The LitMiner software is a literature data-mining tool that facilitates the identification of major gene regulation key players related to a user-defined field of interest in PubMed abstracts. The prediction of gene-regulatory relationships is based on co-occurrence analysis of key terms within the abstracts. LitMiner predicts relationships between key terms from the biomedical domain in four categories (genes, chemical compounds, diseases and tissues). Owing to the limitations (no direction, unverified automatic prediction) of the co-occurrence approach, the primary data in the LitMiner database represent postulated basic gene–gene relationships. The usefulness of the LitMiner system has been demonstrated recently in a study that reconstructed disease-related regulatory networks by promoter modelling that was initiated by a LitMiner generated primary gene list. To overcome the limitations and to verify and improve the data, we developed WikiGene, a Wiki-based curation tool that allows revision of the data by expert users over the Internet. LitMiner () and WikiGene () can be used unrestricted with any Internet browser.
BioLit is a web server which provides metadata describing the semantic content of all open access, peer-reviewed articles which describe research from the major life sciences literature archive, PubMed Central. Specifically, these metadata include database identifiers and ontology terms found within the full text of the article. BioLit delivers these metadata in the form of XML-based article files and as a custom web-based article viewer that provides context-specific functionality to the metadata. This resource aims to integrate the traditional scientific publication directly into existing biological databases, thus obviating the need for a user to search in multiple locations for information relating to a specific item of interest, for example published experimental results associated with a particular biological database entry. As an example of a possible use of BioLit, we also present an instance of the Protein Data Bank fully integrated with BioLit data. We expect that the community of life scientists in general will be the primary end-users of the web-based viewer, while biocurators will make use of the metadata-containing XML files and the BioLit database of article data. BioLit is available at http://biolit.ucsd.edu.
LitInspector is a literature search tool providing gene and signal transduction pathway mining within NCBI's PubMed database. The automatic gene recognition and color coding increases the readability of abstracts and significantly speeds up literature research. A main challenge in gene recognition is the resolution of homonyms and rejection of identical abbreviations used in a ‘non-gene’ context. LitInspector uses automatically generated and manually refined filtering lists for this purpose. The quality of the LitInspector results was assessed with a published dataset of 181 PubMed sentences. LitInspector achieved a precision of 96.8%, a recall of 86.6% and an F-measure of 91.4%. To further demonstrate the homonym resolution qualities, LitInspector was compared to three other literature search tools using some challenging examples. The homonym MIZ-1 (gene IDs 7709 and 9063) was correctly resolved in 87% of the abstracts by LitInspector, whereas the other tools achieved recognition rates between 35% and 67%. The LitInspector signal transduction pathway mining is based on a manually curated database of pathway names (e.g. wingless type), pathway components (e.g. WNT1, FZD1), and general pathway keywords (e.g. signaling cascade). The performance was checked for 10 randomly selected genes. Eighty-two per cent of the 38 predicted pathway associations were correct. LitInspector is freely available at http://www.litinspector.org/.
Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The Support Vector Machine (SVM), a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies.
The data source for this research was the HuGE Navigator, formerly known as the HuGE Pub Lit database. Weighted SVM feature selection based on a keyword list obtained by the two-way z score method demonstrated the best screening performance, achieving 97.5% recall, 98.3% specificity and 31.9% precision in performance testing. Compared with the traditional screening process based on a complex PubMed query, the SVM tool reduced by about 90% the number of abstracts requiring individual review by the database curator. The tool also ascertained 47 articles that were missed by the traditional literature screening process during the 4-week test period. We examined the literature on genetic associations with preterm birth as an example. Compared with the traditional, manual process, the GAPscreener both reduced effort and improved accuracy.
GAPscreener is the first free SVM-based application available for screening the human genetic association literature in PubMed with high recall and specificity. The user-friendly graphical user interface makes this a practical, stand-alone application. The software can be downloaded at no charge.
