Biomedical properties of a series of ruthenium-N-heterocyclic carbene complexes based on oxidant activity in vitro and assessment in vivo of biosafety in zebrafish embryos.

PubMed

Alfaro, Juan M; Prades, Amparo; del Carmen Ramos, María; Peris, Eduardo; Ripoll-Gómez, Jorge; Poyatos, Macarena; Burgos, Javier S

2010-03-01

N-Heterocyclic carbene (NHC) ligands have attracted great interest over the last decade for their use in the design of homogenous catalysts. NHC-based metal complexes have interesting potential biomedical applications, such as in antimicrobial and cancer therapy, which are beginning to be explored more fully. We have studied here the oxidant activities of a series of Ru(II) complexes in vitro and zebrafish (Danio rerio) have been used as a model in vivo to investigate and characterize the toxicity of some of these compounds. Dual behavior was observed for the NHC-based complexes as they behaved as antioxidants at low concentrations but showed pro-oxidant capacity at higher concentrations. Zebrafish embryos were exposed to Ru(II) complexes under several different conditions (0 or 24 h postfertilization, with or without the chorion) and various parameters, such as viability, edema, heart rate, blood coagulation, pigmentation, scoliosis, malformation, and hatching, were tested. In general, zebrafish embryos were not harmed by exposure to Ru(II) complexes whatever the experimental conditions. Several toxicity profiles were observed depending upon the chemical structure of the compound in question. Their characteristics as pro-oxidant and/or antioxidant agents together with their biosafety may point to their having biomedical applications as antitumoral or neuroprotective drugs.

Extracting biomedical events from pairs of text entities

PubMed Central

2015-01-01

Background Huge amounts of electronic biomedical documents, such as molecular biology reports or genomic papers are generated daily. Nowadays, these documents are mainly available in the form of unstructured free texts, which require heavy processing for their registration into organized databases. This organization is instrumental for information retrieval, enabling to answer the advanced queries of researchers and practitioners in biology, medicine, and related fields. Hence, the massive data flow calls for efficient automatic methods of text-mining that extract high-level information, such as biomedical events, from biomedical text. The usual computational tools of Natural Language Processing cannot be readily applied to extract these biomedical events, due to the peculiarities of the domain. Indeed, biomedical documents contain highly domain-specific jargon and syntax. These documents also describe distinctive dependencies, making text-mining in molecular biology a specific discipline. Results We address biomedical event extraction as the classification of pairs of text entities into the classes corresponding to event types. The candidate pairs of text entities are recursively provided to a multiclass classifier relying on Support Vector Machines. This recursive process extracts events involving other events as arguments. Compared to joint models based on Markov Random Fields, our model simplifies inference and hence requires shorter training and prediction times along with lower memory capacity. Compared to usual pipeline approaches, our model passes over a complex intermediate problem, while making a more extensive usage of sophisticated joint features between text entities. Our method focuses on the core event extraction of the Genia task of BioNLP challenges yielding the best result reported so far on the 2013 edition. PMID:26201478

Gimli: open source and high-performance biomedical name recognition

PubMed Central

2013-01-01

Background Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. Results We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Conclusions Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli. PMID:23413997

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving (SSLE) of Glass Crystals.

PubMed

Betts, Aislinn M; McGoldrick, Matthew T; Dethlefs, Christopher R; Piotrowicz, Justin; Van Avermaete, Tony; Maki, Jeff; Gerstler, Steve; Leevy, W M

2017-04-25

Biomedical imaging modalities like computed tomography (CT) and magnetic resonance (MR) provide excellent platforms for collecting three-dimensional data sets of patient or specimen anatomy in clinical or preclinical settings. However, the use of a virtual, on-screen display limits the ability of these tomographic images to fully convey the anatomical information embedded within. One solution is to interface a biomedical imaging data set with 3D printing technology to generate a physical replica. Here we detail a complementary method to visualize tomographic imaging data with a hand-held model: Sub Surface Laser Engraving (SSLE) of crystal glass. SSLE offers several unique benefits including: the facile ability to include anatomical labels, as well as a scale bar; streamlined multipart assembly of complex structures in one medium; high resolution in the X, Y, and Z planes; and semi-transparent shells for visualization of internal anatomical substructures. Here we demonstrate the process of SSLE with CT data sets derived from pre-clinical and clinical sources. This protocol will serve as a powerful and inexpensive new tool with which to visualize complex anatomical structures for scientists and students in a number of educational and research settings.

Object-oriented biomedical system modelling--the language.

PubMed

Hakman, M; Groth, T

1999-11-01

The paper describes a new object-oriented biomedical continuous system modelling language (OOBSML). It is fully object-oriented and supports model inheritance, encapsulation, and model component instantiation and behaviour polymorphism. Besides the traditional differential and algebraic equation expressions the language includes also formal expressions for documenting models and defining model quantity types and quantity units. It supports explicit definition of model input-, output- and state quantities, model components and component connections. The OOBSML model compiler produces self-contained, independent, executable model components that can be instantiated and used within other OOBSML models and/or stored within model and model component libraries. In this way complex models can be structured as multilevel, multi-component model hierarchies. Technically the model components produced by the OOBSML compiler are executable computer code objects based on distributed object and object request broker technology. This paper includes both the language tutorial and the formal language syntax and semantic description.

Homeostatis and Complexity as Integrating Tools in Gerontological Education.

ERIC Educational Resources Information Center

Richardson, Daniel; McCulloch, B. Jan; Rowles, Graham D.

2001-01-01

A gerontology doctoral program used the concepts of homeostasis and complexity to present biomedical and psychosocial issues. Data from 14 students showed that homeostasis was more useful for biomedical than psychosocial issues. Complexity helped in understanding interactions between the two. (SK)

The center for causal discovery of biomedical knowledge from big data.

PubMed

Cooper, Gregory F; Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-11-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Utility and translatability of mathematical modeling, cell culture and small and large animal models in magnetic nanoparticle hyperthermia cancer treatment research

NASA Astrophysics Data System (ADS)

Hoopes, P. J.; Petryk, Alicia A.; Misra, Adwiteeya; Kastner, Elliot J.; Pearce, John A.; Ryan, Thomas P.

2015-03-01

For more than 50 years, hyperthermia-based cancer researchers have utilized mathematical models, cell culture studies and animal models to better understand, develop and validate potential new treatments. It has been, and remains, unclear how and to what degree these research techniques depend on, complement and, ultimately, translate accurately to a successful clinical treatment. In the past, when mathematical models have not proven accurate in a clinical treatment situation, the initiating quantitative scientists (engineers, mathematicians and physicists) have tended to believe the biomedical parameters provided to them were inaccurately determined or reported. In a similar manner, experienced biomedical scientists often tend to question the value of mathematical models and cell culture results since those data typically lack the level of biologic and medical variability and complexity that are essential to accurately study and predict complex diseases and subsequent treatments. Such quantitative and biomedical interdependence, variability, diversity and promise have never been greater than they are within magnetic nanoparticle hyperthermia cancer treatment. The use of hyperthermia to treat cancer is well studied and has utilized numerous delivery techniques, including microwaves, radio frequency, focused ultrasound, induction heating, infrared radiation, warmed perfusion liquids (combined with chemotherapy), and, recently, metallic nanoparticles (NP) activated by near infrared radiation (NIR) and alternating magnetic field (AMF) based platforms. The goal of this paper is to use proven concepts and current research to address the potential pathobiology, modeling and quantification of the effects of treatment as pertaining to the similarities and differences in energy delivered by known external delivery techniques and iron oxide nanoparticles.

Harvest: an open platform for developing web-based biomedical data discovery and reporting applications.

PubMed

Pennington, Jeffrey W; Ruth, Byron; Italia, Michael J; Miller, Jeffrey; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; White, Peter S

2014-01-01

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu.

Harvest: an open platform for developing web-based biomedical data discovery and reporting applications

PubMed Central

Pennington, Jeffrey W; Ruth, Byron; Italia, Michael J; Miller, Jeffrey; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; White, Peter S

2014-01-01

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu PMID:24131510

Environmental/Biomedical Terminology Index

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huffstetler, J.K.; Dailey, N.S.; Rickert, L.W.

1976-12-01

The Information Center Complex (ICC), a centrally administered group of information centers, provides information support to environmental and biomedical research groups and others within and outside Oak Ridge National Laboratory. In-house data base building and development of specialized document collections are important elements of the ongoing activities of these centers. ICC groups must be concerned with language which will adequately classify and insure retrievability of document records. Language control problems are compounded when the complexity of modern scientific problem solving demands an interdisciplinary approach. Although there are several word lists, indexes, and thesauri specific to various scientific disciplines usually groupedmore » as Environmental Sciences, no single generally recognized authority can be used as a guide to the terminology of all environmental science. If biomedical terminology for the description of research on environmental effects is also needed, the problem becomes even more complex. The building of a word list which can be used as a general guide to the environmental/biomedical sciences has been a continuing activity of the Information Center Complex. This activity resulted in the publication of the Environmental Biomedical Terminology Index (EBTI).« less

DataMed - an open source discovery index for finding biomedical datasets.

PubMed

Chen, Xiaoling; Gururaj, Anupama E; Ozyurt, Burak; Liu, Ruiling; Soysal, Ergin; Cohen, Trevor; Tiryaki, Firat; Li, Yueling; Zong, Nansu; Jiang, Min; Rogith, Deevakar; Salimi, Mandana; Kim, Hyeon-Eui; Rocca-Serra, Philippe; Gonzalez-Beltran, Alejandra; Farcas, Claudiu; Johnson, Todd; Margolis, Ron; Alter, George; Sansone, Susanna-Assunta; Fore, Ian M; Ohno-Machado, Lucila; Grethe, Jeffrey S; Xu, Hua

2018-01-13

Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain. DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health-funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine. Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community. © The Author 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Convolving engineering and medical pedagogies for training of tomorrow's health care professionals.

PubMed

Lee, Raphael C

2013-03-01

Several fundamental benefits justify why biomedical engineering and medicine should form a more convergent alliance, especially for the training of tomorrow's physicians and biomedical engineers. Herein, we review the rationale underlying the benefits. Biological discovery has advanced beyond the era of molecular biology well into today's era of molecular systems biology, which focuses on understanding the rules that govern the behavior of complex living systems. This has important medical implications. To realize cost-effective personalized medicine, it is necessary to translate the advances in molecular systems biology to higher levels of biological organization (organ, system, and organismal levels) and then to develop new medical therapeutics based on simulation and medical informatics analysis. Higher education in biological and medical sciences must adapt to a new set of training objectives. This will involve a shifting away from reductionist problem solving toward more integrative, continuum, and predictive modeling approaches which traditionally have been more associated with engineering science. Future biomedical engineers and MDs must be able to predict clinical response to therapeutic intervention. Medical education will involve engineering pedagogies, wherein basic governing rules of complex system behavior and skill sets in manipulating these systems to achieve a practical desired outcome are taught. Similarly, graduate biomedical engineering programs will include more practical exposure to clinical problem solving.

Clique-based data mining for related genes in a biomedical database.

PubMed

Matsunaga, Tsutomu; Yonemori, Chikara; Tomita, Etsuji; Muramatsu, Masaaki

2009-07-01

Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. There is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. Here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph. We constructed a graph whose nodes were gene or disease pages, and edges were the hyperlink connections between those pages in the Online Mendelian Inheritance in Man (OMIM) database. We obtained over 20,000 sets of related genes (called 'gene modules') by enumerating cliques computationally. The modules included genes in the same family, genes for proteins that form a complex, and genes for components of the same signaling pathway. The results of experiments using 'metabolic syndrome'-related gene modules show that the gene modules can be used to get a coherent holistic picture helpful for interpreting relations among genes. We presented a data mining approach extracting related genes by enumerating cliques. The extracted gene sets provide a holistic picture useful for comprehending complex disease mechanisms.

[Construction and validation of a three-dimensional finite element model of cranio-maxillary complex with sutures in unilateral cleft lip and palate patient].

PubMed

Wu, Zhi-fang; Lei, Yong-hua; Li, Wen-jie; Liao, Sheng-hui; Zhao, Zi-jin

2013-02-01

To explore an effective method to construct and validate a finite element model of the unilateral cleft lip and palate(UCLP) craniomaxillary complex with sutures, which could be applied in further three-dimensional finite element analysis (FEA). One male patient aged 9 with left complete lip and palate cleft was selected and CT scan was taken at 0.75mm intervals on the skull. The CT data was saved in Dicom format, which was, afterwards, imported into Software Mimics 10.0 to generate a three-dimensional anatomic model. Then Software Geomagic Studio 12.0 was used to match, smoothen and transfer the anatomic model into a CAD model with NURBS patches. Then, 12 circum-maxillary sutures were integrated into the CAD model by Solidworks (2011 version). Finally meshing by E-feature Biomedical Modeler was done and a three-dimensional finite element model with sutures was obtained. A maxillary protraction force (500 g per side, 20° downward and forward from the occlusal plane) was applied. Displacement and stress distribution of some important craniofacial structures were measured and compared with the results of related researches in the literature. A three-dimensional finite element model of UCLP craniomaxillary complex with 12 sutures was established from the CT scan data. This simulation model consisted of 206 753 individual elements with 260 662 nodes, which was a more precise simulation and a better representation of human craniomaxillary complex than the formerly available FEA models. By comparison, this model was proved to be valid. It is an effective way to establish the three-dimensional finite element model of UCLP cranio-maxillary complex with sutures from CT images with the help of the following softwares: Mimics 10.0, Geomagic Studio 12.0, Solidworks and E-feature Biomedical Modeler.

The cost of quality: Implementing generalization and suppression for anonymizing biomedical data with minimal information loss.

PubMed

Kohlmayer, Florian; Prasser, Fabian; Kuhn, Klaus A

2015-12-01

With the ARX data anonymization tool structured biomedical data can be de-identified using syntactic privacy models, such as k-anonymity. Data is transformed with two methods: (a) generalization of attribute values, followed by (b) suppression of data records. The former method results in data that is well suited for analyses by epidemiologists, while the latter method significantly reduces loss of information. Our tool uses an optimal anonymization algorithm that maximizes output utility according to a given measure. To achieve scalability, existing optimal anonymization algorithms exclude parts of the search space by predicting the outcome of data transformations regarding privacy and utility without explicitly applying them to the input dataset. These optimizations cannot be used if data is transformed with generalization and suppression. As optimal data utility and scalability are important for anonymizing biomedical data, we had to develop a novel method. In this article, we first confirm experimentally that combining generalization with suppression significantly increases data utility. Next, we proof that, within this coding model, the outcome of data transformations regarding privacy and utility cannot be predicted. As a consequence, existing algorithms fail to deliver optimal data utility. We confirm this finding experimentally. The limitation of previous work can be overcome at the cost of increased computational complexity. However, scalability is important for anonymizing data with user feedback. Consequently, we identify properties of datasets that may be predicted in our context and propose a novel and efficient algorithm. Finally, we evaluate our solution with multiple datasets and privacy models. This work presents the first thorough investigation of which properties of datasets can be predicted when data is anonymized with generalization and suppression. Our novel approach adopts existing optimization strategies to our context and combines different search methods. The experiments show that our method is able to efficiently solve a broad spectrum of anonymization problems. Our work shows that implementing syntactic privacy models is challenging and that existing algorithms are not well suited for anonymizing data with transformation models which are more complex than generalization alone. As such models have been recommended for use in the biomedical domain, our results are of general relevance for de-identifying structured biomedical data. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving (SSLE) of Glass Crystals

PubMed Central

Dethlefs, Christopher R.; Piotrowicz, Justin; Van Avermaete, Tony; Maki, Jeff; Gerstler, Steve; Leevy, W. M.

2017-01-01

Biomedical imaging modalities like computed tomography (CT) and magnetic resonance (MR) provide excellent platforms for collecting three-dimensional data sets of patient or specimen anatomy in clinical or preclinical settings. However, the use of a virtual, on-screen display limits the ability of these tomographic images to fully convey the anatomical information embedded within. One solution is to interface a biomedical imaging data set with 3D printing technology to generate a physical replica. Here we detail a complementary method to visualize tomographic imaging data with a hand-held model: Sub Surface Laser Engraving (SSLE) of crystal glass. SSLE offers several unique benefits including: the facile ability to include anatomical labels, as well as a scale bar; streamlined multipart assembly of complex structures in one medium; high resolution in the X, Y, and Z planes; and semi-transparent shells for visualization of internal anatomical substructures. Here we demonstrate the process of SSLE with CT data sets derived from pre-clinical and clinical sources. This protocol will serve as a powerful and inexpensive new tool with which to visualize complex anatomical structures for scientists and students in a number of educational and research settings. PMID:28518066

Bioinformatics for transporter pharmacogenomics and systems biology: data integration and modeling with UML.

PubMed

Yan, Qing

2010-01-01

Bioinformatics is the rational study at an abstract level that can influence the way we understand biomedical facts and the way we apply the biomedical knowledge. Bioinformatics is facing challenges in helping with finding the relationships between genetic structures and functions, analyzing genotype-phenotype associations, and understanding gene-environment interactions at the systems level. One of the most important issues in bioinformatics is data integration. The data integration methods introduced here can be used to organize and integrate both public and in-house data. With the volume of data and the high complexity, computational decision support is essential for integrative transporter studies in pharmacogenomics, nutrigenomics, epigenetics, and systems biology. For the development of such a decision support system, object-oriented (OO) models can be constructed using the Unified Modeling Language (UML). A methodology is developed to build biomedical models at different system levels and construct corresponding UML diagrams, including use case diagrams, class diagrams, and sequence diagrams. By OO modeling using UML, the problems of transporter pharmacogenomics and systems biology can be approached from different angles with a more complete view, which may greatly enhance the efforts in effective drug discovery and development. Bioinformatics resources of membrane transporters and general bioinformatics databases and tools that are frequently used in transporter studies are also collected here. An informatics decision support system based on the models presented here is available at http://www.pharmtao.com/transporter . The methodology developed here can also be used for other biomedical fields.

A top-level ontology of functions and its application in the Open Biomedical Ontologies.

PubMed

Burek, Patryk; Hoehndorf, Robert; Loebe, Frank; Visagie, Johann; Herre, Heinrich; Kelso, Janet

2006-07-15

A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the community's knowledge about biological functions. We present here a top-level ontological framework for representing knowledge about biological functions. This framework lends greater accuracy, power and expressiveness to biomedical ontologies by providing a means to capture existing functional knowledge in a more formal manner. An initial major application of the ontology of functions is the provision of a principled way in which to curate functional knowledge and annotations in biomedical ontologies. Further potential applications include the facilitation of ontology interoperability and automated reasoning. A major advantage of the proposed implementation is that it is an extension to existing biomedical ontologies, and can be applied without substantial changes to these domain ontologies. The Ontology of Functions (OF) can be downloaded in OWL format from http://onto.eva.mpg.de/. Additionally, a UML profile and supplementary information and guides for using the OF can be accessed from the same website.

Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis.

PubMed

Aboy, Mateo; Hornero, Roberto; Abásolo, Daniel; Alvarez, Daniel

2006-11-01

Lempel-Ziv complexity (LZ) and derived LZ algorithms have been extensively used to solve information theoretic problems such as coding and lossless data compression. In recent years, LZ has been widely used in biomedical applications to estimate the complexity of discrete-time signals. Despite its popularity as a complexity measure for biosignal analysis, the question of LZ interpretability and its relationship to other signal parameters and to other metrics has not been previously addressed. We have carried out an investigation aimed at gaining a better understanding of the LZ complexity itself, especially regarding its interpretability as a biomedical signal analysis technique. Our results indicate that LZ is particularly useful as a scalar metric to estimate the bandwidth of random processes and the harmonic variability in quasi-periodic signals.

Cardiovascular system simulation in biomedical engineering education.

NASA Technical Reports Server (NTRS)

Rideout, V. C.

1972-01-01

Use of complex cardiovascular system models, in conjunction with a large hybrid computer, in biomedical engineering courses. A cardiovascular blood pressure-flow model, driving a compartment model for the study of dye transport, was set up on the computer for use as a laboratory exercise by students who did not have the computer experience or skill to be able to easily set up such a simulation involving some 27 differential equations running at 'real time' rate. The students were given detailed instructions regarding the model, and were then able to study effects such as those due to septal and valve defects upon the pressure, flow, and dye dilution curves. The success of this experiment in the use of involved models in engineering courses was such that it seems that this type of laboratory exercise might be considered for use in physiology courses as an adjunct to animal experiments.

Addressing the translational dilemma: dynamic knowledge representation of inflammation using agent-based modeling.

PubMed

An, Gary; Christley, Scott

2012-01-01

Given the panoply of system-level diseases that result from disordered inflammation, such as sepsis, atherosclerosis, cancer, and autoimmune disorders, understanding and characterizing the inflammatory response is a key target of biomedical research. Untangling the complex behavioral configurations associated with a process as ubiquitous as inflammation represents a prototype of the translational dilemma: the ability to translate mechanistic knowledge into effective therapeutics. A critical failure point in the current research environment is a throughput bottleneck at the level of evaluating hypotheses of mechanistic causality; these hypotheses represent the key step toward the application of knowledge for therapy development and design. Addressing the translational dilemma will require utilizing the ever-increasing power of computers and computational modeling to increase the efficiency of the scientific method in the identification and evaluation of hypotheses of mechanistic causality. More specifically, development needs to focus on facilitating the ability of non-computer trained biomedical researchers to utilize and instantiate their knowledge in dynamic computational models. This is termed "dynamic knowledge representation." Agent-based modeling is an object-oriented, discrete-event, rule-based simulation method that is well suited for biomedical dynamic knowledge representation. Agent-based modeling has been used in the study of inflammation at multiple scales. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggest that this modeling framework is well suited for addressing the translational dilemma. This review describes agent-based modeling, gives examples of its applications in the study of inflammation, and introduces a proposed general expansion of the use of modeling and simulation to augment the generation and evaluation of knowledge by the biomedical research community at large.

Pigs as laboratory animals

USDA-ARS?s Scientific Manuscript database

The pig is increasingly popular as a laboratory animal either as the target species in its own right or as a model for humans in biomedical science. As an intelligent, social animal it has a complex behavioral repertoire reminiscent of its ancestor, the wild boar. Within a laboratory setting, the pi...

Organization of Heterogeneous Scientific Data Using the EAV/CR Representation

PubMed Central

Nadkarni, Prakash M.; Marenco, Luis; Chen, Roland; Skoufos, Emmanouil; Shepherd, Gordon; Miller, Perry

1999-01-01

Entity-attribute-value (EAV) representation is a means of organizing highly heterogeneous data using a relatively simple physical database schema. EAV representation is widely used in the medical domain, most notably in the storage of data related to clinical patient records. Its potential strengths suggest its use in other biomedical areas, in particular research databases whose schemas are complex as well as constantly changing to reflect evolving knowledge in rapidly advancing scientific domains. When deployed for such purposes, the basic EAV representation needs to be augmented significantly to handle the modeling of complex objects (classes) as well as to manage interobject relationships. The authors refer to their modification of the basic EAV paradigm as EAV/CR (EAV with classes and relationships). They describe EAV/CR representation with examples from two biomedical databases that use it. PMID:10579606

Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine.

PubMed

Aurich, Maike K; Thiele, Ines

2016-01-01

Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model's topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine.

Microfluidic on-chip biomimicry for 3D cell culture: a fit-for-purpose investigation from the end user standpoint

PubMed Central

Liu, Ye; Gill, Elisabeth; Shery Huang, Yan Yan

2017-01-01

A plethora of 3D and microfluidics-based culture models have been demonstrated in the recent years with the ultimate aim to facilitate predictive in vitro models for pharmaceutical development. This article summarizes to date the progress in the microfluidics-based tissue culture models, including organ-on-a-chip and vasculature-on-a-chip. Specific focus is placed on addressing the question of what kinds of 3D culture and system complexities are deemed desirable by the biological and biomedical community. This question is addressed through analysis of a research survey to evaluate the potential use of microfluidic cell culture models among the end users. Our results showed a willingness to adopt 3D culture technology among biomedical researchers, although a significant gap still exists between the desired systems and existing 3D culture options. With these results, key challenges and future directions are highlighted. PMID:28670465

Microfluidic on-chip biomimicry for 3D cell culture: a fit-for-purpose investigation from the end user standpoint.

PubMed

Liu, Ye; Gill, Elisabeth; Shery Huang, Yan Yan

2017-06-01

A plethora of 3D and microfluidics-based culture models have been demonstrated in the recent years with the ultimate aim to facilitate predictive in vitro models for pharmaceutical development. This article summarizes to date the progress in the microfluidics-based tissue culture models, including organ-on-a-chip and vasculature-on-a-chip. Specific focus is placed on addressing the question of what kinds of 3D culture and system complexities are deemed desirable by the biological and biomedical community. This question is addressed through analysis of a research survey to evaluate the potential use of microfluidic cell culture models among the end users. Our results showed a willingness to adopt 3D culture technology among biomedical researchers, although a significant gap still exists between the desired systems and existing 3D culture options. With these results, key challenges and future directions are highlighted.

Building dialogues between clinical and biomedical research through cross-species collaborations.

PubMed

Chao, Hsiao-Tuan; Liu, Lucy; Bellen, Hugo J

2017-10-01

Today, biomedical science is equipped with an impressive array of technologies and genetic resources that bolster our basic understanding of fundamental biology and enhance the practice of modern medicine by providing clinicians with a diverse toolkit to diagnose, prognosticate, and treat a plethora of conditions. Many significant advances in our understanding of disease mechanisms and therapeutic interventions have arisen from fruitful dialogues between clinicians and biomedical research scientists. However, the increasingly specialized scientific and medical disciplines, globalization of science and technology, and complex datasets often hinder the development of effective interdisciplinary collaborations between clinical medicine and biomedical research. The goal of this review is to provide examples of diverse strategies to enhance communication and collaboration across diverse disciplines. First, we discuss examples of efforts to foster interdisciplinary collaborations at institutional and multi-institutional levels. Second, we explore resources and tools for clinicians and research scientists to facilitate effective bi-directional dialogues. Third, we use our experiences in neurobiology and human genetics to highlight how communication between clinical medicine and biomedical research lead to effective implementation of cross-species model organism approaches to uncover the biological underpinnings of health and disease. Copyright © 2017 Elsevier Ltd. All rights reserved.

Applications of systems science in biomedical research regarding obesity and noncommunicable chronic diseases: opportunities, promise, and challenges.

PubMed

Wang, Youfa; Xue, Hong; Liu, Shiyong

2015-01-01

Interest in the application of systems science (SS) in biomedical research, particularly regarding obesity and noncommunicable chronic disease (NCD) research, has been growing rapidly over the past decade. SS is a broad term referring to a family of research approaches that include modeling. As an emerging approach being adopted in public health, SS focuses on the complex dynamic interaction between agents (e.g., people) and subsystems defined at different levels. SS provides a conceptual framework for interdisciplinary and transdisciplinary approaches that address complex problems. SS has unique advantages for studying obesity and NCD problems in comparison to the traditional analytic approaches. The application of SS in biomedical research dates back to the 1960s with the development of computing capacity and simulation software. In recent decades, SS has been applied to addressing the growing global obesity epidemic. There is growing appreciation and support for using SS in the public health field, with many promising opportunities. There are also many challenges and uncertainties, including methodologic, funding, and institutional barriers. Integrated efforts by stakeholders that address these challenges are critical for the successful application of SS in the future. © 2015 American Society for Nutrition.

Anatomy and Physiology of Multiscale Modeling and Simulation in Systems Medicine.

PubMed

Mizeranschi, Alexandru; Groen, Derek; Borgdorff, Joris; Hoekstra, Alfons G; Chopard, Bastien; Dubitzky, Werner

2016-01-01

Systems medicine is the application of systems biology concepts, methods, and tools to medical research and practice. It aims to integrate data and knowledge from different disciplines into biomedical models and simulations for the understanding, prevention, cure, and management of complex diseases. Complex diseases arise from the interactions among disease-influencing factors across multiple levels of biological organization from the environment to molecules. To tackle the enormous challenges posed by complex diseases, we need a modeling and simulation framework capable of capturing and integrating information originating from multiple spatiotemporal and organizational scales. Multiscale modeling and simulation in systems medicine is an emerging methodology and discipline that has already demonstrated its potential in becoming this framework. The aim of this chapter is to present some of the main concepts, requirements, and challenges of multiscale modeling and simulation in systems medicine.

Catalyzing Interdisciplinary Research and Training: Initial Outcomes and Evolution of the Affinity Research Collaboratives Model.

PubMed

Ravid, Katya; Seta, Francesca; Center, David; Waters, Gloria; Coleman, David

2017-10-01

Team science has been recognized as critical to solving increasingly complex biomedical problems and advancing discoveries in the prevention, diagnosis, and treatment of human disease. In 2009, the Evans Center for Interdisciplinary Biomedical Research (ECIBR) was established in the Department of Medicine at Boston University School of Medicine as a new organizational paradigm to promote interdisciplinary team science. The ECIBR is made up of affinity research collaboratives (ARCs), consisting of investigators from different departments and disciplines who come together to study biomedical problems that are relevant to human disease and not under interdisciplinary investigation at the university. Importantly, research areas are identified by investigators according to their shared interests. ARC proposals are evaluated by a peer review process, and collaboratives are funded annually for up to three years.Initial outcomes of the first 12 ARCs show the value of this model in fostering successful biomedical collaborations that lead to publications, extramural grants, research networking, and training. The most successful ARCs have been developed into more sustainable organizational entities, including centers, research cores, translational research projects, and training programs.To further expand team science at Boston University, the Interdisciplinary Biomedical Research Office was established in 2015 to more fully engage the entire university, not just the medical campus, in interdisciplinary research using the ARC mechanism. This approach to promoting team science may be useful to other academic organizations seeking to expand interdisciplinary research at their institutions.

Ethical considerations for biomedical scientists and engineers: issues for the rank and file.

PubMed

Kwarteng, K B

2000-01-01

Biomedical science and engineering is inextricably linked with the fields of medicine and surgery. Yet, while physicians and surgeons, nurses, and other medical professionals receive instruction in ethics during their training and must abide by certain codes of ethics during their practice, those engaged in biomedical science and engineering typically receive no formal training in ethics. In fact, the little contact that many biomedical science and engineering professionals have with ethics occurs either when they participate in government-funded research or submit articles for publication in certain journals. Thus, there is a need for biomedical scientists and engineers as a group to become more aware of ethics. Moreover, recent advances in biomedical technology and the ever-increasing use of new devices virtually guarantee that biomedical science and engineering will become even more important in the future. Although they are rarely in direct contact with patients, biomedical scientists and engineers must become aware of ethics in order to be able to deal with the complex ethical issues that arise from our society's increasing reliance on biomedical technology. In this brief communication, the need for ethical awareness among workers in biomedical science and engineering is discussed in terms of certain conflicts that arise in the workaday world of the biomedical scientist in a complex, modern society. It is also recognized that inasmuch as workers in the many branches of bioengineering are not regulated like their counterparts in medicine and surgery, perhaps academic institutions and professional societies are best equipped to heighten ethical awareness among workers in this important field.

Opal web services for biomedical applications.

PubMed

Ren, Jingyuan; Williams, Nadya; Clementi, Luca; Krishnan, Sriram; Li, Wilfred W

2010-07-01

Biomedical applications have become increasingly complex, and they often require large-scale high-performance computing resources with a large number of processors and memory. The complexity of application deployment and the advances in cluster, grid and cloud computing require new modes of support for biomedical research. Scientific Software as a Service (sSaaS) enables scalable and transparent access to biomedical applications through simple standards-based Web interfaces. Towards this end, we built a production web server (http://ws.nbcr.net) in August 2007 to support the bioinformatics application called MEME. The server has grown since to include docking analysis with AutoDock and AutoDock Vina, electrostatic calculations using PDB2PQR and APBS, and off-target analysis using SMAP. All the applications on the servers are powered by Opal, a toolkit that allows users to wrap scientific applications easily as web services without any modification to the scientific codes, by writing simple XML configuration files. Opal allows both web forms-based access and programmatic access of all our applications. The Opal toolkit currently supports SOAP-based Web service access to a number of popular applications from the National Biomedical Computation Resource (NBCR) and affiliated collaborative and service projects. In addition, Opal's programmatic access capability allows our applications to be accessed through many workflow tools, including Vision, Kepler, Nimrod/K and VisTrails. From mid-August 2007 to the end of 2009, we have successfully executed 239,814 jobs. The number of successfully executed jobs more than doubled from 205 to 411 per day between 2008 and 2009. The Opal-enabled service model is useful for a wide range of applications. It provides for interoperation with other applications with Web Service interfaces, and allows application developers to focus on the scientific tool and workflow development. Web server availability: http://ws.nbcr.net.

"Speaking the dialect": understanding public discourse in the aftermath of an HIV vaccine trial shutdown.

PubMed

Newman, Peter A; Logie, Carmen; James, Llana; Charles, Tamicka; Maxwell, John; Salam, Khaled; Woodford, Michael

2011-09-01

We investigated how persons from key populations at higher risk of HIV exposure interpreted the process and outcomes of the Step Study HIV-1 vaccine trial, which was terminated early, and implications for willingness to participate in and community support for HIV vaccine research. We used qualitative methods and a community-based approach in 9 focus groups (n = 72) among ethnically and sexually diverse populations and 6 semistructured key informant interviews in Ontario, Canada, in 2007 to 2008. Participants construed social meaning from complex clinical and biomedical phenomena. Social representations and mental models emerged in fears of vaccine-induced infection, conceptualizations of unfair recruitment practices and increased risk behaviors among trial participants, and questioning of informed consent. Narratives of altruism and the common good demonstrated support for future trials. Public discourse on HIV vaccine trials is a productive means of interpreting complex clinical trial processes and outcomes in the context of existing beliefs and experiences regarding HIV vaccines, medical research, and historical disenfranchisement. Strategic engagement with social representations and mental models may promote meaningful community involvement in biomedical HIV prevention research.

The normative dimensions of extending the human lifespan by age-related biomedical innovations.

PubMed

Ehni, Hans-Joerg; Marckmann, Georg

2008-10-01

The current normative debate on age-related biomedical innovations and the extension of the human lifespan has important shortcomings. Mainly, the complexity of the different normative dimensions relevant for ethical and/or juridicial norms is not fully developed and the normative quality of teleological and deontological arguments is not properly distinguished. This article addresses some of these shortcomings and develops the outline of a more comprehensive normative framework covering all relevant dimensions. Such a frame necessarily has to include conceptions of a good life on the individual and societal levels. Furthermore, as a third dimension, a model for the access to and the just distribution of age-related biomedical innovations and technologies extending the human lifespan will be developed. It is argued that such a model has to include the different levels of the general philosophical theories of distributive justice, including social rights and theories of just health care. Furthermore, it has to show how these theories can be applied to the problem area of aging and extending the human lifespan.

Creation of anatomical models from CT data

NASA Astrophysics Data System (ADS)

Alaytsev, Innokentiy K.; Danilova, Tatyana V.; Manturov, Alexey O.; Mareev, Gleb O.; Mareev, Oleg V.

2018-04-01

Computed tomography is a great source of biomedical data because it allows a detailed exploration of complex anatomical structures. Some structures are not visible on CT scans, and some are hard to distinguish due to partial volume effect. CT datasets require preprocessing before using them as anatomical models in a simulation system. The work describes segmentation and data transformation methods for an anatomical model creation from the CT data. The result models may be used for visual and haptic rendering and drilling simulation in a virtual surgery system.

Twitter K-H networks in action: Advancing biomedical literature for drug search.

PubMed

Hamed, Ahmed Abdeen; Wu, Xindong; Erickson, Robert; Fandy, Tamer

2015-08-01

The importance of searching biomedical literature for drug interaction and side-effects is apparent. Current digital libraries (e.g., PubMed) suffer infrequent tagging and metadata annotation updates. Such limitations cause absence of linking literature to new scientific evidence. This demonstrates a great deal of challenges that stand in the way of scientists when searching biomedical repositories. In this paper, we present a network mining approach that provides a bridge for linking and searching drug-related literature. Our contributions here are two fold: (1) an efficient algorithm called HashPairMiner to address the run-time complexity issues demonstrated in its predecessor algorithm: HashnetMiner, and (2) a database of discoveries hosted on the web to facilitate literature search using the results produced by HashPairMiner. Though the K-H network model and the HashPairMiner algorithm are fairly young, their outcome is evidence of the considerable promise they offer to the biomedical science community in general and the drug research community in particular. Copyright © 2015 Elsevier Inc. All rights reserved.

Biomedical Applications of Organometal-Peptide Conjugates

NASA Astrophysics Data System (ADS)

Metzler-Nolte, Nils

Peptides are well suited as targeting vectors for the directed delivery of metal-based drugs or probes for biomedical investigations. This chapter describes synthetic strategies for the preparation of conjugates of medically interesting peptides with covalently bound metal complexes. Peptides that were used include neuropeptides (enkephalin, neuropeptide Y, neurotensin), uptake peptides (TAT and poly-Arg), and intracellular localization sequences. To these peptides, a whole variety of transition metal complexes has been attached in recent years by solid-phase peptide synthesis (SPPS) techniques. The metal complex can be attached to the peptide on the resin as part of the SPPS scheme. Alternatively, the metal complex may be attached to the peptide as a postsynthetic modification. Advantages as well as disadvantages for either strategy are discussed. Biomedical applications include radiopharmaceutical applications, anticancer and antibacterial activity, metal-peptide conjugates as targeted CO-releasing molecules, and metal-peptide conjugates in biosensor applications.

Clinical Complexity in Medicine: A Measurement Model of Task and Patient Complexity.

PubMed

Islam, R; Weir, C; Del Fiol, G

2016-01-01

Complexity in medicine needs to be reduced to simple components in a way that is comprehensible to researchers and clinicians. Few studies in the current literature propose a measurement model that addresses both task and patient complexity in medicine. The objective of this paper is to develop an integrated approach to understand and measure clinical complexity by incorporating both task and patient complexity components focusing on the infectious disease domain. The measurement model was adapted and modified for the healthcare domain. Three clinical infectious disease teams were observed, audio-recorded and transcribed. Each team included an infectious diseases expert, one infectious diseases fellow, one physician assistant and one pharmacy resident fellow. The transcripts were parsed and the authors independently coded complexity attributes. This baseline measurement model of clinical complexity was modified in an initial set of coding processes and further validated in a consensus-based iterative process that included several meetings and email discussions by three clinical experts from diverse backgrounds from the Department of Biomedical Informatics at the University of Utah. Inter-rater reliability was calculated using Cohen's kappa. The proposed clinical complexity model consists of two separate components. The first is a clinical task complexity model with 13 clinical complexity-contributing factors and 7 dimensions. The second is the patient complexity model with 11 complexity-contributing factors and 5 dimensions. The measurement model for complexity encompassing both task and patient complexity will be a valuable resource for future researchers and industry to measure and understand complexity in healthcare.

Finite element models of the human shoulder complex: a review of their clinical implications and modelling techniques.

PubMed

Zheng, Manxu; Zou, Zhenmin; Bartolo, Paulo Jorge Da Silva; Peach, Chris; Ren, Lei

2017-02-01

The human shoulder is a complicated musculoskeletal structure and is a perfect compromise between mobility and stability. The objective of this paper is to provide a thorough review of previous finite element (FE) studies in biomechanics of the human shoulder complex. Those FE studies to investigate shoulder biomechanics have been reviewed according to the physiological and clinical problems addressed: glenohumeral joint stability, rotator cuff tears, joint capsular and labral defects and shoulder arthroplasty. The major findings, limitations, potential clinical applications and modelling techniques of those FE studies are critically discussed. The main challenges faced in order to accurately represent the realistic physiological functions of the shoulder mechanism in FE simulations involve (1) subject-specific representation of the anisotropic nonhomogeneous material properties of the shoulder tissues in both healthy and pathological conditions; (2) definition of boundary and loading conditions based on individualised physiological data; (3) more comprehensive modelling describing the whole shoulder complex including appropriate three-dimensional (3D) representation of all major shoulder hard tissues and soft tissues and their delicate interactions; (4) rigorous in vivo experimental validation of FE simulation results. Fully validated shoulder FE models would greatly enhance our understanding of the aetiology of shoulder disorders, and hence facilitate the development of more efficient clinical diagnoses, non-surgical and surgical treatments, as well as shoulder orthotics and prosthetics. © 2016 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd. © 2016 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.

Complexity and multifractality of neuronal noise in mouse and human hippocampal epileptiform dynamics

NASA Astrophysics Data System (ADS)

Serletis, Demitre; Bardakjian, Berj L.; Valiante, Taufik A.; Carlen, Peter L.

2012-10-01

Fractal methods offer an invaluable means of investigating turbulent nonlinearity in non-stationary biomedical recordings from the brain. Here, we investigate properties of complexity (i.e. the correlation dimension, maximum Lyapunov exponent, 1/fγ noise and approximate entropy) and multifractality in background neuronal noise-like activity underlying epileptiform transitions recorded at the intracellular and local network scales from two in vitro models: the whole-intact mouse hippocampus and lesional human hippocampal slices. Our results show evidence for reduced dynamical complexity and multifractal signal features following transition to the ictal epileptiform state. These findings suggest that pathological breakdown in multifractal complexity coincides with loss of signal variability or heterogeneity, consistent with an unhealthy ictal state that is far from the equilibrium of turbulent yet healthy fractal dynamics in the brain. Thus, it appears that background noise-like activity successfully captures complex and multifractal signal features that may, at least in part, be used to classify and identify brain state transitions in the healthy and epileptic brain, offering potential promise for therapeutic neuromodulatory strategies for afflicted patients suffering from epilepsy and other related neurological disorders. This paper is based on chapter 5 of Serletis (2010 PhD Dissertation Department of Physiology, Institute of Biomaterials and Biomedical Engineering, University of Toronto).

Advances in electronic-nose technologies developed for biomedical applications

Treesearch

Dan Wilson; Manuela Baietto

2011-01-01

The research and development of new electronic-nose applications in the biomedical field has accelerated at a phenomenal rate over the past 25 years. Many innovative e-nose technologies have provided solutions and applications to a wide variety of complex biomedical and healthcare problems. The purposes of this review are to present a comprehensive analysis of past and...

