbene, showed the highest anti-platelet action.Platelet aggregation is characteristic of inflammation. Yucca phenolics have inhibitory activity against platelet aggregation -40. YuccUncaria guianensis) "is a remarkably potent inhibitor of NFkB activity and tumor necrosis factor production." Evaluation of the anti-inflammatory activity of a combination of yucca and cat's claw would be of interest.Another botanical product with anti-inflammatory activity is cat's claw . As reviThe evidence presented in this review indicates that yucca has potential in vivo anti-inflammatory activity, and warrants more in-depth investigation.Yucca schidigera is a medicinal plant which may have beneficial effects in the prevention and treatment of arthritis. Active components of yucca include steroidal saponins and polyphenolics such as resveratrol and yuccaols. Saponins may have anti-arthritic effects associated with their anti-protozoal activity. Yucca polyphenolics may have several roles in anti-arthritic activity. They inhibit NFkB, a transcription factor which stimulates iNOS, an inducible enzyme which produces the inflammatory agent nitric oxide. Yucca phenolics also are antioxidants and free-radical scavengers, which may aid in suppressing reactive oxygen species (ROS) that stimulate inflammatory responses. Folk medicine and anecdotal reports suggest that whole yucca plant powder aids in prevention and treatment of arthritis. Further studies on the anti-arthritic effects of yucca are warranted.PRC is a consultant to Desert King International (DKI), a privately-held company which produces and markets yucca extracts and yucca powder as commodities. He has no equity interest in this company. SP and WO have no relationships with DKI.PRC wrote the paper.SP provided Figures WO provided Figures