Polyhydroxybutyrate and polyhydroxydodecanoate produced by Burkholderia contaminans IPT553.

AIMS: In this paper, we introduce a new Burkholderia contaminans capable of producing a newly characterized polymer. METHODS AND RESULTS: CG-MS and magnetic nuclear resonance 1 H and 13 C were used to determine the constitution of polymers produced in glucose, glucose with casein, sucrose and sucrose with casein. Three pairs of primers were used to find the polyhydroxyalkanoates (PHA) synthase class and sequence. The synthesized polymers were composed by short-chain length PHA (scl-PHA), especially polyhydroxybutyrate (PHB), and medium chain length PHA (mcl-PHA), especially polyhydroxydodecanoate (PHDd), and their concentration, constitution and molecular weight depend on carbon source used. The bacterium showed only class I synthase which could not explain the mcl-PHA production. CONCLUSIONS: Burkholderia contaminans has a class I PHA synthase and produces PHB combined to PHDd when cultivated in sucrose or glucose, and PHDd concentration is affected when casein is used. SIGNIFICANCE AND IMPACT OF THE STUDY: PHA are natural polymers produced by a wide range of bacteria. The presence of PHDd monomers confers to the polymer elastomeric properties. Previously, PHDd was only obtained when bacteria were cultivated in related carbon source. In this work, B. contaminansIPT553 produced PHB with PHDd using simple and low-cost carbon sources that can make possible the cheaper production of a more flexible biopolymer with crystallinity and elasticity different from the more common PHAs.

Validating a natural language processing tool to exclude psychogenic nonepileptic seizures in electronic medical record-based epilepsy research.

RATIONALE: As electronic health record (EHR) systems become more available, they will serve as an important resource for collecting epidemiologic data in epilepsy research. However, since clinicians do not have a systematic method for coding psychogenic nonepileptic seizures (PNES), patients with PNES are often misclassified as having epilepsy, leading to sampling error. This study validates a natural language processing (NLP) tool that uses linguistic information to help identify patients with PNES. METHODS: Using the VA national clinical database, 2200 notes of Iraq and Afghanistan veterans who completed video electroencephalograph (VEEG) monitoring were reviewed manually, and the veterans were identified as having documented PNES or not. Reviewers identified PNES-related vocabulary to inform a NLP tool called Yale cTakes Extension (YTEX). Using NLP techniques, YTEX annotates syntactic constructs, named entities, and their negation context in the EHR. These annotations are passed to a classifier to detect patients without PNES. The classifier was evaluated by calculating positive predictive values (PPVs), sensitivity, and F-score. RESULTS: Of the 742 Iraq and Afghanistan veterans who received a diagnosis of epilepsy or seizure disorder by VEEG, 44 had documented events on VEEG: 22 veterans (3.0%) had definite PNES only, 20 (2.7%) had probable PNES, and 2 (0.3%) had both PNES and epilepsy documented. The remaining 698 veterans did not have events captured during the VEEG admission and/or did not have a definitive diagnosis. Our classifier achieved a PPV of 93%, a sensitivity of 99%, and a F-score of 96%. CONCLUSION: Our study demonstrates that the YTEX NLP tool and classifier is highly accurate in excluding PNES, diagnosed with VEEG, in EHR systems. The tool may be very valuable in preventing false positive identification of patients with epilepsy in EHR-based epidemiologic research.

The Common Data Elements for cancer research: remarks on functions and structure.

OBJECTIVES: The National Cancer Institute (NCI) has developed the Common Data Elements (CDE) to serve as a controlled vocabulary of data descriptors for cancer research, to facilitate data interchange and inter-operability between cancer research centers. We evaluated CDE's structure to see whether it could represent the elements necessary to support its intended purpose, and whether it could prevent errors and inconsistencies from being accidentally introduced. We also performed automated checks for certain types of content errors that provided a rough measure of curation quality. METHODS: Evaluation was performed on CDE content downloaded via the NCI's CDE Browser, and transformed into relational database form. Evaluation was performed under three categories: 1) compatibility with the ISO/IEC 11179 metadata model, on which CDE structure is based, 2) features necessary for controlled vocabulary support, and 3) support for a stated NCI goal, set up of data collection forms for cancer research. RESULTS: Various limitations were identified both with respect to content (inconsistency, insufficient definition of elements, redundancy) as well as structure--particularly the need for term and relationship support, as well as the need for metadata supporting the explicit representation of electronic forms that utilize sets of common data elements. CONCLUSIONS: While there are numerous positive aspects to the CDE effort, there is considerable opportunity for improvement. Our recommendations include review of existing content by diverse experts in the cancer community; integration with the NCI thesaurus to take advantage of the latter's links to nationally used controlled vocabularies, and various schema enhancements required for electronic form support.