Calixarenes in bio-medical researches.

PubMed

Rodik, Roman V; Boyko, Vyacheslav I; Kalchenko, Vitaly I

2009-01-01

Application of calixarene derivatives in bio-medical researches is reviewed in this article. Antiviral, bactericidal, antithrombothic, antituberculosis, anticancer activity as well as specific protein complexation, membranotropic properties and toxicity of modified calixarenes are discussed.

Unsupervised Structure Detection in Biomedical Data.

PubMed

Vogt, Julia E

2015-01-01

A major challenge in computational biology is to find simple representations of high-dimensional data that best reveal the underlying structure. In this work, we present an intuitive and easy-to-implement method based on ranked neighborhood comparisons that detects structure in unsupervised data. The method is based on ordering objects in terms of similarity and on the mutual overlap of nearest neighbors. This basic framework was originally introduced in the field of social network analysis to detect actor communities. We demonstrate that the same ideas can successfully be applied to biomedical data sets in order to reveal complex underlying structure. The algorithm is very efficient and works on distance data directly without requiring a vectorial embedding of data. Comprehensive experiments demonstrate the validity of this approach. Comparisons with state-of-the-art clustering methods show that the presented method outperforms hierarchical methods as well as density based clustering methods and model-based clustering. A further advantage of the method is that it simultaneously provides a visualization of the data. Especially in biomedical applications, the visualization of data can be used as a first pre-processing step when analyzing real world data sets to get an intuition of the underlying data structure. We apply this model to synthetic data as well as to various biomedical data sets which demonstrate the high quality and usefulness of the inferred structure.

Spatiotemporal integration of molecular and anatomical data in virtual reality using semantic mapping.

PubMed

Soh, Jung; Turinsky, Andrei L; Trinh, Quang M; Chang, Jasmine; Sabhaney, Ajay; Dong, Xiaoli; Gordon, Paul Mk; Janzen, Ryan Pw; Hau, David; Xia, Jianguo; Wishart, David S; Sensen, Christoph W

2009-01-01

We have developed a computational framework for spatiotemporal integration of molecular and anatomical datasets in a virtual reality environment. Using two case studies involving gene expression data and pharmacokinetic data, respectively, we demonstrate how existing knowledge bases for molecular data can be semantically mapped onto a standardized anatomical context of human body. Our data mapping methodology uses ontological representations of heterogeneous biomedical datasets and an ontology reasoner to create complex semantic descriptions of biomedical processes. This framework provides a means to systematically combine an increasing amount of biomedical imaging and numerical data into spatiotemporally coherent graphical representations. Our work enables medical researchers with different expertise to simulate complex phenomena visually and to develop insights through the use of shared data, thus paving the way for pathological inference, developmental pattern discovery and biomedical hypothesis testing.

Multiscale information modelling for heart morphogenesis

NASA Astrophysics Data System (ADS)

Abdulla, T.; Imms, R.; Schleich, J. M.; Summers, R.

2010-07-01

Science is made feasible by the adoption of common systems of units. As research has become more data intensive, especially in the biomedical domain, it requires the adoption of a common system of information models, to make explicit the relationship between one set of data and another, regardless of format. This is being realised through the OBO Foundry to develop a suite of reference ontologies, and NCBO Bioportal to provide services to integrate biomedical resources and functionality to visualise and create mappings between ontology terms. Biomedical experts tend to be focused at one level of spatial scale, be it biochemistry, cell biology, or anatomy. Likewise, the ontologies they use tend to be focused at a particular level of scale. There is increasing interest in a multiscale systems approach, which attempts to integrate between different levels of scale to gain understanding of emergent effects. This is a return to physiological medicine with a computational emphasis, exemplified by the worldwide Physiome initiative, and the European Union funded Network of Excellence in the Virtual Physiological Human. However, little work has been done on how information modelling itself may be tailored to a multiscale systems approach. We demonstrate how this can be done for the complex process of heart morphogenesis, which requires multiscale understanding in both time and spatial domains. Such an effort enables the integration of multiscale metrology.

S3DB core: a framework for RDF generation and management in bioinformatics infrastructures

PubMed Central

2010-01-01

Background Biomedical research is set to greatly benefit from the use of semantic web technologies in the design of computational infrastructure. However, beyond well defined research initiatives, substantial issues of data heterogeneity, source distribution, and privacy currently stand in the way towards the personalization of Medicine. Results A computational framework for bioinformatic infrastructure was designed to deal with the heterogeneous data sources and the sensitive mixture of public and private data that characterizes the biomedical domain. This framework consists of a logical model build with semantic web tools, coupled with a Markov process that propagates user operator states. An accompanying open source prototype was developed to meet a series of applications that range from collaborative multi-institution data acquisition efforts to data analysis applications that need to quickly traverse complex data structures. This report describes the two abstractions underlying the S3DB-based infrastructure, logical and numerical, and discusses its generality beyond the immediate confines of existing implementations. Conclusions The emergence of the "web as a computer" requires a formal model for the different functionalities involved in reading and writing to it. The S3DB core model proposed was found to address the design criteria of biomedical computational infrastructure, such as those supporting large scale multi-investigator research, clinical trials, and molecular epidemiology. PMID:20646315

Tackling the challenges of matching biomedical ontologies.

PubMed

Faria, Daniel; Pesquita, Catia; Mott, Isabela; Martins, Catarina; Couto, Francisco M; Cruz, Isabel F

2018-01-15

Biomedical ontologies pose several challenges to ontology matching due both to the complexity of the biomedical domain and to the characteristics of the ontologies themselves. The biomedical tracks in the Ontology Matching Evaluation Initiative (OAEI) have spurred the development of matching systems able to tackle these challenges, and benchmarked their general performance. In this study, we dissect the strategies employed by matching systems to tackle the challenges of matching biomedical ontologies and gauge the impact of the challenges themselves on matching performance, using the AgreementMakerLight (AML) system as the platform for this study. We demonstrate that the linear complexity of the hash-based searching strategy implemented by most state-of-the-art ontology matching systems is essential for matching large biomedical ontologies efficiently. We show that accounting for all lexical annotations (e.g., labels and synonyms) in biomedical ontologies leads to a substantial improvement in F-measure over using only the primary name, and that accounting for the reliability of different types of annotations generally also leads to a marked improvement. Finally, we show that cross-references are a reliable source of information and that, when using biomedical ontologies as background knowledge, it is generally more reliable to use them as mediators than to perform lexical expansion. We anticipate that translating traditional matching algorithms to the hash-based searching paradigm will be a critical direction for the future development of the field. Improving the evaluation carried out in the biomedical tracks of the OAEI will also be important, as without proper reference alignments there is only so much that can be ascertained about matching systems or strategies. Nevertheless, it is clear that, to tackle the various challenges posed by biomedical ontologies, ontology matching systems must be able to efficiently combine multiple strategies into a mature matching approach.

[Thoughts on the complex relationship between medicine and animals: a death prayer for a loyal cat].

PubMed

Cabello C, Felipe

2013-11-01

From its basis in the writings of the philosopher Peter Singer and the bioethical shortcomings of animal experimentation and animal husbandry, the animal rights movement has evolved into an important societal movement critical of animal experimentation in biomedical research. A lack of dialogue and transparency, an absence of understanding and an unreasonable radicalization of different positions regarding animal experimentation has frequently resulted in an adversarial relationship between some members of the scientific community and societal groups aggressively protecting animal rights. In response to this problem, both the bioethical regulations pertaining to biomedical experimentation with animals and the powers of animal care committees (IACUCs) have been strengthened. Careful analysis of the relevance of animal models to human conditions, replacement of these models with non-animal models when possible, adequate re-examination of existing knowledge before undertaking new experimental projects involving animals, and the improvement of methods to avoid animal stress and pain have further strengthened the bioethical basis of animal experimentation. To improve the ethical integrity of research conducted with animals, it is also necessary to increase the editorial scrutiny of the bioethical standards of potentially publishable research utilizing animals. Of note is also the recent use of animals in alternative animal associated therapies (AAT) to ameliorate several medical conditions. Education of the biomedical community, including students and professionals, and of societal groups concerned about this issue as well as directness and continuous dialogue among all the stakeholders are essential to insure the wellbeing of animals and the ethical integrity of biomedical research.

Combining biomedical preventions for HIV: Vaccines with pre-exposure prophylaxis, microbicides or other HIV preventions

PubMed Central

McNicholl, Janet M.

2016-01-01

ABSTRACT Biomedical preventions for HIV, such as vaccines, microbicides or pre-exposure prophylaxis (PrEP) with antiretroviral drugs, can each only partially prevent HIV-1 infection in most human trials. Oral PrEP is now FDA approved for HIV-prevention in high risk groups, but partial adherence reduces efficacy. If combined as biomedical preventions (CBP) an HIV vaccine could provide protection when PrEP adherence is low and PrEP could prevent vaccine breakthroughs. Other types of PrEP or microbicides may also be partially protective. When licensed, first generation HIV vaccines are likely to be partially effective. Individuals at risk for HIV may receive an HIV vaccine combined with other biomedical preventions, in series or in parallel, in clinical trials or as part of standard of care, with the goal of maximally increasing HIV prevention. In human studies, it is challenging to determine which preventions are best combined, how they interact and how effective they are. Animal models can determine CBP efficacy, whether additive or synergistic, the efficacy of different products and combinations, dose, timing and mechanisms. CBP studies in macaques have shown that partially or minimally effective candidate HIV vaccines combined with partially effective oral PrEP, vaginal PrEP or microbicide generally provided greater protection than either prevention alone against SIV or SHIV challenges. Since human CBP trials will be complex, animal models can guide their design, sample size, endpoints, correlates and surrogates of protection. This review focuses on animal studies and human models of CBP and discusses implications for HIV prevention. PMID:27679928

Combining biomedical preventions for HIV: Vaccines with pre-exposure prophylaxis, microbicides or other HIV preventions.

PubMed

McNicholl, Janet M

2016-12-01

Biomedical preventions for HIV, such as vaccines, microbicides or pre-exposure prophylaxis (PrEP) with antiretroviral drugs, can each only partially prevent HIV-1 infection in most human trials. Oral PrEP is now FDA approved for HIV-prevention in high risk groups, but partial adherence reduces efficacy. If combined as biomedical preventions (CBP) an HIV vaccine could provide protection when PrEP adherence is low and PrEP could prevent vaccine breakthroughs. Other types of PrEP or microbicides may also be partially protective. When licensed, first generation HIV vaccines are likely to be partially effective. Individuals at risk for HIV may receive an HIV vaccine combined with other biomedical preventions, in series or in parallel, in clinical trials or as part of standard of care, with the goal of maximally increasing HIV prevention. In human studies, it is challenging to determine which preventions are best combined, how they interact and how effective they are. Animal models can determine CBP efficacy, whether additive or synergistic, the efficacy of different products and combinations, dose, timing and mechanisms. CBP studies in macaques have shown that partially or minimally effective candidate HIV vaccines combined with partially effective oral PrEP, vaginal PrEP or microbicide generally provided greater protection than either prevention alone against SIV or SHIV challenges. Since human CBP trials will be complex, animal models can guide their design, sample size, endpoints, correlates and surrogates of protection. This review focuses on animal studies and human models of CBP and discusses implications for HIV prevention.

From Nonclinical Research to Clinical Trials and Patient-registries: Challenges and Opportunities in Biomedical Research

PubMed Central

de la Torre Hernández, José M.; Edelman, Elazer R.

2018-01-01

The most important challenge faced by human beings is health. The only way to provide better solutions for health care is innovation, true innovation. The only source of true innovation is research, good research indeed. The pathway from a basic science study to a randomized clinical trial is long and not free of bumps and even landmines. These are all the obstacles and barriers that limit the availability of resources, entangle administrative-regulatory processes, and restrain investigators’ initiatives. There is increasing demand for evidence to guide clinical practice but, paradoxically, biomedical research has become increasingly complex, expensive, and difficult to integrate into clinical care with increased barriers to performing the practical aspects of investigation. We face the challenge of increasing the volume of biomedical research and simultaneously improving the efficiency and output of this research. In this article, we review the main stages and methods of biomedical research, from nonclinical studies with animal and computational models to randomized trials and clinical registries, focusing on their limitations and challenges, but also providing alternative solutions to overcome them. Fortunately, challenges are always opportunities in disguise. PMID:28838647

Developments in the use of rare earth metal complexes as efficient catalysts for ring-opening polymerization of cyclic esters used in biomedical applications

NASA Astrophysics Data System (ADS)

Cota, Iuliana

2017-04-01

Biodegradable polymers represent a class of particularly useful materials for many biomedical and pharmaceutical applications. Among these types of polyesters, poly(ɛ-caprolactone) and polylactides are considered very promising for controlled drug delivery devices. These polymers are mainly produced by ring-opening polymerization of their respective cyclic esters, since this method allows a strict control of the molecular parameters (molecular weight and distribution) of the obtained polymers. The most widely used catalysts for ring-opening polymerization of cyclic esters are tin- and aluminium-based organometallic complexes; however since the contamination of the aliphatic polyesters by potentially toxic metallic residues is particularly of concern for biomedical applications, the possibility of replacing organometallic initiators by novel less toxic or more efficient organometallic complexes has been intensively studied. Thus, in the recent years, the use of highly reactive rare earth initiators/catalysts leading to lower polymer contamination has been developed. The use of rare earth complexes is considered a valuable strategy to decrease the polyester contamination by metallic residues and represents an attractive alternative to traditional organometallic complexes.

“Speaking the Dialect”: Understanding Public Discourse in the Aftermath of an HIV Vaccine Trial Shutdown

PubMed Central

Logie, Carmen; James, LLana; Charles, Tamicka; Maxwell, John; Salam, Khaled; Woodford, Michael

2011-01-01

Objectives. We investigated how persons from key populations at higher risk of HIV exposure interpreted the process and outcomes of the Step Study HIV-1 vaccine trial, which was terminated early, and implications for willingness to participate in and community support for HIV vaccine research. Methods. We used qualitative methods and a community-based approach in 9 focus groups (n = 72) among ethnically and sexually diverse populations and 6 semistructured key informant interviews in Ontario, Canada, in 2007 to 2008. Results. Participants construed social meaning from complex clinical and biomedical phenomena. Social representations and mental models emerged in fears of vaccine-induced infection, conceptualizations of unfair recruitment practices and increased risk behaviors among trial participants, and questioning of informed consent. Narratives of altruism and the common good demonstrated support for future trials. Conclusions. Public discourse on HIV vaccine trials is a productive means of interpreting complex clinical trial processes and outcomes in the context of existing beliefs and experiences regarding HIV vaccines, medical research, and historical disenfranchisement. Strategic engagement with social representations and mental models may promote meaningful community involvement in biomedical HIV prevention research. PMID:21778490

Computer-based learning in neuroanatomy: A longitudinal study of learning, transfer, and retention

NASA Astrophysics Data System (ADS)

Chariker, Julia H.

A longitudinal experiment was conducted to explore computer-based learning of neuroanatomy. Using a realistic 3D graphical model of neuroanatomy, and sections derived from the model, exploratory graphical tools were integrated into interactive computer programs so as to allow adaptive exploration. 72 participants learned either sectional anatomy alone or learned whole anatomy followed by sectional anatomy. Sectional anatomy was explored either in perceptually continuous animation or discretely, as in the use of an anatomical atlas. Learning was measured longitudinally to a high performance criterion. After learning, transfer to biomedical images and long-term retention was tested. Learning whole anatomy prior to learning sectional anatomy led to a more efficient learning experience. Learners demonstrated high levels of transfer from whole anatomy to sectional anatomy and from sectional anatomy to complex biomedical images. All learning groups demonstrated high levels of retention at 2--3 weeks.

Considerations for Reporting Finite Element Analysis Studies in Biomechanics

PubMed Central

Erdemir, Ahmet; Guess, Trent M.; Halloran, Jason; Tadepalli, Srinivas C.; Morrison, Tina M.

2012-01-01

Simulation-based medicine and the development of complex computer models of biological structures is becoming ubiquitous for advancing biomedical engineering and clinical research. Finite element analysis (FEA) has been widely used in the last few decades to understand and predict biomechanical phenomena. Modeling and simulation approaches in biomechanics are highly interdisciplinary, involving novice and skilled developers in all areas of biomedical engineering and biology. While recent advances in model development and simulation platforms offer a wide range of tools to investigators, the decision making process during modeling and simulation has become more opaque. Hence, reliability of such models used for medical decision making and for driving multiscale analysis comes into question. Establishing guidelines for model development and dissemination is a daunting task, particularly with the complex and convoluted models used in FEA. Nonetheless, if better reporting can be established, researchers will have a better understanding of a model’s value and the potential for reusability through sharing will be bolstered. Thus, the goal of this document is to identify resources and considerate reporting parameters for FEA studies in biomechanics. These entail various levels of reporting parameters for model identification, model structure, simulation structure, verification, validation, and availability. While we recognize that it may not be possible to provide and detail all of the reporting considerations presented, it is possible to establish a level of confidence with selective use of these parameters. More detailed reporting, however, can establish an explicit outline of the decision-making process in simulation-based analysis for enhanced reproducibility, reusability, and sharing. PMID:22236526

LOCAL ORTHOGONAL CUTTING METHOD FOR COMPUTING MEDIAL CURVES AND ITS BIOMEDICAL APPLICATIONS

PubMed Central

Einstein, Daniel R.; Dyedov, Vladimir

2010-01-01

Medial curves have a wide range of applications in geometric modeling and analysis (such as shape matching) and biomedical engineering (such as morphometry and computer assisted surgery). The computation of medial curves poses significant challenges, both in terms of theoretical analysis and practical efficiency and reliability. In this paper, we propose a definition and analysis of medial curves and also describe an efficient and robust method called local orthogonal cutting (LOC) for computing medial curves. Our approach is based on three key concepts: a local orthogonal decomposition of objects into substructures, a differential geometry concept called the interior center of curvature (ICC), and integrated stability and consistency tests. These concepts lend themselves to robust numerical techniques and result in an algorithm that is efficient and noise resistant. We illustrate the effectiveness and robustness of our approach with some highly complex, large-scale, noisy biomedical geometries derived from medical images, including lung airways and blood vessels. We also present comparisons of our method with some existing methods. PMID:20628546

Microfluidic 3D models of cancer

PubMed Central

Sung, Kyung Eun; Beebe, David J.

2014-01-01

Despite advances in medicine and biomedical sciences, cancer still remains a major health issue. Complex interactions between tumors and their microenvironment contribute to tumor initiation and progression and also contribute to the development of drug resistant tumor cell populations. The complexity and heterogeneity of tumors and their microenvironment make it challenging to both study and treat cancer. Traditional animal cancer models and in vitro cancer models are limited in their ability to recapitulate human structures and functions, thus hindering the identification of appropriate drug targets and therapeutic strategies. The development and application of microfluidic 3D cancer models has the potential to overcome some of the limitations inherent to traditional models. This review summarizes the progress in microfluidic 3D cancer models, their benefits, and their broad application to basic cancer biology, drug screening, and drug discovery. PMID:25017040

A hybrid model based on neural networks for biomedical relation extraction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Zhang, Shaowu; Sun, Yuanyuan; Yang, Liang

2018-05-01

Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two major neural network models for biomedical relation extraction. Neural network-based methods for biomedical relation extraction typically focus on the sentence sequence and employ RNNs or CNNs to learn the latent features from sentence sequences separately. However, RNNs and CNNs have their own advantages for biomedical relation extraction. Combining RNNs and CNNs may improve biomedical relation extraction. In this paper, we present a hybrid model for the extraction of biomedical relations that combines RNNs and CNNs. First, the shortest dependency path (SDP) is generated based on the dependency graph of the candidate sentence. To make full use of the SDP, we divide the SDP into a dependency word sequence and a relation sequence. Then, RNNs and CNNs are employed to automatically learn the features from the sentence sequence and the dependency sequences, respectively. Finally, the output features of the RNNs and CNNs are combined to detect and extract biomedical relations. We evaluate our hybrid model using five public (protein-protein interaction) PPI corpora and a (drug-drug interaction) DDI corpus. The experimental results suggest that the advantages of RNNs and CNNs in biomedical relation extraction are complementary. Combining RNNs and CNNs can effectively boost biomedical relation extraction performance. Copyright © 2018 Elsevier Inc. All rights reserved.

Mapping annotations with textual evidence using an scLDA model.

PubMed

Jin, Bo; Chen, Vicky; Chen, Lujia; Lu, Xinghua

2011-01-01

Most of the knowledge regarding genes and proteins is stored in biomedical literature as free text. Extracting information from complex biomedical texts demands techniques capable of inferring biological concepts from local text regions and mapping them to controlled vocabularies. To this end, we present a sentence-based correspondence latent Dirichlet allocation (scLDA) model which, when trained with a corpus of PubMed documents with known GO annotations, performs the following tasks: 1) learning major biological concepts from the corpus, 2) inferring the biological concepts existing within text regions (sentences), and 3) identifying the text regions in a document that provides evidence for the observed annotations. When applied to new gene-related documents, a trained scLDA model is capable of predicting GO annotations and identifying text regions as textual evidence supporting the predicted annotations. This study uses GO annotation data as a testbed; the approach can be generalized to other annotated data, such as MeSH and MEDLINE documents.

Are Behavioural Interventions Doomed to Fail? Challenges to Self-Management Support in Chronic Diseases.

PubMed

Vallis, Michael

2015-08-01

Self-management and self-management support are concepts very familiar to those of us in diabetes care. These concepts require openness to understanding the behaviours of persons with diabetes broadly, not only behaviours restricted to the biomedical perspective. Understanding the importance of health behaviour change and working within the Expanded Chronic Care Model define the context within which self-management support should occur. The purpose of this perspective is to identify a potential limitation in existing self-management support initiatives. This potential limitation reflects provider issues, not patient issues; that is, true self-management support might require changes by healthcare providers. Specifically, although behavioural interventions within the context of academic research studies are evidence based, behaviour change interventions implemented in general practice settings might prove less effective unless healthcare providers are able to shift from a practice based on the biomedical model to a practice based on the self-management support model. The purpose of this article is to facilitate effective self-management support by encouraging providers to switch from a model of care based on the expert clinician encountering the uninformed help seeker (the biomedical model) to one guided by collaboration grounded in the principles of description, prediction and choice. Key to understanding the value of making this shift are patient-centered communication principles and the tenets of complexity theory. Copyright © 2015 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.

A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

PubMed

Moore, Jason H; Shestov, Maksim; Schmitt, Peter; Olson, Randal S

2018-01-01

A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important role in this process by making it easy for computational researchers to easily access real data for this purpose. Genomics has in some examples taken a leading role in the open data effort starting with DNA microarrays. While real data from experimental and observational studies is necessary for developing computational methods it is not sufficient. This is because it is not possible to know what the ground truth is in real data. This must be accompanied by simulated data where that balance between signal and noise is known and can be directly evaluated. Unfortunately, there is a lack of methods and software for simulating data with the kind of complexity found in real biological and biomedical systems. We present here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating complex biological and biomedical data. Further, we introduce new methods for developing simulation models that generate data that specifically allows discrimination between different machine learning methods.

The potential of induced pluripotent stem cells in models of neurological disorders: implications on future therapy.

PubMed

Crook, Jeremy Micah; Wallace, Gordon; Tomaskovic-Crook, Eva

2015-03-01

There is an urgent need for new and advanced approaches to modeling the pathological mechanisms of complex human neurological disorders. This is underscored by the decline in pharmaceutical research and development efficiency resulting in a relative decrease in new drug launches in the last several decades. Induced pluripotent stem cells represent a new tool to overcome many of the shortcomings of conventional methods, enabling live human neural cell modeling of complex conditions relating to aberrant neurodevelopment, such as schizophrenia, epilepsy and autism as well as age-associated neurodegeneration. This review considers the current status of induced pluripotent stem cell-based modeling of neurological disorders, canvassing proven and putative advantages, current constraints, and future prospects of next-generation culture systems for biomedical research and translation.

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

PubMed Central

Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon

2018-01-01

Background With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. Objective This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. Methods We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. Results The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. Conclusions In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. PMID:29305341

Providing Social Support for Underrepresented Racial and Ethnic Minority PhD Students in the Biomedical Sciences: A Career Coaching Model

ERIC Educational Resources Information Center

Williams, Simon N.; Thakore, Bhoomi K.; McGee, Richard

2017-01-01

Improvement in the proportion of underrepresented racial and ethnic minorities (URMs) in academic positions has been unsatisfactory. Although this is a complex problem, one key issue is that graduate students often rely on research mentors for career-related support, the effectiveness of which can be variable. We present results from a novel…

Teaching biomedical applications to secondary students.

PubMed

Openshaw, S; Fleisher, A; Ljunggren, C

1999-01-01

Certain aspects of biomedical engineering applications lend themselves well to experimentation that can be done by high school students. This paper describes two experiments done during a six-week summer internship program in which two high school students used electrodes, circuit boards, and computers to mimic a sophisticated heart monitor and also to control a robotic car. Our experience suggests that simple illustrations of complex instrumentation can be effective in introducing adolescents to the biomedical engineering field.

User needs analysis and usability assessment of DataMed - a biomedical data discovery index.

PubMed

Dixit, Ram; Rogith, Deevakar; Narayana, Vidya; Salimi, Mandana; Gururaj, Anupama; Ohno-Machado, Lucila; Xu, Hua; Johnson, Todd R

2017-11-30

To present user needs and usability evaluations of DataMed, a Data Discovery Index (DDI) that allows searching for biomedical data from multiple sources. We conducted 2 phases of user studies. Phase 1 was a user needs analysis conducted before the development of DataMed, consisting of interviews with researchers. Phase 2 involved iterative usability evaluations of DataMed prototypes. We analyzed data qualitatively to document researchers' information and user interface needs. Biomedical researchers' information needs in data discovery are complex, multidimensional, and shaped by their context, domain knowledge, and technical experience. User needs analyses validate the need for a DDI, while usability evaluations of DataMed show that even though aggregating metadata into a common search engine and applying traditional information retrieval tools are promising first steps, there remain challenges for DataMed due to incomplete metadata and the complexity of data discovery. Biomedical data poses distinct problems for search when compared to websites or publications. Making data available is not enough to facilitate biomedical data discovery: new retrieval techniques and user interfaces are necessary for dataset exploration. Consistent, complete, and high-quality metadata are vital to enable this process. While available data and researchers' information needs are complex and heterogeneous, a successful DDI must meet those needs and fit into the processes of biomedical researchers. Research directions include formalizing researchers' information needs, standardizing overviews of data to facilitate relevance judgments, implementing user interfaces for concept-based searching, and developing evaluation methods for open-ended discovery systems such as DDIs. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Modeling and mining term association for improving biomedical information retrieval performance.

PubMed

Hu, Qinmin; Huang, Jimmy Xiangji; Hu, Xiaohua

2012-06-11

The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages.

Modeling and mining term association for improving biomedical information retrieval performance

PubMed Central

2012-01-01

Background The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. Results We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. Conclusions First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages. PMID:22901087

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis.

PubMed

Kim, Seongsoon; Park, Donghyeon; Choi, Yonghwa; Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon; Kang, Jaewoo

2018-01-05

With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. ©Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.01.2018.

A modular framework for biomedical concept recognition

PubMed Central

2013-01-01

Background Concept recognition is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. The development of such solutions is typically performed in an ad-hoc manner or using general information extraction frameworks, which are not optimized for the biomedical domain and normally require the integration of complex external libraries and/or the development of custom tools. Results This article presents Neji, an open source framework optimized for biomedical concept recognition built around four key characteristics: modularity, scalability, speed, and usability. It integrates modules for biomedical natural language processing, such as sentence splitting, tokenization, lemmatization, part-of-speech tagging, chunking and dependency parsing. Concept recognition is provided through dictionary matching and machine learning with normalization methods. Neji also integrates an innovative concept tree implementation, supporting overlapped concept names and respective disambiguation techniques. The most popular input and output formats, namely Pubmed XML, IeXML, CoNLL and A1, are also supported. On top of the built-in functionalities, developers and researchers can implement new processing modules or pipelines, or use the provided command-line interface tool to build their own solutions, applying the most appropriate techniques to identify heterogeneous biomedical concepts. Neji was evaluated against three gold standard corpora with heterogeneous biomedical concepts (CRAFT, AnEM and NCBI disease corpus), achieving high performance results on named entity recognition (F1-measure for overlap matching: species 95%, cell 92%, cellular components 83%, gene and proteins 76%, chemicals 65%, biological processes and molecular functions 63%, disorders 85%, and anatomical entities 82%) and on entity normalization (F1-measure for overlap name matching and correct identifier included in the returned list of identifiers: species 88%, cell 71%, cellular components 72%, gene and proteins 64%, chemicals 53%, and biological processes and molecular functions 40%). Neji provides fast and multi-threaded data processing, annotating up to 1200 sentences/second when using dictionary-based concept identification. Conclusions Considering the provided features and underlying characteristics, we believe that Neji is an important contribution to the biomedical community, streamlining the development of complex concept recognition solutions. Neji is freely available at http://bioinformatics.ua.pt/neji. PMID:24063607

Recent development and biomedical applications of probabilistic Boolean networks

PubMed Central

2013-01-01

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered. A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed. A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels. PMID:23815817

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

PubMed Central

Ahmed, Zeeshan; Zeeshan, Saman; Dandekar, Thomas

2016-01-01

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries. PMID:27538578

Biomedical Engineering Education in Perspective

ERIC Educational Resources Information Center

Gowen, Richard J.

1973-01-01

Discusses recent developments in the health care industry and their impact on the future of biomedical engineering education. Indicates that a more thorough understanding of the complex functions of the living organism can be acquired through the application of engineering techniques to problems of life sciences. (CC)

Characterizing semantic mappings adaptation via biomedical KOS evolution: a case study investigating SNOMED CT and ICD.

PubMed

Dos Reis, Julio Cesar; Pruski, Cédric; Da Silveira, Marcos; Reynaud-Delaître, Chantal

2013-01-01

Mappings established between Knowledge Organization Systems (KOS) increase semantic interoperability between biomedical information systems. However, biomedical knowledge is highly dynamic and changes affecting KOS entities can potentially invalidate part or the totality of existing mappings. Understanding how mappings evolve and what the impacts of KOS evolution on mappings are is therefore crucial for the definition of an automatic approach to maintain mappings valid and up-to-date over time. In this article, we study variations of a specific KOS complex change (split) for two biomedical KOS (SNOMED CT and ICD-9-CM) through a rigorous method of investigation for identifying and refining complex changes, and for selecting representative cases. We empirically analyze and explain their influence on the evolution of associated mappings. Results point out the importance of considering various dimensions of the information described in KOS, like the semantic structure of concepts, the set of relevant information used to define the mappings and the change operations interfering with this set of information.

Characterizing Semantic Mappings Adaptation via Biomedical KOS Evolution: A Case Study Investigating SNOMED CT and ICD

PubMed Central

Reis, Julio Cesar Dos; Pruski, Cédric; Da Silveira, Marcos; Reynaud-Delaître, Chantal

2013-01-01

Mappings established between Knowledge Organization Systems (KOS) increase semantic interoperability between biomedical information systems. However, biomedical knowledge is highly dynamic and changes affecting KOS entities can potentially invalidate part or the totality of existing mappings. Understanding how mappings evolve and what the impacts of KOS evolution on mappings are is therefore crucial for the definition of an automatic approach to maintain mappings valid and up-to-date over time. In this article, we study variations of a specific KOS complex change (split) for two biomedical KOS (SNOMED CT and ICD-9-CM) through a rigorous method of investigation for identifying and refining complex changes, and for selecting representative cases. We empirically analyze and explain their influence on the evolution of associated mappings. Results point out the importance of considering various dimensions of the information described in KOS, like the semantic structure of concepts, the set of relevant information used to define the mappings and the change operations interfering with this set of information. PMID:24551341

Urgent Need for Improved Mental Health Care and a More Collaborative Model of Care

PubMed Central

Lake, James; Turner, Mason Spain

2017-01-01

Current treatments and the dominant model of mental health care do not adequately address the complex challenges of mental illness, which accounts for roughly one-third of adult disability globally. These circumstances call for radical change in the paradigm and practices of mental health care, including improving standards of clinician training, developing new research methods, and re-envisioning current models of mental health care delivery. Because of its dominant position in the US health care marketplace and its commitment to research and innovation, Kaiser Permanente (KP) is strategically positioned to make important contributions that will shape the future of mental health care nationally and globally. This article reviews challenges facing mental health care and proposes an agenda for developing a collaborative care model in primary care settings that incorporates conventional biomedical therapies and complementary and alternative medicine approaches. By moving beyond treatment delivery via telephone and secure video and providing earlier interventions through primary care clinics, KP is shifting the paradigm of mental health care to a collaborative care model focusing on prevention. Recommendations are to expand current practices to include integrative treatment strategies incorporating evidence-based biomedical and complementary and alternative medicine modalities that can be provided to patients using a collaborative care model. Recommendations also are made for an internal research program aimed at investigating the efficacy and cost-effectiveness of promising complementary and alternative medicine and integrative treatments addressing the complex needs of patients with severe psychiatric disorders, many of whom respond poorly to treatments available in KP mental health clinics. PMID:28898197

Supervised Learning Based Hypothesis Generation from Biomedical Literature.

PubMed

Sang, Shengtian; Yang, Zhihao; Li, Zongyao; Lin, Hongfei

2015-01-01

Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system.

The Challenging Road towards a Unified Animal Research Network in Europe

PubMed Central

Martinez-Sanchez, Emma; Leech, Kirk

2015-01-01

Animal models are key in biomedical research as a proof of concept to study complex processes in a physiological context. Despite the small yet crucial role animals play in fundamental and applied research, the value of animal research is recurrently undermined. Lack of openness and transparency encourages misconceptions, which can have a dramatic negative impact on science and medicine. Research centres should use all available resources to ensure that relevant details about their use of animals in research are readily accessible. More concerted efforts by professional advocacy groups devoted to informing about the benefits of biomedical animal research are also crucial. The European Animal Research Association acts as an umbrella organisation providing support to national advocacy groups and coordinating actions in countries in which no advocacy group exists. PMID:26018997

Resolving Complex Research Data Management Issues in Biomedical Laboratories: Qualitative Study of an Industry-Academia Collaboration

PubMed Central

Myneni, Sahiti; Patel, Vimla L.; Bova, G. Steven; Wang, Jian; Ackerman, Christopher F.; Berlinicke, Cynthia A.; Chen, Steve H.; Lindvall, Mikael; Zack, Donald J.

2016-01-01

This paper describes a distributed collaborative effort between industry and academia to systematize data management in an academic biomedical laboratory. Heterogeneous and voluminous nature of research data created in biomedical laboratories make information management difficult and research unproductive. One such collaborative effort was evaluated over a period of four years using data collection methods including ethnographic observations, semi-structured interviews, web-based surveys, progress reports, conference call summaries, and face-to-face group discussions. Data were analyzed using qualitative methods of data analysis to 1) characterize specific problems faced by biomedical researchers with traditional information management practices, 2) identify intervention areas to introduce a new research information management system called Labmatrix, and finally to 3) evaluate and delineate important general collaboration (intervention) characteristics that can optimize outcomes of an implementation process in biomedical laboratories. Results emphasize the importance of end user perseverance, human-centric interoperability evaluation, and demonstration of return on investment of effort and time of laboratory members and industry personnel for success of implementation process. In addition, there is an intrinsic learning component associated with the implementation process of an information management system. Technology transfer experience in a complex environment such as the biomedical laboratory can be eased with use of information systems that support human and cognitive interoperability. Such informatics features can also contribute to successful collaboration and hopefully to scientific productivity. PMID:26652980

Composite annotations: requirements for mapping multiscale data and models to biomedical ontologies

PubMed Central

Cook, Daniel L.; Mejino, Jose L. V.; Neal, Maxwell L.; Gennari, John H.

2009-01-01

Current methods for annotating biomedical data resources rely on simple mappings between data elements and the contents of a variety of biomedical ontologies and controlled vocabularies. Here we point out that such simple mappings are inadequate for large-scale multiscale, multidomain integrative “virtual human” projects. For such integrative challenges, we describe a “composite annotation” schema that is simple yet sufficiently extensible for mapping the biomedical content of a variety of data sources and biosimulation models to available biomedical ontologies. PMID:19964601

Applicability Analysis of Validation Evidence for Biomedical Computational Models

DOE PAGES

Pathmanathan, Pras; Gray, Richard A.; Romero, Vicente J.; ...

2017-09-07

Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibilitymore » of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. In this paper, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. Finally, the proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.« less

Applicability Analysis of Validation Evidence for Biomedical Computational Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pathmanathan, Pras; Gray, Richard A.; Romero, Vicente J.

Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibilitymore » of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. In this paper, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. Finally, the proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.« less

Combinatorial nanodiamond in pharmaceutical and biomedical applications.

PubMed

Lim, Dae Gon; Prim, Racelly Ena; Kim, Ki Hyun; Kang, Eunah; Park, Kinam; Jeong, Seong Hoon

2016-11-30

One of the newly emerging carbon materials, nanodiamond (ND), has been exploited for use in traditional electric materials and this has extended into biomedical and pharmaceutical applications. Recently, NDs have attained significant interests as a multifunctional and combinational drug delivery system. ND studies have provided insights into granting new potentials with their wide ranging surface chemistry, complex formation with biopolymers, and combination with biomolecules. The studies that have proved ND inertness, biocompatibility, and low toxicity have made NDs much more feasible for use in real in vivo applications. This review gives an understanding of NDs in biomedical engineering and pharmaceuticals, focusing on the classified introduction of ND/drug complexes. In addition, the diverse potential applications that can be obtained with chemical modification are presented. Copyright © 2016 Elsevier B.V. All rights reserved.

Mining biomedical images towards valuable information retrieval in biomedical and life sciences.

PubMed

Ahmed, Zeeshan; Zeeshan, Saman; Dandekar, Thomas

2016-01-01

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries. © The Author(s) 2016. Published by Oxford University Press.

Local Orthogonal Cutting Method for Computing Medial Curves and Its Biomedical Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiao, Xiangmin; Einstein, Daniel R.; Dyedov, Volodymyr

2010-03-24

Medial curves have a wide range of applications in geometric modeling and analysis (such as shape matching) and biomedical engineering (such as morphometry and computer assisted surgery). The computation of medial curves poses significant challenges, both in terms of theoretical analysis and practical efficiency and reliability. In this paper, we propose a definition and analysis of medial curves and also describe an efficient and robust method for computing medial curves. Our approach is based on three key concepts: a local orthogonal decomposition of objects into substructures, a differential geometry concept called the interior center of curvature (ICC), and integrated stabilitymore » and consistency tests. These concepts lend themselves to robust numerical techniques including eigenvalue analysis, weighted least squares approximations, and numerical minimization, resulting in an algorithm that is efficient and noise resistant. We illustrate the effectiveness and robustness of our approach with some highly complex, large-scale, noisy biomedical geometries derived from medical images, including lung airways and blood vessels. We also present comparisons of our method with some existing methods.« less

Data Mining Algorithms for Classification of Complex Biomedical Data

ERIC Educational Resources Information Center

Lan, Liang

2012-01-01

In my dissertation, I will present my research which contributes to solve the following three open problems from biomedical informatics: (1) Multi-task approaches for microarray classification; (2) Multi-label classification of gene and protein prediction from multi-source biological data; (3) Spatial scan for movement data. In microarray…

A neural joint model for entity and relation extraction from biomedical text.

PubMed

Li, Fei; Zhang, Meishan; Fu, Guohong; Ji, Donghong

2017-03-31

Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

From the NIH: A Systems Approach to Increasing the Diversity of the Biomedical Research Workforce

PubMed Central

Valantine, Hannah A.; Lund, P. Kay; Gammie, Alison E.

2016-01-01

The National Institutes of Health (NIH) is committed to attracting, developing, and supporting the best scientists from all groups as an integral part of excellence in training. Biomedical research workforce diversity, capitalizing on the full spectrum of skills, talents, and viewpoints, is essential for solving complex human health challenges. Over the past few decades, the biomedical research workforce has benefited from NIH programs aimed at enhancing diversity. However, there is considerable room for improvement, particularly at the level of independent scientists and within scientific leadership. We provide a rationale and specific opportunities to develop and sustain a diverse biomedical research workforce through interventions that promote the successful transitions to different stages on the path toward completion of training and entry into the biomedical workforce. PMID:27587850

Effects of research complexity and competition on the incidence and growth of coauthorship in biomedicine

PubMed Central

Wang, Xiaoyan; Laubenbacher, Reinhard C.

2017-01-01

Background Investigations into the factors behind coauthorship growth in biomedical research have mostly focused on specific disciplines or journals, and have rarely controlled for factors in combination or considered changes in their effects over time. Observers often attribute the growth to the increasing complexity or competition (or both) of research practices, but few attempts have been made to parse the contributions of these two likely causes. Objectives We aimed to assess the effects of complexity and competition on the incidence and growth of coauthorship, using a sample of the biomedical literature spanning multiple journals and disciplines. Methods Article-level bibliographic data from PubMed were combined with publicly available bibliometric data from Web of Science and SCImago over the years 1999–2007. We selected four predictors of coauthorship were selected, two (study type, topical scope of the study) associated with complexity and two (financial support for the project, popularity of the publishing journal) associated with competition. A negative binomial regression model was used to estimate the effects of each predictor on coauthorship incidence and growth. A second, mixed-effect model included the journal as a random effect. Results Coauthorship increased at about one author per article per decade. Clinical trials, supported research, and research of broader scope produced articles with more authors, while review articles credited fewer; and more popular journals published higher-authorship articles. Incidence and growth rates varied widely across journals and were themselves uncorrelated. Most effects remained statistically discernible after controlling for the publishing journal. The effects of complexity-associated factors held constant or diminished over time, while competition-related effects strengthened. These trends were similar in size but not discernible from subject-specific subdata. Conclusions Coauthorship incidence rates are multifactorial and vary with factors associated with both complexity and competition. Coauthorship growth is likewise multifactorial and increasingly associated with research competition. PMID:28329003

Effects of research complexity and competition on the incidence and growth of coauthorship in biomedicine.