Many scientists now manage the bulk of their bibliographic information electronically, thereby organizing their publications and citation material from digital libraries. However, a library has been described as “thought in cold storage,” and unfortunately many digital libraries can be cold, impersonal, isolated, and inaccessible places. In this Review, we discuss the current chilly state of digital libraries for the computational biologist, including PubMed, IEEE Xplore, the ACM digital library, ISI Web of Knowledge, Scopus, Citeseer, arXiv, DBLP, and Google Scholar. We illustrate the current process of using these libraries with a typical workflow, and highlight problems with managing data and metadata using URIs. We then examine a range of new applications such as Zotero, Mendeley, Mekentosj Papers, MyNCBI, CiteULike, Connotea, and HubMed that exploit the Web to make these digital libraries more personal, sociable, integrated, and accessible places. We conclude with how these applications may begin to help achieve a digital defrost, and discuss some of the issues that will help or hinder this in terms of making libraries on the Web warmer places in the future, becoming resources that are considerably more useful to both humans and machines.
Summary: Identifying mentions of named entities, such as genes or diseases, and normalizing them to database identifiers have become an important step in many text and data mining pipelines. Despite this need, very few entity normalization systems are publicly available as source code or web services for biomedical text mining. Here we present the Gnat Java library for text retrieval, named entity recognition, and normalization of gene and protein mentions in biomedical text. The library can be used as a component to be integrated with other text-mining systems, as a framework to add user-specific extensions, and as an efficient stand-alone application for the identification of gene and protein names for data analysis. On the BioCreative III test data, the current version of Gnat achieves a Tap-20 score of 0.1987.
Availability: The library and web services are implemented in Java and the sources are available from http://gnat.sourceforge.net.
The practice of evidence-based medicine requires efficient biomedical literature search such as PubMed/MEDLINE. Retrieval performance relies highly on the efficient use of search field tags. The purpose of this study was to analyze PubMed log data in order to understand the usage pattern of search tags by the end user in PubMed/MEDLINE search.
A PubMed query log file was obtained from the National Library of Medicine containing anonymous user identification, timestamp, and query text. Inconsistent records were removed from the dataset and the search tags were extracted from the query texts. A total of 2,917,159 queries were selected for this study issued by a total of 613,061 users. The analysis of frequent co-occurrences and usage patterns of the search tags was conducted using an association mining algorithm.
The percentage of search tag usage was low (11.38% of the total queries) and only 2.95% of queries contained two or more tags. Three out of four users used no search tag and about two-third of them issued less than four queries. Among the queries containing at least one tagged search term, the average number of search tags was almost half of the number of total search terms. Navigational search tags are more frequently used than informational search tags. While no strong association was observed between informational and navigational tags, six (out of 19) informational tags and six (out of 29) navigational tags showed strong associations in PubMed searches.
The low percentage of search tag usage implies that PubMed/MEDLINE users do not utilize the features of PubMed/MEDLINE widely or they are not aware of such features or solely depend on the high recall focused query translation by the PubMed’s Automatic Term Mapping. The users need further education and interactive search application for effective use of the search tags in order to fulfill their biomedical information needs from PubMed/MEDLINE.
Retrieving pertinent information from biological scientific literature requires cutting-edge text mining methods which may be able to recognize the meaning of the very ambiguous names of biological entities. Aliases of a gene share a common vocabulary in their respective collections of PubMed abstracts. This may be true even when these aliases are not associated with the same subset of documents. This gene-specific vocabulary defines a unique fingerprint that can be used to disclose ambiguous aliases. The present work describes an original method for automatically assessing the ambiguity levels of gene aliases in large gene terminologies based exclusively in the content of their associated literature. The method can deal with the two major problems restricting the usage of current text mining tools: 1) different names associated with the same gene; and 2) one name associated with multiple genes, or even with non-gene entities. Important, this method does not require training examples.