Informatics tools to improve clinical research.

During the conduct of complex clinical trials, there are numerous sources and types of data collection and project coordination problems. Methods and approaches to address the conduct of a trial vary in both the cost and time to perform and the potential benefit. Informatics tools can help trial coordinators and investigators ensure the collection of high quality research data during all phases of a clinical trial.

Automating identification of adverse events related to abnormal lab results using standard vocabularies.

Laboratory data need to be imported automatically into central Clinical Study Data Management Systems (CSDMSs), and abnormal laboratory data need to be linked to clinically related adverse events. This import of laboratory data can be automated through mapping to standard vocabularies with HL7/LOINC mapping to the metadata within a CSDMS. We have designed a system that uses the UMLS metathesaurus as a common source to map or link abnormal laboratory values to adverse event CTCAE coded terms and grades in the metadata of TrialDB, a generic CSDMS.

Metadata driven generation of reports for clinical studies.

We have developed a reporting engine to dynamically create Web-based reports for multiple types of clinical studies in a large-scale clinical study data management system (CSDMS), TrialDB. The engine, currently in production use, generates reports based on metadata that is defined through a GUI, thus eliminating the need to write code for every report.

Integration of Web-based and PC-based clinical research databases.

UNLABELLED: We have created a Web-based repository or data library of information about measurement instruments used in studies of multi-factorial geriatric health conditions (the Geriatrics Research Instrument Library - GRIL) based upon existing features of two separate clinical study data management systems. GRIL allows browsing, searching, and selecting measurement instruments based upon criteria such as keywords and areas of applicability. Measurement instruments selected can be printed and/or included in an automatically generated standalone microcomputer database application, which can be downloaded by investigators for use in data collection and data management. METHODS: Integration of database applications requires the creation of a common semantic model, and mapping from each system to this model. Various database schema conflicts at the table and attribute level must be identified and resolved prior to integration. Using a conflict taxonomy and a mapping schema facilitates this process. RESULTS: Critical conflicts at the table level that required resolution included name and relationship differences. CONCLUSIONS: A major benefit of integration efforts is the sharing of features and cross-fertilization of applications created for similar purposes in different operating environments. Integration of applications mandates some degree of metadata model unification.

Approaches and informatics tools to assist in the integration of similar clinical research questionnaires.

OBJECTIVE: The integration of similar clinical research questionnaires is a complex process that can benefit from informatics approaches and tools that provide a systematic structure for performing mapping and integration. This systematic approach is necessary to address complex issues in integration such as data heterogeneity, differing levels of granularity of questions and responses, and other issues involving semantic differences. Informatics tools and approaches have been successfully applied to various standard clinical vocabulary integration processes but not for questionnaire integration or mapping. METHODS: A systematic approach to questionnaire integration was developed in the context of a collaboration of researchers using Trial/DB, a database designed to support clinical research. This approach was applied to the integration of questionnaires involving breast cancer risk factors from each of three research sites. RESULTS: From 375 questions on the three original questionnaires, we identified 65 concepts that were measured by two or three of the sites. An algorithm was developed and used to formalize the process of mapping questions and answers across the questionnaires. The approach was applied to previously collected data and prospective data in disparate data-base systems to import and merge the data from these three sites into Trial/DB. CONCLUSION: Informatics tools that support a systematic approach to mapping questionnaires can be used throughout the research process from questionnaire integration and creation, legacy data integration to data library maintenance and curation.

TrialDB: A web-based Clinical Study Data Management System.

Clinical Study Data Management Systems (CSDMSs) are a class of software that support centralized management of data generated during the conduct of clinical studies. Commercial CSDMSs include Oracle Clinical, ClinTrial and MetaTrial. Such systems, which are typically deployed at an institutional or organizational level, must accommodate diverse types of data from different clinical domains that is generated by different groups of clinical investigators. Large-scale CSDMSs typically employ a high-end database engine that is usually accessed over an intranet or the Internet using Web-based technologies. CSDMSs in institution-wide use for a variety of clinical domains are best served by entity-attribute-value (EAV) modeling for the clinical data: all the commercial CSDMSs that we are aware of use EAV design. However, de novo development of EAV databases for data management is a challenging task. A large body of generic metadata-driven code must be developed before a basic EAV application can be written. Clearly, the availability of pre-existing software with the requisite functionality would be very valuable. We will discuss the benefits of such software being in open-source form.