PubMed

Brunson, Jason Cory; Wang, Xiaoyan; Laubenbacher, Reinhard C

2017-01-01

Investigations into the factors behind coauthorship growth in biomedical research have mostly focused on specific disciplines or journals, and have rarely controlled for factors in combination or considered changes in their effects over time. Observers often attribute the growth to the increasing complexity or competition (or both) of research practices, but few attempts have been made to parse the contributions of these two likely causes. We aimed to assess the effects of complexity and competition on the incidence and growth of coauthorship, using a sample of the biomedical literature spanning multiple journals and disciplines. Article-level bibliographic data from PubMed were combined with publicly available bibliometric data from Web of Science and SCImago over the years 1999-2007. We selected four predictors of coauthorship were selected, two (study type, topical scope of the study) associated with complexity and two (financial support for the project, popularity of the publishing journal) associated with competition. A negative binomial regression model was used to estimate the effects of each predictor on coauthorship incidence and growth. A second, mixed-effect model included the journal as a random effect. Coauthorship increased at about one author per article per decade. Clinical trials, supported research, and research of broader scope produced articles with more authors, while review articles credited fewer; and more popular journals published higher-authorship articles. Incidence and growth rates varied widely across journals and were themselves uncorrelated. Most effects remained statistically discernible after controlling for the publishing journal. The effects of complexity-associated factors held constant or diminished over time, while competition-related effects strengthened. These trends were similar in size but not discernible from subject-specific subdata. Coauthorship incidence rates are multifactorial and vary with factors associated with both complexity and competition. Coauthorship growth is likewise multifactorial and increasingly associated with research competition.

Optimizing the chances of success in the search for epigenetic biomarkers: Embracing genetic variation.

PubMed

Philibert, Robert; Glatt, Stephen J

2017-09-01

The emphasis on clinical translation in biomedical research continues to grow. This focus has been particularly notable in those investigators using epigenetic approaches to decipher the biology of complex behavioral disorders. As a result of these efforts, reproducible findings for several disorders, such as smoking, have been generated, giving rise to hopes that biomarkers for other behavioral illnesses would be forthcoming. Unfortunately, that biomedical cornucopia has not yet materialized. In this editorial, we review progress to date and discuss barriers to generating epigenetic biomarkers for complex behavioral disorders. We highlight the need to incorporate information on genetic variation and develop more powerful bioinformatics tools in order to optimize the likelihood of success. We emphasize that searches should focus on clearly defined, readily distinguishable behavioral constructs and suggest that some well-intentioned methods, such as correction for cellular heterogeneity, may actually impede the identification of clinically relevant biomarkers in peripheral blood. Finally, we describe how the understanding created by the development of these biomarkers may lead to more valid animal models of neuropsychiatric illness. We conclude that the prospects for epigenetic biomarkers for complex disorders are bright, but emphasize that the journey to the clinical implementation of these findings will be a slow, iterative process. © 2017 Wiley Periodicals, Inc.

Role of Kinetic Modeling in Biomedical Imaging

PubMed Central

Huang, Sung-Cheng

2009-01-01

Biomedical imaging can reveal clear 3-dimensional body morphology non-invasively with high spatial resolution. Its efficacy, in both clinical and pre-clinical settings, is enhanced with its capability to provide in vivo functional/biological information in tissue. The role of kinetic modeling in providing biological/functional information in biomedical imaging is described. General characteristics and limitations in extracting biological information are addressed and practical approaches to solve the problems are discussed and illustrated with examples. Some future challenges and opportunities for kinetic modeling to expand the capability of biomedical imaging are also presented. PMID:20640185

ENABLE 2017, the First EUROPEAN PhD and Post-Doc Symposium. Session 3: In Vitro to In Vivo: Modeling Life in 3D.

PubMed

Di Mauro, Gianmarco; Dondi, Ambra; Giangreco, Giovanni; Hogrebe, Alexander; Louer, Elja; Magistrati, Elisa; Mullari, Meeli; Turon, Gemma; Verdurmen, Wouter; Cortada, Helena Xicoy; Zivanovic, Sanja

2018-05-22

The EUROPEAN ACADEMY FOR BIOMEDICAL SCIENCE (ENABLE) is an initiative funded by the European Union Horizon 2020 program involving four renowned European research institutes (Institute for Research in Biomedicine-IRB Barcelona, Spain; Radboud Institute for Molecular Life Sciences-RIMLS, the Netherlands; Novo Nordisk Foundation Center for Protein Research-NNF CPR, Denmark; European School of Molecular Medicine-SEMM, Italy) and an innovative science communication agency (Scienseed). With the aim to promote biomedical science of excellence in Europe, ENABLE organizes an annual three-day international event. This gathering includes a top-level scientific symposium bringing together leading scientists, PhD students, and post-doctoral fellows; career development activities supporting the progression of young researchers and fostering discussion about opportunities beyond the bench; outreach activities stimulating the interaction between science and society. The first European PhD and Postdoc Symposium, entitled "Breaking Down Complexity: Innovative models and techniques in biomedicine", was hosted by the vibrant city of Barcelona. The scientific program of the conference was focused on the most recent advances and applications of modern techniques and models in biomedical research and covered a wide range of topics, from synthetic biology to translational medicine. Overall, the event was a great success, with more than 200 attendees from all over Europe actively participating in the symposium by presenting their research and exchanging ideas with their peers and world-renowned scientists.

Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry-academia collaboration.

PubMed

Myneni, Sahiti; Patel, Vimla L; Bova, G Steven; Wang, Jian; Ackerman, Christopher F; Berlinicke, Cynthia A; Chen, Steve H; Lindvall, Mikael; Zack, Donald J

2016-04-01

This paper describes a distributed collaborative effort between industry and academia to systematize data management in an academic biomedical laboratory. Heterogeneous and voluminous nature of research data created in biomedical laboratories make information management difficult and research unproductive. One such collaborative effort was evaluated over a period of four years using data collection methods including ethnographic observations, semi-structured interviews, web-based surveys, progress reports, conference call summaries, and face-to-face group discussions. Data were analyzed using qualitative methods of data analysis to (1) characterize specific problems faced by biomedical researchers with traditional information management practices, (2) identify intervention areas to introduce a new research information management system called Labmatrix, and finally to (3) evaluate and delineate important general collaboration (intervention) characteristics that can optimize outcomes of an implementation process in biomedical laboratories. Results emphasize the importance of end user perseverance, human-centric interoperability evaluation, and demonstration of return on investment of effort and time of laboratory members and industry personnel for success of implementation process. In addition, there is an intrinsic learning component associated with the implementation process of an information management system. Technology transfer experience in a complex environment such as the biomedical laboratory can be eased with use of information systems that support human and cognitive interoperability. Such informatics features can also contribute to successful collaboration and hopefully to scientific productivity. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Microfluidic Organ/Body-on-a-Chip Devices at the Convergence of Biology and Microengineering.

PubMed

Perestrelo, Ana Rubina; Águas, Ana C P; Rainer, Alberto; Forte, Giancarlo

2015-12-10

Recent advances in biomedical technologies are mostly related to the convergence of biology with microengineering. For instance, microfluidic devices are now commonly found in most research centers, clinics and hospitals, contributing to more accurate studies and therapies as powerful tools for drug delivery, monitoring of specific analytes, and medical diagnostics. Most remarkably, integration of cellularized constructs within microengineered platforms has enabled the recapitulation of the physiological and pathological conditions of complex tissues and organs. The so-called "organ-on-a-chip" technology, which represents a new avenue in the field of advanced in vitro models, with the potential to revolutionize current approaches to drug screening and toxicology studies. This review aims to highlight recent advances of microfluidic-based devices towards a body-on-a-chip concept, exploring their technology and broad applications in the biomedical field.

The importance of Zebrafish in biomedical research.

PubMed

Tavares, Bárbara; Santos Lopes, Susana

2013-01-01

Zebrafish (Danio rerio) is an ideal model organism for the study of vertebrate development. This is due to the large clutches that each couple produces, with up to 200 embryos every 7 days, and to the fact that the embryos and larvae are small, transparent and undergo rapid external development. Using scientific literature research tools available online and the keywords Zebrafish, biomedical research, human disease, and drug screening, we reviewed original studies and reviews indexed in PubMed. In this review we summarized work conducted with this model for the advancement of our knowledge related to several human diseases. We also focused on the biomedical research being performed in Portugal with the zebrafish model. Powerful live imaging and genetic tools are currently available for zebrafish making it a valuable model in biomedical research. The combination of these properties with the optimization of automated systems for drug screening has transformed the zebrafish into "a top model" in biomedical research, drug discovery and toxicity testing. Furthermore, with the optimization of xenografts technology it will be possible to use zebrafish to aide in the choice of the best therapy for each patient. Zebrafish is an excellent model organism in biomedical research, drug development and in clinical therapy.

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

PubMed Central

Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González

2016-01-01

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering. PMID:27872840

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation.

PubMed

Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González

2016-01-01

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

PubMed

Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

2017-03-01

Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Explanatory models of diabetes in urban poor communities in Accra, Ghana.

PubMed

de-Graft Aikins, Ama; Awuah, Raphael Baffour; Pera, Tuula Anneli; Mendez, Montserrat; Ogedegbe, Gbenga

2015-01-01

The objective of the study was to examine explanatory models of diabetes and diabetes complications among urban poor Ghanaians living with diabetes and implications for developing secondary prevention strategies. Twenty adults with type 2 diabetes were recruited from three poor communities in Accra. Qualitative data were obtained using interviews that run between 40 and 90 minutes. The interviews were audio-taped, transcribed and analysed thematically, informed by the 'explanatory model of disease' concept. Respondents associated diabetes and its complications with diet, family history, lifestyle factors (smoking, excessive alcohol consumption and physical inactivity), psychological stress and supernatural factors (witchcraft and sorcery). These associations were informed by biomedical and cultural models of diabetes and disease. Subjective experience, through a process of 'body-listening,' constituted a third model on which respondents drew to theorise diabetes complications. Poverty was an important mediator of poor self-care practices, including treatment non-adherence. The biomedical model of diabetes was a major source of legitimate information for self-care practices. However, this was understood and applied through a complex framework of cultural theories of chronic disease, the biopsychological impact of everyday illness experience and the disempowering effects of poverty. An integrated biopsychosocial approach is proposed for diabetes intervention in this research community.

Securely and Flexibly Sharing a Biomedical Data Management System

PubMed Central

Wang, Fusheng; Hussels, Phillip; Liu, Peiya

2011-01-01

Biomedical database systems need not only to address the issues of managing complex data, but also to provide data security and access control to the system. These include not only system level security, but also instance level access control such as access of documents, schemas, or aggregation of information. The latter is becoming more important as multiple users can share a single scientific data management system to conduct their research, while data have to be protected before they are published or IP-protected. This problem is challenging as users’ needs for data security vary dramatically from one application to another, in terms of who to share with, what resources to be shared, and at what access level. We develop a comprehensive data access framework for a biomedical data management system SciPort. SciPort provides fine-grained multi-level space based access control of resources at not only object level (documents and schemas), but also space level (resources set aggregated in a hierarchy way). Furthermore, to simplify the management of users and privileges, customizable role-based user model is developed. The access control is implemented efficiently by integrating access privileges into the backend XML database, thus efficient queries are supported. The secure access approach we take makes it possible for multiple users to share the same biomedical data management system with flexible access management and high data security. PMID:21625285

Semantic relatedness and similarity of biomedical terms: examining the effects of recency, size, and section of biomedical publications on the performance of word2vec.

PubMed

Zhu, Yongjun; Yan, Erjia; Wang, Fei

2017-07-03

Understanding semantic relatedness and similarity between biomedical terms has a great impact on a variety of applications such as biomedical information retrieval, information extraction, and recommender systems. The objective of this study is to examine word2vec's ability in deriving semantic relatedness and similarity between biomedical terms from large publication data. Specifically, we focus on the effects of recency, size, and section of biomedical publication data on the performance of word2vec. We download abstracts of 18,777,129 articles from PubMed and 766,326 full-text articles from PubMed Central (PMC). The datasets are preprocessed and grouped into subsets by recency, size, and section. Word2vec models are trained on these subtests. Cosine similarities between biomedical terms obtained from the word2vec models are compared against reference standards. Performance of models trained on different subsets are compared to examine recency, size, and section effects. Models trained on recent datasets did not boost the performance. Models trained on larger datasets identified more pairs of biomedical terms than models trained on smaller datasets in relatedness task (from 368 at the 10% level to 494 at the 100% level) and similarity task (from 374 at the 10% level to 491 at the 100% level). The model trained on abstracts produced results that have higher correlations with the reference standards than the one trained on article bodies (i.e., 0.65 vs. 0.62 in the similarity task and 0.66 vs. 0.59 in the relatedness task). However, the latter identified more pairs of biomedical terms than the former (i.e., 344 vs. 498 in the similarity task and 339 vs. 503 in the relatedness task). Increasing the size of dataset does not always enhance the performance. Increasing the size of datasets can result in the identification of more relations of biomedical terms even though it does not guarantee better precision. As summaries of research articles, compared with article bodies, abstracts excel in accuracy but lose in coverage of identifiable relations.

USNCTAM perspectives on mechanics in medicine

PubMed Central

Bao, Gang; Bazilevs, Yuri; Chung, Jae-Hyun; Decuzzi, Paolo; Espinosa, Horacio D.; Ferrari, Mauro; Gao, Huajian; Hossain, Shaolie S.; Hughes, Thomas J. R.; Kamm, Roger D.; Liu, Wing Kam; Marsden, Alison; Schrefler, Bernhard

2014-01-01

Over decades, the theoretical and applied mechanics community has developed sophisticated approaches for analysing the behaviour of complex engineering systems. Most of these approaches have targeted systems in the transportation, materials, defence and energy industries. Applying and further developing engineering approaches for understanding, predicting and modulating the response of complicated biomedical processes not only holds great promise in meeting societal needs, but also poses serious challenges. This report, prepared for the US National Committee on Theoretical and Applied Mechanics, aims to identify the most pressing challenges in biological sciences and medicine that can be tackled within the broad field of mechanics. This echoes and complements a number of national and international initiatives aiming at fostering interdisciplinary biomedical research. This report also comments on cultural/educational challenges. Specifically, this report focuses on three major thrusts in which we believe mechanics has and will continue to have a substantial impact. (i) Rationally engineering injectable nano/microdevices for imaging and therapy of disease. Within this context, we discuss nanoparticle carrier design, vascular transport and adhesion, endocytosis and tumour growth in response to therapy, as well as uncertainty quantification techniques to better connect models and experiments. (ii) Design of biomedical devices, including point-of-care diagnostic systems, model organ and multi-organ microdevices, and pulsatile ventricular assistant devices. (iii) Mechanics of cellular processes, including mechanosensing and mechanotransduction, improved characterization of cellular constitutive behaviour, and microfluidic systems for single-cell studies. PMID:24872502

Recent advances in engineering microparticles and their nascent utilization in biomedical delivery and diagnostic applications.

PubMed

Choi, Andrew; Seo, Kyoung Duck; Kim, Do Wan; Kim, Bum Chang; Kim, Dong Sung

2017-02-14

Complex microparticles (MPs) bearing unique characteristics such as well-tailored sizes, various morphologies, and multi-compartments have been attempted to be produced by many researchers in the past decades. However, a conventionally used method of fabricating MPs, emulsion polymerization, has a limitation in achieving the aforementioned characteristics and several approaches such as the microfluidics-assisted (droplet-based microfluidics and flow lithography-based microfluidics), electrohydrodynamics (EHD)-based, centrifugation-based, and template-based methods have been recently suggested to overcome this limitation. The outstanding features of complex MPs engineered through these suggested methods have provided new opportunities for MPs to be applied in a wider range of applications including cell carriers, drug delivery agents, active pigments for display, microsensors, interface stabilizers, and catalyst substrates. Overall, the engineered MPs expose their potential particularly in the field of biomedical engineering as the increased complexity in the engineered MPs fulfills well the requirements of the high-end applications. This review outlines the current trends of newly developed techniques used for engineered MPs fabrication and focuses on the current state of engineered MPs in biomedical applications.

From the NIH: A Systems Approach to Increasing the Diversity of the Biomedical Research Workforce

ERIC Educational Resources Information Center

Valantine, Hannah A.; Lund, P. Kay; Gammie, Alison E.

2016-01-01

The National Institutes of Health (NIH) is committed to attracting, developing, and supporting the best scientists from all groups as an integral part of excellence in training. Biomedical research workforce diversity, capitalizing on the full spectrum of skills, talents, and viewpoints, is essential for solving complex human health challenges.…

Evidence-based Modeling of Critical Illness: An Initial Consensus from the Society for Complexity in Acute Illness

PubMed Central

Vodovotz, Yoram; Clermont, Gilles; Hunt, C. Anthony; Lefering, Rolf; Bartels, John; Seydel, Ruediger; Hotchkiss, John; Ta'asan, Shlomo; Neugebauer, Edmund; An, Gary

2007-01-01

Introduction: Given the complexity of biological systems, understanding their dynamic behaviors, such as the Acute Inflammatory Response (AIR), requires a formal synthetic process. Dynamic Mathematical Modeling (DMM) represents a suite of methods intended for inclusion within the required synthetic framework. DMM, however, is a relatively novel approach in the practice of biomedical research. The Society for Complexity in Acute Illness (SCAI) was formed in 2004 from the leading research groups utilizing DMM in the study of acute inflammation. This Society believes that it is important to offer guidelines for the design, development and utilization of DMM in the setting of AIR research to avoid the “garbage-in garbage-out” problem. Accordingly, SCAI identified a need for and carried out a critical appraisal of DMM as currently used in the setting of acute illness. Methods: The SCAI annual meeting in 2005, the 4th International Conference on Complexity in Acute Illness (ICCAI; Cologne, Germany), was structured with the intent of developing a consensus statement on the methods and execution of DMM in AIR research. The conference was organized to include a series of interactive breakout sessions that included thought leaders from both the DMM and acute illness fields, the results of which were then presented in summary form to the entire group for discussion and consensus. The information in this manuscript represents the concatenation of those presentations. Results: The output from the 4th ICCAI involved consensus statements for the following topics: 1) the need for DMM, 2) a suggested approach for the process of establishing a modeling project, 3) the type of “wet” lab experiments and data needed to establish a modeling project, 4) general quality measures for data to be input to a modeling project, and 5) a descriptive list of several types of DMM to provide guidance in selection of a method for a project. Conclusion: We believe that the complexity of biological systems requires that DMM needs to be among the methods used to improve understanding and make progress with attempts to characterize and manipulate the AIR. We believe that this consensus statement will help guide the integration, rational implementation, and standardization of DMM into general biomedical research. PMID:17371750

Constraint reasoning in deep biomedical models.

PubMed

Cruz, Jorge; Barahona, Pedro

2005-05-01

Deep biomedical models are often expressed by means of differential equations. Despite their expressive power, they are difficult to reason about and make decisions, given their non-linearity and the important effects that the uncertainty on data may cause. The objective of this work is to propose a constraint reasoning framework to support safe decisions based on deep biomedical models. The methods used in our approach include the generic constraint propagation techniques for reducing the bounds of uncertainty of the numerical variables complemented with new constraint reasoning techniques that we developed to handle differential equations. The results of our approach are illustrated in biomedical models for the diagnosis of diabetes, tuning of drug design and epidemiology where it was a valuable decision-supporting tool notwithstanding the uncertainty on data. The main conclusion that follows from the results is that, in biomedical decision support, constraint reasoning may be a worthwhile alternative to traditional simulation methods, especially when safe decisions are required.

Project-based learning with international collaboration for training biomedical engineers.

PubMed

Krishnan, Shankar

2011-01-01

Training biomedical engineers while effectively keeping up with the fast paced scientific breakthroughs and the growth in technical innovations poses arduous challenges for educators. Traditional pedagogical methods are employed for coping with the increasing demands in biomedical engineering (BME) training and continuous improvements have been attempted with some success. Project-based learning (PBL) is an academic effort that challenges students by making them carry out interdisciplinary projects aimed at accomplishing a wide range of student learning outcomes. PBL has been shown to be effective in the medical field and has been adopted by other fields including engineering. The impact of globalization in healthcare appears to be steadily increasing which necessitates the inclusion of awareness of relevant international activities in the curriculum. Numerous difficulties are encountered when the formation of a collaborative team is tried, and additional difficulties occur as the collaboration team is extended to international partners. Understanding and agreement of responsibilities becomes somewhat complex and hence the collaborative project has to be planned and executed with clear understanding by all partners and participants. A model for training BME students by adopting PBL with international collaboration is proposed. The results of previous BME project work with international collaboration fit partially into the model. There were many logistic issues and constraints; however, the collaborative projects themselves greatly enhanced the student learning outcomes. This PBL type of learning experience tends to promote long term retention of multidisciplinary material and foster high-order cognitive activities such as analysis, synthesis and evaluation. In addition to introducing the students to experiences encountered in the real-life workforce, the proposed approach enhances developing professional contracts and global networking. In conclusion, despite initial challenges, adopting project-based learning with international collaboration has strong potentials to be valuable in the training of biomedical engineering students.

Sociodemographic Factors Associated With Changes in Successful Aging in Spain: A Follow-Up Study.

PubMed

Domènech-Abella, Joan; Perales, Jaime; Lara, Elvira; Moneta, Maria Victoria; Izquierdo, Ana; Rico-Uribe, Laura Alejandra; Mundó, Jordi; Haro, Josep Maria

2017-06-01

Successful aging (SA) refers to maintaining well-being in old age. Several definitions or models of SA exist (biomedical, psychosocial, and mixed). We examined the longitudinal association between various SA models and sociodemographic factors, and analyzed the patterns of change within these models. This was a nationally representative follow-up in Spain including 3,625 individuals aged ≥50 years. Some 1,970 individuals were interviewed after 3 years. Linear regression models were used to analyze the survey data. Age, sex, and occupation predicted SA in the biomedical model, while marital status, educational level, and urbanicity predicted SA in the psychosocial model. The remaining models included different sets of these predictors as significant. In the psychosocial model, individuals tended to improve over time but this was not the case in the biomedical model. The biomedical and psychosocial components of SA need to be addressed specifically to achieve the best aging trajectories.

Digital fabrication of multi-material biomedical objects.

PubMed

Cheung, H H; Choi, S H

2009-12-01

This paper describes a multi-material virtual prototyping (MMVP) system for modelling and digital fabrication of discrete and functionally graded multi-material objects for biomedical applications. The MMVP system consists of a DMMVP module, an FGMVP module and a virtual reality (VR) simulation module. The DMMVP module is used to model discrete multi-material (DMM) objects, while the FGMVP module is for functionally graded multi-material (FGM) objects. The VR simulation module integrates these two modules to perform digital fabrication of multi-material objects, which can be subsequently visualized and analysed in a virtual environment to optimize MMLM processes for fabrication of product prototypes. Using the MMVP system, two biomedical objects, including a DMM human spine and an FGM intervertebral disc spacer are modelled and digitally fabricated for visualization and analysis in a VR environment. These studies show that the MMVP system is a practical tool for modelling, visualization, and subsequent fabrication of biomedical objects of discrete and functionally graded multi-materials for biomedical applications. The system may be adapted to control MMLM machines with appropriate hardware for physical fabrication of biomedical objects.

A review on models for count data with extra zeros

NASA Astrophysics Data System (ADS)

Zamri, Nik Sarah Nik; Zamzuri, Zamira Hasanah

2017-04-01

Typically, the zero inflated models are usually used in modelling count data with excess zeros. The existence of the extra zeros could be structural zeros or random which occur by chance. These types of data are commonly found in various disciplines such as finance, insurance, biomedical, econometrical, ecology, and health sciences. As found in the literature, the most popular zero inflated models used are zero inflated Poisson and zero inflated negative binomial. Recently, more complex models have been developed to account for overdispersion and unobserved heterogeneity. In addition, more extended distributions are also considered in modelling data with this feature. In this paper, we review related literature, provide a recent development and summary on models for count data with extra zeros.

Spatial Distributions of Guest Molecule and Hydration Level in Dendrimer-Based Guest–Host Complex

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Chih-Ying; Chen, Hsin-Lung; Do, Changwoo

2016-08-09

Using the electrostatic complex of G4 poly(amidoamine) (PAMAM) dendrimer with an amphiphilic surfactant as a model system, contrast variation small angle neutron scattering (SANS) is implemented to resolve the key structural characteristics of dendrimer-based guest–host system. Quantifications of the radial distributions of the scattering length density and the hydration level within the complex molecule reveal that the surfactant is embedded in the peripheral region of dendrimer and the steric crowding in this region increases the backfolding of the dendritic segments, thereby reducing the hydration level throughout the complex molecule. Here, the insights into the spatial location of the guest moleculesmore » as well as the perturbations of dendrimer conformation and hydration level deduced here are crucial for the delicate design of dendrimer-based guest–host system for biomedical applications.« less

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

PubMed

Bakal, Gokhan; Talari, Preetham; Kakani, Elijah V; Kavuluru, Ramakanth

2018-06-01

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction. Copyright © 2018 Elsevier Inc. All rights reserved.

qPortal: A platform for data-driven biomedical research.

PubMed

Mohr, Christopher; Friedrich, Andreas; Wojnar, David; Kenar, Erhan; Polatkan, Aydin Can; Codrea, Marius Cosmin; Czemmel, Stefan; Kohlbacher, Oliver; Nahnsen, Sven

2018-01-01

Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis. We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software's strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy). qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on high-performance computing systems via coupling of workflow management systems. Integration of project and data management as well as workflow resources in one place present clear advantages over existing solutions.

Improving average ranking precision in user searches for biomedical research datasets

PubMed Central

Gobeill, Julien; Gaudinat, Arnaud; Vachon, Thérèse; Ruch, Patrick

2017-01-01

Abstract Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorization method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries, and provided competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP, being +22.3% higher than the median infAP of the participant’s best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system’s performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. The similarity measure algorithm showed robust performance in different training conditions, with small performance variations compared to the Divergence from Randomness framework. Finally, the result categorization did not have significant impact on the system’s performance. We believe that our solution could be used to enhance biomedical dataset management systems. The use of data driven expansion methods, such as those based on word embeddings, could be an alternative to the complexity of biomedical terminologies. Nevertheless, due to the limited size of the assessment set, further experiments need to be performed to draw conclusive results. Database URL: https://biocaddie.org/benchmark-data PMID:29220475

Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search.

PubMed

Ji, Yanqing; Ying, Hao; Tran, John; Dews, Peter; Massanari, R Michael

2016-07-19

Finding highly relevant articles from biomedical databases is challenging not only because it is often difficult to accurately express a user's underlying intention through keywords but also because a keyword-based query normally returns a long list of hits with many citations being unwanted by the user. This paper proposes a novel biomedical literature search system, called BiomedSearch, which supports complex queries and relevance feedback. The system employed association mining techniques to build a k-profile representing a user's relevance feedback. More specifically, we developed a weighted interest measure and an association mining algorithm to find the strength of association between a query and each concept in the article(s) selected by the user as feedback. The top concepts were utilized to form a k-profile used for the next-round search. BiomedSearch relies on Unified Medical Language System (UMLS) knowledge sources to map text files to standard biomedical concepts. It was designed to support queries with any levels of complexity. A prototype of BiomedSearch software was made and it was preliminarily evaluated using the Genomics data from TREC (Text Retrieval Conference) 2006 Genomics Track. Initial experiment results indicated that BiomedSearch increased the mean average precision (MAP) for a set of queries. With UMLS and association mining techniques, BiomedSearch can effectively utilize users' relevance feedback to improve the performance of biomedical literature search.

Computational Modeling of Single-Cell Migration: The Leading Role of Extracellular Matrix Fibers

PubMed Central

Schlüter, Daniela K.; Ramis-Conde, Ignacio; Chaplain, Mark A.J.

2012-01-01

Cell migration is vitally important in a wide variety of biological contexts ranging from embryonic development and wound healing to malignant diseases such as cancer. It is a very complex process that is controlled by intracellular signaling pathways as well as the cell’s microenvironment. Due to its importance and complexity, it has been studied for many years in the biomedical sciences, and in the last 30 years it also received an increasing amount of interest from theoretical scientists and mathematical modelers. Here we propose a force-based, individual-based modeling framework that links single-cell migration with matrix fibers and cell-matrix interactions through contact guidance and matrix remodelling. With this approach, we can highlight the effect of the cell’s environment on its migration. We investigate the influence of matrix stiffness, matrix architecture, and cell speed on migration using quantitative measures that allow us to compare the results to experiments. PMID:22995486

Using the Weighted Keyword Model to Improve Information Retrieval for Answering Biomedical Questions

PubMed Central

Yu, Hong; Cao, Yong-gang

2009-01-01

Physicians ask many complex questions during the patient encounter. Information retrieval systems that can provide immediate and relevant answers to these questions can be invaluable aids to the practice of evidence-based medicine. In this study, we first automatically identify topic keywords from ad hoc clinical questions with a Condition Random Field model that is trained over thousands of manually annotated clinical questions. We then report on a linear model that assigns query weights based on their automatically identified semantic roles: topic keywords, domain specific terms, and their synonyms. Our evaluation shows that this weighted keyword model improves information retrieval from the Text Retrieval Conference Genomics track data. PMID:21347188

Using the weighted keyword model to improve information retrieval for answering biomedical questions.

PubMed

Yu, Hong; Cao, Yong-Gang

2009-03-01

Physicians ask many complex questions during the patient encounter. Information retrieval systems that can provide immediate and relevant answers to these questions can be invaluable aids to the practice of evidence-based medicine. In this study, we first automatically identify topic keywords from ad hoc clinical questions with a Condition Random Field model that is trained over thousands of manually annotated clinical questions. We then report on a linear model that assigns query weights based on their automatically identified semantic roles: topic keywords, domain specific terms, and their synonyms. Our evaluation shows that this weighted keyword model improves information retrieval from the Text Retrieval Conference Genomics track data.

Compensatory neurofuzzy model for discrete data classification in biomedical

NASA Astrophysics Data System (ADS)

Ceylan, Rahime

2015-03-01

Biomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.

Tissue polarimetry: concepts, challenges, applications, and outlook.

PubMed

Ghosh, Nirmalya; Vitkin, I Alex

2011-11-01

Polarimetry has a long and successful history in various forms of clear media. Driven by their biomedical potential, the use of the polarimetric approaches for biological tissue assessment has also recently received considerable attention. Specifically, polarization can be used as an effective tool to discriminate against multiply scattered light (acting as a gating mechanism) in order to enhance contrast and to improve tissue imaging resolution. Moreover, the intrinsic tissue polarimetry characteristics contain a wealth of morphological and functional information of potential biomedical importance. However, in a complex random medium-like tissue, numerous complexities due to multiple scattering and simultaneous occurrences of many scattering and polarization events present formidable challenges both in terms of accurate measurements and in terms of analysis of the tissue polarimetry signal. In order to realize the potential of the polarimetric approaches for tissue imaging and characterization/diagnosis, a number of researchers are thus pursuing innovative solutions to these challenges. In this review paper, we summarize these and other issues pertinent to the polarized light methodologies in tissues. Specifically, we discuss polarized light basics, Stokes-Muller formalism, methods of polarization measurements, polarized light modeling in turbid media, applications to tissue imaging, inverse analysis for polarimetric results quantification, applications to quantitative tissue assessment, etc.

Combining MEDLINE and publisher data to create parallel corpora for the automatic translation of biomedical text

PubMed Central

2013-01-01

Background Most of the institutional and research information in the biomedical domain is available in the form of English text. Even in countries where English is an official language, such as the United States, language can be a barrier for accessing biomedical information for non-native speakers. Recent progress in machine translation suggests that this technique could help make English texts accessible to speakers of other languages. However, the lack of adequate specialized corpora needed to train statistical models currently limits the quality of automatic translations in the biomedical domain. Results We show how a large-sized parallel corpus can automatically be obtained for the biomedical domain, using the MEDLINE database. The corpus generated in this work comprises article titles obtained from MEDLINE and abstract text automatically retrieved from journal websites, which substantially extends the corpora used in previous work. After assessing the quality of the corpus for two language pairs (English/French and English/Spanish) we use the Moses package to train a statistical machine translation model that outperforms previous models for automatic translation of biomedical text. Conclusions We have built translation data sets in the biomedical domain that can easily be extended to other languages available in MEDLINE. These sets can successfully be applied to train statistical machine translation models. While further progress should be made by incorporating out-of-domain corpora and domain-specific lexicons, we believe that this work improves the automatic translation of biomedical texts. PMID:23631733

Microfluidic Organ/Body-on-a-Chip Devices at the Convergence of Biology and Microengineering

PubMed Central

Perestrelo, Ana Rubina; Águas, Ana C. P.; Rainer, Alberto; Forte, Giancarlo

2015-01-01

Recent advances in biomedical technologies are mostly related to the convergence of biology with microengineering. For instance, microfluidic devices are now commonly found in most research centers, clinics and hospitals, contributing to more accurate studies and therapies as powerful tools for drug delivery, monitoring of specific analytes, and medical diagnostics. Most remarkably, integration of cellularized constructs within microengineered platforms has enabled the recapitulation of the physiological and pathological conditions of complex tissues and organs. The so-called “organ-on-a-chip” technology, which represents a new avenue in the field of advanced in vitro models, with the potential to revolutionize current approaches to drug screening and toxicology studies. This review aims to highlight recent advances of microfluidic-based devices towards a body-on-a-chip concept, exploring their technology and broad applications in the biomedical field. PMID:26690442

Big Biomedical data as the key resource for discovery science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Toga, Arthur W.; Foster, Ian; Kesselman, Carl

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an “-ome to home” approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage,more » aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center’s computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson’s and Alzheimer’s.« less

Big biomedical data as the key resource for discovery science

PubMed Central

Toga, Arthur W; Foster, Ian; Kesselman, Carl; Madduri, Ravi; Chard, Kyle; Deutsch, Eric W; Price, Nathan D; Glusman, Gustavo; Heavner, Benjamin D; Dinov, Ivo D; Ames, Joseph; Van Horn, John; Kramer, Roger; Hood, Leroy

2015-01-01

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an “-ome to home” approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center’s computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson’s and Alzheimer’s. PMID:26198305

Assessing Understanding of Complex Learning Outcomes and Real-World Skills Using an Authentic Software Tool: A Study from Biomedical Sciences

ERIC Educational Resources Information Center

Dermo, John; Boyne, James

2014-01-01

We describe a study conducted during 2009-12 into innovative assessment practice, evaluating an assessed coursework task on a final year Medical Genetics module for Biomedical Science undergraduates. An authentic e-assessment coursework task was developed, integrating objectively marked online questions with an online DNA sequence analysis tool…

Next-generation biomedical implants using additive manufacturing of complex, cellular and functional mesh arrays.

PubMed

Murr, L E; Gaytan, S M; Medina, F; Lopez, H; Martinez, E; Machado, B I; Hernandez, D H; Martinez, L; Lopez, M I; Wicker, R B; Bracke, J

2010-04-28

In this paper, we examine prospects for the manufacture of patient-specific biomedical implants replacing hard tissues (bone), particularly knee and hip stems and large bone (femoral) intramedullary rods, using additive manufacturing (AM) by electron beam melting (EBM). Of particular interest is the fabrication of complex functional (biocompatible) mesh arrays. Mesh elements or unit cells can be divided into different regions in order to use different cell designs in different areas of the component to produce various or continually varying (functionally graded) mesh densities. Numerous design elements have been used to fabricate prototypes by AM using EBM of Ti-6Al-4V powders, where the densities have been compared with the elastic (Young) moduli determined by resonant frequency and damping analysis. Density optimization at the bone-implant interface can allow for bone ingrowth and cementless implant components. Computerized tomography (CT) scans of metal (aluminium alloy) foam have also allowed for the building of Ti-6Al-4V foams by embedding the digital-layered scans in computer-aided design or software models for EBM. Variations in mesh complexity and especially strut (or truss) dimensions alter the cooling and solidification rate, which alters the alpha-phase (hexagonal close-packed) microstructure by creating mixtures of alpha/alpha' (martensite) observed by optical and electron metallography. Microindentation hardness measurements are characteristic of these microstructures and microstructure mixtures (alpha/alpha') and sizes.

Role of Airway Recruitment and Derecruitment in Lung Injury

PubMed Central

Ghadiali, S. N.; Huang, Y.

2011-01-01

The mechanical forces generated during the ventilation of patients with acute lung injury causes significant lung damage and inflammation. Low-volume ventilation protocols are commonly used to prevent stretch-related injury that occurs at high lung volumes. However, the cyclic closure and reopening of pulmonary airways at low lung volumes, i.e., derecruitment and recruitment, also causes significant lung damage and inflammation. In this review, we provide an overview of how biomedical engineering techniques are being used to elucidate the complex physiological and biomechanical mechanisms responsible for cellular injury during recruitment/derecruitment. We focus on the development of multiscale, multiphysics computational models of cell deformation and injury during airway reopening. These models, and the corresponding in vitro experiments, have been used to both elucidate the basic mechanisms responsible for recruitment/derecruitment injury and to develop alternative therapies that make the epithelium more resistant to injury. For example, models and experiments indicate that fluidization of the cytoskeleton is cytoprotective and that changes in cytoskeletal structure and cell mechanics can be used to mitigate the mechanotransduction of oscillatory pressure into inflammatory signaling. The continued application of biomedical engineering techniques to the problem of recruitment/derecruitment injury may therefore lead to novel and more effective therapies. PMID:22011235

Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey

PubMed Central

Belle, Ashwin; Kon, Mark A.; Najarian, Kayvan

2013-01-01

The volumes of current patient data as well as their complexity make clinical decision making more challenging than ever for physicians and other care givers. This situation calls for the use of biomedical informatics methods to process data and form recommendations and/or predictions to assist such decision makers. The design, implementation, and use of biomedical informatics systems in the form of computer-aided decision support have become essential and widely used over the last two decades. This paper provides a brief review of such systems, their application protocols and methodologies, and the future challenges and directions they suggest. PMID:23431259

Enabling Large-Scale Biomedical Analysis in the Cloud

PubMed Central

Lin, Ying-Chih; Yu, Chin-Sheng; Lin, Yen-Jen

2013-01-01

Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data. This work briefly introduces the data intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the vast amount of diversification data meaningful and usable. PMID:24288665

Advances in Electronic-Nose Technologies Developed for Biomedical Applications

PubMed Central

Wilson, Alphus D.; Baietto, Manuela

2011-01-01

The research and development of new electronic-nose applications in the biomedical field has accelerated at a phenomenal rate over the past 25 years. Many innovative e-nose technologies have provided solutions and applications to a wide variety of complex biomedical and healthcare problems. The purposes of this review are to present a comprehensive analysis of past and recent biomedical research findings and developments of electronic-nose sensor technologies, and to identify current and future potential e-nose applications that will continue to advance the effectiveness and efficiency of biomedical treatments and healthcare services for many years. An abundance of electronic-nose applications has been developed for a variety of healthcare sectors including diagnostics, immunology, pathology, patient recovery, pharmacology, physical therapy, physiology, preventative medicine, remote healthcare, and wound and graft healing. Specific biomedical e-nose applications range from uses in biochemical testing, blood-compatibility evaluations, disease diagnoses, and drug delivery to monitoring of metabolic levels, organ dysfunctions, and patient conditions through telemedicine. This paper summarizes the major electronic-nose technologies developed for healthcare and biomedical applications since the late 1980s when electronic aroma detection technologies were first recognized to be potentially useful in providing effective solutions to problems in the healthcare industry. PMID:22346620

Incorporating collaboratory concepts into informatics in support of translational interdisciplinary biomedical research

PubMed Central

Lee, E. Sally; McDonald, David W.; Anderson, Nicholas; Tarczy-Hornoch, Peter

2008-01-01

Due to its complex nature, modern biomedical research has become increasingly interdisciplinary and collaborative in nature. Although a necessity, interdisciplinary biomedical collaboration is difficult. There is, however, a growing body of literature on the study and fostering of collaboration in fields such as computer supported cooperative work (CSCW) and information science (IS). These studies of collaboration provide insight into how to potentially alleviate the difficulties of interdisciplinary collaborative research. We, therefore, undertook a cross cutting study of science and engineering collaboratories to identify emergent themes. We review many relevant collaboratory concepts: (a) general collaboratory concepts across many domains: communication, common workspace and coordination, and data sharing and management, (b) specific collaboratory concepts of particular biomedical relevance: data integration and analysis, security structure, metadata and data provenance, and interoperability and data standards, (c) environmental factors that support collaboratories: administrative and management structure, technical support, and available funding as critical environmental factors, and (d) future considerations for biomedical collaboration: appropriate training and long-term planning. In our opinion, the collaboratory concepts we discuss can guide planning and design of future collaborative infrastructure by biomedical informatics researchers to alleviate some of the difficulties of interdisciplinary biomedical collaboration. PMID:18706852

Livestock in biomedical research: history, current status and future prospective.

PubMed

Polejaeva, Irina A; Rutigliano, Heloisa M; Wells, Kevin D

2016-01-01

Livestock models have contributed significantly to biomedical and surgical advances. Their contribution is particularly prominent in the areas of physiology and assisted reproductive technologies, including understanding developmental processes and disorders, from ancient to modern times. Over the past 25 years, biomedical research that traditionally embraced a diverse species approach shifted to a small number of model species (e.g. mice and rats). The initial reasons for focusing the main efforts on the mouse were the availability of murine embryonic stem cells (ESCs) and genome sequence data. This powerful combination allowed for precise manipulation of the mouse genome (knockouts, knockins, transcriptional switches etc.) leading to ground-breaking discoveries on gene functions and regulation, and their role in health and disease. Despite the enormous contribution to biomedical research, mouse models have some major limitations. Their substantial differences compared with humans in body and organ size, lifespan and inbreeding result in pronounced metabolic, physiological and behavioural differences. Comparative studies of strategically chosen domestic species can complement mouse research and yield more rigorous findings. Because genome sequence and gene manipulation tools are now available for farm animals (cattle, pigs, sheep and goats), a larger number of livestock genetically engineered (GE) models will be accessible for biomedical research. This paper discusses the use of cattle, goats, sheep and pigs in biomedical research, provides an overview of transgenic technology in farm animals and highlights some of the beneficial characteristics of large animal models of human disease compared with the mouse. In addition, status and origin of current regulation of GE biomedical models is also reviewed.