Aliases were considered “ambiguous” when their Jaccard distance to the respective official gene symbol was equal or greater than the smallest distance between the official gene symbol and one of the three internal controls (randomly picked unrelated official gene symbols). Otherwise, they were assigned the status of “synonyms”. We evaluated the coherence of the results by comparing the frequencies of the official gene symbols in the text corpora retrieved with their respective “synonyms” or “ambiguous” aliases. Official gene symbols were mentioned in the abstract collections of 42 % (70/165) of their respective synonyms. No official gene symbol occurred in the abstract collections of any of their respective ambiguous aliases. In overall, querying PubMed with official gene symbols and “synonym” aliases allowed a 3.6-fold increase in the number of unique documents retrieved.
These results confirm that this method is able to distinguish between synonyms and ambiguous gene aliases based exclusively on their vocabulary fingerprint. The approach we describe could be used to enhance the retrieval of relevant literature related to a gene.
Summary:SciMiner is a web-based literature mining and functional analysis tool that identifies genes and proteins using a context specific analysis of MEDLINE abstracts and full texts. SciMiner accepts a free text query (PubMed Entrez search) or a list of PubMed identifiers as input. SciMiner uses both regular expression patterns and dictionaries of gene symbols and names compiled from multiple sources. Ambiguous acronyms are resolved by a scoring scheme based on the co-occurrence of acronyms and corresponding description terms, which incorporates optional user-defined filters. Functional enrichment analyses are used to identify highly relevant targets (genes and proteins), GO (Gene Ontology) terms, MeSH (Medical Subject Headings) terms, pathways and protein–protein interaction networks by comparing identified targets from one search result with those from other searches or to the full HGNC [HUGO (Human Genome Organization) Gene Nomenclature Committee] gene set. The performance of gene/protein name identification was evaluated using the BioCreAtIvE (Critical Assessment of Information Extraction systems in Biology) version 2 (Year 2006) Gene Normalization Task as a gold standard. SciMiner achieved 87.1% recall, 71.3% precision and 75.8% F-measure. SciMiner's literature mining performance coupled with functional enrichment analyses provides an efficient platform for retrieval and summary of rich biological information from corpora of users' interests.
Availability: http://jdrf.neurology.med.umich.edu/SciMiner/. A server version of the SciMiner is also available for download and enables users to utilize their institution's journal subscriptions.
Supplementary information: Supplementary data are available at Bioinformatics online.
Protein–protein interaction (PPI) extraction has been an important research topic in bio-text mining area, since the PPI information is critical for understanding biological processes. However, there are very few open systems available on the Web and most of the systems focus on keyword searching based on predefined PPIs. PIE (Protein Interaction information Extraction system) is a configurable Web service to extract PPIs from literature, including user-provided papers as well as PubMed articles. After providing abstracts or papers, the prediction results are displayed in an easily readable form with essential, yet compact features. The PIE interface supports more features such as PDF file extraction, PubMed search tool and network communication, which are useful for biologists and bio-system developers. The PIE system utilizes natural language processing techniques and machine learning methodologies to predict PPI sentences, which results in high precision performance for Web users. PIE is freely available at http://bi.snu.ac.kr/pie/.
Numerous projects, initiatives, and programs are dedicated to the development of Electronic Health Records (EHR) worldwide. Increasingly more of these plans have recently been brought from a scientific environment to real life applications. In this context, quality is a crucial factor with regard to the acceptance and utility of Electronic Health Records. However, the dissemination of the existing quality approaches is often rather limited.
The present paper aims at the description and comparison of the current major quality certification approaches to EHRs.
A literature analysis was carried out in order to identify the relevant publications with regard to EHR quality certification. PubMed, ACM Digital Library, IEEExplore, CiteSeer, and Google (Scholar) were used to collect relevant sources. The documents that were obtained were analyzed using techniques of qualitative content analysis.
