Aflatoxin M1 Detoxification Ability of Probiotic Lactobacilli of Indian Origin in In vitro Digestion Model.

Aflatoxin M1 (AFM1) is known to be a potent carcinogen and continues to pose a public health concern through the consumption of contaminated dairy foods. It is anticipated that consumption of lactic acid bacteria capable of binding aflatoxins can reduce the risk of AFM1 on human health to a certain extent. Seldom reports have hinted the possibility of using lactic acid bacteria for the biological detoxification of AFM1. Hence, the present study was conducted to assess the ability of selected probiotic Lactobacillus strains for their AFM1 binding ability in PBS and to reduce its bioaccessibility in artificially contaminated skim milk using an in vitro digestion model. Eleven tested probiotic strains illustrated various degrees of AFM1 binding ability ranging from 4.13 to 64.16%. Five among the 11 probiotic strains were subsequently selected for detailed studies on the basis of highest binding potential after 24 h of incubation period. The stability of bacterial-AFM1 complex was assessed by repeated washings with AFM1 free PBS. The observation on bacterial-AFM1 complex stability showed small release of AFM1 in first and second wash (17.30 to 0.98%) where as none was detectable in the third wash. However, upon chloroform extraction, 88.57 to 92.30% of bound AFM1 was released from the bacterial cells which indicate AFM1 binding to the bacterial cell surface rather than absorption or degradation of AFM1 by bacterial cells. During the in vitro digestion test in skim milk, bioaccessibility of AFM1 was reduced to a scale of 32.61 to 52.84% in the presence of selected strains of probiotic lactobacilli. The present findings suggest that selected probiotic strains could be potentially used to mitigate the toxic effects of AFM1 in the contaminated milk and milk products and thereby enhance food safety.

A multi-layered framework for disseminating knowledge for computer-based decision support.

BACKGROUND: There are several challenges in encoding guideline knowledge in a form that is portable to different clinical sites, including the heterogeneity of clinical decision support (CDS) tools, of patient data representations, and of workflows. METHODS: We have developed a multi-layered knowledge representation framework for structuring guideline recommendations for implementation in a variety of CDS contexts. In this framework, guideline recommendations are increasingly structured through four layers, successively transforming a narrative text recommendation into input for a CDS system. We have used this framework to implement rules for a CDS service based on three guidelines. We also conducted a preliminary evaluation, where we asked CDS experts at four institutions to rate the implementability of six recommendations from the three guidelines. CONCLUSION: The experience in using the framework and the preliminary evaluation indicate that this approach has promise in creating structured knowledge, to implement in CDS systems, that is usable across organizations.

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside.

BACKGROUND: Translational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery. RESULTS: We developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action. CONCLUSIONS: This work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine. AVAILABILITY: TMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql.

Creating and sharing clinical decision support content with Web 2.0: Issues and examples.

Clinical decision support is a powerful tool for improving healthcare quality and patient safety. However, developing a comprehensive package of decision support interventions is costly and difficult. If used well, Web 2.0 methods may make it easier and less costly to develop decision support. Web 2.0 is characterized by online communities, open sharing, interactivity and collaboration. Although most previous attempts at sharing clinical decision support content have worked outside of the Web 2.0 framework, several initiatives are beginning to use Web 2.0 to share and collaborate on decision support content. We present case studies of three efforts: the Clinfowiki, a world-accessible wiki for developing decision support content; Partners Healthcare eRooms, web-based tools for developing decision support within a single organization; and Epic Systems Corporation's Community Library, a repository for sharing decision support content for customers of a single clinical system vendor. We evaluate the potential of Web 2.0 technologies to enable collaborative development and sharing of clinical decision support systems through the lens of three case studies; analyzing technical, legal and organizational issues for developers, consumers and organizers of clinical decision support content in Web 2.0. We believe the case for Web 2.0 as a tool for collaborating on clinical decision support content appears strong, particularly for collaborative content development within an organization.

Semiotic web for translational medicine.

The semiotic web for translational medicine generalizes the concept of the semantic web. We present the functions of the semiotic web as a simple ontology with three dimensions, namely: (a) the four steps of semiotics, (b) the two processes in semiotics, and (c) the four types of research. The resulting 32 combinations represent all its functions.

Early experiences in evolving an enterprise-wide information model for laboratory and clinical observations.