DEApp: an interactive web interface for differential expression analysis of next generation sequence data.

PubMed

Li, Yan; Andrade, Jorge

2017-01-01

A growing trend in the biomedical community is the use of Next Generation Sequencing (NGS) technologies in genomics research. The complexity of downstream differential expression (DE) analysis is however still challenging, as it requires sufficient computer programing and command-line knowledge. Furthermore, researchers often need to evaluate and visualize interactively the effect of using differential statistical and error models, assess the impact of selecting different parameters and cutoffs, and finally explore the overlapping consensus of cross-validated results obtained with different methods. This represents a bottleneck that slows down or impedes the adoption of NGS technologies in many labs. We developed DEApp, an interactive and dynamic web application for differential expression analysis of count based NGS data. This application enables models selection, parameter tuning, cross validation and visualization of results in a user-friendly interface. DEApp enables labs with no access to full time bioinformaticians to exploit the advantages of NGS applications in biomedical research. This application is freely available at https://yanli.shinyapps.io/DEAppand https://gallery.shinyapps.io/DEApp.

Active learning-based information structure analysis of full scientific articles and two applications for biomedical literature review.

PubMed

Guo, Yufan; Silins, Ilona; Stenius, Ulla; Korhonen, Anna

2013-06-01

Techniques that are capable of automatically analyzing the information structure of scientific articles could be highly useful for improving information access to biomedical literature. However, most existing approaches rely on supervised machine learning (ML) and substantial labeled data that are expensive to develop and apply to different sub-fields of biomedicine. Recent research shows that minimal supervision is sufficient for fairly accurate information structure analysis of biomedical abstracts. However, is it realistic for full articles given their high linguistic and informational complexity? We introduce and release a novel corpus of 50 biomedical articles annotated according to the Argumentative Zoning (AZ) scheme, and investigate active learning with one of the most widely used ML models-Support Vector Machines (SVM)-on this corpus. Additionally, we introduce two novel applications that use AZ to support real-life literature review in biomedicine via question answering and summarization. We show that active learning with SVM trained on 500 labeled sentences (6% of the corpus) performs surprisingly well with the accuracy of 82%, just 2% lower than fully supervised learning. In our question answering task, biomedical researchers find relevant information significantly faster from AZ-annotated than unannotated articles. In the summarization task, sentences extracted from particular zones are significantly more similar to gold standard summaries than those extracted from particular sections of full articles. These results demonstrate that active learning of full articles' information structure is indeed realistic and the accuracy is high enough to support real-life literature review in biomedicine. The annotated corpus, our AZ classifier and the two novel applications are available at http://www.cl.cam.ac.uk/yg244/12bioinfo.html

Space Science

NASA Image and Video Library

2003-06-01

NASA’s Virtual Glovebox (VGX) was developed to allow astronauts on Earth to train for complex biology research tasks in space. The astronauts may reach into the virtual environment, naturally manipulating specimens, tools, equipment, and accessories in a simulated microgravity environment as they would do in space. Such virtual reality technology also provides engineers and space operations staff with rapid prototyping, planning, and human performance modeling capabilities. Other Earth based applications being explored for this technology include biomedical procedural training and training for disarming bio-terrorism weapons.

Virtual Glovebox (VGX) Aids Astronauts in Pre-Flight Training

NASA Technical Reports Server (NTRS)

2003-01-01

NASA's Virtual Glovebox (VGX) was developed to allow astronauts on Earth to train for complex biology research tasks in space. The astronauts may reach into the virtual environment, naturally manipulating specimens, tools, equipment, and accessories in a simulated microgravity environment as they would do in space. Such virtual reality technology also provides engineers and space operations staff with rapid prototyping, planning, and human performance modeling capabilities. Other Earth based applications being explored for this technology include biomedical procedural training and training for disarming bio-terrorism weapons.

The biomedical model of mental disorder: a critical analysis of its validity, utility, and effects on psychotherapy research.

PubMed

Deacon, Brett J

2013-11-01

The biomedical model posits that mental disorders are brain diseases and emphasizes pharmacological treatment to target presumed biological abnormalities. A biologically-focused approach to science, policy, and practice has dominated the American healthcare system for more than three decades. During this time, the use of psychiatric medications has sharply increased and mental disorders have become commonly regarded as brain diseases caused by chemical imbalances that are corrected with disease-specific drugs. However, despite widespread faith in the potential of neuroscience to revolutionize mental health practice, the biomedical model era has been characterized by a broad lack of clinical innovation and poor mental health outcomes. In addition, the biomedical paradigm has profoundly affected clinical psychology via the adoption of drug trial methodology in psychotherapy research. Although this approach has spurred the development of empirically supported psychological treatments for numerous mental disorders, it has neglected treatment process, inhibited treatment innovation and dissemination, and divided the field along scientist and practitioner lines. The neglected biopsychosocial model represents an appealing alternative to the biomedical approach, and an honest and public dialog about the validity and utility of the biomedical paradigm is urgently needed. Copyright © 2013 Elsevier Ltd. All rights reserved.

ReVeaLD: a user-driven domain-specific interactive search platform for biomedical research.

PubMed

Kamdar, Maulik R; Zeginis, Dimitris; Hasnain, Ali; Decker, Stefan; Deus, Helena F

2014-02-01

Bioinformatics research relies heavily on the ability to discover and correlate data from various sources. The specialization of life sciences over the past decade, coupled with an increasing number of biomedical datasets available through standardized interfaces, has created opportunities towards new methods in biomedical discovery. Despite the popularity of semantic web technologies in tackling the integrative bioinformatics challenge, there are many obstacles towards its usage by non-technical research audiences. In particular, the ability to fully exploit integrated information needs using improved interactive methods intuitive to the biomedical experts. In this report we present ReVeaLD (a Real-time Visual Explorer and Aggregator of Linked Data), a user-centered visual analytics platform devised to increase intuitive interaction with data from distributed sources. ReVeaLD facilitates query formulation using a domain-specific language (DSL) identified by biomedical experts and mapped to a self-updated catalogue of elements from external sources. ReVeaLD was implemented in a cancer research setting; queries included retrieving data from in silico experiments, protein modeling and gene expression. ReVeaLD was developed using Scalable Vector Graphics and JavaScript and a demo with explanatory video is available at http://www.srvgal78.deri.ie:8080/explorer. A set of user-defined graphic rules controls the display of information through media-rich user interfaces. Evaluation of ReVeaLD was carried out as a game: biomedical researchers were asked to assemble a set of 5 challenge questions and time and interactions with the platform were recorded. Preliminary results indicate that complex queries could be formulated under less than two minutes by unskilled researchers. The results also indicate that supporting the identification of the elements of a DSL significantly increased intuitiveness of the platform and usability of semantic web technologies by domain users. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Completion of the swine genome will simplify the production of swine as a large animal biomedical model

PubMed Central

2012-01-01

Background Anatomic and physiological similarities to the human make swine an excellent large animal model for human health and disease. Methods Cloning from a modified somatic cell, which can be determined in cells prior to making the animal, is the only method available for the production of targeted modifications in swine. Results Since some strains of swine are similar in size to humans, technologies that have been developed for swine can be readily adapted to humans and vice versa. Here the importance of swine as a biomedical model, current technologies to produce genetically enhanced swine, current biomedical models, and how the completion of the swine genome will promote swine as a biomedical model are discussed. Conclusions The completion of the swine genome will enhance the continued use and development of swine as models of human health, syndromes and conditions. PMID:23151353

High-Fidelity Simulation in Biomedical and Aerospace Engineering

NASA Technical Reports Server (NTRS)

Kwak, Dochan

2005-01-01

Contents include the following: Introduction / Background. Modeling and Simulation Challenges in Aerospace Engineering. Modeling and Simulation Challenges in Biomedical Engineering. Digital Astronaut. Project Columbia. Summary and Discussion.

Data management integration for biomedical core facilities

NASA Astrophysics Data System (ADS)

Zhang, Guo-Qiang; Szymanski, Jacek; Wilson, David

2007-03-01

We present the design, development, and pilot-deployment experiences of MIMI, a web-based, Multi-modality Multi-Resource Information Integration environment for biomedical core facilities. This is an easily customizable, web-based software tool that integrates scientific and administrative support for a biomedical core facility involving a common set of entities: researchers; projects; equipments and devices; support staff; services; samples and materials; experimental workflow; large and complex data. With this software, one can: register users; manage projects; schedule resources; bill services; perform site-wide search; archive, back-up, and share data. With its customizable, expandable, and scalable characteristics, MIMI not only provides a cost-effective solution to the overarching data management problem of biomedical core facilities unavailable in the market place, but also lays a foundation for data federation to facilitate and support discovery-driven research.

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach.

PubMed

Elgendi, Mohamed

2016-11-02

Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages ("TERMA") involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages ( W 1 and W 2 ) have to follow the inequality ( 8 × W 1 ) ≥ W 2 ≥ ( 2 × W 1 ) . Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions.

Outcome of a Workshop on Applications of Protein Models in Biomedical Research

PubMed Central

Schwede, Torsten; Sali, Andrej; Honig, Barry; Levitt, Michael; Berman, Helen M.; Jones, David; Brenner, Steven E.; Burley, Stephen K.; Das, Rhiju; Dokholyan, Nikolay V.; Dunbrack, Roland L.; Fidelis, Krzysztof; Fiser, Andras; Godzik, Adam; Huang, Yuanpeng Janet; Humblet, Christine; Jacobson, Matthew P.; Joachimiak, Andrzej; Krystek, Stanley R.; Kortemme, Tanja; Kryshtafovych, Andriy; Montelione, Gaetano T.; Moult, John; Murray, Diana; Sanchez, Roberto; Sosnick, Tobin R.; Standley, Daron M.; Stouch, Terry; Vajda, Sandor; Vasquez, Max; Westbrook, John D.; Wilson, Ian A.

2009-01-01

Summary We describe the proceedings and conclusions from a “Workshop on Applications of Protein Models in Biomedical Research” that was held at University of California at San Francisco on 11 and 12 July, 2008. At the workshop, international scientists involved with structure modeling explored (i) how models are currently used in biomedical research, (ii) what the requirements and challenges for different applications are, and (iii) how the interaction between the computational and experimental research communities could be strengthened to advance the field. PMID:19217386

Identifying impact of software dependencies on replicability of biomedical workflows.

PubMed

Miksa, Tomasz; Rauber, Andreas; Mina, Eleni

2016-12-01

Complex data driven experiments form the basis of biomedical research. Recent findings warn that the context in which the software is run, that is the infrastructure and the third party dependencies, can have a crucial impact on the final results delivered by a computational experiment. This implies that in order to replicate the same result, not only the same data must be used, but also it must be run on an equivalent software stack. In this paper we present the VFramework that enables assessing replicability of workflows. It identifies whether any differences in software dependencies among two executions of the same workflow exist and whether they have impact on the produced results. We also conduct a case study in which we investigate the impact of software dependencies on replicability of Taverna workflows used in biomedical research of Huntington's disease. We re-execute analysed workflows in environments differing in operating system distribution and configuration. The results show that the VFramework can be used to identify the impact of software dependencies on the replicability of biomedical workflows. Furthermore, we observe that despite the fact that the workflows are executed in a controlled environment, they still depend on specific tools installed in the environment. The context model used by the VFramework improves the deficiencies of provenance traces and documents also such tools. Based on our findings we define guidelines for workflow owners that enable them to improve replicability of their workflows. Copyright © 2016 Elsevier Inc. All rights reserved.

Healthcare professionals' response to cachexia in advanced cancer: a qualitative study.

PubMed

Millar, Claire; Reid, Joanne; Porter, Sam

2013-11-01

To explore healthcare professionals' experience, understanding, and perception of the needs of patients with cachexia in advanced cancer. A qualitative approach based on symbolic interactionism. A regional cancer center in a large teaching hospital in the United Kingdom. 34 healthcare professionals who had experience providing care to patients with cachexia in advanced cancer. Data collection consisted of two phases: focus group and semistructured interviews. Interviews were digitally recorded and transcribed verbatim for analysis. This article reports on findings from the second phase of data collection. Analysis revealed that professional approaches to cachexia were influenced by three overarching and interthinking themes: knowledge, culture, and resources. Healthcare professionals commonly recognized the impact of the syndrome; however, for nonpalliative healthcare professionals, a culture of avoidance and an overreliance on the biomedical model of care had considerable influence on the management of cachexia in patients with advanced cancer. Cachexia management in patients with advanced cancer can be difficult and is directed by a variable combination of the influence of knowledge, culture of the clinical area, and available resources. Distinct differences exist in the management of cachexia among palliative and nonpalliative care professionals. This study presented a multiprofessional perspective on the management of cachexia in patients with advanced cancer and revealed that cachexia is a complex and challenging syndrome that needs to be addressed from a holistic model of care. Cachexia management in patients with advanced cancer is complex and challenging and is directed by a combination of variables. An overreliance on the biomedical model of health and illness occurs in the management of cachexia in patients with advanced cancer. Cachexia needs to be addressed from a holistic model of care to reflect the multidimensional needs of patients and their families.

FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining.

PubMed

Bachman, John A; Gyori, Benjamin M; Sorger, Peter K

2018-06-28

For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or "grounding." Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers. In a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., "AKT") and complexes with multiple subunits (e.g."NF- κB"). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15 to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity Normalization Task dataset demonstrated the utility of FamPlex in other settings. FamPlex is an effective resource for improving named entity recognition, grounding, and relationship resolution in automated reading of biomedical text. The content in FamPlex is available in both tabular and Open Biomedical Ontology formats at https://github.com/sorgerlab/famplex under the Creative Commons CC0 license and has been integrated into the TRIPS/DRUM and REACH reading systems.

Improving validity of informed consent for biomedical research in Zambia using a laboratory exposure intervention.

PubMed

Zulu, Joseph Mumba; Lisulo, Mpala Mwanza; Besa, Ellen; Kaonga, Patrick; Chisenga, Caroline C; Chomba, Mumba; Simuyandi, Michelo; Banda, Rosemary; Kelly, Paul

2014-01-01

Complex biomedical research can lead to disquiet in communities with limited exposure to scientific discussions, leading to rumours or to high drop-out rates. We set out to test an intervention designed to address apprehensions commonly encountered in a community where literacy is uncommon, and where complex biomedical research has been conducted for over a decade. We aimed to determine if it could improve the validity of consent. Data were collected using focus group discussions, key informant interviews and observations. We designed an intervention that exposed participants to a detailed demonstration of laboratory processes. Each group was interviewed twice in a day, before and after exposure to the intervention in order to assess changes in their views. Factors that motivated people to participate in invasive biomedical research included a desire to stay healthy because of the screening during the recruitment process, regular advice from doctors, free medical services, and trust in the researchers. Inhibiting factors were limited knowledge about samples taken from their bodies during endoscopic procedures, the impact of endoscopy on the function of internal organs, and concerns about the use of biomedical samples. The belief that blood can be used for Satanic practices also created insecurities about drawing of blood samples. Further inhibiting factors included a fear of being labelled as HIV positive if known to consult heath workers repeatedly, and gender inequality. Concerns about the use and storage of blood and tissue samples were overcome by a laboratory exposure intervention. Selecting a group of members from target community and engaging them in a laboratory exposure intervention could be a useful tool for enhancing specific aspects of consent for biomedical research. Further work is needed to determine the extent to which improved understanding permeates beyond the immediate group participating in the intervention.

The role of fractional calculus in modeling biological phenomena: A review

NASA Astrophysics Data System (ADS)

Ionescu, C.; Lopes, A.; Copot, D.; Machado, J. A. T.; Bates, J. H. T.

2017-10-01

This review provides the latest developments and trends in the application of fractional calculus (FC) in biomedicine and biology. Nature has often showed to follow rather simple rules that lead to the emergence of complex phenomena as a result. Of these, the paper addresses the properties in respiratory lung tissue, whose natural solutions arise from the midst of FC in the form of non-integer differ-integral solutions and non-integer parametric models. Diffusion of substances in human body, e.g. drug diffusion, is also a phenomena well known to be captured with such mathematical models. FC has been employed in neuroscience to characterize the generation of action potentials and spiking patters but also in characterizing bio-systems (e.g. vegetable tissues). Despite the natural complexity, biological systems belong as well to this class of systems, where FC has offered parsimonious yet accurate models. This review paper is a collection of results and literature reports who are essential to any versed engineer with multidisciplinary applications and bio-medical in particular.

A common layer of interoperability for biomedical ontologies based on OWL EL.

PubMed

Hoehndorf, Robert; Dumontier, Michel; Oellrich, Anika; Wimalaratne, Sarala; Rebholz-Schuhmann, Dietrich; Schofield, Paul; Gkoutos, Georgios V

2011-04-01

Ontologies are essential in biomedical research due to their ability to semantically integrate content from different scientific databases and resources. Their application improves capabilities for querying and mining biological knowledge. An increasing number of ontologies is being developed for this purpose, and considerable effort is invested into formally defining them in order to represent their semantics explicitly. However, current biomedical ontologies do not facilitate data integration and interoperability yet, since reasoning over these ontologies is very complex and cannot be performed efficiently or is even impossible. We propose the use of less expressive subsets of ontology representation languages to enable efficient reasoning and achieve the goal of genuine interoperability between ontologies. We present and evaluate EL Vira, a framework that transforms OWL ontologies into the OWL EL subset, thereby enabling the use of tractable reasoning. We illustrate which OWL constructs and inferences are kept and lost following the conversion and demonstrate the performance gain of reasoning indicated by the significant reduction of processing time. We applied EL Vira to the open biomedical ontologies and provide a repository of ontologies resulting from this conversion. EL Vira creates a common layer of ontological interoperability that, for the first time, enables the creation of software solutions that can employ biomedical ontologies to perform inferences and answer complex queries to support scientific analyses. The EL Vira software is available from http://el-vira.googlecode.com and converted OBO ontologies and their mappings are available from http://bioonto.gen.cam.ac.uk/el-ont.

Computer-based Learning of Neuroanatomy: A Longitudinal Study of Learning, Transfer, and Retention

PubMed Central

Chariker, Julia H.; Naaz, Farah; Pani, John R.

2013-01-01

A longitudinal experiment was conducted to evaluate the effectiveness of new methods for learning neuroanatomy with computer-based instruction. Using a 3D graphical model of the human brain, and sections derived from the model, tools for exploring neuroanatomy were developed to encourage adaptive exploration. This is an instructional method which incorporates graphical exploration in the context of repeated testing and feedback. With this approach, 72 participants learned either sectional anatomy alone or whole anatomy followed by sectional anatomy. Sectional anatomy was explored either with perceptually continuous navigation through the sections or with discrete navigation (as in the use of an anatomical atlas). Learning was measured longitudinally to a high performance criterion. Subsequent tests examined transfer of learning to the interpretation of biomedical images and long-term retention. There were several clear results of this study. On initial exposure to neuroanatomy, whole anatomy was learned more efficiently than sectional anatomy. After whole anatomy was mastered, learners demonstrated high levels of transfer of learning to sectional anatomy and from sectional anatomy to the interpretation of complex biomedical images. Learning whole anatomy prior to learning sectional anatomy led to substantially fewer errors overall than learning sectional anatomy alone. Use of continuous or discrete navigation through sectional anatomy made little difference to measured outcomes. Efficient learning, good long-term retention, and successful transfer to the interpretation of biomedical images indicated that computer-based learning using adaptive exploration can be a valuable tool in instruction of neuroanatomy and similar disciplines. PMID:23349552

ENABLE 2017, the First EUROPEAN PhD and Post-Doc Symposium. Session 4: From Discovery to Cure: The Future of Therapeutics.

PubMed

Di Mauro, Gianmarco; Dondi, Ambra; Giangreco, Giovanni; Hogrebe, Alexander; Louer, Elja; Magistrati, Elisa; Mullari, Meeli; Turon, Gemma; Verdurmen, Wouter; Xicoy Cortada, Helena; Zivanovic, Sanja

2018-05-28

The EUROPEAN ACADEMY FOR BIOMEDICAL SCIENCE (ENABLE) is an initiative funded by the European Union Horizon 2020 program involving four renowned European Research Institutes (Institute for Research in Biomedicine-IRB Barcelona, Spain; Radboud Institute for Molecular Life Sciences-RIMLS, the Netherlands; Novo Nordisk Foundation Center for Protein Research-NNF CPR, Denmark; European School of Molecular Medicine-SEMM, Italy) and an innovative science communication agency (Scienseed). With the aim of promoting biomedical science of excellence in Europe, ENABLE organizes an annual three-day international event. This gathering includes a top-level scientific symposium bringing together leading scientists, PhD students, and post-doctoral fellows; career development activities supporting the progression of young researchers and fostering discussion about opportunities beyond the bench; and outreach activities stimulating the interaction between science and society. The first European PhD and Postdoc Symposium, entitled "Breaking Down Complexity: Innovative Models and Techniques in Biomedicine", was hosted by the vibrant city of Barcelona. The scientific program of the conference was focused on the most recent advances and applications of modern techniques and models in biomedical research and covered a wide range of topics, from synthetic biology to translational medicine. Overall, the event was a great success, with more than 200 attendees from all over Europe actively participating in the symposium by presenting their research and exchanging ideas with their peers and world-renowned scientists.

USNCTAM perspectives on mechanics in medicine.

PubMed

Bao, Gang; Bazilevs, Yuri; Chung, Jae-Hyun; Decuzzi, Paolo; Espinosa, Horacio D; Ferrari, Mauro; Gao, Huajian; Hossain, Shaolie S; Hughes, Thomas J R; Kamm, Roger D; Liu, Wing Kam; Marsden, Alison; Schrefler, Bernhard

2014-08-06

Over decades, the theoretical and applied mechanics community has developed sophisticated approaches for analysing the behaviour of complex engineering systems. Most of these approaches have targeted systems in the transportation, materials, defence and energy industries. Applying and further developing engineering approaches for understanding, predicting and modulating the response of complicated biomedical processes not only holds great promise in meeting societal needs, but also poses serious challenges. This report, prepared for the US National Committee on Theoretical and Applied Mechanics, aims to identify the most pressing challenges in biological sciences and medicine that can be tackled within the broad field of mechanics. This echoes and complements a number of national and international initiatives aiming at fostering interdisciplinary biomedical research. This report also comments on cultural/educational challenges. Specifically, this report focuses on three major thrusts in which we believe mechanics has and will continue to have a substantial impact. (i) Rationally engineering injectable nano/microdevices for imaging and therapy of disease. Within this context, we discuss nanoparticle carrier design, vascular transport and adhesion, endocytosis and tumour growth in response to therapy, as well as uncertainty quantification techniques to better connect models and experiments. (ii) Design of biomedical devices, including point-of-care diagnostic systems, model organ and multi-organ microdevices, and pulsatile ventricular assistant devices. (iii) Mechanics of cellular processes, including mechanosensing and mechanotransduction, improved characterization of cellular constitutive behaviour, and microfluidic systems for single-cell studies. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response.

PubMed

Binder, Harald; Sauerbrei, Willi; Royston, Patrick

2013-06-15

In observational studies, many continuous or categorical covariates may be related to an outcome. Various spline-based procedures or the multivariable fractional polynomial (MFP) procedure can be used to identify important variables and functional forms for continuous covariates. This is the main aim of an explanatory model, as opposed to a model only for prediction. The type of analysis often guides the complexity of the final model. Spline-based procedures and MFP have tuning parameters for choosing the required complexity. To compare model selection approaches, we perform a simulation study in the linear regression context based on a data structure intended to reflect realistic biomedical data. We vary the sample size, variance explained and complexity parameters for model selection. We consider 15 variables. A sample size of 200 (1000) and R(2)  = 0.2 (0.8) is the scenario with the smallest (largest) amount of information. For assessing performance, we consider prediction error, correct and incorrect inclusion of covariates, qualitative measures for judging selected functional forms and further novel criteria. From limited information, a suitable explanatory model cannot be obtained. Prediction performance from all types of models is similar. With a medium amount of information, MFP performs better than splines on several criteria. MFP better recovers simpler functions, whereas splines better recover more complex functions. For a large amount of information and no local structure, MFP and the spline procedures often select similar explanatory models. Copyright © 2012 John Wiley & Sons, Ltd.

Collaborative mining and interpretation of large-scale data for biomedical research insights.

PubMed

Tsiliki, Georgia; Karacapilidis, Nikos; Christodoulou, Spyros; Tzagarakis, Manolis

2014-01-01

Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence.

Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights

PubMed Central

Tsiliki, Georgia; Karacapilidis, Nikos; Christodoulou, Spyros; Tzagarakis, Manolis

2014-01-01

Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence. PMID:25268270

UCSC genome browser: deep support for molecular biomedical research.

PubMed

Mangan, Mary E; Williams, Jennifer M; Lathe, Scott M; Karolchik, Donna; Lathe, Warren C

2008-01-01

The volume and complexity of genomic sequence data, and the additional experimental data required for annotation of the genomic context, pose a major challenge for display and access for biomedical researchers. Genome browsers organize this data and make it available in various ways to extract useful information to advance research projects. The UCSC Genome Browser is one of these resources. The official sequence data for a given species forms the framework to display many other types of data such as expression, variation, cross-species comparisons, and more. Visual representations of the data are available for exploration. Data can be queried with sequences. Complex database queries are also easily achieved with the Table Browser interface. Associated tools permit additional query types or access to additional data sources such as images of in situ localizations. Support for solving researcher's issues is provided with active discussion mailing lists and by providing updated training materials. The UCSC Genome Browser provides a source of deep support for a wide range of biomedical molecular research (http://genome.ucsc.edu).

Crowdsourcing biomedical research: leveraging communities as innovation engines

PubMed Central

Saez-Rodriguez, Julio; Costello, James C.; Friend, Stephen H.; Kellen, Michael R.; Mangravite, Lara; Meyer, Pablo; Norman, Thea; Stolovitzky, Gustavo

2018-01-01

The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories. PMID:27418159

Crowdsourcing biomedical research: leveraging communities as innovation engines.

PubMed

Saez-Rodriguez, Julio; Costello, James C; Friend, Stephen H; Kellen, Michael R; Mangravite, Lara; Meyer, Pablo; Norman, Thea; Stolovitzky, Gustavo

2016-07-15

The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.

Increased co-first authorships in biomedical and clinical publications: a call for recognition.

PubMed

Conte, Marisa L; Maat, Stacy L; Omary, M Bishr

2013-10-01

There has been a dramatic increase in the number and percentage of publications in biomedical and clinical journals in which two or more coauthors claim first authorship, with a change in some journals from no joint first authorship in 1990 to co-first authorship of >30% of all research publications in 2012. As biomedical and clinical research become increasingly complex and team-driven, and given the importance attributed to first authorship by grant reviewers and promotion and tenure committees, the time is ripe for journals, bibliographic databases, and authors to highlight equal first author contributions of published original research.

Dynamic knowledge representation using agent-based modeling: ontology instantiation and verification of conceptual models.

PubMed

An, Gary

2009-01-01

The sheer volume of biomedical research threatens to overwhelm the capacity of individuals to effectively process this information. Adding to this challenge is the multiscale nature of both biological systems and the research community as a whole. Given this volume and rate of generation of biomedical information, the research community must develop methods for robust representation of knowledge in order for individuals, and the community as a whole, to "know what they know." Despite increasing emphasis on "data-driven" research, the fact remains that researchers guide their research using intuitively constructed conceptual models derived from knowledge extracted from publications, knowledge that is generally qualitatively expressed using natural language. Agent-based modeling (ABM) is a computational modeling method that is suited to translating the knowledge expressed in biomedical texts into dynamic representations of the conceptual models generated by researchers. The hierarchical object-class orientation of ABM maps well to biomedical ontological structures, facilitating the translation of ontologies into instantiated models. Furthermore, ABM is suited to producing the nonintuitive behaviors that often "break" conceptual models. Verification in this context is focused at determining the plausibility of a particular conceptual model, and qualitative knowledge representation is often sufficient for this goal. Thus, utilized in this fashion, ABM can provide a powerful adjunct to other computational methods within the research process, as well as providing a metamodeling framework to enhance the evolution of biomedical ontologies.

Finding and accessing diagrams in biomedical publications.

PubMed

Kuhn, Tobias; Luong, ThaiBinh; Krauthammer, Michael

2012-01-01

Complex relationships in biomedical publications are often communicated by diagrams such as bar and line charts, which are a very effective way of summarizing and communicating multi-faceted data sets. Given the ever-increasing amount of published data, we argue that the precise retrieval of such diagrams is of great value for answering specific and otherwise hard-to-meet information needs. To this end, we demonstrate the use of advanced image processing and classification for identifying bar and line charts by the shape and relative location of the different image elements that make up the charts. With recall and precisions of close to 90% for the detection of relevant figures, we discuss the use of this technology in an existing biomedical image search engine, and outline how it enables new forms of literature queries over biomedical relationships that are represented in these charts.

Immediate detailed feedback to test-enhanced learning: an effective online educational tool.

PubMed

Wojcikowski, Ken; Kirk, Leslie

2013-11-01

Test-enhanced learning has gained popularity because it is an effective way to increase retention of knowledge; provided the student receives the correct answer soon after the test is taken. To determine whether detailed feedback provided to test-enhanced learning questions is an effective online educational tool for improving performance on complex biomedical information exams. A series of online multiple choice tests were developed to test knowledge of biomedical information that students were expected to know after each patient-case. Following submission of the student answers, one cohort (n = 52) received answers only while the following year, a second cohort (n = 51) received the answers with detailed feedback explaining why each answer was correct or incorrect. Students in both groups progressed through the series of online tests with little assessor intervention. Students receiving the answers along with the explanations within their feedback performed significantly better in the final biomedical information exam than those students receiving correct answers only. This pilot study found that the detailed feedback to test-enhanced learning questions is an important online learning tool. The increase in student performance in the complex biomedical information exam in this study suggests that detailed feedback should be investigated not only for increasing knowledge, but also be investigated for its effect on retention and application of knowledge.

An analytical model for inductively coupled implantable biomedical devices with ferrite rods.

PubMed

Theilmann, P T; Asbeck, P M

2009-02-01

Using approximations applicable to near field coupled implants simplified expressions for the complex mutual inductance of coaxial aligned coils with and without a cylindrical ferrite rod are derived. Experimental results for ferrite rods of various sizes and permeabilities are presented to verify the accuracy of this expression. An equivalent circuit model for the inductive link between an implant and power coil is then presented and used to investigate how ferrite size, permeability and loss affect the power available to the implant device. Enhancements in coupling provided by high frequency, low permeability nickel zinc rods are compared with low frequency high permeability manganese zinc rods.

Biosignals learning and synthesis using deep neural networks.

PubMed

Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo

2017-09-25

Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

Tsinghua-Johns Hopkins Joint Center for Biomedical Engineering Research: scientific and cultural exchange in undergraduate engineering.

PubMed

Wisneski, Andrew D; Huang, Lixia; Hong, Bo; Wang, Xiaoqin

2011-01-01

A model for an international undergraduate biomedical engineering research exchange program is outlined. In 2008, the Johns Hopkins University in collaboration with Tsinghua University in Beijing, China established the Tsinghua-Johns Hopkins Joint Center for Biomedical Engineering Research. Undergraduate biomedical engineering students from both universities are offered the opportunity to participate in research at the overseas institution. Programs such as these will not only provide research experiences for undergraduates but valuable cultural exchange and enrichment as well. Currently, strict course scheduling and rigorous curricula in most biomedical engineering programs may present obstacles for students to partake in study abroad opportunities. Universities are encouraged to harbor abroad opportunities for undergraduate engineering students, for which this particular program can serve as a model.

Peptide-mediated vectorization of metal complexes: conjugation strategies and biomedical applications.

PubMed

Soler, Marta; Feliu, Lidia; Planas, Marta; Ribas, Xavi; Costas, Miquel

2016-08-16

The rich chemical and structural versatility of transition metal complexes provides numerous novel paths to be pursued in the design of molecules that exert particular chemical or physicochemical effects that could operate over specific biological targets. However, the poor cell permeability of metallodrugs represents an important barrier for their therapeutic use. The conjugation between metal complexes and a functional peptide vector can be regarded as a versatile and potential strategy to improve their bioavailability and accumulation inside cells, and the site selectivity of their effect. This perspective lies in reviewing the recent advances in the design of metallopeptide conjugates for biomedical applications. Additionally, we highlight the studies where this approach has been directed towards the incorporation of redox active metal centers into living organisms for modulating the cellular redox balance, as a tool with application in anticancer therapy.

Biomedical and veterinary science can increase our understanding of coral disease

USGS Publications Warehouse

Work, Thierry M.; Richardson, Laurie L.; Reynolds, T.L.; Willis, Bette L.

2008-01-01

A balanced approach to coral disease investigation is critical for understanding the global decline of corals. Such an approach should involve the proper use of biomedical concepts, tools, and terminology to address confusion and promote clarity in the coral disease literature. Investigating disease in corals should follow a logical series of steps including identification of disease, systematic morphologic descriptions of lesions at the gross and cellular levels, measurement of health indices, and experiments to understand disease pathogenesis and the complex interactions between host, pathogen, and the environment. This model for disease investigation is widely accepted in the medical, veterinary and invertebrate pathology disciplines. We present standard biomedical rationale behind the detection, description, and naming of diseases and offer examples of the application of Koch's postulates to elucidate the etiology of some infectious diseases. Basic epidemiologic concepts are introduced to help investigators think systematically about the cause(s) of complex diseases. A major goal of disease investigation in corals and other organisms is to gather data that will enable the establishment of standardized case definitions to distinguish among diseases. Concepts and facts amassed from empirical studies over the centuries by medical and veterinary pathologists have standardized disease investigation and are invaluable to coral researchers because of the robust comparisons they enable; examples of these are given throughout this paper. Arguments over whether coral diseases are caused by primary versus opportunistic pathogens reflect the lack of data available to prove or refute such hypotheses and emphasize the need for coral disease investigations that focus on: characterizing the normal microbiota and physiology of the healthy host; defining ecological interactions within the microbial community associated with the host; and investigating host immunity, host-agent interactions, pathology, pathogenesis, and factors that promote the pathogenicity of the causative agent(s) of disease.

KaBOB: ontology-based semantic integration of biomedical databases.

PubMed

Livingston, Kevin M; Bada, Michael; Baumgartner, William A; Hunter, Lawrence E

2015-04-23

The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources. We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license. KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.

OBO to UML: Support for the development of conceptual models in the biomedical domain.

PubMed

Waldemarin, Ricardo C; de Farias, Cléver R G

2018-04-01

A conceptual model abstractly defines a number of concepts and their relationships for the purposes of understanding and communication. Once a conceptual model is available, it can also be used as a starting point for the development of a software system. The development of conceptual models using the Unified Modeling Language (UML) facilitates the representation of modeled concepts and allows software developers to directly reuse these concepts in the design of a software system. The OBO Foundry represents the most relevant collaborative effort towards the development of ontologies in the biomedical domain. The development of UML conceptual models in the biomedical domain may benefit from the use of domain-specific semantics and notation. Further, the development of these models may also benefit from the reuse of knowledge contained in OBO ontologies. This paper investigates the support for the development of conceptual models in the biomedical domain using UML as a conceptual modeling language and using the support provided by the OBO Foundry for the development of biomedical ontologies, namely entity kind and relationship types definitions provided by the Basic Formal Ontology (BFO) and the OBO Core Relations Ontology (OBO Core), respectively. Further, the paper investigates the support for the reuse of biomedical knowledge currently available in OBOFFF ontologies in the development these conceptual models. The paper describes a UML profile for the OBO Core Relations Ontology, which basically defines a number of stereotypes to represent BFO entity kinds and OBO Core relationship types definitions. The paper also presents a support toolset consisting of a graphical editor named OBO-RO Editor, which directly supports the development of UML models using the extensions defined by our profile, and a command-line tool named OBO2UML, which directly converts an OBOFFF ontology into a UML model. Copyright © 2018 Elsevier Inc. All rights reserved.

The function of dog models in developing gene therapy strategies for human health.

PubMed

Nowend, Keri L; Starr-Moss, Alison N; Murphy, Keith E

2011-08-01

The domestic dog is of great benefit to humankind, not only through companionship and working activities cultivated through domestication and selective breeding, but also as a model for biomedical research. Many single-gene traits have been well-characterized at the genomic level, and recent advances in whole-genome association studies will allow for better understanding of complex, multigenic hereditary diseases. Additionally, the dog serves as an invaluable large animal model for assessment of novel therapeutic agents. Thus, the dog has filled a crucial step in the translation of basic research to new treatment regimens for various human diseases. Four well-characterized diseases in canine models are discussed as they relate to other animal model availability, novel therapeutic approach, and extrapolation to human gene therapy trials.

Genetically engineered livestock for biomedical models.

PubMed

Rogers, Christopher S

2016-06-01

To commemorate Transgenic Animal Research Conference X, this review summarizes the recent progress in developing genetically engineered livestock species as biomedical models. The first of these conferences was held in 1997, which turned out to be a watershed year for the field, with two significant events occurring. One was the publication of the first transgenic livestock animal disease model, a pig with retinitis pigmentosa. Before that, the use of livestock species in biomedical research had been limited to wild-type animals or disease models that had been induced or were naturally occurring. The second event was the report of Dolly, a cloned sheep produced by somatic cell nuclear transfer. Cloning subsequently became an essential part of the process for most of the models developed in the last 18 years and is stilled used prominently today. This review is intended to highlight the biomedical modeling achievements that followed those key events, many of which were first reported at one of the previous nine Transgenic Animal Research Conferences. Also discussed are the practical challenges of utilizing livestock disease models now that the technical hurdles of model development have been largely overcome.

Using Semantic Web technologies for the generation of domain-specific templates to support clinical study metadata standards.

PubMed

Jiang, Guoqian; Evans, Julie; Endle, Cory M; Solbrig, Harold R; Chute, Christopher G

2016-01-01

The Biomedical Research Integrated Domain Group (BRIDG) model is a formal domain analysis model for protocol-driven biomedical research, and serves as a semantic foundation for application and message development in the standards developing organizations (SDOs). The increasing sophistication and complexity of the BRIDG model requires new approaches to the management and utilization of the underlying semantics to harmonize domain-specific standards. The objective of this study is to develop and evaluate a Semantic Web-based approach that integrates the BRIDG model with ISO 21090 data types to generate domain-specific templates to support clinical study metadata standards development. We developed a template generation and visualization system based on an open source Resource Description Framework (RDF) store backend, a SmartGWT-based web user interface, and a "mind map" based tool for the visualization of generated domain-specific templates. We also developed a RESTful Web Service informed by the Clinical Information Modeling Initiative (CIMI) reference model for access to the generated domain-specific templates. A preliminary usability study is performed and all reviewers (n = 3) had very positive responses for the evaluation questions in terms of the usability and the capability of meeting the system requirements (with the average score of 4.6). Semantic Web technologies provide a scalable infrastructure and have great potential to enable computable semantic interoperability of models in the intersection of health care and clinical research.

VarioML framework for comprehensive variation data representation and exchange.

PubMed

Byrne, Myles; Fokkema, Ivo Fac; Lancaster, Owen; Adamusiak, Tomasz; Ahonen-Bishopp, Anni; Atlan, David; Béroud, Christophe; Cornell, Michael; Dalgleish, Raymond; Devereau, Andrew; Patrinos, George P; Swertz, Morris A; Taschner, Peter Em; Thorisson, Gudmundur A; Vihinen, Mauno; Brookes, Anthony J; Muilu, Juha

2012-10-03

Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement. The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components. VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity.

VarioML framework for comprehensive variation data representation and exchange

PubMed Central

2012-01-01

Background Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement. Results The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components. Conclusions VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity. PMID:23031277

BiOSS: A system for biomedical ontology selection.

PubMed

Martínez-Romero, Marcos; Vázquez-Naya, José M; Pereira, Javier; Pazos, Alejandro

2014-04-01

In biomedical informatics, ontologies are considered a key technology for annotating, retrieving and sharing the huge volume of publicly available data. Due to the increasing amount, complexity and variety of existing biomedical ontologies, choosing the ones to be used in a semantic annotation problem or to design a specific application is a difficult task. As a consequence, the design of approaches and tools addressed to facilitate the selection of biomedical ontologies is becoming a priority. In this paper we present BiOSS, a novel system for the selection of biomedical ontologies. BiOSS evaluates the adequacy of an ontology to a given domain according to three different criteria: (1) the extent to which the ontology covers the domain; (2) the semantic richness of the ontology in the domain; (3) the popularity of the ontology in the biomedical community. BiOSS has been applied to 5 representative problems of ontology selection. It also has been compared to existing methods and tools. Results are promising and show the usefulness of BiOSS to solve real-world ontology selection problems. BiOSS is openly available both as a web tool and a web service. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

RX for science literacy: The what, where, how, and why of health science research (A teacher`s manual about biomedical research)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoffman, K.S.

1994-12-31

When the North Carolina Association for Biomedical Research (NCABR) surveyed the state`s science teachers in March 1993, 92% of those responding requested information related to biomedical research. Most of the teachers requested lesson plans and activities designed to help them give students an accurate and balanced perspective on research. In response to that need, NCABR has recently completed production of a 300-page teacher`s manual that provides an overview of the biomedical research process and describes the role and care of animals in that process. Rx for Science Literacy incorporates background information, lesson plans, handouts and activities to assist teachers inmore » K-12 classrooms. Developed by a science teacher with assistance from science and education experts, the manual captures the complex biomedical research process in an easy-to-follow, easy-to-use format. In North Carolina, NCABR plans to begin these workshops in fall 1994. The workshops will include a tour of a biomedical research laboratory and on-site presentations by bench scientists. Teacher evaluation of the manual will be structured into the workshop program. The manual is available at cost to all interested individuals and organizations.« less

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

PubMed Central

Elgendi, Mohamed

2016-01-01

Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages (“TERMA”) involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages (W1 and W2) have to follow the inequality (8×W1)≥W2≥(2×W1). Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions. PMID:27827852

The diversity of experimental organisms in biomedical research may be influenced by biomedical funding.