The analysis discusses and compares the quality approaches of CCHIT, EuroRec, IHE, openEHR, and EN13606. These approaches differ with regard to their focus, support of service-oriented EHRs, process of (re-)certification and testing, number of systems certified and tested, supporting organizations, and regional relevance.
The analyzed approaches show differences with regard to their structure and processes. System vendors can exploit these approaches in order to improve and certify their information systems. Health care organizations can use these approaches to support selection processes or to assess the quality of their own information systems.
Quality; certification; electronic health record; medical records
Although highly effective prevention interventions exist, the epidemic of paediatric HIV continues to challenge control efforts in resource-limited settings. We reviewed the cost-effectiveness of interventions to prevent mother-to-child transmission (MTCT) of HIV in low- and middle-income countries (LMICs). This article presents syntheses of evidence on the costs, effects and cost-effectiveness of HIV MTCT strategies for LMICs from the published literature and evaluates their implications for policy and future research.
Candidate studies were identified through a comprehensive database search including PubMed, Embase, Cochrane Library, and EconLit restricted by language (English or French), date (January 1st, 1994 to January 17th, 2011) and article type (original research). Articles reporting full economic evaluations of interventions to prevent or reduce HIV MTCT were eligible for inclusion. We searched article bibliographies to identify additional studies. Two authors independently assessed eligibility and extracted data from studies retained for review. Study quality was appraised using a modified BMJ checklist for economic evaluations. Data were synthesised in narrative form.
We identified 19 articles published in 9 journals from 1996 to 2010, 16 concerning sub-Saharan Africa. Collectively, the articles suggest that interventions to prevent paediatric infections are cost-effective in a variety of LMIC settings as measured against accepted international benchmarks. In concentrated epidemics where HIV prevalence in the general population is very low, MTCT strategies based on universal testing of pregnant women may not compare well against cost-effectiveness benchmarks, or may satisfy formal criteria for cost-effectiveness but offer a low relative value as compared to competing interventions to improve population health.
Conclusions and Recommendations
Interventions to prevent HIV MTCT are compelling on economic grounds in many resource-limited settings and should remain at the forefront of global HIV prevention efforts. Future cost-effectiveness analyses can help to ensure that pMTCT interventions for LMICs reach their full potential by focussing on unanswered questions in four areas: local assessment of rapidly evolving HIV MTCT options; strategies to improve coverage and reach underserved populations; evaluation of a more comprehensive set of MTCT approaches including primary HIV prevention and reproductive counselling; integration of HIV MTCT and other sexual and reproductive health services.
The colon and rectum are common sites of food-related cancer in developed countries. Recent studies strongly suggest that red meat intake is associated with colon cancer, whereas for rectal cancer such an association still needs to be proved. The aim of the study was to assess the role of total amount and frequency of red meat intake in colorectal carcinogenesis based on published data using meta-analysis methods.
Material and methods
The literature published until 2009 was selected from: MEDLINE, PubMed, Scopus, Embase, CancerLit, Google Scholar and Cochrane Library databases. The used search terms were: colorectal cancer, colon cancer, rectal cancer, meat intake, red meat intake, red meat consumption, meat consumption, colorectal cancer risk, colon cancer risk, rectal cancer risk and lifestyle. Articles investigating red meat intake of more often than once a day or 50 g per day were reviewed and selected for further analysis.
Twenty-two studies fulfilled the established criteria. A meta-analysis confirmed the carcinogenic effect of the consumption of over 50 g of red meat per day for the colon (relative risk 1.21, 1.07–1.37) but not for the rectum (relative risk 1.30, 0.90–1.89). Red meat intake more frequently than once a day can induce both colonic (relative risk 1.37, 1.09–1.71) and rectal cancer (relative risk 1.43, 1.24–1.64).