As Electronic Healthcare Records become more prevalent, there is an increasing need to ensure unambiguous data capture, interpretation, and exchange within and across heterogeneous applications. To address this need, a common, uniform, and comprehensive approach for representing clinical information is essential. At Partners HealthCare System, we are investigating the development and implementation of enterprise-wide information models to specify the representation of clinical information to support semantic interoperability. This paper summarizes our early experiences in: (1) defining a process for information model development, (2) reviewing and comparing existing healthcare information models, (3) identifying requirements for representation of laboratory and clinical observations, and (4) exploring linkages to existing terminology and data standards. These initial findings provide insight to the various challenges ahead and guidance on next steps for adoption of information models at our organization.

Advancing translational research with the Semantic Web.

BACKGROUND: A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen Translational Research, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature. RESULTS: We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine. CONCLUSION: Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work.

On implementing clinical decision support: achieving scalability and maintainability by combining business rules and ontologies.

We present an approach and architecture for implementing scalable and maintainable clinical decision support at the Partners HealthCare System. The architecture integrates a business rules engine that executes declarative if-then rules stored in a rule-base referencing objects and methods in a business object model. The rules engine executes object methods by invoking services implemented on the clinical data repository. Specialized inferences that support classification of data and instances into classes are identified and an approach to implement these inferences using an OWL based ontology engine is presented. Alternative representations of these specialized inferences as if-then rules or OWL axioms are explored and their impact on the scalability and maintenance of the system is presented. Architectural alternatives for integration of clinical decision support functionality with the invoking application and the underlying clinical data repository; and their associated trade-offs are discussed and presented.

Comparative evaluation of accuracy of extraction of medication information from narrative physician notes by commercial and academic natural language processing software packages.

Medication information is frequently only found in narrative physician notes. It is now possible to extract medication data from narrative documents using NLP technology. A number of commercial and academic NLP software packages can perform this function. In this paper we report the first comparative evaluation of their accuracy. Evaluation was carried out on 150 notes randomly selected from electronic medical record. NLP software results were compared to manual abstraction of medication data by two independent reviewers. Recall, precision and F-measure for identification of medication names, doses, frequencies, routes and inactive status were computed. For different data categories, recall ranged from 6.6% to 90.6%, and precision from 16.7% to 96.6%. Recall was highest for medication names and lowest for identification of inactive medications; there were no significant differences in precision between data categories. NLP software accuracy improved significantly over the last decade but further improvements are needed, particularly in analysis of complex sentences.

Creation of structured documentation templates using Natural Language Processing techniques.

Structured Clinical Documentation is a fundamental component of the healthcare enterprise, linking both clinical (e.g., electronic health record, clinical decision support) and administrative functions (e.g., evaluation and management coding, billing). One of the challenges in creating good quality documentation templates has been the inability to address specialized clinical disciplines and adapt to local clinical practices. A one-size-fits-all approach leads to poor adoption and inefficiencies in the documentation process. On the other hand, the cost associated with manual generation of documentation templates is significant. Consequently there is a need for at least partial automation of the template generation process. We propose an approach and methodology for the creation of structured documentation templates for diabetes using Natural Language Processing (NLP).

The UMLS Semantic Network and the Semantic Web.

The Unified Medical Language System is an extensive source of biomedical knowledge developed and maintained by the US National Library of Medicine (NLM) and is being currently used in a wide variety of biomedical applications. The Semantic Network, a component of the UMLS is a structured description of core biomedical knowledge consisting of well defined semantic types and relationships between them. We investigate the expressiveness of DAML+OIL, a markup language proposed for ontologies on the Semantic Web, for representing the knowledge contained in the Semantic Network. Requirements specific to the Semantic Network, such as polymorphic relationships and blocking relationship inheritance are discussed and approaches to represent these in DAML+OIL are presented. Finally, conclusions are presented along with a discussion of ongoing and future work.

Toward (semi-)automatic generation of bio-medical ontologies.

The design and construction of domain specific ontologies and taxonomies requires allocation of huge resources in terms of cost and time. These efforts are human intensive and we need to explore ways of minimizing human involvement and other resources. In the biomedical domain, we seek to leverage resources such as the UMLS Metathesaurus and NLP-based applications such as MetaMap in conjunction with statistical clustering techniques, to (partially) automate the process. This is expected to be useful to the team involved in developing MeSH and other biomedical taxonomies to identify gaps in the existing taxonomies, and to be able to quickly bootstrap taxonomy generation for new research areas in biomedical informatics.