PubMed

Erick Peirson, B R; Kropp, Heather; Damerow, Julia; Laubichler, Manfred D

2017-05-01

Contrary to concerns of some critics, we present evidence that biomedical research is not dominated by a small handful of model organisms. An exhaustive analysis of research literature suggests that the diversity of experimental organisms in biomedical research has increased substantially since 1975. There has been a longstanding worry that organism-centric funding policies can lead to biases in experimental organism choice, and thus negatively impact the direction of research and the interpretation of results. Critics have argued that a focus on model organisms has unduly constrained the diversity of experimental organisms. The availability of large electronic databases of scientific literature, combined with interest in quantitative methods among philosophers of science, presents new opportunities for data-driven investigations into organism choice in biomedical research. The diversity of organisms used in NIH-funded research may be considerably lower than in the broader biomedical sciences, and may be subject to greater constraints on organism choice. © 2017 WILEY Periodicals, Inc.

Symposium on Career Opportunities in Biomedical and Public Health Sciences

NASA Technical Reports Server (NTRS)

Sullivan, Walter W.

1997-01-01

The goal of the Symposium on Career Opportunities in Biomedical and Public Health Sciences is to encourage minority collegiate and junior and senior high school students to pursue careers in biomedical and public health sciences. The objectives of the Symposium are to: (1) Provide information to participants concerning biomedical and public health science careers in government, academe and industry; (2) Provide information to minority students about training activities necessary to pursue a biomedical or public health science career and the fiscal support that one can obtain for such training; and (3) Provide opportunities for participating minority biomedical and public health role models to interact with participants.

Formation mechanism and biological activity of novel thiolated human-like collagen iron complex.

PubMed

Zhu, Chenhui; Liu, Lingyun; Deng, Jianjun; Ma, Xiaoxuan; Hui, Junfeng; Fan, Daidi

2016-03-01

To develop an iron supplement that is effectively absorbed and utilized, thiolated human-like collagen was created to improve the iron binding capacity of human-like collagen. A thiolated human-like collagen-iron complex was prepared in a phosphate buffer, and one mole of thiolated human-like collagen-iron possessed approximately 28.83 moles of iron. The characteristics of thiolated human-like collagen-iron were investigated by ultraviolet-visible absorption spectroscopy, Fourier transform infrared spectroscopy, circular dichroism, and differential scanning calorimetry. The results showed that the thiolated human-like collagen-iron complex retained the secondary structure of human-like collagen and had greater thermodynamic stability than human-like collagen, although interactions between iron ions and human-like collagen occurred during the formation of the complex. In addition, to evaluate the bioavailability of thiolated human-like collagen-iron, an in vitro Caco-2 cell model and an in vivo iron deficiency anemia mouse model were employed. The data demonstrated that the thiolated human-like collagen-iron complex exhibited greater bioavailability and was more easily utilized than FeSO4, ferric ammonium citrate, or ferrous glycinate. These results indicated that the thiolated human-like collagen-iron complex is a potential iron supplement in the biomedical field. © The Author(s) 2016.

Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research.

PubMed

An, Gary C

2010-01-01

The greatest challenge facing the biomedical research community is the effective translation of basic mechanistic knowledge into clinically effective therapeutics. This challenge is most evident in attempts to understand and modulate "systems" processes/disorders, such as sepsis, cancer, and wound healing. Formulating an investigatory strategy for these issues requires the recognition that these are dynamic processes. Representation of the dynamic behavior of biological systems can aid in the investigation of complex pathophysiological processes by augmenting existing discovery procedures by integrating disparate information sources and knowledge. This approach is termed Translational Systems Biology. Focusing on the development of computational models capturing the behavior of mechanistic hypotheses provides a tool that bridges gaps in the understanding of a disease process by visualizing "thought experiments" to fill those gaps. Agent-based modeling is a computational method particularly well suited to the translation of mechanistic knowledge into a computational framework. Utilizing agent-based models as a means of dynamic hypothesis representation will be a vital means of describing, communicating, and integrating community-wide knowledge. The transparent representation of hypotheses in this dynamic fashion can form the basis of "knowledge ecologies," where selection between competing hypotheses will apply an evolutionary paradigm to the development of community knowledge.

Diagnostic reasoning and underlying knowledge of students with preclinical patient contacts in PBL.

PubMed

Diemers, Agnes D; van de Wiel, Margje W J; Scherpbier, Albert J J A; Baarveld, Frank; Dolmans, Diana H J M

2015-12-01

Medical experts have access to elaborate and integrated knowledge networks consisting of biomedical and clinical knowledge. These coherent knowledge networks enable them to generate more accurate diagnoses in a shorter time. However, students' knowledge networks are less organised and students have difficulties linking theory and practice and transferring acquired knowledge. Therefore we wanted to explore the development and transfer of knowledge of third-year preclinical students on a problem-based learning (PBL) course with real patient contacts. Before and after a 10-week PBL course with real patients, third-year medical students were asked to think out loud while diagnosing four types of paper patient problems (two course cases and two transfer cases), and explain the underlying pathophysiological mechanisms of the patient features. Diagnostic accuracy and time needed to think through the cases were measured. The think-aloud protocols were transcribed verbatim and different types of knowledge were coded and quantitatively analysed. The written pathophysiological explanations were translated into networks of concepts. Both the concepts and the links between concepts in students' networks were compared to model networks. Over the course diagnostic accuracy increased, case-processing time decreased, and students used less biomedical and clinical knowledge during diagnostic reasoning. The quality of the pathophysiological explanations increased: the students used more concepts, especially more model concepts, and they used fewer wrong concepts and links. The findings differed across course and transfer cases. The effects were generally less strong for transfer cases. Students' improved diagnostic accuracy and the improved quality of their knowledge networks suggest that integration of biomedical and clinical knowledge took place during a 10-week course. The differences between course and transfer cases demonstrate that transfer is complex and time-consuming. We therefore suggest offering students many varied patient contacts with the same underlying pathophysiological mechanism and encouraging students to link biomedical and clinical knowledge. © 2015 John Wiley & Sons Ltd.

Comparative study analysing women's childbirth satisfaction and obstetric outcomes across two different models of maternity care

PubMed Central

Conesa Ferrer, Ma Belén; Canteras Jordana, Manuel; Ballesteros Meseguer, Carmen; Carrillo García, César; Martínez Roche, M Emilia

2016-01-01

Objectives To describe the differences in obstetrical results and women's childbirth satisfaction across 2 different models of maternity care (biomedical model and humanised birth). Setting 2 university hospitals in south-eastern Spain from April to October 2013. Design A correlational descriptive study. Participants A convenience sample of 406 women participated in the study, 204 of the biomedical model and 202 of the humanised model. Results The differences in obstetrical results were (biomedical model/humanised model): onset of labour (spontaneous 66/137, augmentation 70/1, p=0.0005), pain relief (epidural 172/132, no pain relief 9/40, p=0.0005), mode of delivery (normal vaginal 140/165, instrumental 48/23, p=0.004), length of labour (0–4 hours 69/93, >4 hours 133/108, p=0.011), condition of perineum (intact perineum or tear 94/178, episiotomy 100/24, p=0.0005). The total questionnaire score (100) gave a mean (M) of 78.33 and SD of 8.46 in the biomedical model of care and an M of 82.01 and SD of 7.97 in the humanised model of care (p=0.0005). In the analysis of the results per items, statistical differences were found in 8 of the 9 subscales. The highest scores were reached in the humanised model of maternity care. Conclusions The humanised model of maternity care offers better obstetrical outcomes and women's satisfaction scores during the labour, birth and immediate postnatal period than does the biomedical model. PMID:27566632

Multiscale Integration of -Omic, Imaging, and Clinical Data in Biomedical Informatics

PubMed Central

Phan, John H.; Quo, Chang F.; Cheng, Chihwen; Wang, May Dongmei

2016-01-01

This paper reviews challenges and opportunities in multiscale data integration for biomedical informatics. Biomedical data can come from different biological origins, data acquisition technologies, and clinical applications. Integrating such data across multiple scales (e.g., molecular, cellular/tissue, and patient) can lead to more informed decisions for personalized, predictive, and preventive medicine. However, data heterogeneity, community standards in data acquisition, and computational complexity are big challenges for such decision making. This review describes genomic and proteomic (i.e., molecular), histopathological imaging (i.e., cellular/tissue), and clinical (i.e., patient) data; it includes case studies for single-scale (e.g., combining genomic or histopathological image data), multiscale (e.g., combining histopathological image and clinical data), and multiscale and multiplatform (e.g., the Human Protein Atlas and The Cancer Genome Atlas) data integration. Numerous opportunities exist in biomedical informatics research focusing on integration of multiscale and multiplatform data. PMID:23231990

Multiscale integration of -omic, imaging, and clinical data in biomedical informatics.

PubMed

Phan, John H; Quo, Chang F; Cheng, Chihwen; Wang, May Dongmei

2012-01-01

This paper reviews challenges and opportunities in multiscale data integration for biomedical informatics. Biomedical data can come from different biological origins, data acquisition technologies, and clinical applications. Integrating such data across multiple scales (e.g., molecular, cellular/tissue, and patient) can lead to more informed decisions for personalized, predictive, and preventive medicine. However, data heterogeneity, community standards in data acquisition, and computational complexity are big challenges for such decision making. This review describes genomic and proteomic (i.e., molecular), histopathological imaging (i.e., cellular/tissue), and clinical (i.e., patient) data; it includes case studies for single-scale (e.g., combining genomic or histopathological image data), multiscale (e.g., combining histopathological image and clinical data), and multiscale and multiplatform (e.g., the Human Protein Atlas and The Cancer Genome Atlas) data integration. Numerous opportunities exist in biomedical informatics research focusing on integration of multiscale and multiplatform data.

A Diagram Editor for Efficient Biomedical Knowledge Capture and Integration

PubMed Central

Yu, Bohua; Jakupovic, Elvis; Wilson, Justin; Dai, Manhong; Xuan, Weijian; Mirel, Barbara; Athey, Brian; Watson, Stanley; Meng, Fan

2008-01-01

Understanding the molecular mechanisms underlying complex disorders requires the integration of data and knowledge from different sources including free text literature and various biomedical databases. To facilitate this process, we created the Biomedical Concept Diagram Editor (BCDE) to help researchers distill knowledge from data and literature and aid the process of hypothesis development. A key feature of BCDE is the ability to capture information with a simple drag-and-drop. This is a vast improvement over manual methods of knowledge and data recording and greatly increases the efficiency of the biomedical researcher. BCDE also provides a unique concept matching function to enforce consistent terminology, which enables conceptual relationships deposited by different researchers in the BCDE database to be mined and integrated for intelligible and useful results. We hope BCDE will promote the sharing and integration of knowledge from different researchers for effective hypothesis development. PMID:21347131

Finding and Accessing Diagrams in Biomedical Publications

PubMed Central

Kuhn, Tobias; Luong, ThaiBinh; Krauthammer, Michael

2012-01-01

Complex relationships in biomedical publications are often communicated by diagrams such as bar and line charts, which are a very effective way of summarizing and communicating multi-faceted data sets. Given the ever-increasing amount of published data, we argue that the precise retrieval of such diagrams is of great value for answering specific and otherwise hard-to-meet information needs. To this end, we demonstrate the use of advanced image processing and classification for identifying bar and line charts by the shape and relative location of the different image elements that make up the charts. With recall and precisions of close to 90% for the detection of relevant figures, we discuss the use of this technology in an existing biomedical image search engine, and outline how it enables new forms of literature queries over biomedical relationships that are represented in these charts. PMID:23304318

A passage retrieval method based on probabilistic information retrieval model and UMLS concepts in biomedical question answering.

PubMed

Sarrouti, Mourad; Ouatik El Alaoui, Said

2017-04-01

Passage retrieval, the identification of top-ranked passages that may contain the answer for a given biomedical question, is a crucial component for any biomedical question answering (QA) system. Passage retrieval in open-domain QA is a longstanding challenge widely studied over the last decades. However, it still requires further efforts in biomedical QA. In this paper, we present a new biomedical passage retrieval method based on Stanford CoreNLP sentence/passage length, probabilistic information retrieval (IR) model and UMLS concepts. In the proposed method, we first use our document retrieval system based on PubMed search engine and UMLS similarity to retrieve relevant documents to a given biomedical question. We then take the abstracts from the retrieved documents and use Stanford CoreNLP for sentence splitter to make a set of sentences, i.e., candidate passages. Using stemmed words and UMLS concepts as features for the BM25 model, we finally compute the similarity scores between the biomedical question and each of the candidate passages and keep the N top-ranked ones. Experimental evaluations performed on large standard datasets, provided by the BioASQ challenge, show that the proposed method achieves good performances compared with the current state-of-the-art methods. The proposed method significantly outperforms the current state-of-the-art methods by an average of 6.84% in terms of mean average precision (MAP). We have proposed an efficient passage retrieval method which can be used to retrieve relevant passages in biomedical QA systems with high mean average precision. Copyright © 2017 Elsevier Inc. All rights reserved.

Cluster-Based Query Expansion Using Language Modeling for Biomedical Literature Retrieval

ERIC Educational Resources Information Center

Xu, Xuheng

2011-01-01

The tremendously huge volume of biomedical literature, scientists' specific information needs, long terms of multiples words, and fundamental problems of synonym and polysemy have been challenging issues facing the biomedical information retrieval community researchers. Search engines have significantly improved the efficiency and effectiveness of…

Structural DNA Nanotechnology: Artificial Nanostructures for Biomedical Research.

PubMed

Ke, Yonggang; Castro, Carlos; Choi, Jong Hyun

2018-06-04

Structural DNA nanotechnology utilizes synthetic or biologic DNA as designer molecules for the self-assembly of artificial nanostructures. The field is founded upon the specific interactions between DNA molecules, known as Watson-Crick base pairing. After decades of active pursuit, DNA has demonstrated unprecedented versatility in constructing artificial nanostructures with significant complexity and programmability. The nanostructures could be either static, with well-controlled physicochemical properties, or dynamic, with the ability to reconfigure upon external stimuli. Researchers have devoted considerable effort to exploring the usability of DNA nanostructures in biomedical research. We review the basic design methods for fabricating both static and dynamic DNA nanostructures, along with their biomedical applications in fields such as biosensing, bioimaging, and drug delivery.

Big biomedical data as the key resource for discovery science.

PubMed

Toga, Arthur W; Foster, Ian; Kesselman, Carl; Madduri, Ravi; Chard, Kyle; Deutsch, Eric W; Price, Nathan D; Glusman, Gustavo; Heavner, Benjamin D; Dinov, Ivo D; Ames, Joseph; Van Horn, John; Kramer, Roger; Hood, Leroy

2015-11-01

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an "-ome to home" approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center's computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson's and Alzheimer's. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Theory, simulations and the design of functionalized nanoparticles for biomedical applications: A Soft Matter Perspective

NASA Astrophysics Data System (ADS)

Angioletti-Uberti, Stefano

2017-11-01

Functionalised nanoparticles for biomedical applications represents an incredibly exciting and rapidly growing field of research. Considering the complexity of the nano-bio interface, an important question is to what extent can theory and simulations be used to study these systems in a realistic, meaningful way. In this review, we will argue for a positive answer to this question. Approaching the issue from a "Soft Matter" perspective, we will consider those properties of functionalised nanoparticles that can be captured within a classical description. We will thus not concentrate on optical and electronic properties, but rather on the way nanoparticles' interactions with the biological environment can be tuned by functionalising their surface and exploited in different contexts relevant to applications. In particular, we wish to provide a critical overview of theoretical and computational coarse-grained models, developed to describe these interactions and present to the readers some of the latest results in this fascinating area of research.

Progress of statistical analysis in biomedical research through the historical review of the development of the Framingham score.

PubMed

Ignjatović, Aleksandra; Stojanović, Miodrag; Milošević, Zoran; Anđelković Apostolović, Marija

2017-12-02

The interest in developing risk models in medicine not only is appealing, but also associated with many obstacles in different aspects of predictive model development. Initially, the association of biomarkers or the association of more markers with the specific outcome was proven by statistical significance, but novel and demanding questions required the development of new and more complex statistical techniques. Progress of statistical analysis in biomedical research can be observed the best through the history of the Framingham study and development of the Framingham score. Evaluation of predictive models comes from a combination of the facts which are results of several metrics. Using logistic regression and Cox proportional hazards regression analysis, the calibration test, and the ROC curve analysis should be mandatory and eliminatory, and the central place should be taken by some new statistical techniques. In order to obtain complete information related to the new marker in the model, recently, there is a recommendation to use the reclassification tables by calculating the net reclassification index and the integrated discrimination improvement. Decision curve analysis is a novel method for evaluating the clinical usefulness of a predictive model. It may be noted that customizing and fine-tuning of the Framingham risk score initiated the development of statistical analysis. Clinically applicable predictive model should be a trade-off between all abovementioned statistical metrics, a trade-off between calibration and discrimination, accuracy and decision-making, costs and benefits, and quality and quantity of patient's life.

The RICORDO approach to semantic interoperability for biomedical data and models: strategy, standards and solutions

PubMed Central

2011-01-01

Background The practice and research of medicine generates considerable quantities of data and model resources (DMRs). Although in principle biomedical resources are re-usable, in practice few can currently be shared. In particular, the clinical communities in physiology and pharmacology research, as well as medical education, (i.e. PPME communities) are facing considerable operational and technical obstacles in sharing data and models. Findings We outline the efforts of the PPME communities to achieve automated semantic interoperability for clinical resource documentation in collaboration with the RICORDO project. Current community practices in resource documentation and knowledge management are overviewed. Furthermore, requirements and improvements sought by the PPME communities to current documentation practices are discussed. The RICORDO plan and effort in creating a representational framework and associated open software toolkit for the automated management of PPME metadata resources is also described. Conclusions RICORDO is providing the PPME community with tools to effect, share and reason over clinical resource annotations. This work is contributing to the semantic interoperability of DMRs through ontology-based annotation by (i) supporting more effective navigation and re-use of clinical DMRs, as well as (ii) sustaining interoperability operations based on the criterion of biological similarity. Operations facilitated by RICORDO will range from automated dataset matching to model merging and managing complex simulation workflows. In effect, RICORDO is contributing to community standards for resource sharing and interoperability. PMID:21878109

A corpus of full-text journal articles is a robust evaluation tool for revealing differences in performance of biomedical natural language processing tools

PubMed Central

2012-01-01

Background We introduce the linguistic annotation of a corpus of 97 full-text biomedical publications, known as the Colorado Richly Annotated Full Text (CRAFT) corpus. We further assess the performance of existing tools for performing sentence splitting, tokenization, syntactic parsing, and named entity recognition on this corpus. Results Many biomedical natural language processing systems demonstrated large differences between their previously published results and their performance on the CRAFT corpus when tested with the publicly available models or rule sets. Trainable systems differed widely with respect to their ability to build high-performing models based on this data. Conclusions The finding that some systems were able to train high-performing models based on this corpus is additional evidence, beyond high inter-annotator agreement, that the quality of the CRAFT corpus is high. The overall poor performance of various systems indicates that considerable work needs to be done to enable natural language processing systems to work well when the input is full-text journal articles. The CRAFT corpus provides a valuable resource to the biomedical natural language processing community for evaluation and training of new models for biomedical full text publications. PMID:22901054

A multiple distributed representation method based on neural network for biomedical event extraction.

PubMed

Wang, Anran; Wang, Jian; Lin, Hongfei; Zhang, Jianhai; Yang, Zhihao; Xu, Kan

2017-12-20

Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction.

Journal selection decisions: a biomedical library operations research model. I. The framework.

PubMed Central

Kraft, D H; Polacsek, R A; Soergel, L; Burns, K; Klair, A

1976-01-01

The problem of deciding which journal titles to select for acquisition in a biomedical library is modeled. The approach taken is based on cost/benefit ratios. Measures of journal worth, methods of data collection, and journal cost data are considered. The emphasis is on the development of a practical process for selecting journal titles, based on the objectivity and rationality of the model; and on the collection of the approprate data and library statistics in a reasonable manner. The implications of this process towards an overall management information system (MIS) for biomedical serials handling are discussed. PMID:820391

Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research.

PubMed

Girardi, Dominic; Küng, Josef; Kleiser, Raimund; Sonnberger, Michael; Csillag, Doris; Trenkler, Johannes; Holzinger, Andreas

2016-09-01

Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks. We evaluated this approach in the medical domain for the case of cerebral aneurysms research.

Biomedical surface analysis: Evolution and future directions (Review)

PubMed Central

Castner, David G.

2017-01-01

This review describes some of the major advances made in biomedical surface analysis over the past 30–40 years. Starting from a single technique analysis of homogeneous surfaces, it has been developed into a complementary, multitechnique approach for obtaining detailed, comprehensive information about a wide range of surfaces and interfaces of interest to the biomedical community. Significant advances have been made in each surface analysis technique, as well as how the techniques are combined to provide detailed information about biological surfaces and interfaces. The driving force for these advances has been that the surface of a biomaterial is the interface between the biological environment and the biomaterial, and so, the state-of-the-art in instrumentation, experimental protocols, and data analysis methods need to be developed so that the detailed surface structure and composition of biomedical devices can be determined and related to their biological performance. Examples of these advances, as well as areas for future developments, are described for immobilized proteins, complex biomedical surfaces, nanoparticles, and 2D/3D imaging of biological materials. PMID:28438024

The SWAN biomedical discourse ontology.

PubMed

Ciccarese, Paolo; Wu, Elizabeth; Wong, Gwen; Ocana, Marco; Kinoshita, June; Ruttenberg, Alan; Clark, Tim

2008-10-01

Developing cures for highly complex diseases, such as neurodegenerative disorders, requires extensive interdisciplinary collaboration and exchange of biomedical information in context. Our ability to exchange such information across sub-specialties today is limited by the current scientific knowledge ecosystem's inability to properly contextualize and integrate data and discourse in machine-interpretable form. This inherently limits the productivity of research and the progress toward cures for devastating diseases such as Alzheimer's and Parkinson's. SWAN (Semantic Web Applications in Neuromedicine) is an interdisciplinary project to develop a practical, common, semantically structured, framework for biomedical discourse initially applied, but not limited, to significant problems in Alzheimer Disease (AD) research. The SWAN ontology has been developed in the context of building a series of applications for biomedical researchers, as well as in extensive discussions and collaborations with the larger bio-ontologies community. In this paper, we present and discuss the SWAN ontology of biomedical discourse. We ground its development theoretically, present its design approach, explain its main classes and their application, and show its relationship to other ongoing activities in biomedicine and bio-ontologies.

Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

PubMed Central

Vukićević, Milan

2014-01-01

Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data. PMID:24892101

Targeted gene knock-in by CRISPR/Cas ribonucleoproteins in porcine zygotes

USDA-ARS?s Scientific Manuscript database

The domestic pig is an important “dual purpose” animal model for agricultural and biomedical applications. There is an emerging consensus in the biomedical community that even though mouse is a powerhouse genetic model, there is a requirement for large animal models such as pigs that can either ser...

A Suggested Model for Building Robust Biomedical Implants Registries.

PubMed

Aloufi, Bader; Alshagathrah, Fahad; Househ, Mowafa

2017-01-01

Registries are an essential source of information for clinical and non-clinical decision-makers; because they provide evidence for post-market clinical follow-up and early detection of safety signals for biomedical implants. Yet, many of todays biomedical implants registries are facing a variety of challenges relating to a poorly designed dataset, the reliability of inputted data and low clinician and patient participation. The purpose of this paper is to present a best practice model for the implementation and use of biomedical implants registries to monitor the safety and effectiveness of implantable medical devices. Based on a literature review and an analysis of multiple national relevant registries, we identified six factors that address contemporary challenges and are believed to be the keys for building a successful biomedical implants registry, which include: sustainable development, international comparability, data reliability, purposeful design, ease of patient participation, and collaborative development at the national level.

Aging and the complexity of cardiovascular dynamics

NASA Technical Reports Server (NTRS)

Kaplan, D. T.; Furman, M. I.; Pincus, S. M.; Ryan, S. M.; Lipsitz, L. A.; Goldberger, A. L.

1991-01-01

Biomedical signals often vary in a complex and irregular manner. Analysis of variability in such signals generally does not address directly their complexity, and so may miss potentially useful information. We analyze the complexity of heart rate and beat-to-beat blood pressure using two methods motivated by nonlinear dynamics (chaos theory). A comparison of a group of healthy elderly subjects with healthy young adults indicates that the complexity of cardiovascular dynamics is reduced with aging. This suggests that complexity of variability may be a useful physiological marker.

The impact of socioeconomic status on the association between biomedical and psychosocial well-being and all-cause mortality in older Spanish adults.

PubMed

Doménech-Abella, Joan; Mundó, Jordi; Moneta, Maria Victoria; Perales, Jaime; Ayuso-Mateos, José Luis; Miret, Marta; Haro, Josep Maria; Olaya, Beatriz

2018-03-01

The aim of this paper was to analyze the effect of biomedical and psychosocial well-being, based on distinct successful aging models (SA), on time to mortality, and determine whether this effect was modified by socioeconomic status (SES) in a nationally representative sample of older Spanish adults. Data were taken from a 3-year follow-up study with 2783 participants aged 50 or over. Vital status was ascertained using national registers or asking participants' relatives. Kaplan-Meier curves were used to estimate the time to death by SES, and levels of biomedical and psychosocial SA. Cox proportional hazard regression models were conducted to explore interactions between SES and SA models while adjusting for gender, age, and marital status. Lower levels of SES and biomedical and psychosocial SA were associated with low probability of survival. Only the interaction between SES and biomedical SA was significant. Biomedical SA impacted on mortality rates among individuals with low SES but not on those with medium or high SES, whereas psychosocial SA affected mortality regardless of SES. Promoting equal access to health care system and improved psychosocial well-being could be a protective factor against premature mortality in older Spanish adults with low SES.

Improving Validity of Informed Consent for Biomedical Research in Zambia Using a Laboratory Exposure Intervention

PubMed Central

Zulu, Joseph Mumba; Lisulo, Mpala Mwanza; Besa, Ellen; Kaonga, Patrick; Chisenga, Caroline C.; Chomba, Mumba; Simuyandi, Michelo; Banda, Rosemary; Kelly, Paul

2014-01-01

Background Complex biomedical research can lead to disquiet in communities with limited exposure to scientific discussions, leading to rumours or to high drop-out rates. We set out to test an intervention designed to address apprehensions commonly encountered in a community where literacy is uncommon, and where complex biomedical research has been conducted for over a decade. We aimed to determine if it could improve the validity of consent. Methods Data were collected using focus group discussions, key informant interviews and observations. We designed an intervention that exposed participants to a detailed demonstration of laboratory processes. Each group was interviewed twice in a day, before and after exposure to the intervention in order to assess changes in their views. Results Factors that motivated people to participate in invasive biomedical research included a desire to stay healthy because of the screening during the recruitment process, regular advice from doctors, free medical services, and trust in the researchers. Inhibiting factors were limited knowledge about samples taken from their bodies during endoscopic procedures, the impact of endoscopy on the function of internal organs, and concerns about the use of biomedical samples. The belief that blood can be used for Satanic practices also created insecurities about drawing of blood samples. Further inhibiting factors included a fear of being labelled as HIV positive if known to consult heath workers repeatedly, and gender inequality. Concerns about the use and storage of blood and tissue samples were overcome by a laboratory exposure intervention. Conclusion Selecting a group of members from target community and engaging them in a laboratory exposure intervention could be a useful tool for enhancing specific aspects of consent for biomedical research. Further work is needed to determine the extent to which improved understanding permeates beyond the immediate group participating in the intervention. PMID:25254378

The Cell Collective: Toward an open and collaborative approach to systems biology

PubMed Central

2012-01-01

Background Despite decades of new discoveries in biomedical research, the overwhelming complexity of cells has been a significant barrier to a fundamental understanding of how cells work as a whole. As such, the holistic study of biochemical pathways requires computer modeling. Due to the complexity of cells, it is not feasible for one person or group to model the cell in its entirety. Results The Cell Collective is a platform that allows the world-wide scientific community to create these models collectively. Its interface enables users to build and use models without specifying any mathematical equations or computer code - addressing one of the major hurdles with computational research. In addition, this platform allows scientists to simulate and analyze the models in real-time on the web, including the ability to simulate loss/gain of function and test what-if scenarios in real time. Conclusions The Cell Collective is a web-based platform that enables laboratory scientists from across the globe to collaboratively build large-scale models of various biological processes, and simulate/analyze them in real time. In this manuscript, we show examples of its application to a large-scale model of signal transduction. PMID:22871178

ReRouting biomedical innovation: observations from a mapping of the alternative research and development (R&D) landscape.

PubMed

Greenberg, Alexandra; Kiddell-Monroe, Rachel

2016-09-14

In recent years, the world has witnessed the tragic outcomes of multiple global health crises. From Ebola to high prices to antibiotic resistance, these events highlight the fundamental constraints of the current biomedical research and development (R&D) system in responding to patient needs globally.To mitigate this lack of responsiveness, over 100 self-identified "alternative" R&D initiatives, have emerged in the past 15 years. To begin to make sense of this panoply of initiatives working to overcome the constraints of the current system, UAEM began an extensive, though not comprehensive, mapping of the alternative biomedical R&D landscape. We developed a two phase approach: (1) an investigation, via the RE:Route Mapping, of both existing and proposed initiatives that claim to offer an alternative approach to R&D, and (2) evaluation of those initiatives to determine which are in fact achieving increased access to and innovation in medicines. Through phase 1, the RE:Route Mapping, we examined 81 initiatives that claim to redress the inequity perpetuated by the current system via one of five commonly recognized mechanisms necessary for truly alternative R&D.Preliminary analysis of phase 1 provides the following conclusions: 1. No initiative presents a completely alternative model of biomedical R&D. 2. The majority of initiatives focus on developing incentives for drug discovery. 3. The majority of initiatives focus on rare diseases or diseases of the poor and marginalized. 4. There is an increasing emphasis on the use of push, pull, pool, collaboration and open mechanisms alongside the concept of delinkage in alternative R&D. 5. There is a trend towards public funding and launching of initiatives by the Global South. Given the RE:Route Mapping's inevitable limitations and the assumptions made in its methodology, it is not intended to be the final word on a constantly evolving and complex field; however, its findings are significant. The Mapping's value lies in its timely and unique insight into the importance of ongoing efforts to develop a new global framework for biomedical R&D. As we progress to phase 2, an evaluation tool for initiatives focused on identifying which approaches have truly achieved increased innovation and access for patients, we aim to demonstrate that there are a handful of initiatives which represent some, but not all, of the building blocks for a new approach to R&D.Through this mapping and our forthcoming evaluation, UAEM aims to initiate an evidence-based conversation around a truly alternative biomedical R&D model that serves people rather than profits.

Recent advances in 3D printing of biomaterials.

PubMed

Chia, Helena N; Wu, Benjamin M

2015-01-01

3D Printing promises to produce complex biomedical devices according to computer design using patient-specific anatomical data. Since its initial use as pre-surgical visualization models and tooling molds, 3D Printing has slowly evolved to create one-of-a-kind devices, implants, scaffolds for tissue engineering, diagnostic platforms, and drug delivery systems. Fueled by the recent explosion in public interest and access to affordable printers, there is renewed interest to combine stem cells with custom 3D scaffolds for personalized regenerative medicine. Before 3D Printing can be used routinely for the regeneration of complex tissues (e.g. bone, cartilage, muscles, vessels, nerves in the craniomaxillofacial complex), and complex organs with intricate 3D microarchitecture (e.g. liver, lymphoid organs), several technological limitations must be addressed. In this review, the major materials and technology advances within the last five years for each of the common 3D Printing technologies (Three Dimensional Printing, Fused Deposition Modeling, Selective Laser Sintering, Stereolithography, and 3D Plotting/Direct-Write/Bioprinting) are described. Examples are highlighted to illustrate progress of each technology in tissue engineering, and key limitations are identified to motivate future research and advance this fascinating field of advanced manufacturing.

Conducting requirements analyses for research using routinely collected health data: a model driven approach.

PubMed

de Lusignan, Simon; Cashman, Josephine; Poh, Norman; Michalakidis, Georgios; Mason, Aaron; Desombre, Terry; Krause, Paul

2012-01-01

Medical research increasingly requires the linkage of data from different sources. Conducting a requirements analysis for a new application is an established part of software engineering, but rarely reported in the biomedical literature; and no generic approaches have been published as to how to link heterogeneous health data. Literature review, followed by a consensus process to define how requirements for research, using, multiple data sources might be modeled. We have developed a requirements analysis: i-ScheDULEs - The first components of the modeling process are indexing and create a rich picture of the research study. Secondly, we developed a series of reference models of progressive complexity: Data flow diagrams (DFD) to define data requirements; unified modeling language (UML) use case diagrams to capture study specific and governance requirements; and finally, business process models, using business process modeling notation (BPMN). These requirements and their associated models should become part of research study protocols.

Autophagy in Dictyostelium: Mechanisms, regulation and disease in a simple biomedical model.

PubMed

Mesquita, Ana; Cardenal-Muñoz, Elena; Dominguez, Eunice; Muñoz-Braceras, Sandra; Nuñez-Corcuera, Beatriz; Phillips, Ben A; Tábara, Luis C; Xiong, Qiuhong; Coria, Roberto; Eichinger, Ludwig; Golstein, Pierre; King, Jason S; Soldati, Thierry; Vincent, Olivier; Escalante, Ricardo

2017-01-02

Autophagy is a fast-moving field with an enormous impact on human health and disease. Understanding the complexity of the mechanism and regulation of this process often benefits from the use of simple experimental models such as the social amoeba Dictyostelium discoideum. Since the publication of the first review describing the potential of D. discoideum in autophagy, significant advances have been made that demonstrate both the experimental advantages and interest in using this model. Since our previous review, research in D. discoideum has shed light on the mechanisms that regulate autophagosome formation and contributed significantly to the study of autophagy-related pathologies. Here, we review these advances, as well as the current techniques to monitor autophagy in D. discoideum. The comprehensive bioinformatics search of autophagic proteins that was a substantial part of the previous review has not been revisited here except for those aspects that challenged previous predictions such as the composition of the Atg1 complex. In recent years our understanding of, and ability to investigate, autophagy in D. discoideum has evolved significantly and will surely enable and accelerate future research using this model.

A resource-saving collective approach to biomedical semantic role labeling

PubMed Central

2014-01-01

Background Biomedical semantic role labeling (BioSRL) is a natural language processing technique that identifies the semantic roles of the words or phrases in sentences describing biological processes and expresses them as predicate-argument structures (PAS’s). Currently, a major problem of BioSRL is that most systems label every node in a full parse tree independently; however, some nodes always exhibit dependency. In general SRL, collective approaches based on the Markov logic network (MLN) model have been successful in dealing with this problem. However, in BioSRL such an approach has not been attempted because it would require more training data to recognize the more specialized and diverse terms found in biomedical literature, increasing training time and computational complexity. Results We first constructed a collective BioSRL system based on MLN. This system, called collective BIOSMILE (CBIOSMILE), is trained on the BioProp corpus. To reduce the resources used in BioSRL training, we employ a tree-pruning filter to remove unlikely nodes from the parse tree and four argument candidate identifiers to retain candidate nodes in the tree. Nodes not recognized by any candidate identifier are discarded. The pruned annotated parse trees are used to train a resource-saving MLN-based system, which is referred to as resource-saving collective BIOSMILE (RCBIOSMILE). Our experimental results show that our proposed CBIOSMILE system outperforms BIOSMILE, which is the top BioSRL system. Furthermore, our proposed RCBIOSMILE maintains the same level of accuracy as CBIOSMILE using 92% less memory and 57% less training time. Conclusions This greatly improved efficiency makes RCBIOSMILE potentially suitable for training on much larger BioSRL corpora over more biomedical domains. Compared to real-world biomedical corpora, BioProp is relatively small, containing only 445 MEDLINE abstracts and 30 event triggers. It is not large enough for practical applications, such as pathway construction. We consider it of primary importance to pursue SRL training on large corpora in the future. PMID:24884358

Challenges facing academic research in commercializing event-detector implantable devices for an in-vivo biomedical subcutaneous device for biomedical analysis

NASA Astrophysics Data System (ADS)

Juanola-Feliu, E.; Colomer-Farrarons, J.; Miribel-Català, P.; Samitier, J.; Valls-Pasola, J.

2011-05-01

It is widely recognized that the welfare of the most advanced economies is at risk, and that the only way to tackle this situation is by controlling the knowledge economies and dealing with. To achieve this ambitious goal, we need to improve the performance of each dimension in the "knowledge triangle": education, research and innovation. Indeed, recent findings point to the importance of strategies of adding-value and marketing during R+D processes so as to bridge the gap between the laboratory and the market and so ensure the successful commercialization of new technology-based products. Moreover, in a global economy in which conventional manufacturing is dominated by developing economies, the future of industry in the most advanced economies must rely on its ability to innovate in those high-tech activities that can offer a differential added-value, rather than on improving existing technologies and products. It seems quite clear, therefore, that the combination of health (medicine) and nanotechnology in a new biomedical device is very capable of meeting these requisites. This work propose a generic CMOS Front-End Self-Powered In-Vivo Implantable Biomedical Device, based on a threeelectrode amperometric biosensor approach, capable of detecting threshold values for targeted concentrations of pathogens, ions, oxygen concentration, etc. Given the speed with which diabetes can spread, as diabetes is the fastest growing disease in the world, the nano-enabled implantable device for in-vivo biomedical analysis needs to be introduced into the global diabetes care devices market. In the case of glucose monitoring, the detection of a threshold decrease in the glucose level it is mandatory to avoid critic situations like the hypoglycemia. Although the case study reported in this paper is complex because it involves multiple organizations and sources of data, it contributes to extend experience to the best practices and models on nanotechnology applications and commercialization.

New Developments in NASA's Rodent Research Hardware for Conducting Long Duration Biomedical and Basic Research in Space

NASA Technical Reports Server (NTRS)

Shirazi, Yasaman; Choi, S.; Harris, C.; Gong, C.; Fisher, R. J.; Beegle, J. E.; Stube, K. C.; Martin, K. J.; Nevitt, R. G.; Globus, R. K.

2017-01-01

Animal models, particularly rodents, are the foundation of pre-clinical research to understand human diseases and evaluate new therapeutics, and play a key role in advancing biomedical discoveries both on Earth and in space. The National Research Councils Decadal survey emphasized the importance of expanding NASA's life sciences research to perform long duration, rodent experiments on the International Space Station (ISS) to study effects of the space environment on the musculoskeletal and neurological systems of mice as model organisms of human health and disease, particularly in areas of muscle atrophy, bone loss, and fracture healing. To accomplish this objective, flight hardware, operations, and science capabilities were developed at NASA Ames Research Center (ARC) to enhance science return for both commercial (CASIS) and government-sponsored rodent research. The Rodent Research Project at NASA ARC has pioneered a new research capability on the International Space Station and has progressed toward translating research to the ISS utilizing commercial rockets, collaborating with academia and science industry, while training crewmembers to assist in performing research on orbit. The Rodent Research Habitat provides a living environment for animals on ISS according to standard animal welfare requirements, and daily health checks can be performed using the habitats camera system. Results from these studies contribute to the science community via both the primary investigation and banked samples that are shared in publicly available data repository such as GeneLab. Following each flight, through the Biospecimen Sharing Program (BSP), numerous tissues and thousands of samples will be harvested, and distributed from the Space Life and Physical Sciences (SLPS) to Principal Investigators (PIs) through the Ames Life Science Data Archive (ALSDA). Every completed mission sets a foundation to build and design greater complexity into future research and answer questions about common human diseases. Together, the hardware improvements (enrichment, telemetry sensors, cameras), new capabilities (live animal return), and experience that the Rodent Research team has gained working with principal investigator teams and ISS crew to conduct complex experiments on orbit are expanding capabilities for long duration rodent research on the ISS to achieve both basic science and biomedical research objectives.

Towards a 21st century roadmap for biomedical research and ...

EPA Pesticide Factsheets

Decades of costly failures in translating drug candidates from preclinical disease models to human therapeutic use warrant reconsideration of the priority placed on animal models in biomedical research. Following an international workshop attended by experts from academia, government institutions, research funding bodies and the corporate and NGO sectors, this consensus report analyses, as case studies, five disease areas with major unmet needs for new treatments. In view of the scientifically driven transition towards a human pathways-based paradigm in toxicology, a similar paradigm shift appears to be justified in biomedical research. There is a pressing need for an approach that strategically implements advanced, human biology-based models and tools to understand disease pathways at multiple biological scales. We present recommendations to help achieve this. To discover and develop new therapies, we need 21-century roadmaps for biomedical research based on multiscale human disease pathways, and supported by policy and funding strategies that prioritise human relevance.

Biomedical text mining and its applications in cancer research.

PubMed

Zhu, Fei; Patumcharoenpol, Preecha; Zhang, Cheng; Yang, Yang; Chan, Jonathan; Meechai, Asawin; Vongsangnak, Wanwipa; Shen, Bairong

2013-04-01

Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over 100years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research. Copyright © 2012 Elsevier Inc. All rights reserved.

A resource facility for kinetic analysis: modeling using the SAAM computer programs.

PubMed

Foster, D M; Boston, R C; Jacquez, J A; Zech, L

1989-01-01

Kinetic analysis and integrated system modeling have contributed significantly to understanding the physiology and pathophysiology of metabolic systems in humans and animals. Many experimental biologists are aware of the usefulness of these techniques and recognize that kinetic modeling requires special expertise. The Resource Facility for Kinetic Analysis (RFKA) provides this expertise through: (1) development and application of modeling technology for biomedical problems, and (2) development of computer-based kinetic modeling methodologies concentrating on the computer program Simulation, Analysis, and Modeling (SAAM) and its conversational version, CONversational SAAM (CONSAM). The RFKA offers consultation to the biomedical community in the use of modeling to analyze kinetic data and trains individuals in using this technology for biomedical research. Early versions of SAAM were widely applied in solving dosimetry problems; many users, however, are not familiar with recent improvements to the software. The purpose of this paper is to acquaint biomedical researchers in the dosimetry field with RFKA, which, together with the joint National Cancer Institute-National Heart, Lung and Blood Institute project, is overseeing SAAM development and applications. In addition, RFKA provides many service activities to the SAAM user community that are relevant to solving dosimetry problems.