Red meat intake is associated with elevated risk of developing colorectal cancer. The frequency of red meat consumption rather than total amount of consumed meat is associated with a higher risk of colorectal carcinogenesis.
diet; meat consumption; colorectal cancer; cancer risk
The objective of this narrative review is to discuss the potential for burnout in chiropractic practitioners. This discussion is grounded in the job demands-resource model, the conservation of resources model, the unique profession-specific stressors experienced by chiropractors, and information from similar health care professions.
A search using both the indexed (PubMed and PsychLit) and nonindexed psychosocial literature was used. Other resources included the Cochrane Library, articles from governing bodies of the chiropractic profession, trade magazines, and research conferences and symposium proceedings. Articles were analyzed following the grounded theory principles: open coding and memos for conceptual labeling, axial coding and memos for category building, and selective coding for model building.
Potential stressors unique to doctors of chiropractic include factors associated with physical workload, role stress, and mental and emotional demands.
There are unique chiropractic-specific occupational characteristics that possibly contribute to burnout in the chiropractic professionals. These findings emphasize the need for assessing and measuring burnout and attrition within the chiropractic profession.
Psychology; Chiropractic; Manual therapy
The Florida Health Information Network (FHIN) was established in October 1989 to provide biomedical information services to the University of Florida Health Science Center (HSC) and to health professionals throughout the state--especially the northern thirty-nine counties of the state. FHIN services are available to all affiliates of the HSC and by annual subscription to nonaffiliates. At present, FHIN services include database access, circulation services, document delivery, and information services. Training network users has been an objective since the inception. Training has targeted both the HSC Library staff and HSC users and now is expanding to include remote users. Because many users have had insufficient experience with computers, the library has to teach the mechanics of access and network use and to instruct users regarding applications and database searching. This paper describes the development and implementation of the medical informatics education program. Topics include library staff training; educational offerings for HSC faculty, staff, and students; development and implementation of the remote training program; and organizational and budgetary implications for the construction of such a program.
Objectives: The study examines how Loansome Doc services are implemented and used by libraries in the Southeast Region and describe end users' experiences with and attitudes toward Loansome Doc.
Methods: 251 active DOCLINE libraries and 867 Loansome Doc users were surveyed.
Results: Roughly one half of the libraries offered Loansome Doc services. Of those that did not, most indicated no plans to offer it in the future. The majority had a small number of end users and experienced minimal increases in interlibrary loan activity. Problems were relatively rare. Satisfaction with Loansome Doc was high among all types of libraries. End users were usually physicians or other health care professionals who requested articles for research and patient care. Most learned about Loansome Doc through PubMed or Internet Grateful Med. End users appeared to be largely self-taught or received informal instruction in Loansome Doc. Loansome Doc filled document requests in a timely manner, and end users reported being satisfied with the service.
Conclusions: Greater promotion of what Loansome Doc is and how it can benefit libraries can increase the number of participating libraries. While satisfaction of Loansome Doc end users is high, satisfaction could be increased with more help on the PubMed screen, more library training, and faster delivery methods.
Biological data have traditionally been stored and made publicly available through a variety of on-line databases, whereas biological knowledge has traditionally been found in the printed literature. With journals now on-line and providing an increasing amount of open access content, often free of copyright restriction, this distinction between database and literature is blurring. To exploit this opportunity we present the integration of open access literature with the RCSB Protein Data Bank (PDB).
BioLit provides an enhanced view of articles with markup of semantic data and links to biological databases, based on the content of the article. For example, words matching to existing biological ontologies are highlighted and database identifiers are linked to their database of origin. Among other functions, it identifies PDB IDs that are mentioned in the open access literature, by parsing the full text for all research articles in PubMed Central (PMC) and exposing the results as simple XML Web Services. Here, we integrate BioLit results with the RCSB PDB website by using these services to find PDB IDs that are mentioned in research articles and subsequently retrieving abstract, figures, and text excerpts for those articles. A new RCSB PDB literature view permits browsing through the figures and abstracts of the articles that mention a given structure. The BioLit Web Services that are providing the underlying data are publicly accessible. A client library is provided that supports querying these services (Java).