Facilities available for biomedical science research in the public universities in Lagos, Nigeria.

PubMed

John, T A

2010-03-01

Across the world, basic medical scientists and physician scientists work on common platforms in state-of-the-arts laboratories doing translational research that occasionally results in bedside application. Biotechnology industries capitalise on useful findings for colossal profit.1 In Nigeria and the rest of Africa, biomedical science has not thrived and the contribution of publications to global high impact journals is low.2 This work investigated facilities available for modern biomedical research in Lagos public universities to extract culprit factors. The two public universities in Lagos, Nigeria were investigated by a cross sectional questionnaire survey of the technical staff manning biomedical science departments. They were asked about availability of 47 modern biomedical science research laboratory components such as cold room and microscopes and six research administration components such as director of research and grants administration. For convenient basic laboratory components such as autoclaves and balances, 50% responses indicated "well maintained and always functional" whereas for less convenient complex, high maintenance, state-of-the-arts equipment 19% responses indicated "well maintained and always functional." Respondents indicated that components of modern biomedical science research administration were 44% of expectation. The survey reveal a deficit in state-of the-arts research equipment and also a deficit in high maintenance, expensive equipment indicating that biomedical science in the investigated environment lacks the momentum of global trends and also lacks buoyant funding. In addition, administration supporting biomedical science is below expectation and may also account for the low contributions of research articles to global high impact journals.

EXACT2: the semantics of biomedical protocols

PubMed Central

2014-01-01

Background The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility. Methods We have developed the ontology EXACT2 (EXperimental ACTions) that is designed to capture the full semantics of biomedical protocols required for their reproducibility. To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously 'unseen' (not used for the construction of EXACT2) protocols. Results The paper reports on a fundamentally new version EXACT2 that supports the semantically-defined representation of biomedical protocols. The ability of EXACT2 to capture the semantics of biomedical procedures was verified through a text mining use case. In this EXACT2 is used as a reference model for text mining tools to identify terms pertinent to experimental actions, and their properties, in biomedical protocols expressed in natural language. An EXACT2-based framework for the translation of biomedical protocols to a machine amenable format is proposed. Conclusions The EXACT2 ontology is sufficient to record, in a machine processable form, the essential information about biomedical protocols. EXACT2 defines explicit semantics of experimental actions, and can be used by various computer applications. It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format. PMID:25472549

SOA-based digital library services and composition in biomedical applications.

PubMed

Zhao, Xia; Liu, Enjie; Clapworthy, Gordon J; Viceconti, Marco; Testi, Debora

2012-06-01

Carefully collected, high-quality data are crucial in biomedical visualization, and it is important that the user community has ready access to both this data and the high-performance computing resources needed by the complex, computational algorithms that will process it. Biological researchers generally require data, tools and algorithms from multiple providers to achieve their goals. This paper illustrates our response to the problems that result from this. The Living Human Digital Library (LHDL) project presented in this paper has taken advantage of Web Services to build a biomedical digital library infrastructure that allows clinicians and researchers not only to preserve, trace and share data resources, but also to collaborate at the data-processing level. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

A Novel Multiple Choice Question Generation Strategy: Alternative Uses for Controlled Vocabulary Thesauri in Biomedical-Sciences Education.

PubMed

Lopetegui, Marcelo A; Lara, Barbara A; Yen, Po-Yin; Çatalyürek, Ümit V; Payne, Philip R O

2015-01-01

Multiple choice questions play an important role in training and evaluating biomedical science students. However, the resource intensive nature of question generation limits their open availability, reducing their contribution to evaluation purposes mainly. Although applied-knowledge questions require a complex formulation process, the creation of concrete-knowledge questions (i.e., definitions, associations) could be assisted by the use of informatics methods. We envisioned a novel and simple algorithm that exploits validated knowledge repositories and generates concrete-knowledge questions by leveraging concepts' relationships. In this manuscript we present the development and validation of a prototype which successfully produced meaningful concrete-knowledge questions, opening new applications for existing knowledge repositories, potentially benefiting students of all biomedical sciences disciplines.

A vector space model approach to identify genetically related diseases.

PubMed

Sarkar, Indra Neil

2012-01-01

The relationship between diseases and their causative genes can be complex, especially in the case of polygenic diseases. Further exacerbating the challenges in their study is that many genes may be causally related to multiple diseases. This study explored the relationship between diseases through the adaptation of an approach pioneered in the context of information retrieval: vector space models. A vector space model approach was developed that bridges gene disease knowledge inferred across three knowledge bases: Online Mendelian Inheritance in Man, GenBank, and Medline. The approach was then used to identify potentially related diseases for two target diseases: Alzheimer disease and Prader-Willi Syndrome. In the case of both Alzheimer Disease and Prader-Willi Syndrome, a set of plausible diseases were identified that may warrant further exploration. This study furthers seminal work by Swanson, et al. that demonstrated the potential for mining literature for putative correlations. Using a vector space modeling approach, information from both biomedical literature and genomic resources (like GenBank) can be combined towards identification of putative correlations of interest. To this end, the relevance of the predicted diseases of interest in this study using the vector space modeling approach were validated based on supporting literature. The results of this study suggest that a vector space model approach may be a useful means to identify potential relationships between complex diseases, and thereby enable the coordination of gene-based findings across multiple complex diseases.

omniClassifier: a Desktop Grid Computing System for Big Data Prediction Modeling

PubMed Central

Phan, John H.; Kothari, Sonal; Wang, May D.

2016-01-01

Robust prediction models are important for numerous science, engineering, and biomedical applications. However, best-practice procedures for optimizing prediction models can be computationally complex, especially when choosing models from among hundreds or thousands of parameter choices. Computational complexity has further increased with the growth of data in these fields, concurrent with the era of “Big Data”. Grid computing is a potential solution to the computational challenges of Big Data. Desktop grid computing, which uses idle CPU cycles of commodity desktop machines, coupled with commercial cloud computing resources can enable research labs to gain easier and more cost effective access to vast computing resources. We have developed omniClassifier, a multi-purpose prediction modeling application that provides researchers with a tool for conducting machine learning research within the guidelines of recommended best-practices. omniClassifier is implemented as a desktop grid computing system using the Berkeley Open Infrastructure for Network Computing (BOINC) middleware. In addition to describing implementation details, we use various gene expression datasets to demonstrate the potential scalability of omniClassifier for efficient and robust Big Data prediction modeling. A prototype of omniClassifier can be accessed at http://omniclassifier.bme.gatech.edu/. PMID:27532062

Multistate Lempel-Ziv (MLZ) index interpretation as a measure of amplitude and complexity changes.

PubMed

Sarlabous, Leonardo; Torres, Abel; Fiz, Jose A; Gea, Joaquim; Galdiz, Juan B; Jane, Raimon

2009-01-01

The Lempel-Ziv complexity (LZ) has been widely used to evaluate the randomness of finite sequences. In general, the LZ complexity has been used to determine the complexity grade present in biomedical signals. The LZ complexity is not able to discern between signals with different amplitude variations and similar random components. On the other hand, amplitude parameters, as the root mean square (RMS), are not able to discern between signals with similar power distributions and different random components. In this work, we present a novel method to quantify amplitude and complexity variations in biomedical signals by means of the computation of the LZ coefficient using more than two quantification states, and with thresholds fixed and independent of the dynamic range or standard deviation of the analyzed signal: the Multistate Lempel-Ziv (MLZ) index. Our results indicate that MLZ index with few quantification levels only evaluate the complexity changes of the signal, with high number of levels, the amplitude variations, and with an intermediate number of levels informs about both amplitude and complexity variations. The study performed in diaphragmatic mechanomyographic signals shows that the amplitude variations of this signal are more correlated with the respiratory effort than the complexity variations. Furthermore, it has been observed that the MLZ index with high number of levels practically is not affected by the existence of impulsive, sinusoidal, constant and Gaussian noises compared with the RMS amplitude parameter.

Towards a 21st century roadmap for biomedical research and drug discovery: Consensus report and recommendations

EPA Science Inventory

Decades of costly failures in translating drug candidates from preclinical disease models to human therapeutic use warrant reconsideration of the priority placed on animal models in biomedical research. Following an international workshop attended by experts from academia, govern...

A cognitive task analysis of a visual analytic workflow: Exploring molecular interaction networks in systems biology.

PubMed

Mirel, Barbara; Eichinger, Felix; Keller, Benjamin J; Kretzler, Matthias

2011-03-21

Bioinformatics visualization tools are often not robust enough to support biomedical specialists’ complex exploratory analyses. Tools need to accommodate the workflows that scientists actually perform for specific translational research questions. To understand and model one of these workflows, we conducted a case-based, cognitive task analysis of a biomedical specialist’s exploratory workflow for the question: What functional interactions among gene products of high throughput expression data suggest previously unknown mechanisms of a disease? From our cognitive task analysis four complementary representations of the targeted workflow were developed. They include: usage scenarios, flow diagrams, a cognitive task taxonomy, and a mapping between cognitive tasks and user-centered visualization requirements. The representations capture the flows of cognitive tasks that led a biomedical specialist to inferences critical to hypothesizing. We created representations at levels of detail that could strategically guide visualization development, and we confirmed this by making a trial prototype based on user requirements for a small portion of the workflow. Our results imply that visualizations should make available to scientific users “bundles of features” consonant with the compositional cognitive tasks purposefully enacted at specific points in the workflow. We also highlight certain aspects of visualizations that: (a) need more built-in flexibility; (b) are critical for negotiating meaning; and (c) are necessary for essential metacognitive support.

Nonhuman Primate Models in the Genomic Era: A Paradigm Shift

PubMed Central

Vallender, Eric J.; Miller, Gregory M.

2013-01-01

Because of their strong similarities to humans across physiologic, developmental, behavioral, immunologic, and genetic levels, nonhuman primates are essential models for a wide spectrum of biomedical research. But unlike other animal models, nonhuman primates possess substantial outbred genetic variation, reducing statistical power and potentially confounding interpretation of results in research studies. Although unknown genetic variation is a hindrance in studies that allocate animals randomly, taking genetic variation into account in study design affords an opportunity to transform the way that nonhuman primates are used in biomedical research. New understandings of how the function of individual genes in rhesus macaques mimics that seen in humans are greatly advancing the rhesus macaques utility as research models, but epistatic interaction, epigenetic regulatory mechanisms, and the intricacies of gene networks limit model development. We are now entering a new era of nonhuman primate research, brought on by the proliferation and rapid expansion of genomic data. Already the cost of a rhesus macaque genome is dwarfed by its purchase and husbandry costs, and complete genomic datasets will inevitably encompass each rhesus macaque used in biomedical research. Advancing this outcome is paramount. It represents an opportunity to transform the way animals are assigned and used in biomedical research and to develop new models of human disease. The genetic and genomic revolution brings with it a paradigm shift for nonhuman primates and new mandates on how nonhuman primates are used in biomedical research. PMID:24174439

Chapter 1: Biomedical knowledge integration.

PubMed

Payne, Philip R O

2012-01-01

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.

Chapter 1: Biomedical Knowledge Integration

PubMed Central

Payne, Philip R. O.

2012-01-01

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems. PMID:23300416

Complexity and multifractality of neuronal noise in mouse and human hippocampal epileptiform dynamics.

PubMed

Serletis, Demitre; Bardakjian, Berj L; Valiante, Taufik A; Carlen, Peter L

2012-10-01

Fractal methods offer an invaluable means of investigating turbulent nonlinearity in non-stationary biomedical recordings from the brain. Here, we investigate properties of complexity (i.e. the correlation dimension, maximum Lyapunov exponent, 1/f(γ) noise and approximate entropy) and multifractality in background neuronal noise-like activity underlying epileptiform transitions recorded at the intracellular and local network scales from two in vitro models: the whole-intact mouse hippocampus and lesional human hippocampal slices. Our results show evidence for reduced dynamical complexity and multifractal signal features following transition to the ictal epileptiform state. These findings suggest that pathological breakdown in multifractal complexity coincides with loss of signal variability or heterogeneity, consistent with an unhealthy ictal state that is far from the equilibrium of turbulent yet healthy fractal dynamics in the brain. Thus, it appears that background noise-like activity successfully captures complex and multifractal signal features that may, at least in part, be used to classify and identify brain state transitions in the healthy and epileptic brain, offering potential promise for therapeutic neuromodulatory strategies for afflicted patients suffering from epilepsy and other related neurological disorders.

CNN-based ranking for biomedical entity normalization.

PubMed

Li, Haodi; Chen, Qingcai; Tang, Buzhou; Wang, Xiaolong; Xu, Hua; Wang, Baohua; Huang, Dong

2017-10-03

Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities. The CNN-based ranking method first generates candidates using handcrafted rules, and then ranks the candidates according to their semantic information modeled by CNN as well as their morphological information. Experiments on two benchmark datasets for biomedical entity normalization show that our proposed CNN-based ranking method outperforms traditional rule-based method with state-of-the-art performance. We propose a CNN architecture that regards biomedical entity normalization as a ranking problem. Comparison results show that semantic information is beneficial to biomedical entity normalization and can be well combined with morphological information in our CNN architecture for further improvement.

Mixed Methods in Biomedical and Health Services Research

PubMed Central

Curry, Leslie A.; Krumholz, Harlan M.; O’Cathain, Alicia; Plano Clark, Vicki L.; Cherlin, Emily; Bradley, Elizabeth H.

2013-01-01

Mixed methods studies, in which qualitative and quantitative methods are combined in a single program of inquiry, can be valuable in biomedical and health services research, where the complementary strengths of each approach can yield greater insight into complex phenomena than either approach alone. Although interest in mixed methods is growing among science funders and investigators, written guidance on how to conduct and assess rigorous mixed methods studies is not readily accessible to the general readership of peer-reviewed biomedical and health services journals. Furthermore, existing guidelines for publishing mixed methods studies are not well known or applied by researchers and journal editors. Accordingly, this paper is intended to serve as a concise, practical resource for readers interested in core principles and practices of mixed methods research. We briefly describe mixed methods approaches and present illustrations from published biomedical and health services literature, including in cardiovascular care, summarize standards for the design and reporting of these studies, and highlight four central considerations for investigators interested in using these methods. PMID:23322807

Nanodiamond-Based Composite Structures for Biomedical Imaging and Drug Delivery.

PubMed

Rosenholm, Jessica M; Vlasov, Igor I; Burikov, Sergey A; Dolenko, Tatiana A; Shenderova, Olga A

2015-02-01

Nanodiamond particles are widely recognized candidates for biomedical applications due to their excellent biocompatibility, bright photoluminescence based on color centers and outstanding photostability. Recently, more complex architectures with a nanodiamond core and an external shell or nanostructure which provides synergistic benefits have been developed, and their feasibility for biomedical applications has been demonstrated. This review is aimed at summarizing recent achievements in the fabrication and functional demonstrations of nanodiamond-based composite structures, along with critical considerations that should be taken into account in the design of such structures from a biomedical point of view. A particular focus of the review is core/shell structures of nanodiamond surrounded by porous silica shells, which demonstrate a remarkable increase in drug loading efficiency; as well as nanodiamonds decorated with carbon dots, which have excellent potential as bioimaging probes. Other combinations are also considered, relying on the discussed inherent properties of the inorganic materials being integrated in a way to advance inorganic nanomedicine in the quest for better health-related nanotechnology.

Cell Uptake and Validation of Novel PECs for Biomedical Applications.

PubMed

Palamà, Ilaria E; Musarò, Mariarosaria; Coluccia, Addolorata M L; D'Amone, Stefania; Gigli, Giuseppe

2011-01-01

This pilot study provides the proof of principle for biomedical application of novel polyelectrolyte complexes (PECs) obtained via electrostatic interactions between dextran sulphate (DXS) and poly(allylamine hydrochloride) (PAH). Scanning electron microscopy (SEM) and atomic force microscopy (AFM) showed that DXS/PAH polyelectrolyte complexes were Monodispersed with regular rounded-shape features and average diameters of 250 nm at 2 : 1 weight ratios of DXS/PAH. Fluorescently labelled DXS and fluorescein-isothiocyanate- (FITC-)conjugate DXS were used to follow cell uptake efficiency of PECs and biodegradability of their enzymatically degradable DXS-layers by using confocal laser scanning microscopy (CLSM). Moreover, quantitative MTT and Trypan Blue assays were employed to validate PECs as feasible and safe nanoscaled carriers at single-cell level without adverse effects on metabolism and viability.

Cell Uptake and Validation of Novel PECs for Biomedical Applications

PubMed Central

Palamà, Ilaria E.; Musarò, Mariarosaria; Coluccia, Addolorata M. L.; D'Amone, Stefania; Gigli, Giuseppe

2011-01-01

This pilot study provides the proof of principle for biomedical application of novel polyelectrolyte complexes (PECs) obtained via electrostatic interactions between dextran sulphate (DXS) and poly(allylamine hydrochloride) (PAH). Scanning electron microscopy (SEM) and atomic force microscopy (AFM) showed that DXS/PAH polyelectrolyte complexes were Monodispersed with regular rounded-shape features and average diameters of 250 nm at 2 : 1 weight ratios of DXS/PAH. Fluorescently labelled DXS and fluorescein-isothiocyanate- (FITC-)conjugate DXS were used to follow cell uptake efficiency of PECs and biodegradability of their enzymatically degradable DXS-layers by using confocal laser scanning microscopy (CLSM). Moreover, quantitative MTT and Trypan Blue assays were employed to validate PECs as feasible and safe nanoscaled carriers at single-cell level without adverse effects on metabolism and viability. PMID:21876815

A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data.

PubMed

Delussu, Giovanni; Lianas, Luca; Frexia, Francesca; Zanetti, Gianluigi

2016-01-01

This work presents a scalable data access layer, called PyEHR, designed to support the implementation of data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts the openEHR's formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structure indexing to accelerate searches. Data persistence is guaranteed by a driver layer with a common driver interface. Interfaces for two NoSQL Database Management Systems are already implemented: MongoDB and Elasticsearch. We evaluated the scalability of PyEHR experimentally through two types of tests, called "Constant Load" and "Constant Number of Records", with queries of increasing complexity on synthetic datasets of ten million records each, containing very complex openEHR archetype structures, distributed on up to ten computing nodes.

A histological ontology of the human cardiovascular system.

PubMed

Mazo, Claudia; Salazar, Liliana; Corcho, Oscar; Trujillo, Maria; Alegre, Enrique

2017-10-02

In this paper, we describe a histological ontology of the human cardiovascular system developed in collaboration among histology experts and computer scientists. The histological ontology is developed following an existing methodology using Conceptual Models (CMs) and validated using OOPS!, expert evaluation with CMs, and how accurately the ontology can answer the Competency Questions (CQ). It is publicly available at http://bioportal.bioontology.org/ontologies/HO and https://w3id.org/def/System . The histological ontology is developed to support complex tasks, such as supporting teaching activities, medical practices, and bio-medical research or having natural language interactions.

Reproducibility Issues: Avoiding Pitfalls in Animal Inflammation Models.

PubMed

Laman, Jon D; Kooistra, Susanne M; Clausen, Björn E

2017-01-01

In light of an enhanced awareness of ethical questions and ever increasing costs when working with animals in biomedical research, there is a dedicated and sometimes fierce debate concerning the (lack of) reproducibility of animal models and their relevance for human inflammatory diseases. Despite evident advancements in searching for alternatives, that is, replacing, reducing, and refining animal experiments-the three R's of Russel and Burch (1959)-understanding the complex interactions of the cells of the immune system, the nervous system and the affected tissue/organ during inflammation critically relies on in vivo models. Consequently, scientific advancement and ultimately novel therapeutic interventions depend on improving the reproducibility of animal inflammation models. As a prelude to the remaining hands-on protocols described in this volume, here, we summarize potential pitfalls of preclinical animal research and provide resources and background reading on how to avoid them.

Biomedical progress rates as new parameters for models of economic growth in developed countries.

PubMed

Zhavoronkov, Alex; Litovchenko, Maria

2013-11-08

While the doubling of life expectancy in developed countries during the 20th century can be attributed mostly to decreases in child mortality, the trillions of dollars spent on biomedical research by governments, foundations and corporations over the past sixty years are also yielding longevity dividends in both working and retired population. Biomedical progress will likely increase the healthy productive lifespan and the number of years of government support in the old age. In this paper we introduce several new parameters that can be applied to established models of economic growth: the biomedical progress rate, the rate of clinical adoption and the rate of change in retirement age. The biomedical progress rate is comprised of the rejuvenation rate (extending the productive lifespan) and the non-rejuvenating rate (extending the lifespan beyond the age at which the net contribution to the economy becomes negative). While staying within the neoclassical economics framework and extending the overlapping generations (OLG) growth model and assumptions from the life cycle theory of saving behavior, we provide an example of the relations between these new parameters in the context of demographics, labor, households and the firm.

Biomedical Progress Rates as New Parameters for Models of Economic Growth in Developed Countries

PubMed Central

Zhavoronkov, Alex; Litovchenko, Maria

2013-01-01

While the doubling of life expectancy in developed countries during the 20th century can be attributed mostly to decreases in child mortality, the trillions of dollars spent on biomedical research by governments, foundations and corporations over the past sixty years are also yielding longevity dividends in both working and retired population. Biomedical progress will likely increase the healthy productive lifespan and the number of years of government support in the old age. In this paper we introduce several new parameters that can be applied to established models of economic growth: the biomedical progress rate, the rate of clinical adoption and the rate of change in retirement age. The biomedical progress rate is comprised of the rejuvenation rate (extending the productive lifespan) and the non-rejuvenating rate (extending the lifespan beyond the age at which the net contribution to the economy becomes negative). While staying within the neoclassical economics framework and extending the overlapping generations (OLG) growth model and assumptions from the life cycle theory of saving behavior, we provide an example of the relations between these new parameters in the context of demographics, labor, households and the firm. PMID:24217179

Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

PubMed

Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

2016-06-14

In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

Physical properties of biological entities: an introduction to the ontology of physics for biology.

PubMed

Cook, Daniel L; Bookstein, Fred L; Gennari, John H

2011-01-01

As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities-molecules, cells, organs-are well-established, there are no principled ontologies of physical properties-energies, volumes, flow rates-of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration. © 2011 Cook et al.

From the NIH: A Systems Approach to Increasing the Diversity of the Biomedical Research Workforce.

PubMed

Valantine, Hannah A; Lund, P Kay; Gammie, Alison E

The National Institutes of Health (NIH) is committed to attracting, developing, and supporting the best scientists from all groups as an integral part of excellence in training. Biomedical research workforce diversity, capitalizing on the full spectrum of skills, talents, and viewpoints, is essential for solving complex human health challenges. Over the past few decades, the biomedical research workforce has benefited from NIH programs aimed at enhancing diversity. However, there is considerable room for improvement, particularly at the level of independent scientists and within scientific leadership. We provide a rationale and specific opportunities to develop and sustain a diverse biomedical research workforce through interventions that promote the successful transitions to different stages on the path toward completion of training and entry into the biomedical workforce. © 2016 H. A. Valantine et al. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

A systematic approach to embedded biomedical decision making.

PubMed

Song, Zhe; Ji, Zhongkai; Ma, Jian-Guo; Sputh, Bernhard; Acharya, U Rajendra; Faust, Oliver

2012-11-01

An embedded decision making is a key feature for many biomedical systems. In most cases human life directly depends on correct decisions made by these systems, therefore they have to work reliably. This paper describes how we applied systems engineering principles to design a high performance embedded classification system in a systematic and well structured way. We introduce the structured design approach by discussing requirements capturing, specifications refinement, implementation and testing. Thereby, we follow systems engineering principles and execute each of these processes as formal as possible. The requirements, which motivate the system design, describe an automated decision making system for diagnostic support. These requirements are refined into the implementation of a support vector machine (SVM) algorithm which enables us to integrate automated decision making in embedded systems. With a formal model we establish functionality, stability and reliability of the system. Furthermore, we investigated different parallel processing configurations of this computationally complex algorithm. We found that, by adding SVM processes, an almost linear speedup is possible. Once we established these system properties, we translated the formal model into an implementation. The resulting implementation was tested using XMOS processors with both normal and failure cases, to build up trust in the implementation. Finally, we demonstrated that our parallel implementation achieves the speedup, predicted by the formal model. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Manpower development for the biomedical industry space.

PubMed

Goh, James C H

2013-01-01

The Biomedical Sciences (BMS) Cluster is one of four key pillars of the Singapore economy. The Singapore Government has injected research funding for basic and translational research to attract companies to carry out their commercial R&D activities. To further intensify the R&D efforts, the National Research Foundation (NRF) was set up to coordinate the research activities of different agencies within the larger national framework and to fund strategic R&D initiatives. In recent years, funding agencies began to focus on support of translational and clinical research, particularly those with potential for commercialization. Translational research is beginning to have traction, in particular research funding for the development of innovation medical devices. Therefore, the Biomedical Sciences sector is projected to grow which means that there is a need to invest in human capital development to achieve sustainable growth. In support of this, education and training programs to strengthen the manpower capabilities for the Biomedical Sciences industry have been developed. In recent years, undergraduate and graduate degree courses in biomedical engineering/bioengineering have been developing at a rapid rate. The goal is to train students with skills to understand complex issues of biomedicine and to develop and implement of advanced technological applications to these problems. There are a variety of career opportunities open to graduates in biomedical engineering, however regardless of the type of career choices, students must not only focus on achieving good grades. They have to develop their marketability to employers through internships, overseas exchange programs, and involvement in leadership-type activities. Furthermore, curriculum has to be developed with biomedical innovation in mind and ensure relevance to the industry. The objective of this paper is to present the NUS Bioengineering undergraduate program in relation to manpower development for the biomedical industry in Singapore.

Generation of open biomedical datasets through ontology-driven transformation and integration processes.

PubMed

Carmen Legaz-García, María Del; Miñarro-Giménez, José Antonio; Menárguez-Tortosa, Marcos; Fernández-Breis, Jesualdo Tomás

2016-06-03

Biomedical research usually requires combining large volumes of data from multiple heterogeneous sources, which makes difficult the integrated exploitation of such data. The Semantic Web paradigm offers a natural technological space for data integration and exploitation by generating content readable by machines. Linked Open Data is a Semantic Web initiative that promotes the publication and sharing of data in machine readable semantic formats. We present an approach for the transformation and integration of heterogeneous biomedical data with the objective of generating open biomedical datasets in Semantic Web formats. The transformation of the data is based on the mappings between the entities of the data schema and the ontological infrastructure that provides the meaning to the content. Our approach permits different types of mappings and includes the possibility of defining complex transformation patterns. Once the mappings are defined, they can be automatically applied to datasets to generate logically consistent content and the mappings can be reused in further transformation processes. The results of our research are (1) a common transformation and integration process for heterogeneous biomedical data; (2) the application of Linked Open Data principles to generate interoperable, open, biomedical datasets; (3) a software tool, called SWIT, that implements the approach. In this paper we also describe how we have applied SWIT in different biomedical scenarios and some lessons learned. We have presented an approach that is able to generate open biomedical repositories in Semantic Web formats. SWIT is able to apply the Linked Open Data principles in the generation of the datasets, so allowing for linking their content to external repositories and creating linked open datasets. SWIT datasets may contain data from multiple sources and schemas, thus becoming integrated datasets.

Examination for the Certificate in Advanced Practice in Cervical Cytology--the first year's experience.

PubMed

Smith, P A; Hewer, E M

2003-06-01

Following the decision to establish an Advanced Biomedical Scientist Practitioner grade for senior biomedical scientists in the NHS Cervical Screening Programme, a conjoint examination board has been appointed by the Royal College of Pathologists and Institute of Biomedical Science to oversee the Certificate in Advanced Practice in Cervical Cytology examination. The examination consists of a multiple-choice paper, short-answer written questions and practical microscopy sections covering screening of unmarked slides, and more complex discussion cases. In the first year there were 58 entries with 29 successful candidates, a pass rate of 50%. The standard of performance in the examination showed a wide range, and some candidates appear to have underestimated the degree of preparation, knowledge or level of microscopy skill required.

A sub-microwatt asynchronous level-crossing ADC for biomedical applications.

PubMed

Li, Yongjia; Zhao, Duan; Serdijn, Wouter A

2013-04-01

A continuous-time level-crossing analog-to-digital converter (LC-ADC) for biomedical applications is presented. When compared to uniform-sampling (US) ADCs LC-ADCs generate fewer samples for various sparse biomedical signals. Lower power consumption and reduced design complexity with respect to conventional LC-ADCs are achieved due to: 1) replacing the n-bit digital-to-analog converter (DAC) with a 1-bit DAC; 2) splitting the level-crossing detections; and 3) fixing the comparison window. Designed and implemented in 0.18 μm CMOS technology, the proposed ADC uses a chip area of 220 × 203 μm(2). Operating from a supply voltage of 0.8 V, the ADC consumes 313-582 nW from 5 Hz to 5 kHz and achieves an ENOB up to 7.9 bits.

The Annals of Biomedical Engineering: inception to signature journal.

PubMed

Fagette, Paul

2012-03-01

The Annals of Biomedical Engineering, the flagship journal of the Biomedical Engineering Society, developed through four distinct stages. Once an editorial infrastructure was in place and a publisher was secured, a long-lived struggle for sufficient manuscripts and financial stability ensued. The journal achieved a degree of stableness by the mid-1980s. Electronic communication and on-line publishing in the 1990s allowed more rapid turn around but the increased acceptance of quality manuscripts created pressures from insufficient available pages. The journal finally turned to self-publication. The Board of Directors and the Publications Board carefully nurtured the journal over the years with financial support and policy. Still, the bulk of the effort was carried by the editors. They dealt with an ever increasing complex publishing process that now supports three Society journals.

Volume and Value of Big Healthcare Data.

PubMed

Dinov, Ivo D

Modern scientific inquiries require significant data-driven evidence and trans-disciplinary expertise to extract valuable information and gain actionable knowledge about natural processes. Effective evidence-based decisions require collection, processing and interpretation of vast amounts of complex data. The Moore's and Kryder's laws of exponential increase of computational power and information storage, respectively, dictate the need rapid trans-disciplinary advances, technological innovation and effective mechanisms for managing and interrogating Big Healthcare Data. In this article, we review important aspects of Big Data analytics and discuss important questions like: What are the challenges and opportunities associated with this biomedical, social, and healthcare data avalanche? Are there innovative statistical computing strategies to represent, model, analyze and interpret Big heterogeneous data? We present the foundation of a new compressive big data analytics (CBDA) framework for representation, modeling and inference of large, complex and heterogeneous datasets. Finally, we consider specific directions likely to impact the process of extracting information from Big healthcare data, translating that information to knowledge, and deriving appropriate actions.

Volume and Value of Big Healthcare Data

PubMed Central

Dinov, Ivo D.

2016-01-01

Modern scientific inquiries require significant data-driven evidence and trans-disciplinary expertise to extract valuable information and gain actionable knowledge about natural processes. Effective evidence-based decisions require collection, processing and interpretation of vast amounts of complex data. The Moore's and Kryder's laws of exponential increase of computational power and information storage, respectively, dictate the need rapid trans-disciplinary advances, technological innovation and effective mechanisms for managing and interrogating Big Healthcare Data. In this article, we review important aspects of Big Data analytics and discuss important questions like: What are the challenges and opportunities associated with this biomedical, social, and healthcare data avalanche? Are there innovative statistical computing strategies to represent, model, analyze and interpret Big heterogeneous data? We present the foundation of a new compressive big data analytics (CBDA) framework for representation, modeling and inference of large, complex and heterogeneous datasets. Finally, we consider specific directions likely to impact the process of extracting information from Big healthcare data, translating that information to knowledge, and deriving appropriate actions. PMID:26998309

Omni Directional Multimaterial Soft Cylindrical Actuator and Its Application as a Steerable Catheter.

PubMed

Gul, Jahan Zeb; Yang, Young Jin; Su, Kim Young; Choi, Kyung Hyun

2017-09-01

Soft actuators with complex range of motion lead to strong interest in applying devices like biomedical catheters and steerable soft pipe inspectors. To facilitate the use of soft actuators in devices where controlled, complex, precise, and fast motion is required, a structurally controlled Omni directional soft cylindrical actuator is fabricated in a modular way using multilayer composite of polylactic acid based conductive Graphene, shape memory polymer, shape memory alloy, and polyurethane. Multiple fabrication techniques are discussed step by step that mainly include fused deposition modeling based 3D printing, dip coating, and UV curing. A mathematical control model is used to generate patterned electrical signals for the Omni directional deformations. Characterizations like structural control, bending, recovery, path, and thermal effect are carried out with and without load (10 g) to verify the new cylindrical design concept. Finally, the application of Omni directional actuator as a steerable catheter is explored by fabricating a scaled version of carotid artery through 3D printing using a semitransparent material.

Nanoparticles speckled by ready-to-conjugate lanthanide complexes for multimodal imaging

NASA Astrophysics Data System (ADS)

Biju, Vasudevanpillai; Hamada, Morihiko; Ono, Kenji; Sugino, Sakiko; Ohnishi, Takashi; Shibu, Edakkattuparambil Sidharth; Yamamura, Shohei; Sawada, Makoto; Nakanishi, Shunsuke; Shigeri, Yasushi; Wakida, Shin-Ichi

2015-09-01

Multimodal and multifunctional contrast agents receive enormous attention in the biomedical imaging field. Such contrast agents are routinely prepared by the incorporation of organic molecules and inorganic nanoparticles (NPs) into host materials such as gold NPs, silica NPs, polymer NPs, and liposomes. Despite their non-cytotoxic nature, the large size of these NPs limits the in vivo distribution and clearance and inflames complex pharmacokinetics, which hinder the regulatory approval for clinical applications. Herein, we report a unique method that combines magnetic resonance imaging (MRI) and fluorescence imaging modalities together in nanoscale entities by the simple, direct and stable conjugation of novel biotinylated coordination complexes of gadolinium(iii) to CdSe/ZnS quantum dots (QD) and terbium(iii) to super paramagnetic iron oxide NPs (SPION) but without any host material. Subsequently, we evaluate the potentials of such lanthanide-speckled fluorescent-magnetic NPs for bioimaging at single-molecule, cell and in vivo levels. The simple preparation and small size make such fluorescent-magnetic NPs promising contrast agents for biomedical imaging.

Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals

NASA Astrophysics Data System (ADS)

Azami, Hamed; Escudero, Javier

2017-01-01

Multiscale entropy (MSE) is an appealing tool to characterize the complexity of time series over multiple temporal scales. Recent developments in the field have tried to extend the MSE technique in different ways. Building on these trends, we propose the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) whose coarse-graining step uses variance (RCmvMFEσ2) or mean (RCmvMFEμ). We investigate the behavior of these multivariate methods on multichannel white Gaussian and 1/ f noise signals, and two publicly available biomedical recordings. Our simulations demonstrate that RCmvMFEσ2 and RCmvMFEμ lead to more stable results and are less sensitive to the signals' length in comparison with the other existing multivariate multiscale entropy-based methods. The classification results also show that using both the variance and mean in the coarse-graining step offers complexity profiles with complementary information for biomedical signal analysis. We also made freely available all the Matlab codes used in this paper.

Organization and integration of biomedical knowledge with concept maps for key peroxisomal pathways.

PubMed

Willemsen, A M; Jansen, G A; Komen, J C; van Hooff, S; Waterham, H R; Brites, P M T; Wanders, R J A; van Kampen, A H C

2008-08-15

One important area of clinical genomics research involves the elucidation of molecular mechanisms underlying (complex) disorders which eventually may lead to new diagnostic or drug targets. To further advance this area of clinical genomics one of the main challenges is the acquisition and integration of data, information and expert knowledge for specific biomedical domains and diseases. Currently the required information is not very well organized but scattered over biological and biomedical databases, basic text books, scientific literature and experts' minds and may be highly specific, heterogeneous, complex and voluminous. We present a new framework to construct knowledge bases with concept maps for presentation of information and the web ontology language OWL for the representation of information. We demonstrate this framework through the construction of a peroxisomal knowledge base, which focuses on four key peroxisomal pathways and several related genetic disorders. All 155 concept maps in our knowledge base are linked to at least one other concept map, which allows the visualization of one big network of related pieces of information. The peroxisome knowledge base is available from www.bioinformaticslaboratory.nl (Support-->Web applications). Supplementary data is available from www.bioinformaticslaboratory.nl (Research-->Output--> Publications--> KB_SuppInfo)

Children's Naive Concepts of OCD and How They Are Affected by Biomedical Versus Cognitive Behavioural Psychoeducation.

PubMed

Butlin, B; Wilson, C

2018-04-04

How we conceptualize mental health conditions is important as it impacts on a wide range of mediators of treatment outcome. We do not know how children intuitively conceptualize obsessive-compulsive disorder (OCD), nor do we know the relative impact of biomedical or cognitive behavioural conceptual explanations, yet both are being widely used in psychoeducation for children with OCD. This study identified children's naive concepts of OCD, and the comparative impact of biomedical versus cognitive behavioural psychoeducation on perceived prognosis. A within- and between-subjects experimental design was used. After watching a video of a young person describing their OCD, 202 children completed a questionnaire examining their concepts of the condition. They repeated the questionnaire following a second equivalent video, this time preceded by either biomedical or cognitive behavioural psychoeducation. Participants' naive concepts of OCD reflected predominant models of OCD in healthcare. Even at the minimal dose of psychoeducation, participants' conceptualizations of OCD changed. Prior exposure to OCD resulted in a stronger alignment with the biomedical model. Exposure to biomedical psychoeducation resulted in participants predicting a slower recovery with less chance of complete remission. Psychoeducation for childhood OCD is impactful. Despite its wide use by clinicians and mental health services, biomedical psychoeducation appears to have deleterious effects. Children's concepts of OCD merit attention but caution should be applied in how they are targeted.

Publishing biomedical journals on the World-Wide Web using an open architecture model.

PubMed Central

Shareck, E. P.; Greenes, R. A.

1996-01-01

BACKGROUND: In many respects, biomedical publications are ideally suited for distribution via the World-Wide Web, but economic concerns have prevented the rapid adoption of an on-line publishing model. PURPOSE: We report on our experiences with assisting biomedical journals in developing an online presence, issues that were encountered, and methods used to address these issues. Our approach is based on an open architecture that fosters adaptation and interconnection of biomedical resources. METHODS: We have worked with the New England Journal of Medicine (NEJM), as well as five other publishers. A set of tools and protocols was employed to develop a scalable and customizable solution for publishing journals on-line. RESULTS: In March, 1996, the New England Journal of Medicine published its first World-Wide Web issue. Explorations with other publishers have helped to generalize the model. CONCLUSIONS: Economic and technical issues play a major role in developing World-Wide Web publishing solutions. PMID:8947685

Visceral obesity and psychosocial stress: a generalised control theory model

NASA Astrophysics Data System (ADS)

Wallace, Rodrick

2016-07-01

The linking of control theory and information theory via the Data Rate Theorem and its generalisations allows for construction of necessary conditions statistical models of body mass regulation in the context of interaction with a complex dynamic environment. By focusing on the stress-related induction of central obesity via failure of HPA axis regulation, we explore implications for strategies of prevention and treatment. It rapidly becomes evident that individual-centred biomedical reductionism is an inadequate paradigm. Without mitigation of HPA axis or related dysfunctions arising from social pathologies of power imbalance, economic insecurity, and so on, it is unlikely that permanent changes in visceral obesity for individuals can be maintained without constant therapeutic effort, an expensive - and likely unsustainable - public policy.

Performance of Ultra Wideband On-Body Communication Based on Statistical Channel Model

NASA Astrophysics Data System (ADS)

Wang, Qiong; Wang, Jianqing

Ultra wideband (UWB) on-body communication is attracting much attention in biomedical applications. In this paper, the performance of UWB on-body communication is investigated based on a statistically extracted on-body channel model, which provides detailed characteristics of the multi-path-affected channel with an emphasis on various body postures or body movement. The possible data rate, the possible communication distance, as well as the bit error rate (BER) performance are clarified via computer simulation. It is found that the conventional correlation receiver is incompetent in the multi-path-affected on-body channel, while the RAKE receiver outperforms the conventional correlation receiver at a cost of structure complexity. Different RAKE receiver structures are compared to show the improvement of the BER performance.

MOPED enables discoveries through consistently processed proteomics data

PubMed Central

Higdon, Roger; Stewart, Elizabeth; Stanberry, Larissa; Haynes, Winston; Choiniere, John; Montague, Elizabeth; Anderson, Nathaniel; Yandl, Gregory; Janko, Imre; Broomall, William; Fishilevich, Simon; Lancet, Doron; Kolker, Natali; Kolker, Eugene

2014-01-01

The Model Organism Protein Expression Database (MOPED, http://moped.proteinspire.org), is an expanding proteomics resource to enable biological and biomedical discoveries. MOPED aggregates simple, standardized and consistently processed summaries of protein expression and metadata from proteomics (mass spectrometry) experiments from human and model organisms (mouse, worm and yeast). The latest version of MOPED adds new estimates of protein abundance and concentration, as well as relative (differential) expression data. MOPED provides a new updated query interface that allows users to explore information by organism, tissue, localization, condition, experiment, or keyword. MOPED supports the Human Proteome Project’s efforts to generate chromosome and diseases specific proteomes by providing links from proteins to chromosome and disease information, as well as many complementary resources. MOPED supports a new omics metadata checklist in order to harmonize data integration, analysis and use. MOPED’s development is driven by the user community, which spans 90 countries guiding future development that will transform MOPED into a multi-omics resource. MOPED encourages users to submit data in a simple format. They can use the metadata a checklist generate a data publication for this submission. As a result, MOPED will provide even greater insights into complex biological processes and systems and enable deeper and more comprehensive biological and biomedical discoveries. PMID:24350770

Bacterial vaginosis: a public health problem for women.

PubMed

Rauh, V A; Culhane, J F; Hogan, V K

2000-01-01

Bacterial vaginosis (BV) remains a moderately prevalent condition with clearly observed links to adverse reproductive, gynecological, and other outcomes in women, including human immunodeficiency virus infection. Because of inconsistent findings from clinical studies concerning BV's etiologic role, no definitive policies with respect to screening and treatment have yet been established. Of concern is the high, unexplained prevalence of BV among African-American women, who are also at extremely high risk for preterm birth. The complexity of the sociodemographic picture challenges the field of public health to continue to explore the role of BV and its relationship to a whole host of social and biomedical conditions that may contribute to adverse health outcomes among society's most vulnerable members. Future decisions about screening and treatment, currently based on the biomedical model, may need to take into consideration issues of social context and expanded views of causality if we are to better understand and eliminate those factors that place individual women at risk of adverse outcomes, as well as the conditions that underlie racial and ethnic disparities in health.