The integration between literature and websites, as demonstrated here with the RCSB PDB, provides a broader view for how a given structure has been analyzed and used. This approach detects the mention of a PDB structure even if it is not formally cited in the paper. Other structures related through the same literature references can also be identified, possibly providing new scientific insight. To our knowledge this is the first time that database and literature have been integrated in this way and it speaks to the opportunities afforded by open and free access to both database and literature content.
Ontologies have become a critical component of many applications in biomedical informatics. However, the landscape of the ontology tools today is largely fragmented, with independent tools for ontology editing, publishing, and peer review: users develop an ontology in an ontology editor, such as Protégé; and publish it on a Web server or in an ontology library, such as BioPortal, in order to share it with the community; they use the tools provided by the library or mailing lists and bug trackers to collect feedback from users. In this paper, we present a set of tools that bring the ontology editing and publishing closer together, in an integrated platform for the entire ontology lifecycle. This integration streamlines the workflow for collaborative development and increases integration between the ontologies themselves through the reuse of terms.
MitoMiner (http://mitominer.mrc-mbu.cam.ac.uk/) is a data warehouse for the storage and analysis of mitochondrial proteomics data gathered from publications of mass spectrometry and green fluorescent protein tagging studies. In MitoMiner, these data are integrated with data from UniProt, Gene Ontology, Online Mendelian Inheritance in Man, HomoloGene, Kyoto Encyclopaedia of Genes and Genomes and PubMed. The latest release of MitoMiner stores proteomics data sets from 46 studies covering 11 different species from eumetazoa, viridiplantae, fungi and protista. MitoMiner is implemented by using the open source InterMine data warehouse system, which provides a user interface allowing users to upload data for analysis, personal accounts to store queries and results and enables queries of any data in the data model. MitoMiner also provides lists of proteins for use in analyses, including the new MitoMiner mitochondrial proteome reference sets that specify proteins with substantial experimental evidence for mitochondrial localization. As further mitochondrial proteomics data sets from normal and diseased tissue are published, MitoMiner can be used to characterize the variability of the mitochondrial proteome between tissues and investigate how changes in the proteome may contribute to mitochondrial dysfunction and mitochondrial-associated diseases such as cancer, neurodegenerative diseases, obesity, diabetes, heart failure and the ageing process.
BIOSMILE web search (BWS), a web-based NCBI-PubMed search application, which can analyze articles for selected biomedical verbs and give users relational information, such as subject, object, location, manner, time, etc. After receiving keyword query input, BWS retrieves matching PubMed abstracts and lists them along with snippets by order of relevancy to protein–protein interaction. Users can then select articles for further analysis, and BWS will find and mark up biomedical relations in the text. The analysis results can be viewed in the abstract text or in table form. To date, BWS has been field tested by over 30 biologists and questionnaires have shown that subjects are highly satisfied with its capabilities and usability. BWS is accessible free of charge at http://bioservices.cse.yzu.edu.tw/BWS.
If evaluation of economic evidence is to be used increasingly in Saudi Arabia, a review of the published literature would be useful to inform policy decision-makers of the current state of research and plan future research agendas. The purpose of this paper is to provide a critical review of the state of health economic evaluation research within the Saudi context with regard to the number, characteristics, and quality of published articles.
A literature search was conducted on May 8, 2011 to identify health economic articles pertaining to Saudi Arabia in the PubMed, Embase, and EconLit databases, using the following terms alone or in combination: “cost*”, “economics”, “health economics”, “cost-effectiveness”, “cost-benefit”, “cost minimization”, “cost utility analysis”, and “Saudi”. Reference lists of the articles identified were also searched for further articles. The tables of contents of the Saudi Pharmaceutical Journal and the Saudi Medical Journal were reviewed for the previous 5 years.
The search identified 535 citations. Based on a reading of abstracts and titles, 477 papers were excluded. Upon reviewing the full text of the remaining 58 papers, 43 were excluded. Fifteen papers were included. Ten were categorized as full economic evaluations and five as partial economic evaluations. These articles were published between 1997 and 2010. The majority of the studies identified did not clearly state the perspective of their evaluation. There are many concerns about the methods used to collect outcome and costs data. Only one study used some sort of sensitivity analysis to assess the effects of uncertainty on the robustness of its conclusions.
This review highlights major flaws in the design, analysis, and reporting of the identified economic analyses. Such deficiencies mean that the local economic evidence available to decision-makers is not very useful. Thus, building research capability in health economics is warranted.
cost-effective analysis; pharmacoeconomics; economic evaluation; quality assessment; Saudi Arabia
Tuberculosis (TB) is known to disproportionately affect the most economically disadvantaged strata of society. Many studies have assessed the association between poverty and TB, but only a few have assessed the direct financial burden TB treatment and care can place on households. Patient costs can be particularly burdensome for TB-affected households in sub-Saharan Africa where poverty levels are high; these costs include the direct costs of medical and non-medical expenditures and the indirect costs of time utilizing healthcare or lost wages. In order to comprehensively assess the existing evidence on the costs that TB patients incur, we undertook a systematic review of the literature.
PubMed, EMBASE, Science Citation Index, Social Science Citation Index, EconLit, Dissertation Abstracts, CINAHL, and Sociological Abstracts databases were searched, and 5,114 articles were identified. Articles were included in the final review if they contained a quantitative measure of direct or indirect patient costs for treatment or care for pulmonary TB in sub-Saharan Africa and were published from January 1, 1994 to Dec 31, 2010. Cost data were extracted from each study and converted to 2010 international dollars (I$).
Thirty articles met all of the inclusion criteria. Twenty-one studies reported both direct and indirect costs; eight studies reported only direct costs; and one study reported only indirect costs. Depending on type of costs, costs varied from less than I$1 to almost I$600 or from a small fraction of mean monthly income for average annual income earners to over 10 times average annual income for income earners in the income-poorest 20% of the population. Out of the eleven types of TB patient costs identified in this review, the costs for hospitalization, medication, transportation, and care in the private sector were largest.
TB patients and households in sub-Saharan Africa often incurred high costs when utilizing TB treatment and care, both within and outside of Directly Observed Therapy Short-course (DOTS) programs. For many households, TB treatment and care-related costs were considered to be catastrophic because the patient costs incurred commonly amounted to 10% or more of per capita incomes in the countries where the primary studies included in this review were conducted. Our results suggest that policies to decrease direct and indirect TB patient costs are urgently needed to prevent poverty due to TB treatment and care for those affected by the disease.
Tuberculosis; Economic impact; Out-of-pocket costs; Africa
Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from high-throughput analyses. Although many tools and databases are currently available for accessing such data, they are left unutilized by bench scientists as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by scientists with limited computational expertise. We describe BioNetwork Bench, an open source, user-friendly suite of database and software tools for constructing, querying, and analyzing gene and protein network models. It enables biologists to analyze public as well as private gene expression; interactively query gene expression datasets; integrate data from multiple networks; store and selectively share the data and results. Finally, we describe an application of BioNetwork Bench to the assembly and iterative expansion of a gene network that controls the differentiation of retinal progenitor cells into rod photoreceptors. The tool is available from http://bionetworkbench.sourceforge.net/
The emergence of high-throughput technologies has allowed many biological investigators to collect a great deal of information about the behavior of genes and gene products over time or during a particular disease state. Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from such high-throughput analyses. There are a growing number of public databases, as well as tools for visualization and analysis of networks. However, such databases and tools have yet to be widely utilized by bench scientists, as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by biological scientists with limited computational expertise.