A method for exploring implicit concept relatedness in biomedical knowledge network.

PubMed

Bai, Tian; Gong, Leiguang; Wang, Ye; Wang, Yan; Kulikowski, Casimir A; Huang, Lan

2016-07-19

Biomedical information and knowledge, structural and non-structural, stored in different repositories can be semantically connected to form a hybrid knowledge network. How to compute relatedness between concepts and discover valuable but implicit information or knowledge from it effectively and efficiently is of paramount importance for precision medicine, and a major challenge facing the biomedical research community. In this study, a hybrid biomedical knowledge network is constructed by linking concepts across multiple biomedical ontologies as well as non-structural biomedical knowledge sources. To discover implicit relatedness between concepts in ontologies for which potentially valuable relationships (implicit knowledge) may exist, we developed a Multi-Ontology Relatedness Model (MORM) within the knowledge network, for which a relatedness network (RN) is defined and computed across multiple ontologies using a formal inference mechanism of set-theoretic operations. Semantic constraints are designed and implemented to prune the search space of the relatedness network. Experiments to test examples of several biomedical applications have been carried out, and the evaluation of the results showed an encouraging potential of the proposed approach to biomedical knowledge discovery.

High performance flexible electronics for biomedical devices.

PubMed

Salvatore, Giovanni A; Munzenrieder, Niko; Zysset, Christoph; Kinkeldei, Thomas; Petti, Luisa; Troster, Gerhard

2014-01-01

Plastic electronics is soft, deformable and lightweight and it is suitable for the realization of devices which can form an intimate interface with the body, be implanted or integrated into textile for wearable and biomedical applications. Here, we present flexible electronics based on amorphous oxide semiconductors (a-IGZO) whose performance can achieve MHz frequency even when bent around hair. We developed an assembly technique to integrate complex electronic functionalities into textile while preserving the softness of the garment. All this and further developments can open up new opportunities in health monitoring, biotechnology and telemedicine.

Environmental and biomedical applications of natural metal stable isotope variations

USGS Publications Warehouse

Bullen, T.D.; Walczyk, T.

2009-01-01

etal stable isotopes are now being used to trace metal contaminants in the environment and as indicators of human systemic function where metals play a role. Stable isotope abundance variations provide information about metal sources and the processes affecting metals in complex natural systems, complementing information gained from surrogate tracers, such as metal abundance ratios or biochemical markers of metal metabolism. The science is still in its infancy, but the results of initial studies confirm that metal stable isotopes can provide a powerful tool for forensic and biomedical investigations.

Efficient Cisplatin Pro-Drug Delivery Visualized with Sub-100 nm Resolution: Interfacing Engineered Thermosensitive Magnetomicelles with a Living System

DOE PAGES

Vitol, Elina A.; Rozhkova, Elena A.; Rose, Volker; ...

2014-06-06

Temperature-responsive magnetic nanomicelles can serve as thermal energy and cargo carriers with controlled drug release functionality. In view of their potential biomedical applications, understanding the modes of interaction between nanomaterials and living systems and evaluation of efficiency of cargo delivery is of the utmost importance. In this paper, we investigate the interaction between the hybrid magnetic nanomicelles engineered for controlled platinum complex drug delivery and a biological system at three fundamental levels: subcellular compartments, a single cell and whole living animal. Nanomicelles with polymeric P(NIPAAm-co-AAm)-b-PCL core-shell were loaded with a hydrophobic Pt(IV) complex and Fe 3O 4 nanoparticles though self-assembly.more » The distribution of a platinum complex on subcellular level is visualized using hard X-ray fluorescence microscopy with unprecedented level of detail at sub-100 nm spatial resolution. We then study the cytotoxic effects of platinum complex-loaded micelles in vitro on a head and neck cancer cell culture model SQ20B. In conclusion, by employing the magnetic functionality of the micelles and additionally loading them with a near infrared fluorescent dye, we magnetically target them to a tumor site in a live animal xenografted model which allows to visualize their biodistribution in vivo.« less

Pain and dualism: Which dualism?

PubMed

Arnaudo, Elisa

2017-10-01

In the literature, the allegation to pursue approaches grounded on Cartesian dualism in medicine is widespread. In this paper, I argue that dualism informing biomedicine has not only to do with a Cartesian view of mind and body, but that multiple dualisms are at play in biomedical approach to disease, especially regarding complex pain conditions. A form of epistemological dualism entailing the primacy of the physician's model of knowledge on the patient's direct experience of suffering is pointed out as the source of the inadequate medical attitude towards pain, particularly in relation to its complex forms such as fibromyalgic syndrome. The analysis, in particular, pays attention to the way in which dualism underlies and reinforces the delegitimization of chronic pain patients and suggests that solutions have to be found in the material organization of the health care service. © 2017 John Wiley & Sons, Ltd.

How French media have portrayed ADHD to the lay public and to social workers

PubMed Central

Ponnou, Sébastien; Gonon, François

2017-01-01

ABSTRACT Two models of attention deficit hyperactivity disorder (ADHD) coexist: the biomedical and the psychosocial. We identified in nine French newspapers 159 articles giving facts and opinions about ADHD from 1995 to 2015. We classified them according to the model they mainly supported and on the basis of what argument. Two thirds (104/159) mainly supported the biomedical model. The others either defended the psychodynamic understanding of ADHD or voiced both models. Neurological dysfunctions and genetic risk factors were mentioned in support of the biomedical model in only 26 and eight articles, respectively. These biological arguments were less frequent in the most recent years. There were fewer articles mentioning medication other than asserting that medication must be combined with psychosocial interventions (14 versus 57 articles). Only 11/159 articles claimed that medication protects from school failure. These results were compared to those of our two previous studies. Thus, both French newspapers and the specialized press read by social workers mainly defended either the psychodynamic understanding of ADHD or a nuanced version of the biomedical model. In contrast, most French TV programmes described ADHD as an inherited neurological disease whose consequences on school failure can be counteracted by a very effective medication. PMID:28532330

In vivo assessment of the effect of taxifolin glycoside on atopic dermatitis-like skin lesions using biomedical tools in NC/Nga mice.

PubMed

Kim, J Y; Lee, O S; Ha, S; Kim, J H; Park, G; Kim, J K; Oh, C H

2015-07-01

Noninvasive methods of assessment are widely used in clinical trials. However, such methods have not been established in atopic dermatitis (AD), which is a chronic inflammatory skin disease. To demonstrate, using biomedical tools, the benefits of a new substance, taxifolin glycoside (TAX), in an AD model, the NC/Nga mouse. We evaluated the efficacy of topical TAX for AD by measuring clinical skin severity score, cytokine expression and serum IgE level, and by using biomedical measures (vapometry and corneometry). Topical TAX was applied to AD-induced NC/Nga mice for 3 weeks. The anti-inflammatory effects of this compound were demonstrated noninvasively using biomedical tools and immunological assays. Our method of AD assessment using biomedical tools is more objective and accurate than visual inspection. The results obtained using the biomedical tools were identical to those obtained using immunological assays. In vivo biomedical tools are useful for diagnosing and monitoring treatment effects in AD. © 2014 British Association of Dermatologists.

Informatics in radiology: an information model of the DICOM standard.

PubMed

Kahn, Charles E; Langlotz, Curtis P; Channin, David S; Rubin, Daniel L

2011-01-01

The Digital Imaging and Communications in Medicine (DICOM) Standard is a key foundational technology for radiology. However, its complexity creates challenges for information system developers because the current DICOM specification requires human interpretation and is subject to nonstandard implementation. To address this problem, a formally sound and computationally accessible information model of the DICOM Standard was created. The DICOM Standard was modeled as an ontology, a machine-accessible and human-interpretable representation that may be viewed and manipulated by information-modeling tools. The DICOM Ontology includes a real-world model and a DICOM entity model. The real-world model describes patients, studies, images, and other features of medical imaging. The DICOM entity model describes connections between real-world entities and the classes that model the corresponding DICOM information entities. The DICOM Ontology was created to support the Cancer Biomedical Informatics Grid (caBIG) initiative, and it may be extended to encompass the entire DICOM Standard and serve as a foundation of medical imaging systems for research and patient care. RSNA, 2010

Emerging paradigms in medicine: implications for the future of psychiatry.

PubMed

Lake, James

2007-01-01

The causes of mental illness remain obscure in spite of rapid progress in the neurosciences. This is due in part to the fact that contemporary biomedical psychiatry rests on philosophically and scientifically ambiguous ground. In Western medicine paradigms, theories from physics, chemistry, and biology form the basis of an explanatory model of illness, including mental illness. Symptoms are conceptualized as subjective descriptions of effects caused by factors characterized in empirical terms. Conventional biomedicine asserts that all causes of illness, and by extension, mechanisms of action underlying legitimate treatment approaches, rest on biological processes that can be described in the reductionist language of Western science. However, in contemporary Western psychiatry, there is no single adequate explanatory model of the causes of mental illness. What remains are competing psychodynamic, genetic, endocrinologic, and neurobiological models of symptom formation reflecting disparate ideological positions and diverse clinical training backgrounds of mental health professionals. There is no unifying theory in psychiatry because no single explanatory model has been confirmed as more valid than any other. I hypothesize in this article that the synthesis of ideas and clinical approaches from Western biomedicine and non-Western systems of medicine based on understandings of human consciousness, the neurosciences, complexity theory, and quantum field theory, will lead to rapid evolution of conventional Western biomedical psychiatry toward truly integrative mental healthcare. The result will be the emergence of an integrative mental healthcare model that will more adequately address the disparate causes, conditions, and meanings of symptoms combining multimodal approaches from Western biomedicine and non-Western systems of medicine.

Genetics Home Reference: tuberous sclerosis complex

MedlinePlus

... Accessibility FOIA Viewers & Players U.S. Department of Health & Human Services National Institutes of Health National Library of Medicine Lister Hill National Center for Biomedical Communications 8600 Rockville Pike, Bethesda, MD 20894, USA HONCode ...

Collaborative Initiative in Biomedical Imaging to Study Complex Diseases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Weili; Fiddy, Michael A.

2012-03-31

The work reported addressed these topics: Fluorescence imaging; Optical coherence tomography; X-ray interferometer/phase imaging system; Quantitative imaging from scattered fields, Terahertz imaging and spectroscopy; and Multiphoton and Raman microscopy.

Translations on USSR Science and Technology, Biomedical and Behavioral Sciences, Number 15

DTIC Science & Technology

1977-11-16

processed. By applying systems theory to synthesis of complex man-machine systems we form ergatic organisms which not only have external and internal...without exception (and this is extremely important to emphasize) as a complex , integral formation, which through various traditions has acquired a...and outputs of the whole, which has a complex internal organization and structure, which we can no longer ignore in our analysis. Thus analysis and

A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data

PubMed Central

Lianas, Luca; Frexia, Francesca; Zanetti, Gianluigi

2016-01-01

This work presents a scalable data access layer, called PyEHR, designed to support the implementation of data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts the openEHR’s formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structure indexing to accelerate searches. Data persistence is guaranteed by a driver layer with a common driver interface. Interfaces for two NoSQL Database Management Systems are already implemented: MongoDB and Elasticsearch. We evaluated the scalability of PyEHR experimentally through two types of tests, called “Constant Load” and “Constant Number of Records”, with queries of increasing complexity on synthetic datasets of ten million records each, containing very complex openEHR archetype structures, distributed on up to ten computing nodes. PMID:27936191

Genome typing of nonhuman primate models: implications for biomedical research.

PubMed

Haus, Tanja; Ferguson, Betsy; Rogers, Jeffrey; Doxiadis, Gaby; Certa, Ulrich; Rose, Nicola J; Teepe, Robert; Weinbauer, Gerhard F; Roos, Christian

2014-11-01

The success of personalized medicine rests on understanding the genetic variation between individuals. Thus, as medical practice evolves and variation among individuals becomes a fundamental aspect of clinical medicine, a thorough consideration of the genetic and genomic information concerning the animals used as models in biomedical research also becomes critical. In particular, nonhuman primates (NHPs) offer great promise as models for many aspects of human health and disease. These are outbred species exhibiting substantial levels of genetic variation; however, understanding of the contribution of this variation to phenotypes is lagging behind in NHP species. Thus, there is a pivotal need to address this gap and define strategies for characterizing both genomic content and variability within primate models of human disease. Here, we discuss the current state of genomics of NHP models and offer guidelines for future work to ensure continued improvement and utility of this line of biomedical research. Copyright © 2014 Elsevier Ltd. All rights reserved.

Development of an informatics infrastructure for data exchange of biomolecular simulations: architecture, data models and ontology$

PubMed Central

Thibault, J. C.; Roe, D. R.; Eilbeck, K.; Cheatham, T. E.; Facelli, J. C.

2015-01-01

Biomolecular simulations aim to simulate structure, dynamics, interactions, and energetics of complex biomolecular systems. With the recent advances in hardware, it is now possible to use more complex and accurate models, but also reach time scales that are biologically significant. Molecular simulations have become a standard tool for toxicology and pharmacology research, but organizing and sharing data – both within the same organization and among different ones – remains a substantial challenge. In this paper we review our recent work leading to the development of a comprehensive informatics infrastructure to facilitate the organization and exchange of biomolecular simulations data. Our efforts include the design of data models and dictionary tools that allow the standardization of the metadata used to describe the biomedical simulations, the development of a thesaurus and ontology for computational reasoning when searching for biomolecular simulations in distributed environments, and the development of systems based on these models to manage and share the data at a large scale (iBIOMES), and within smaller groups of researchers at laboratory scale (iBIOMES Lite), that take advantage of the standardization of the meta data used to describe biomolecular simulations. PMID:26387907

Development of an informatics infrastructure for data exchange of biomolecular simulations: Architecture, data models and ontology.

PubMed

Thibault, J C; Roe, D R; Eilbeck, K; Cheatham, T E; Facelli, J C

2015-01-01

Biomolecular simulations aim to simulate structure, dynamics, interactions, and energetics of complex biomolecular systems. With the recent advances in hardware, it is now possible to use more complex and accurate models, but also reach time scales that are biologically significant. Molecular simulations have become a standard tool for toxicology and pharmacology research, but organizing and sharing data - both within the same organization and among different ones - remains a substantial challenge. In this paper we review our recent work leading to the development of a comprehensive informatics infrastructure to facilitate the organization and exchange of biomolecular simulations data. Our efforts include the design of data models and dictionary tools that allow the standardization of the metadata used to describe the biomedical simulations, the development of a thesaurus and ontology for computational reasoning when searching for biomolecular simulations in distributed environments, and the development of systems based on these models to manage and share the data at a large scale (iBIOMES), and within smaller groups of researchers at laboratory scale (iBIOMES Lite), that take advantage of the standardization of the meta data used to describe biomolecular simulations.

Atomic model of a cell-wall cross-linking enzyme in complex with an intact bacterial peptidoglycan.

PubMed

Schanda, Paul; Triboulet, Sébastien; Laguri, Cédric; Bougault, Catherine M; Ayala, Isabel; Callon, Morgane; Arthur, Michel; Simorre, Jean-Pierre

2014-12-24

The maintenance of bacterial cell shape and integrity is largely attributed to peptidoglycan, a highly cross-linked biopolymer. The transpeptidases that perform this cross-linking are important targets for antibiotics. Despite this biomedical importance, to date no structure of a protein in complex with an intact bacterial peptidoglycan has been resolved, primarily due to the large size and flexibility of peptidoglycan sacculi. Here we use solid-state NMR spectroscopy to derive for the first time an atomic model of an l,d-transpeptidase from Bacillus subtilis bound to its natural substrate, the intact B. subtilis peptidoglycan. Importantly, the model obtained from protein chemical shift perturbation data shows that both domains-the catalytic domain as well as the proposed peptidoglycan recognition domain-are important for the interaction and reveals a novel binding motif that involves residues outside of the classical enzymatic pocket. Experiments on mutants and truncated protein constructs independently confirm the binding site and the implication of both domains. Through measurements of dipolar-coupling derived order parameters of bond motion we show that protein binding reduces the flexibility of peptidoglycan. This first report of an atomic model of a protein-peptidoglycan complex paves the way for the design of new antibiotic drugs targeting l,d-transpeptidases. The strategy developed here can be extended to the study of a large variety of enzymes involved in peptidoglycan morphogenesis.

BioMOL: a computer-assisted biological modeling tool for complex chemical mixtures and biological processes at the molecular level.

PubMed Central

Klein, Michael T; Hou, Gang; Quann, Richard J; Wei, Wei; Liao, Kai H; Yang, Raymond S H; Campain, Julie A; Mazurek, Monica A; Broadbelt, Linda J

2002-01-01

A chemical engineering approach for the rigorous construction, solution, and optimization of detailed kinetic models for biological processes is described. This modeling capability addresses the required technical components of detailed kinetic modeling, namely, the modeling of reactant structure and composition, the building of the reaction network, the organization of model parameters, the solution of the kinetic model, and the optimization of the model. Even though this modeling approach has enjoyed successful application in the petroleum industry, its application to biomedical research has just begun. We propose to expand the horizons on classic pharmacokinetics and physiologically based pharmacokinetics (PBPK), where human or animal bodies were often described by a few compartments, by integrating PBPK with reaction network modeling described in this article. If one draws a parallel between an oil refinery, where the application of this modeling approach has been very successful, and a human body, the individual processing units in the oil refinery may be considered equivalent to the vital organs of the human body. Even though the cell or organ may be much more complicated, the complex biochemical reaction networks in each organ may be similarly modeled and linked in much the same way as the modeling of the entire oil refinery through linkage of the individual processing units. The integrated chemical engineering software package described in this article, BioMOL, denotes the biological application of molecular-oriented lumping. BioMOL can build a detailed model in 1-1,000 CPU sec using standard desktop hardware. The models solve and optimize using standard and widely available hardware and software and can be presented in the context of a user-friendly interface. We believe this is an engineering tool with great promise in its application to complex biological reaction networks. PMID:12634134

Capillary Origami Inspired Fabrication of Complex 3D Hydrogel Constructs.

PubMed

Li, Moxiao; Yang, Qingzhen; Liu, Hao; Qiu, Mushu; Lu, Tian Jian; Xu, Feng

2016-09-01

Hydrogels have found broad applications in various engineering and biomedical fields, where the shape and size of hydrogels can profoundly influence their functions. Although numerous methods have been developed to tailor 3D hydrogel structures, it is still challenging to fabricate complex 3D hydrogel constructs. Inspired by the capillary origami phenomenon where surface tension of a droplet on an elastic membrane can induce spontaneous folding of the membrane into 3D structures along with droplet evaporation, a facile strategy is established for the fabrication of complex 3D hydrogel constructs with programmable shapes and sizes by crosslinking hydrogels during the folding process. A mathematical model is further proposed to predict the temporal structure evolution of the folded 3D hydrogel constructs. Using this model, precise control is achieved over the 3D shapes (e.g., pyramid, pentahedron, and cube) and sizes (ranging from hundreds of micrometers to millimeters) through tuning membrane shape, dimensionless parameter of the process (elastocapillary number Ce ), and evaporation time. This work would be favorable to multiple areas, such as flexible electronics, tissue regeneration, and drug delivery. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Complex networks for data-driven medicine: the case of Class III dentoskeletal disharmony

NASA Astrophysics Data System (ADS)

Scala, A.; Auconi, P.; Scazzocchio, M.; Caldarelli, G.; McNamara, JA; Franchi, L.

2014-11-01

In the last decade, the availability of innovative algorithms derived from complexity theory has inspired the development of highly detailed models in various fields, including physics, biology, ecology, economy, and medicine. Due to the availability of novel and ever more sophisticated diagnostic procedures, all biomedical disciplines face the problem of using the increasing amount of information concerning each patient to improve diagnosis and prevention. In particular, in the discipline of orthodontics the current diagnostic approach based on clinical and radiographic data is problematic due to the complexity of craniofacial features and to the numerous interacting co-dependent skeletal and dentoalveolar components. In this study, we demonstrate the capability of computational methods such as network analysis and module detection to extract organizing principles in 70 patients with excessive mandibular skeletal protrusion with underbite, a condition known in orthodontics as Class III malocclusion. Our results could possibly constitute a template framework for organising the increasing amount of medical data available for patients’ diagnosis.

SemaTyP: a knowledge graph based literature mining method for drug discovery.

PubMed

Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian

2018-05-30

Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

Evolving technologies drive the new roles of Biomedical Engineering.

PubMed

Frisch, P H; St Germain, J; Lui, W

2008-01-01

Rapidly changing technology coupled with the financial impact of organized health care, has required hospital Biomedical Engineering organizations to augment their traditional operational and business models to increase their role in developing enhanced clinical applications utilizing new and evolving technologies. The deployment of these technology based applications has required Biomedical Engineering organizations to re-organize to optimize the manner in which they provide and manage services. Memorial Sloan-Kettering Cancer Center has implemented a strategy to explore evolving technologies integrating them into enhanced clinical applications while optimally utilizing the expertise of the traditional Biomedical Engineering component (Clinical Engineering) to provide expanded support in technology / equipment management, device repair, preventive maintenance and integration with legacy clinical systems. Specifically, Biomedical Engineering is an integral component of the Medical Physics Department which provides comprehensive and integrated support to the Center in advanced physical, technical and engineering technology. This organizational structure emphasizes the integration and collaboration between a spectrum of technical expertise for clinical support and equipment management roles. The high cost of clinical equipment purchases coupled with the increasing cost of service has driven equipment management responsibilities to include significant business and financial aspects to provide a cost effective service model. This case study details the dynamics of these expanded roles, future initiatives and benefits for Biomedical Engineering and Memorial Sloan Kettering Cancer Center.

A COMPUTER MODELING STUDY OF BINDING PROPERTIES OF CHIRAL NUCLEOPEPTIDE FOR BIOMEDICAL APPLICATIONS.

PubMed

Pirtskhalava, M; Egoyan, A; Mirtskhulava, M; Roviello, G

2017-12-01

Nucleopeptides often show interesting properties of molecular binding that render them good candidates for development of innovative drugs for anticancer and antiviral therapies. In this work we present results of computer modeling of interactions between the molecules of hexathymine nucleopeptide (T6) and poly rA RNA (A18). The results of geometry optimization calculated using Hyperchem software and our own computer program for molecular docking show that molecules establish stable complexes due to the complementary-nucleobase interaction and the electrostatic interaction between the negative phosphate group of poly rA and the positively-charged residues present in the cationic nucleopeptide structure. Computer modeling makes it possible to find the optimal binding configuration of the molecules of a nucleopeptide and poly rA RNA and to estimate the binding energy between the molecules.

Nanotechnology meets 3D in vitro models: tissue engineered tumors and cancer therapies.

PubMed

da Rocha, E L; Porto, L M; Rambo, C R

2014-01-01

Advances in nanotechnology are providing to medicine a new dimension. Multifunctional nanomaterials with diagnostics and treatment modalities integrated in one nanoparticle or in cooperative nanosystems are promoting new insights to cancer treatment and diagnosis. The recent convergence between tissue engineering and cancer is gradually moving towards the development of 3D disease models that more closely resemble in vivo characteristics of tumors. However, the current nanomaterials based therapies are accomplished mainly in 2D cell cultures or in complex in vivo models. The development of new platforms to evaluate nano-based therapies in parallel with possible toxic effects will allow the design of nanomaterials for biomedical applications prior to in vivo studies. Therefore, this review focuses on how 3D in vitro models can be applied to study tumor biology, nanotoxicology and to evaluate nanomaterial based therapies. © 2013.

Image segmentation for biomedical applications based on alternating sequential filtering and watershed transformation

NASA Astrophysics Data System (ADS)

Gorpas, D.; Yova, D.

2009-07-01

One of the major challenges in biomedical imaging is the extraction of quantified information from the acquired images. Light and tissue interaction leads to the acquisition of images that present inconsistent intensity profiles and thus the accurate identification of the regions of interest is a rather complicated process. On the other hand, the complex geometries and the tangent objects that very often are present in the acquired images, lead to either false detections or to the merging, shrinkage or expansion of the regions of interest. In this paper an algorithm, which is based on alternating sequential filtering and watershed transformation, is proposed for the segmentation of biomedical images. This algorithm has been tested over two applications, each one based on different acquisition system, and the results illustrate its accuracy in segmenting the regions of interest.

Beyond the zebrafish: diverse fish species for modeling human disease

PubMed Central

Schartl, Manfred

2014-01-01

ABSTRACT In recent years, zebrafish, and to a lesser extent medaka, have become widely used small animal models for human diseases. These organisms have convincingly demonstrated the usefulness of fish for improving our understanding of the molecular and cellular mechanisms leading to pathological conditions, and for the development of new diagnostic and therapeutic tools. Despite the usefulness of zebrafish and medaka in the investigation of a wide spectrum of traits, there is evidence to suggest that other fish species could be better suited for more targeted questions. With the emergence of new, improved sequencing technologies that enable genomic resources to be generated with increasing efficiency and speed, the potential of non-mainstream fish species as disease models can now be explored. A key feature of these fish species is that the pathological condition that they model is often related to specific evolutionary adaptations. By exploring these adaptations, new disease-causing and disease-modifier genes might be identified; thus, diverse fish species could be exploited to better understand the complexity of disease processes. In addition, non-mainstream fish models could allow us to study the impact of environmental factors, as well as genetic variation, on complex disease phenotypes. This Review will discuss the opportunities that such fish models offer for current and future biomedical research. PMID:24271780

An open annotation ontology for science on web 3.0

PubMed Central

2011-01-01

Background There is currently a gap between the rich and expressive collection of published biomedical ontologies, and the natural language expression of biomedical papers consumed on a daily basis by scientific researchers. The purpose of this paper is to provide an open, shareable structure for dynamic integration of biomedical domain ontologies with the scientific document, in the form of an Annotation Ontology (AO), thus closing this gap and enabling application of formal biomedical ontologies directly to the literature as it emerges. Methods Initial requirements for AO were elicited by analysis of integration needs between biomedical web communities, and of needs for representing and integrating results of biomedical text mining. Analysis of strengths and weaknesses of previous efforts in this area was also performed. A series of increasingly refined annotation tools were then developed along with a metadata model in OWL, and deployed for feedback and additional requirements the ontology to users at a major pharmaceutical company and a major academic center. Further requirements and critiques of the model were also elicited through discussions with many colleagues and incorporated into the work. Results This paper presents Annotation Ontology (AO), an open ontology in OWL-DL for annotating scientific documents on the web. AO supports both human and algorithmic content annotation. It enables “stand-off” or independent metadata anchored to specific positions in a web document by any one of several methods. In AO, the document may be annotated but is not required to be under update control of the annotator. AO contains a provenance model to support versioning, and a set model for specifying groups and containers of annotation. AO is freely available under open source license at http://purl.org/ao/, and extensive documentation including screencasts is available on AO’s Google Code page: http://code.google.com/p/annotation-ontology/ . Conclusions The Annotation Ontology meets critical requirements for an open, freely shareable model in OWL, of annotation metadata created against scientific documents on the Web. We believe AO can become a very useful common model for annotation metadata on Web documents, and will enable biomedical domain ontologies to be used quite widely to annotate the scientific literature. Potential collaborators and those with new relevant use cases are invited to contact the authors. PMID:21624159

An open annotation ontology for science on web 3.0.

PubMed

Ciccarese, Paolo; Ocana, Marco; Garcia Castro, Leyla Jael; Das, Sudeshna; Clark, Tim

2011-05-17

There is currently a gap between the rich and expressive collection of published biomedical ontologies, and the natural language expression of biomedical papers consumed on a daily basis by scientific researchers. The purpose of this paper is to provide an open, shareable structure for dynamic integration of biomedical domain ontologies with the scientific document, in the form of an Annotation Ontology (AO), thus closing this gap and enabling application of formal biomedical ontologies directly to the literature as it emerges. Initial requirements for AO were elicited by analysis of integration needs between biomedical web communities, and of needs for representing and integrating results of biomedical text mining. Analysis of strengths and weaknesses of previous efforts in this area was also performed. A series of increasingly refined annotation tools were then developed along with a metadata model in OWL, and deployed for feedback and additional requirements the ontology to users at a major pharmaceutical company and a major academic center. Further requirements and critiques of the model were also elicited through discussions with many colleagues and incorporated into the work. This paper presents Annotation Ontology (AO), an open ontology in OWL-DL for annotating scientific documents on the web. AO supports both human and algorithmic content annotation. It enables "stand-off" or independent metadata anchored to specific positions in a web document by any one of several methods. In AO, the document may be annotated but is not required to be under update control of the annotator. AO contains a provenance model to support versioning, and a set model for specifying groups and containers of annotation. AO is freely available under open source license at http://purl.org/ao/, and extensive documentation including screencasts is available on AO's Google Code page: http://code.google.com/p/annotation-ontology/ . The Annotation Ontology meets critical requirements for an open, freely shareable model in OWL, of annotation metadata created against scientific documents on the Web. We believe AO can become a very useful common model for annotation metadata on Web documents, and will enable biomedical domain ontologies to be used quite widely to annotate the scientific literature. Potential collaborators and those with new relevant use cases are invited to contact the authors.

A commentary on domestic animals as dual-purpose models that benefit agricultural and biomedical research.

PubMed

Ireland, J J; Roberts, R M; Palmer, G H; Bauman, D E; Bazer, F W

2008-10-01

Research on domestic animals (cattle, swine, sheep, goats, poultry, horses, and aquatic species) at land grant institutions is integral to improving the global competitiveness of US animal agriculture and to resolving complex animal and human diseases. However, dwindling federal and state budgets, years of stagnant funding from USDA for the Competitive State Research, Education, and Extension Service National Research Initiative (CSREES-NRI) Competitive Grants Program, significant reductions in farm animal species and in numbers at land grant institutions, and declining enrollment for graduate studies in animal science are diminishing the resources necessary to conduct research on domestic species. Consequently, recruitment of scientists who use such models to conduct research relevant to animal agriculture and biomedicine at land grant institutions is in jeopardy. Concerned stakeholders have addressed this critical problem by conducting workshops, holding a series of meetings with USDA and National Institutes of Health (NIH) officials, and developing a white paper to propose solutions to obstacles impeding the use of domestic species as dual-purpose animal models for high-priority problems common to agriculture and biomedicine. In addition to shortfalls in research support and human resources, overwhelming use of mouse models in biomedicine, lack of advocacy from university administrators, long-standing cultural barriers between agriculture and human medicine, inadequate grantsmanship by animal scientists, and a scarcity of key reagents and resources are major roadblocks to progress. Solutions will require a large financial enhancement of USDA's Competitive Grants Program, educational programs geared toward explaining how research using agricultural animals benefits both animal agriculture and human health, and the development of a new mind-set in land grant institutions that fosters greater cooperation among basic and applied researchers. Recruitment of outstanding scientists dedicated to using domestic animal models for agricultural and biomedical research, strong incentives for scientists to take advantage of training opportunities to write NIH grants, and greater NIH and USDA cooperation to sponsor the use of agricultural animals as dual-purpose animal models that benefit agriculture and biomedicine will also be necessary. In conclusion, the broad diversity of animal models needed for agricultural and biomedical research is at risk unless research priorities at the land grant universities are critically evaluated and financial support for such research is dramatically increased.

Modeling and simulation of multi-physics multi-scale transport phenomenain bio-medical applications

NASA Astrophysics Data System (ADS)

Kenjereš, Saša

2014-08-01

We present a short overview of some of our most recent work that combines the mathematical modeling, advanced computer simulations and state-of-the-art experimental techniques of physical transport phenomena in various bio-medical applications. In the first example, we tackle predictions of complex blood flow patterns in the patient-specific vascular system (carotid artery bifurcation) and transfer of the so-called "bad" cholesterol (low-density lipoprotein, LDL) within the multi-layered artery wall. This two-way coupling between the blood flow and corresponding mass transfer of LDL within the artery wall is essential for predictions of regions where atherosclerosis can develop. It is demonstrated that a recently developed mathematical model, which takes into account the complex multi-layer arterial-wall structure, produced LDL profiles within the artery wall in good agreement with in-vivo experiments in rabbits, and it can be used for predictions of locations where the initial stage of development of atherosclerosis may take place. The second example includes a combination of pulsating blood flow and medical drug delivery and deposition controlled by external magnetic field gradients in the patient specific carotid artery bifurcation. The results of numerical simulations are compared with own PIV (Particle Image Velocimetry) and MRI (Magnetic Resonance Imaging) in the PDMS (silicon-based organic polymer) phantom. A very good agreement between simulations and experiments is obtained for different stages of the pulsating cycle. Application of the magnetic drug targeting resulted in an increase of up to ten fold in the efficiency of local deposition of the medical drug at desired locations. Finally, the LES (Large Eddy Simulation) of the aerosol distribution within the human respiratory system that includes up to eight bronchial generations is performed. A very good agreement between simulations and MRV (Magnetic Resonance Velocimetry) measurements is obtained. Magnetic steering of aerosols towards the left or right part of lungs proved to be possible, which can open new strategies for medical treatment of respiratory diseases.

Accessing the Phenotype Gap: Enabling Systematic Investigation of Paralog Functional Complexity with CRISPR.

PubMed

Ewen-Campen, Ben; Mohr, Stephanie E; Hu, Yanhui; Perrimon, Norbert

2017-10-09

Single-gene knockout experiments can fail to reveal function in the context of redundancy, which is frequently observed among duplicated genes (paralogs) with overlapping functions. We discuss the complexity associated with studying paralogs and outline how recent advances in CRISPR will help address the "phenotype gap" and impact biomedical research. Copyright © 2017 Elsevier Inc. All rights reserved.

Unlocking Proteomic Heterogeneity in Complex Diseases through Visual Analytics

PubMed Central

Bhavnani, Suresh K.; Dang, Bryant; Bellala, Gowtham; Divekar, Rohit; Visweswaran, Shyam; Brasier, Allan; Kurosky, Alex

2015-01-01

Despite years of preclinical development, biological interventions designed to treat complex diseases like asthma often fail in phase III clinical trials. These failures suggest that current methods to analyze biomedical data might be missing critical aspects of biological complexity such as the assumption that cases and controls come from homogeneous distributions. Here we discuss why and how methods from the rapidly evolving field of visual analytics can help translational teams (consisting of biologists, clinicians, and bioinformaticians) to address the challenge of modeling and inferring heterogeneity in the proteomic and phenotypic profiles of patients with complex diseases. Because a primary goal of visual analytics is to amplify the cognitive capacities of humans for detecting patterns in complex data, we begin with an overview of the cognitive foundations for the field of visual analytics. Next, we organize the primary ways in which a specific form of visual analytics called networks have been used to model and infer biological mechanisms, which help to identify the properties of networks that are particularly useful for the discovery and analysis of proteomic heterogeneity in complex diseases. We describe one such approach called subject-protein networks, and demonstrate its application on two proteomic datasets. This demonstration provides insights to help translational teams overcome theoretical, practical, and pedagogical hurdles for the widespread use of subject-protein networks for analyzing molecular heterogeneities, with the translational goal of designing biomarker-based clinical trials, and accelerating the development of personalized approaches to medicine. PMID:25684269

Biomedical semantics in the Semantic Web

PubMed Central

2011-01-01

The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences? We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th. PMID:21388570

Biomedical semantics in the Semantic Web.

PubMed

Splendiani, Andrea; Burger, Albert; Paschke, Adrian; Romano, Paolo; Marshall, M Scott

2011-03-07

The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences?We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th.

Culture and biomedical care in Africa: the influence of culture on biomedical care in a traditional African society, Nigeria, West Africa.

PubMed

Chukwuneke, F N; Ezeonu, C T; Onyire, B N; Ezeonu, P O

2012-01-01

Biomedical Care in Africa and the influence of culture on the health-seeking behaviour of Africans can not be underestimated; many African cultures have different understanding of the causes of disease which more often affect our public health system, policy, planning and implementations. The traditional African healer unlike a doctor trained in western biomedicine, looks for the cause of the patient's ailments as misfortune in relationship between the patient and the social, natural and spiritual environments. The complexity of African society with different cultural and religious practices also reflects on the people's attitude and understanding of their health matters. This paper is an overview of the cultural influence on biomedical care in a traditional African society, Nigeria, West Africa. A research on the patients' health seeking behaviour and Primary Health Care service organization in 10 health centres in the five eastern states of the Federal Republic of Nigeria was carried out using a multistage cross-sectional study. A semi-structured questionnaire was administered to the health care providers and patients while an in-depth semi- structured interview was also conducted. We observed there is underutilization of health care services at the primary level because most people do not accept the model of health care system provided for them. Most people believe diseases are caused by supernatural beings, the handiwork of neighbours or vengeance from an offended god as a result of transgressions committed in the past by an individual or parents. This group of people therefore prefers seeking traditional medicine to seeking orthodox medicine and often ends up in the hands of witch doctors who claim to have cure to almost all the diseases. Biomedical care in Africa is influence by culture because of different understanding of what ailment is and also due to limited knowledge of health matters, poverty and ignorance. There is a need therefore to focus on health out-reach programme, communication and enlightment campaign in Africa especially in the rural areas that are more vulnerable and are burdened with many of these diseases.

Biomedical ontologies: toward scientific debate.

PubMed

Maojo, V; Crespo, J; García-Remesal, M; de la Iglesia, D; Perez-Rey, D; Kulikowski, C

2011-01-01

Biomedical ontologies have been very successful in structuring knowledge for many different applications, receiving widespread praise for their utility and potential. Yet, the role of computational ontologies in scientific research, as opposed to knowledge management applications, has not been extensively discussed. We aim to stimulate further discussion on the advantages and challenges presented by biomedical ontologies from a scientific perspective. We review various aspects of biomedical ontologies going beyond their practical successes, and focus on some key scientific questions in two ways. First, we analyze and discuss current approaches to improve biomedical ontologies that are based largely on classical, Aristotelian ontological models of reality. Second, we raise various open questions about biomedical ontologies that require further research, analyzing in more detail those related to visual reasoning and spatial ontologies. We outline significant scientific issues that biomedical ontologies should consider, beyond current efforts of building practical consensus between them. For spatial ontologies, we suggest an approach for building "morphospatial" taxonomies, as an example that could stimulate research on fundamental open issues for biomedical ontologies. Analysis of a large number of problems with biomedical ontologies suggests that the field is very much open to alternative interpretations of current work, and in need of scientific debate and discussion that can lead to new ideas and research directions.

Reframing Person-Centered Nursing Care for Persons With Dementia

PubMed Central

Penrod, Janice; Yu, Fang; Kolanowski, Ann; Fick, Donna M.; Loeb, Susan J.; Hupcey, Judith E.

2010-01-01

Alzheimer’s dementia manifests in a complex clinical presentation that has been addressed from both biomedical and phenomenological perspectives. Although each of these paradigmatic perspectives has contributed to advancement of the science, neither is adequate for theoretically framing a person-centered approach to nursing care. The need-driven dementia-compromised behavior (NDB) model is discussed as an exemplar of midrange nursing theory that promotes the integration of these paradigmatic views to promote a new level of excellence in person-centered dementia care. Clinical application of the NDB promotes a new level of praxis, or thoughtful action, in the care of persons with dementia. PMID:17378465

Computational modeling in melanoma for novel drug discovery.

PubMed

Pennisi, Marzio; Russo, Giulia; Di Salvatore, Valentina; Candido, Saverio; Libra, Massimo; Pappalardo, Francesco

2016-06-01

There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches. This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials. Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.

Object-oriented design tools for supramolecular devices and biomedical nanotechnology.

PubMed

Lee, Stephen C; Bhalerao, Khaustaub; Ferrari, Mauro

2004-05-01

Nanotechnology provides multifunctional agents for in vivo use that increasingly blur the distinction between pharmaceuticals and medical devices. Realization of such therapeutic nanodevices requires multidisciplinary effort that is difficult for individual device developers to sustain, and identification of appropriate collaborations outside ones own field can itself be challenging. Further, as in vivo nanodevices become increasingly complex, their design will increasingly demand systems level thinking. System engineering tools such as object-oriented analysis, object-oriented design (OOA/D) and unified modeling language (UML) are applicable to nanodevices built from biological components, help logically manage the knowledge needed to design them, and help identify useful collaborative relationships for device designers. We demonstrate the utility of these systems engineering tools by reverse engineering an existing molecular device (the bacmid molecular cloning system) using them, and illustrate how object-oriented approaches identify fungible components (objects) in nanodevices in a way that facilitates design of families of related devices, rather than single inventions. We also explore the utility of object-oriented approaches for design of another class of therapeutic nanodevices, vaccines. While they are useful for design of current nanodevices, the power of systems design tools for biomedical nanotechnology will become increasingly apparent as the complexity and sophistication of in vivo nanosystems increases. The nested, hierarchical nature of object-oriented approaches allows treatment of devices as objects in higher-order structures, and so will facilitate concatenation of multiple devices into higher-order, higher-function nanosystems.

Quantifying the complexity of medical research.

PubMed

Rodriguez-Esteban, Raul; Loging, William T

2013-11-15

A crucial phenomenon of our times is the diminishing marginal returns of investments in pharmaceutical research and development. A potential reason is that research into diseases is becoming increasingly complex, and thus more burdensome, for humans to handle. We sought to investigate whether we could measure research complexity by analyzing the published literature. Through the text mining of the publication record of multiple diseases, we have found that the complexity and novelty of disease research has been increasing over the years. Surprisingly, we have also found that research on diseases with higher publication rate does not possess greater complexity or novelty than that on less-studied diseases. We have also shown that the research produced about a disease can be seen as a differentiated area of knowledge within the wider biomedical research. For our analysis, we have conceptualized disease research as a parallel multi-agent search in which each scientific agent (a scientist) follows a search path based on a model of a disease. We have looked at trends in facts published for diseases, measured their diversity and turnover using the entropy measure and found similar patterns across disease areas. raul.rodriguez-esteban@roche.com.