We describe BioNetwork Bench, an open source, user-friendly suite of database and software tools for constructing, querying, and analyzing gene and protein network models. BioNetwork Bench currently supports a broad class of gene and protein network models (eg, weighted and un-weighted, undirected graphs, multi-graphs). It enables biologists to analyze public as well as private gene expression, macromolecular interaction and annotation data; interactively query gene expression datasets; integrate data from multiple networks; query multiple networks for interactions of interest; store and selectively share the data as well as results of analyses. BioNetwork Bench is implemented as a plug-in for, and hence is fully interoperable with, Cytoscape, a popular open-source software suite for visualizing macromolecular interaction networks. Finally, we describe an application of BioNetwork Bench to the problem of assembly and iterative expansion of a gene network that controls the differentiation of retinal progenitor cells into rod photoreceptors.
BioNetwork Bench provides a suite of open source software for construction, querying, and selective sharing of gene and protein networks. Although initially aimed at a community of biologists interested in retinal development, the tool can be adapted easily to work with other biological systems simply by populating the associated database with the relevant datasets.
network analysis; software; network contruction; network integration
The technological advances in the past decade have lead to massive progress in the field of biotechnology. The documentation of the progress made exists in the form of research articles. The PubMed is the current most used repository for bio-literature. PubMed consists of about 17 million abstracts as of 2007 that require methods to efficiently retrieve and browse large volume of relevant information. The State-of-the-art technologies such as GOPubmed use simple keyword-based techniques for retrieving abstracts from the PubMed and linking them to the Gene Ontology (GO). This paper changes the paradigm by introducing semantics enabled technique to link the PubMed to the Gene Ontology, called, SEGOPubmed for ontology-based browsing. Latent Semantic Analysis (LSA) framework is used to semantically interface PubMed abstracts to the Gene Ontology.
The Empirical analysis is performed to compare the performance of the SEGOPubmed with the GOPubmed. The analysis is initially performed using a few well-referenced query words. Further, statistical analysis is performed using GO curated dataset as ground truth. The analysis suggests that the SEGOPubmed performs better than the classic GOPubmed as it incorporates semantics.
The LSA technique is applied on the PubMed abstracts obtained based on the user query and the semantic similarity between the query and the abstracts. The analyses using well-referenced keywords show that the proposed semantic-sensitive technique outperformed the string comparison based techniques in associating the relevant abstracts to the GO terms. The SEGOPubmed also extracted the abstracts in which the keywords do not appear in isolation (i.e. they appear in combination with other terms) that could not be retrieved by simple term matching techniques.
AIM: To evaluate the efficacy of simple and extended cholecystectomy for mucosa (T1a) or muscularis (T1b) gallbladder (GB) cancer.
METHODS: Original studies on simple and extended cholecystectomy for T1a or T1b GB cancer were searched from MEDLINE (PubMed), Cochrane Library, EMBase, and CancerLit using the search terms of GB, cancer/carcinoma/tumor/neoplasm.
RESULTS: Twenty-nine out of the 2312 potentially relevant publications met the eligibility criteria. Of the 1266 patients with GB cancer included in the publications, 706 (55.8%) and 560 (44.2%) had T1a and T1b GB cancer, respectively. Simple cholecystectomy for T1a and T1b GB cancer was performed in 590 (83.6%) and 375 (67.0%) patients, respectively (P < 0.01). In most series, the treatment of choice was simple cholecystectomy for T1a GB cancer patients with a 5-year survival rate of 100%. Lymph node metastasis was detected in 10.9% of the T1b GB cancer patients and in 1.8% of the T1a GB cancer patients, respectively (P < 0.01). Eight patients (1.1%) with T1a GB cancer and 52 patients (9.3%) with T1b GB cancer died of recurrent GB cancer (P < 0.01).
CONCLUSION: Simple cholecystectomy represents the adequate treatment of T1a GB cancer. There is no definite evidence that extended cholecystectomy is advantageous over simple cholecystectomy for T1b GB cancer.
Gallbladder; Cancer; Cholecystectomy; Simple; Extended