Text Mining for Protein Docking

PubMed Central

Badal, Varsha D.; Kundrotas, Petras J.; Vakser, Ilya A.

2015-01-01

The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate. PMID:26650466

Progress and Prospects for Genetic Modification of Nonhuman Primate Models in Biomedical Research

PubMed Central

Chan, Anthony W. S.

2013-01-01

The growing interest of modeling human diseases using genetically modified (transgenic) nonhuman primates (NHPs) is a direct result of NHPs (rhesus macaque, etc.) close relation to humans. NHPs share similar developmental paths with humans in their anatomy, physiology, genetics, and neural functions; and in their cognition, emotion, and social behavior. The NHP model within biomedical research has played an important role in the development of vaccines, assisted reproductive technologies, and new therapies for many diseases. Biomedical research has not been the primary role of NHPs. They have mainly been used for safety evaluation and pharmacokinetics studies, rather than determining therapeutic efficacy. The development of the first transgenic rhesus macaque (2001) revolutionized the role of NHP models in biomedicine. Development of the transgenic NHP model of Huntington's disease (2008), with distinctive clinical features, further suggested the uniqueness of the model system; and the potential role of the NHP model for human genetic disorders. Modeling human genetic diseases using NHPs will continue to thrive because of the latest advances in molecular, genetic, and embryo technologies. NHPs rising role in biomedical research, specifically pre-clinical studies, is foreseeable. The path toward the development of transgenic NHPs and the prospect of transgenic NHPs in their new role in future biomedicine needs to be reviewed. This article will focus on the advancement of transgenic NHPs in the past decade, including transgenic technologies and disease modeling. It will outline new technologies that may have significant impact in future NHP modeling and will conclude with a discussion of the future prospects of the transgenic NHP model. PMID:24174443

A preliminary investigation of the growth of an aneurysm with a multiscale monolithic Fluid-Structure interaction solver

NASA Astrophysics Data System (ADS)

Cerroni, D.; Manservisi, S.; Pozzetti, G.

2015-11-01

In this work we investigate the potentialities of multi-scale engineering techniques to approach complex problems related to biomedical and biological fields. In particular we study the interaction between blood and blood vessel focusing on the presence of an aneurysm. The study of each component of the cardiovascular system is very difficult due to the fact that the movement of the fluid and solid is determined by the rest of system through dynamical boundary conditions. The use of multi-scale techniques allows us to investigate the effect of the whole loop on the aneurysm dynamic. A three-dimensional fluid-structure interaction model for the aneurysm is developed and coupled to a mono-dimensional one for the remaining part of the cardiovascular system, where a point zero-dimensional model for the heart is provided. In this manner it is possible to achieve rigorous and quantitative investigations of the cardiovascular disease without loosing the system dynamic. In order to study this biomedical problem we use a monolithic fluid-structure interaction (FSI) model where the fluid and solid equations are solved together. The use of a monolithic solver allows us to handle the convergence issues caused by large deformations. By using this monolithic approach different solid and fluid regions are treated as a single continuum and the interface conditions are automatically taken into account. In this way the iterative process characteristic of the commonly used segregated approach, it is not needed any more.

Strategic planning: a biomedical communications model.

PubMed

Barrett, J E

1991-01-01

This article describes a biomedical communications approach to strategic planning. This model produces a short-term plan that allows a department to take the competitive advantage, react to technological change, and make timely decisions on new courses of action. The model calls for self-study, involving staff in brainstorming sessions where options are identified and ideas are prioritized into possible strategies for success. The article recommends that an evaluation and monitoring schedule be implemented after decisions have been made.

Biomedical signal and image processing.

PubMed

Cerutti, Sergio; Baselli, Giuseppe; Bianchi, Anna; Caiani, Enrico; Contini, Davide; Cubeddu, Rinaldo; Dercole, Fabio; Rienzo, Luca; Liberati, Diego; Mainardi, Luca; Ravazzani, Paolo; Rinaldi, Sergio; Signorini, Maria; Torricelli, Alessandro

2011-01-01

Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly [1], [2]. This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain–computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.

The role of real-time in biomedical science: a meta-analysis on computational complexity, delay and speedup.

PubMed

Faust, Oliver; Yu, Wenwei; Rajendra Acharya, U

2015-03-01

The concept of real-time is very important, as it deals with the realizability of computer based health care systems. In this paper we review biomedical real-time systems with a meta-analysis on computational complexity (CC), delay (Δ) and speedup (Sp). During the review we found that, in the majority of papers, the term real-time is part of the thesis indicating that a proposed system or algorithm is practical. However, these papers were not considered for detailed scrutiny. Our detailed analysis focused on papers which support their claim of achieving real-time, with a discussion on CC or Sp. These papers were analyzed in terms of processing system used, application area (AA), CC, Δ, Sp, implementation/algorithm (I/A) and competition. The results show that the ideas of parallel processing and algorithm delay were only recently introduced and journal papers focus more on Algorithm (A) development than on implementation (I). Most authors compete on big O notation (O) and processing time (PT). Based on these results, we adopt the position that the concept of real-time will continue to play an important role in biomedical systems design. We predict that parallel processing considerations, such as Sp and algorithm scaling, will become more important. Copyright © 2015 Elsevier Ltd. All rights reserved.

Glycobiology of Reproductive Processes in Marine Animals: The State of the Art

PubMed Central

Gallo, Alessandra; Costantini, Maria

2012-01-01

Glycobiology is the study of complex carbohydrates in biological systems and represents a developing field of science that has made huge advances in the last half century. In fact, it combines all branches of biomedical research, revealing the vast and diverse forms of carbohydrate structures that exist in nature. Advances in structure determination have enabled scientists to study the function of complex carbohydrates in more depth and to determine the role that they play in a wide range of biological processes. Glycobiology research in marine systems has primarily focused on reproduction, in particular for what concern the chemical communication between the gametes. The current status of marine glycobiology is primarily descriptive, devoted to characterizing marine glycoconjugates with potential biomedical and biotechnological applications. In this review, we describe the current status of the glycobiology in the reproductive processes from gametogenesis to fertilization and embryo development of marine animals. PMID:23247316

CodeSlinger: a case study in domain-driven interactive tool design for biomedical coding scheme exploration and use.

PubMed

Flowers, Natalie L

2010-01-01

CodeSlinger is a desktop application that was developed to aid medical professionals in the intertranslation, exploration, and use of biomedical coding schemes. The application was designed to provide a highly intuitive, easy-to-use interface that simplifies a complex business problem: a set of time-consuming, laborious tasks that were regularly performed by a group of medical professionals involving manually searching coding books, searching the Internet, and checking documentation references. A workplace observation session with a target user revealed the details of the current process and a clear understanding of the business goals of the target user group. These goals drove the design of the application's interface, which centers on searches for medical conditions and displays the codes found in the application's database that represent those conditions. The interface also allows the exploration of complex conceptual relationships across multiple coding schemes.

Moral Enhancement Using Non-invasive Brain Stimulation

PubMed Central

Darby, R. Ryan; Pascual-Leone, Alvaro

2017-01-01

Biomedical enhancement refers to the use of biomedical interventions to improve capacities beyond normal, rather than to treat deficiencies due to diseases. Enhancement can target physical or cognitive capacities, but also complex human behaviors such as morality. However, the complexity of normal moral behavior makes it unlikely that morality is a single capacity that can be deficient or enhanced. Instead, our central hypothesis will be that moral behavior results from multiple, interacting cognitive-affective networks in the brain. First, we will test this hypothesis by reviewing evidence for modulation of moral behavior using non-invasive brain stimulation. Next, we will discuss how this evidence affects ethical issues related to the use of moral enhancement. We end with the conclusion that while brain stimulation has the potential to alter moral behavior, such alteration is unlikely to improve moral behavior in all situations, and may even lead to less morally desirable behavior in some instances. PMID:28275345

New Software Developments for Quality Mesh Generation and Optimization from Biomedical Imaging Data

PubMed Central

Yu, Zeyun; Wang, Jun; Gao, Zhanheng; Xu, Ming; Hoshijima, Masahiko

2013-01-01

In this paper we present a new software toolkit for generating and optimizing surface and volumetric meshes from three-dimensional (3D) biomedical imaging data, targeted at image-based finite element analysis of some biomedical activities in a single material domain. Our toolkit includes a series of geometric processing algorithms including surface re-meshing and quality-guaranteed tetrahedral mesh generation and optimization. All methods described have been encapsulated into a user-friendly graphical interface for easy manipulation and informative visualization of biomedical images and mesh models. Numerous examples are presented to demonstrate the effectiveness and efficiency of the described methods and toolkit. PMID:24252469

Biomedical applications of thermally activated shape memory polymers†

PubMed Central

Small, Ward; Singhal, Pooja; Wilson, Thomas S.

2011-01-01

Shape memory polymers (SMPs) are smart materials that can remember a primary shape and can return to this primary shape from a deformed secondary shape when given an appropriate stimulus. This property allows them to be delivered in a compact form via minimally invasive surgeries in humans, and deployed to achieve complex final shapes. Here we review the various biomedical applications of SMPs and the challenges they face with respect to actuation and biocompatibility. While shape memory behavior has been demonstrated with heat, light and chemical environment, here we focus our discussion on thermally stimulated SMPs. PMID:21258605

Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers.

PubMed

Grüning, Björn A; Rasche, Eric; Rebolledo-Jaramillo, Boris; Eberhard, Carl; Houwaart, Torsten; Chilton, John; Coraor, Nate; Backofen, Rolf; Taylor, James; Nekrutenko, Anton

2017-05-01

What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., the list of variable sites). The subsequent exploratory stage is much more ad hoc and requires the development of custom scripts and pipelines, making it problematic for biomedical researchers. Here, we describe a hybrid platform combining common analysis pathways with the ability to explore data interactively. It aims to fully encompass and simplify the "raw data-to-publication" pathway and make it reproducible.

Biomedical Applications of Thermally Activated Shape Memory Polymers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Small IV, W; Singhal, P; Wilson, T S

2009-04-10

Shape memory polymers (SMPs) are smart materials that can remember a primary shape and can return to this primary shape from a deformed secondary shape when given an appropriate stimulus. This property allows them to be delivered in a compact form via minimally invasive surgeries in humans, and deployed to achieve complex final shapes. Here we review the various biomedical applications of SMPs and the challenges they face with respect to actuation and biocompatibility. While shape memory behavior has been demonstrated with heat, light and chemical environment, here we focus our discussion on thermally stimulated SMPs.

[A web-based biomedical image mosaicing system].

PubMed

Zhang, Meng; Yan, Zhuang-zhi; Pan, Zhi-jun; Shao, Shi-jie

2006-11-01

This paper describes a web service for biomedical image mosaicing. A web site based on CGI (Common Gateway Interface) is implemented. The system is based on Browser/Server model and is tested in www. Finally implementation examples and experiment results are provided.

Modeling Patient Treatment With Medical Records: An Abstraction Hierarchy to Understand User Competencies and Needs.

PubMed

St-Maurice, Justin D; Burns, Catherine M

2017-07-28

Health care is a complex sociotechnical system. Patient treatment is evolving and needs to incorporate the use of technology and new patient-centered treatment paradigms. Cognitive work analysis (CWA) is an effective framework for understanding complex systems, and work domain analysis (WDA) is useful for understanding complex ecologies. Although previous applications of CWA have described patient treatment, due to their scope of work patients were previously characterized as biomedical machines, rather than patient actors involved in their own care. An abstraction hierarchy that characterizes patients as beings with complex social values and priorities is needed. This can help better understand treatment in a modern approach to care. The purpose of this study was to perform a WDA to represent the treatment of patients with medical records. The methods to develop this model included the analysis of written texts and collaboration with subject matter experts. Our WDA represents the ecology through its functional purposes, abstract functions, generalized functions, physical functions, and physical forms. Compared with other work domain models, this model is able to articulate the nuanced balance between medical treatment, patient education, and limited health care resources. Concepts in the analysis were similar to the modeling choices of other WDAs but combined them in as a comprehensive, systematic, and contextual overview. The model is helpful to understand user competencies and needs. Future models could be developed to model the patient's domain and enable the exploration of the shared decision-making (SDM) paradigm. Our work domain model links treatment goals, decision-making constraints, and task workflows. This model can be used by system developers who would like to use ecological interface design (EID) to improve systems. Our hierarchy is the first in a future set that could explore new treatment paradigms. Future hierarchies could model the patient as a controller and could be useful for mobile app development. ©Justin D St-Maurice, Catherine M Burns. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 28.07.2017.

Modeling Patient Treatment With Medical Records: An Abstraction Hierarchy to Understand User Competencies and Needs

PubMed Central

2017-01-01

Background Health care is a complex sociotechnical system. Patient treatment is evolving and needs to incorporate the use of technology and new patient-centered treatment paradigms. Cognitive work analysis (CWA) is an effective framework for understanding complex systems, and work domain analysis (WDA) is useful for understanding complex ecologies. Although previous applications of CWA have described patient treatment, due to their scope of work patients were previously characterized as biomedical machines, rather than patient actors involved in their own care. Objective An abstraction hierarchy that characterizes patients as beings with complex social values and priorities is needed. This can help better understand treatment in a modern approach to care. The purpose of this study was to perform a WDA to represent the treatment of patients with medical records. Methods The methods to develop this model included the analysis of written texts and collaboration with subject matter experts. Our WDA represents the ecology through its functional purposes, abstract functions, generalized functions, physical functions, and physical forms. Results Compared with other work domain models, this model is able to articulate the nuanced balance between medical treatment, patient education, and limited health care resources. Concepts in the analysis were similar to the modeling choices of other WDAs but combined them in as a comprehensive, systematic, and contextual overview. The model is helpful to understand user competencies and needs. Future models could be developed to model the patient’s domain and enable the exploration of the shared decision-making (SDM) paradigm. Conclusion Our work domain model links treatment goals, decision-making constraints, and task workflows. This model can be used by system developers who would like to use ecological interface design (EID) to improve systems. Our hierarchy is the first in a future set that could explore new treatment paradigms. Future hierarchies could model the patient as a controller and could be useful for mobile app development. PMID:28754650

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

PubMed Central

Zur, Hadas; Tuller, Tamir

2016-01-01

mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field. PMID:27591251

Combining clinical and genomics queries using i2b2 – Three methods

PubMed Central

Murphy, Shawn N.; Avillach, Paul; Bellazzi, Riccardo; Phillips, Lori; Gabetta, Matteo; Eran, Alal; McDuffie, Michael T.; Kohane, Isaac S.

2017-01-01

We are fortunate to be living in an era of twin biomedical data surges: a burgeoning representation of human phenotypes in the medical records of our healthcare systems, and high-throughput sequencing making rapid technological advances. The difficulty representing genomic data and its annotations has almost by itself led to the recognition of a biomedical “Big Data” challenge, and the complexity of healthcare data only compounds the problem to the point that coherent representation of both systems on the same platform seems insuperably difficult. We investigated the capability for complex, integrative genomic and clinical queries to be supported in the Informatics for Integrating Biology and the Bedside (i2b2) translational software package. Three different data integration approaches were developed: The first is based on Sequence Ontology, the second is based on the tranSMART engine, and the third on CouchDB. These novel methods for representing and querying complex genomic and clinical data on the i2b2 platform are available today for advancing precision medicine. PMID:28388645

Syntactic dependency parsers for biomedical-NLP.

PubMed

Cohen, Raphael; Elhadad, Michael

2012-01-01

Syntactic parsers have made a leap in accuracy and speed in recent years. The high order structural information provided by dependency parsers is useful for a variety of NLP applications. We present a biomedical model for the EasyFirst parser, a fast and accurate parser for creating Stanford Dependencies. We evaluate the models trained in the biomedical domains of EasyFirst and Clear-Parser in a number of task oriented metrics. Both parsers provide stat of the art speed and accuracy in the Genia of over 89%. We show that Clear-Parser excels at tasks relating to negation identification while EasyFirst excels at tasks relating to Named Entities and is more robust to changes in domain.

Laser-induced breakdown spectroscopy (LIBS): an overview of recent progress and future potential for biomedical applications.

PubMed

Rehse, S J; Salimnia, H; Miziolek, A W

2012-02-01

The recent progress made in developing laser-induced breakdown spectroscopy (LIBS) has transformed LIBS from an elemental analysis technique to one that can be applied for the reagentless analysis of molecularly complex biological materials or clinical specimens. Rapid advances in the LIBS technology have spawned a growing number of recently published articles in peer-reviewed journals which have consistently demonstrated the capability of LIBS to rapidly detect, biochemically characterize and analyse, and/or accurately identify various biological, biomedical or clinical samples. These analyses are inherently real-time, require no sample preparation, and offer high sensitivity and specificity. This overview of the biomedical applications of LIBS is meant to summarize the research that has been performed to date, as well as to suggest to health care providers several possible specific future applications which, if successfully implemented, would be significantly beneficial to humankind.

Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.

PubMed

Amith, Muhammad; He, Zhe; Bian, Jiang; Lossio-Ventura, Juan Antonio; Tao, Cui

2018-04-01

With the proliferation of heterogeneous health care data in the last three decades, biomedical ontologies and controlled biomedical terminologies play a more and more important role in knowledge representation and management, data integration, natural language processing, as well as decision support for health information systems and biomedical research. Biomedical ontologies and controlled terminologies are intended to assure interoperability. Nevertheless, the quality of biomedical ontologies has hindered their applicability and subsequent adoption in real-world applications. Ontology evaluation is an integral part of ontology development and maintenance. In the biomedicine domain, ontology evaluation is often conducted by third parties as a quality assurance (or auditing) effort that focuses on identifying modeling errors and inconsistencies. In this work, we first organized four categorical schemes of ontology evaluation methods in the existing literature to create an integrated taxonomy. Further, to understand the ontology evaluation practice in the biomedicine domain, we reviewed a sample of 200 ontologies from the National Center for Biomedical Ontology (NCBO) BioPortal-the largest repository for biomedical ontologies-and observed that only 15 of these ontologies have documented evaluation in their corresponding inception papers. We then surveyed the recent quality assurance approaches for biomedical ontologies and their use. We also mapped these quality assurance approaches to the ontology evaluation criteria. It is our anticipation that ontology evaluation and quality assurance approaches will be more widely adopted in the development life cycle of biomedical ontologies. Copyright © 2018 Elsevier Inc. All rights reserved.

The Nature of Conceptual Understanding in Biomedicine: The Deep Structure of Complex Ideas and the Development of Misconceptions. Technical Report No. 440.

ERIC Educational Resources Information Center

Feltovich, Paul J.; And Others

This report presents a general framework for studying the acquisition and cognitive representation of biomedical concepts and analyzing the nature and development of misconceptions. The central approach of the report is a selective and highly concentrated analysis of the true nature of clusters of complex concepts and the manner in which they are…

Aggregated Indexing of Biomedical Time Series Data

PubMed Central

Woodbridge, Jonathan; Mortazavi, Bobak; Sarrafzadeh, Majid; Bui, Alex A.T.

2016-01-01

Remote and wearable medical sensing has the potential to create very large and high dimensional datasets. Medical time series databases must be able to efficiently store, index, and mine these datasets to enable medical professionals to effectively analyze data collected from their patients. Conventional high dimensional indexing methods are a two stage process. First, a superset of the true matches is efficiently extracted from the database. Second, supersets are pruned by comparing each of their objects to the query object and rejecting any objects falling outside a predetermined radius. This pruning stage heavily dominates the computational complexity of most conventional search algorithms. Therefore, indexing algorithms can be significantly improved by reducing the amount of pruning. This paper presents an online algorithm to aggregate biomedical times series data to significantly reduce the search space (index size) without compromising the quality of search results. This algorithm is built on the observation that biomedical time series signals are composed of cyclical and often similar patterns. This algorithm takes in a stream of segments and groups them to highly concentrated collections. Locality Sensitive Hashing (LSH) is used to reduce the overall complexity of the algorithm, allowing it to run online. The output of this aggregation is used to populate an index. The proposed algorithm yields logarithmic growth of the index (with respect to the total number of objects) while keeping sensitivity and specificity simultaneously above 98%. Both memory and runtime complexities of time series search are improved when using aggregated indexes. In addition, data mining tasks, such as clustering, exhibit runtimes that are orders of magnitudes faster when run on aggregated indexes. PMID:27617298

A new paradigm for improved co-ordination and efficacy of European biomedical research: taking diabetes as a model.

PubMed

Halban, P A; Boulton, A J M; Smith, U

2013-03-01

Today, European biomedical and health-related research is insufficiently well funded and is fragmented, with no common vision, less-than-optimal sharing of resources, and inadequate support and training in clinical research. Improvements to the competitiveness of European biomedical research will depend on the creation of new infrastructures that must be dynamic and free of bureaucracy, involve all stakeholders and facilitate faster delivery of new discoveries from bench to bedside. Taking diabetes research as the model, a new paradigm for European biomedical research is presented, which offers improved co-ordination and common resources that will benefit both academic and industrial clinical research. This includes the creation of a European Council for Health Research, first proposed by the Alliance for Biomedical Research in Europe, which will bring together and consult with all health stakeholders to develop strategic and multidisciplinary research programmes addressing the full innovation cycle. A European Platform for Clinical Research in Diabetes is proposed by the Alliance for European Diabetes Research (EURADIA) in response to the special challenges and opportunities presented by research across the European region, with the need for common standards and shared expertise and data.

Excluding the poor from accessing biomedical literature: a rights violation that impedes global health.

PubMed

Yamey, Gavin

2008-01-01

Most biomedical journals charge readers a hefty access toll to read the full text version of a published research article. These tolls bring enormous profits to the traditional corporate publishing industry, but they make it impossible for most people worldwide--particularly in low and middle income countries--to access the biomedical literature. Traditional publishers also insist on owning the copyright on these articles, making it illegal for readers to freely distribute and photocopy papers, translate them, or create derivative educational works. This article argues that excluding the poor from accessing and freely using the biomedical research literature is harming global public health. Health care workers, for example, are prevented from accessing the information they need to practice effective medicine, while policymakers are prevented from accessing the essential knowledge they require to build better health care systems. The author proposes that the biomedical literature should be considered a global public good, basing his arguments upon longstanding and recent international declarations that enshrine access to scientific and medical knowledge as a human right. He presents an emerging alternative publishing model, called open access, and argues that this model is a more socially responsive and equitable approach to knowledge dissemination.

NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation.

PubMed

Martínez-Romero, Marcos; Jonquet, Clement; O'Connor, Martin J; Graybeal, John; Pazos, Alejandro; Musen, Mark A

2017-06-07

Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios. Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender , and via a Web service API).

Neuroendocrine-Immune Circuits, Phenotypes, and Interactions

PubMed Central

Ashley, Noah T.; Demas, Gregory E.

2016-01-01

Multidirectional interactions among the immune, endocrine, and nervous systems have been demonstrated in humans and non-human animal models for many decades by the biomedical community, but ecological and evolutionary perspectives are lacking. Neuroendocrine-immune interactions can be conceptualized using a series of feedback loops, which culminate into distinct neuroendocrine-immune phenotypes. Behavior can exert profound influences on these phenotypes, which can in turn reciprocally modulate behavior. For example, the behavioral aspects of reproduction, including courtship, aggression, mate selection and parental behaviors can impinge upon neuroendocrine-immune interactions. One classic example is the immunocompetence handicap hypothesis (ICHH), which proposes that steroid hormones act as mediators of traits important for female choice while suppressing the immune system. Reciprocally, neuroendocrine-immune pathways can promote the development of altered behavioral states, such as sickness behavior. Understanding the energetic signals that mediate neuroendocrine-immune crosstalk is an active area of research. Although the field of psychoneuroimmunology (PNI) has begun to explore this crosstalk from a biomedical standpoint, the neuroendocrine-immune-behavior nexus has been relatively underappreciated in comparative species. The field of ecoimmunology, while traditionally emphasizing the study of non-model systems from an ecological evolutionary perspective, often under natural conditions, has focused less on the physiological mechanisms underlying behavioral responses. This review summarizes neuroendocrine-immune interactions using a comparative framework to understand the ecological and evolutionary forces that shape these complex physiological interactions. PMID:27765499

Neuroendocrine-immune circuits, phenotypes, and interactions.

PubMed

Ashley, Noah T; Demas, Gregory E

2017-01-01

Multidirectional interactions among the immune, endocrine, and nervous systems have been demonstrated in humans and non-human animal models for many decades by the biomedical community, but ecological and evolutionary perspectives are lacking. Neuroendocrine-immune interactions can be conceptualized using a series of feedback loops, which culminate into distinct neuroendocrine-immune phenotypes. Behavior can exert profound influences on these phenotypes, which can in turn reciprocally modulate behavior. For example, the behavioral aspects of reproduction, including courtship, aggression, mate selection and parental behaviors can impinge upon neuroendocrine-immune interactions. One classic example is the immunocompetence handicap hypothesis (ICHH), which proposes that steroid hormones act as mediators of traits important for female choice while suppressing the immune system. Reciprocally, neuroendocrine-immune pathways can promote the development of altered behavioral states, such as sickness behavior. Understanding the energetic signals that mediate neuroendocrine-immune crosstalk is an active area of research. Although the field of psychoneuroimmunology (PNI) has begun to explore this crosstalk from a biomedical standpoint, the neuroendocrine-immune-behavior nexus has been relatively underappreciated in comparative species. The field of ecoimmunology, while traditionally emphasizing the study of non-model systems from an ecological evolutionary perspective, often under natural conditions, has focused less on the physiological mechanisms underlying behavioral responses. This review summarizes neuroendocrine-immune interactions using a comparative framework to understand the ecological and evolutionary forces that shape these complex physiological interactions. Copyright © 2016 Elsevier Inc. All rights reserved.

Ontological approach for safe and effective polypharmacy prescription

PubMed Central

Grando, Adela; Farrish, Susan; Boyd, Cynthia; Boxwala, Aziz

2012-01-01

The intake of multiple medications in patients with various medical conditions challenges the delivery of medical care. Initial empirical studies and pilot implementations seem to indicate that generic safe and effective multi-drug prescription principles could be defined and reused to reduce adverse drug events and to support compliance with medical guidelines and drug formularies. Given that ontologies are known to provide well-principled, sharable, setting-independent and machine-interpretable declarative specification frameworks for modeling and reasoning on biomedical problems, we explore here their use in the context of multi-drug prescription. We propose an ontology for modeling drug-related knowledge and a repository of safe and effective generic prescription principles. To test the usability and the level of granularity of the developed ontology-based specification models and heuristic we implemented a tool that computes the complexity of multi-drug treatments, and a decision aid to check the safeness and effectiveness of prescribed multi-drug treatments. PMID:23304299

Exploring host–microbiota interactions in animal models and humans

PubMed Central

Kostic, Aleksandar D.; Howitt, Michael R.; Garrett, Wendy S.

2013-01-01

The animal and bacterial kingdoms have coevolved and coadapted in response to environmental selective pressures over hundreds of millions of years. The meta'omics revolution in both sequencing and its analytic pipelines is fostering an explosion of interest in how the gut microbiome impacts physiology and propensity to disease. Gut microbiome studies are inherently interdisciplinary, drawing on approaches and technical skill sets from the biomedical sciences, ecology, and computational biology. Central to unraveling the complex biology of environment, genetics, and microbiome interaction in human health and disease is a deeper understanding of the symbiosis between animals and bacteria. Experimental model systems, including mice, fish, insects, and the Hawaiian bobtail squid, continue to provide critical insight into how host–microbiota homeostasis is constructed and maintained. Here we consider how model systems are influencing current understanding of host–microbiota interactions and explore recent human microbiome studies. PMID:23592793

Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support

PubMed Central

Bodenreider, O.

2008-01-01

Summary Objectives To provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support. Methods Biomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles. Results The ontologies under investigation in this survey include SNOMED CT, the Logical Observation Identifiers, Names, and Codes (LOINC), the Foundational Model of Anatomy, the Gene Ontology, RxNorm, the National Cancer Institute Thesaurus, the International Classification of Diseases, the Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). The roles played by biomedical ontologies are classified into three major categories: knowledge management (indexing and retrieval of data and information, access to information, mapping among ontologies); data integration, exchange and semantic interoperability; and decision support and reasoning (data selection and aggregation, decision support, natural language processing applications, knowledge discovery). Conclusions Ontologies play an important role in biomedical research through a variety of applications. While ontologies are used primarily as a source of vocabulary for standardization and integration purposes, many applications also use them as a source of computable knowledge. Barriers to the use of ontologies in biomedical applications are discussed. PMID:18660879

Outlook for Development of High-throughput Cryopreservation for Small-bodied Biomedical Model Fishes★

PubMed Central

Tiersch, Terrence R.; Yang, Huiping; Hu, E.

2011-01-01

With the development of genomic research technologies, comparative genome studies among vertebrate species are becoming commonplace for human biomedical research. Fish offer unlimited versatility for biomedical research. Extensive studies are done using these fish models, yielding tens of thousands of specific strains and lines, and the number is increasing every day. Thus, high-throughput sperm cryopreservation is urgently needed to preserve these genetic resources. Although high-throughput processing has been widely applied for sperm cryopreservation in livestock for decades, application in biomedical model fishes is still in the concept-development stage because of the limited sample volumes and the biological characteristics of fish sperm. High-throughput processing in livestock was developed based on advances made in the laboratory and was scaled up for increased processing speed, capability for mass production, and uniformity and quality assurance. Cryopreserved germplasm combined with high-throughput processing constitutes an independent industry encompassing animal breeding, preservation of genetic diversity, and medical research. Currently, there is no specifically engineered system available for high-throughput of cryopreserved germplasm for aquatic species. This review is to discuss the concepts and needs for high-throughput technology for model fishes, propose approaches for technical development, and overview future directions of this approach. PMID:21440666

Should MD-PhD programs encourage graduate training in disciplines beyond conventional biomedical or clinical sciences?

PubMed

O'Mara, Ryan J; Hsu, Stephen I; Wilson, Daniel R

2015-02-01

The goal of MD-PhD training programs is to produce physician-scientists with unique capacities to lead the future biomedical research workforce. The current dearth of physician-scientists with expertise outside conventional biomedical or clinical sciences raises the question of whether MD-PhD training programs should allow or even encourage scholars to pursue doctoral studies in disciplines that are deemed nontraditional, yet are intrinsically germane to major influences on health. This question is especially relevant because the central value and ultimate goal of the academic medicine community is to help attain the highest level of health and health equity for all people. Advances in medical science and practice, along with improvements in health care access and delivery, are steps toward health equity, but alone they will not come close to eliminating health inequalities. Addressing the complex health issues in our communities and society as a whole requires a biomedical research workforce with knowledge, practice, and research skills well beyond conventional biomedical or clinical sciences. To make real progress in advancing health equity, educational pathways must prepare physician-scientists to treat both micro and macro determinants of health. The authors argue that MD-PhD programs should allow and encourage their scholars to cross boundaries into less traditional disciplines such as epidemiology, statistics, anthropology, sociology, ethics, public policy, management, economics, education, social work, informatics, communications, and marketing. To fulfill current and coming health care needs, nontraditional MD-PhD students should be welcomed and supported as valuable members of our biomedical research workforce.

International biomedical law in search for its normative status.

PubMed

Krajewska, Atina

2012-01-01

The broad and multifaceted problem of global health law and global health governance has been attracting increasing attention in the last few decades. The global community has failed to establish international legal regime that deals comprehensively with the 'technological revolution'. The latter has posed complex questions to regions of the world with widely differing cultural perspectives. At the same time, an increasing number of governmental and non-state actors have become significantly involved in the sector. They use legal, political, and other forms of decision-making that result in regulatory instruments of contrasting normative status. Law created in this heterogeneous environment has been said to be fragmented, inconsistent, and exacerbating uncertainties. Therefore, claims have been made that a centralised and institutionalised system would help address the problems of transparency, legitimacy and efficiency. Nevertheless, little scholarly consideration is paid to the normative status of international biomedical law. This paper explores whether formalisation and "constitutionalisation" of biomedical law are indeed inevitable for its establishment as a separate regulatory regime. It does so by analysing the proliferation of biomedical law in light of two the theory of fragmentation and the theory of global legal pluralism. Investigating the problem in this way helps determine the theoretical framework and methodology of future studies of biomedical law at the international level. This in turn should help its future development in a more consistent and harmonised manner.

Characterization of the influence of external stimulus on protein-nucleic acid complex through multiscale computations

NASA Astrophysics Data System (ADS)

Gosai, Agnivo

The concomitant detection, monitoring and analysis of biomolecules have assumed utmost importance in the field of medical diagnostics as well as in different spheres of biotechnology research such as drug development, environmental hazard detection and biodefense. There is an increased demand for the modulation of the biological response for such detection / sensing schemes which will be facilitated by the sensitive and controllable transmission of external stimuli. Electrostatic actuation for the controlled release/capture of biomolecules through conformational transformations of bioreceptors provides an efficient and feasible mechanism to modulate biological response. In addition, electrostatic actuation mechanism has the advantage of allowing massively parallel schemes and measurement capabilities that could ultimately be essential for biomedical applications. Experiments have previously demonstrated the unbinding of thrombin from its aptamer in presence of small positive electrode potential whereas the complex remained associated in presence of small negative potentials / zero potential. However, the nanoscale physics/chemistry involved in this process is not clearly understood. In this thesis a combination of continuum mechanics based modeling and a variety of atomistic simulation techniques have been utilized to corroborate the aforementioned experimental observations. It is found that the computational approach can satisfactorily predict the dynamics of the electrically excited aptamer-thrombin complex as well as provide an analytical model to characterize the forced binding of the complex.

A complex systems science perspective for whole systems of complementary and alternative medicine research.

PubMed

Koithan, Mary; Bell, Iris R; Niemeyer, Kathryn; Pincus, David

2012-01-01

Whole systems complementary and alternative medicine (WS-CAM) approaches share a basic worldview that embraces interconnectedness; emergent, non-linear outcomes to treatment that include both local and global changes in the human condition; a contextual view of human beings that are inseparable from and responsive to their environments; and interventions that are complex, synergistic, and interdependent. These fundamental beliefs and principles run counter to the assumptions of reductionism and conventional biomedical research methods that presuppose unidimensional simple causes and thus dismantle and individually test various interventions that comprise only single aspects of the WSCAM system. This paper will demonstrate the superior fit and practical advantages of using complex adaptive systems (CAS) and related modeling approaches to develop the scientific basis for WS-CAM. Furthermore, the details of these CAS models will be used to provide working hypotheses to explain clinical phenomena such as (a) persistence of changes for weeks to months between treatments and/or after cessation of treatment, (b) nonlocal and whole systems changes resulting from therapy, (c) Hering's law, and (d) healing crises. Finally, complex systems science will be used to offer an alternative perspective on cause, beyond the simple reductionism of mainstream mechanistic ontology and more parsimonious than the historical vitalism of WS-CAM. Rather, complex systems science provides a scientifically rigorous, yet essentially holistic ontological perspective with which to conceptualize and empirically explore the development of disease and illness experiences, as well as experiences of healing and wellness. Copyright © 2012 S. Karger AG, Basel.

The Faculty Costs to Educate a Biomedical Sciences Graduate Student

ERIC Educational Resources Information Center

Smolka, Adam J.; Halushka, Perry V.; Garrett-Mayer, Elizabeth

2015-01-01

Academic medical centers nationwide face numerous fiscal challenges resulting from implementation of restructured healthcare delivery models, contracting state support for higher education, and increased competition for federal and other sources of biomedical research funding. In pursuing greater accountability and transparency in its fiscal…

Guidelines for the Effective Use of Entity-Attribute-Value Modeling for Biomedical Databases

PubMed Central

Dinu, Valentin; Nadkarni, Prakash

2007-01-01

Purpose To introduce the goals of EAV database modeling, to describe the situations where Entity-Attribute-Value (EAV) modeling is a useful alternative to conventional relational methods of database modeling, and to describe the fine points of implementation in production systems. Methods We analyze the following circumstances: 1) data are sparse and have a large number of applicable attributes, but only a small fraction will apply to a given entity; 2) numerous classes of data need to be represented, each class has a limited number of attributes, but the number of instances of each class is very small. We also consider situations calling for a mixed approach where both conventional and EAV design are used for appropriate data classes. Results and Conclusions In robust production systems, EAV-modeled databases trade a modest data sub-schema for a complex metadata sub-schema. The need to design the metadata effectively makes EAV design potentially more challenging than conventional design. PMID:17098467

Modeling Information Content Via Dirichlet-Multinomial Regression Analysis.

PubMed

Ferrari, Alberto

2017-01-01

Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.

New software developments for quality mesh generation and optimization from biomedical imaging data.

PubMed

Yu, Zeyun; Wang, Jun; Gao, Zhanheng; Xu, Ming; Hoshijima, Masahiko

2014-01-01

In this paper we present a new software toolkit for generating and optimizing surface and volumetric meshes from three-dimensional (3D) biomedical imaging data, targeted at image-based finite element analysis of some biomedical activities in a single material domain. Our toolkit includes a series of geometric processing algorithms including surface re-meshing and quality-guaranteed tetrahedral mesh generation and optimization. All methods described have been encapsulated into a user-friendly graphical interface for easy manipulation and informative visualization of biomedical images and mesh models. Numerous examples are presented to demonstrate the effectiveness and efficiency of the described methods and toolkit. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

The effects of utility evaluations, biomedical knowledge and modernization on intention to exclusively use biomedical health facilities among rural households in Mozambique.

PubMed

Mukolo, Abraham; Cooil, Bruce; Victor, Bart

2015-08-01

In resource-limited settings, the choice between utilizing biomedical health services and/or traditional healers is critical to the success of the public health mission. In the literature, this choice has been predicted to be influenced by three major factors: knowledge about biomedical etiologies; cultural modernization; and rational choice. The current study investigated all three of these predicted determinants, applying data from a general household survey conducted in 2010 in Zambézia Province of Mozambique involving 1045 randomly sampled rural households. Overall, more respondents (N = 802) intended to continue to supplement their biomedical healthcare with traditional healer services in comparison with those intending to utilize biomedical care exclusively (N = 243). The findings strongly supported the predicted association between rational utility (measured as satisfaction with the quality of service and results from past care) with the future intention to continue to supplement or utilize biomedical care exclusively. Odds of moving away from supplementation increase by a factor of 2.5 if the respondent reported seeing their condition improve under government/private biomedical care. Odds of staying with supplementation increase by a factor 3.1 if the respondent was satisfied with traditional care and a factor of 16 if the condition had improved under traditional care. Modernization variables (education, income, religion, and Portuguese language skills) were relevant and provided a significant component of the best scientific model. Amount of biomedical knowledge was not a significant predictor of choice. There was a small effect on choice from knowing the limitations of biomedical care. The findings have implications for public healthcare promotion activities in areas where biomedical care is introduced as an alternative to traditional healing. Copyright © 2015 Elsevier Ltd. All rights reserved.

A simple microbial fuel cell model for improvement of biomedical device powering times.

PubMed

Roxby, Daniel N; Tran, Nham; Nguyen, Hung T

2014-01-01

This study describes a Matlab based Microbial Fuel Cell (MFC) model for a suspended microbial population, in the anode chamber for the use of the MFC in powering biomedical devices. The model contains three main sections including microbial growth, microbial chemical uptake and secretion and electrochemical modeling. The microbial growth portion is based on a Continuously Stirred Tank Reactor (CSTR) model for the microbial growth with substrate and electron acceptors. Microbial stoichiometry is used to determine chemical concentrations and their rates of change and transfer within the MFC. These parameters are then used in the electrochemical modeling for calculating current, voltage and power. The model was tested for typically exhibited MFC characteristics including increased electrode distances and surface areas, overpotentials and operating temperatures. Implantable biomedical devices require long term powering which is the main objective for MFCs. Towards this end, our model was tested with different initial substrate and electron acceptor concentrations, revealing a four-fold increase in concentrations decreased the power output time by 50%. Additionally, the model also predicts that for a 35.7% decrease in specific growth rate, a 50% increase in power longevity is possible.

Peer-to-peer Monte Carlo simulation of photon migration in topical applications of biomedical optics

NASA Astrophysics Data System (ADS)

Doronin, Alexander; Meglinski, Igor

2012-09-01

In the framework of further development of the unified approach of photon migration in complex turbid media, such as biological tissues we present a peer-to-peer (P2P) Monte Carlo (MC) code. The object-oriented programming is used for generalization of MC model for multipurpose use in various applications of biomedical optics. The online user interface providing multiuser access is developed using modern web technologies, such as Microsoft Silverlight, ASP.NET. The emerging P2P network utilizing computers with different types of compute unified device architecture-capable graphics processing units (GPUs) is applied for acceleration and to overcome the limitations, imposed by multiuser access in the online MC computational tool. The developed P2P MC was validated by comparing the results of simulation of diffuse reflectance and fluence rate distribution for semi-infinite scattering medium with known analytical results, results of adding-doubling method, and with other GPU-based MC techniques developed in the past. The best speedup of processing multiuser requests in a range of 4 to 35 s was achieved using single-precision computing, and the double-precision computing for floating-point arithmetic operations provides higher accuracy.

Peer-to-peer Monte Carlo simulation of photon migration in topical applications of biomedical optics.

PubMed

Doronin, Alexander; Meglinski, Igor

2012-09-01

In the framework of further development of the unified approach of photon migration in complex turbid media, such as biological tissues we present a peer-to-peer (P2P) Monte Carlo (MC) code. The object-oriented programming is used for generalization of MC model for multipurpose use in various applications of biomedical optics. The online user interface providing multiuser access is developed using modern web technologies, such as Microsoft Silverlight, ASP.NET. The emerging P2P network utilizing computers with different types of compute unified device architecture-capable graphics processing units (GPUs) is applied for acceleration and to overcome the limitations, imposed by multiuser access in the online MC computational tool. The developed P2P MC was validated by comparing the results of simulation of diffuse reflectance and fluence rate distribution for semi-infinite scattering medium with known analytical results, results of adding-doubling method, and with other GPU-based MC techniques developed in the past. The best speedup of processing multiuser requests in a range of 4 to 35 s was achieved using single-precision computing, and the double-precision computing for floating-point arithmetic operations provides higher accuracy.

Structural characterization of biomedical Co-Cr-Mo components produced by direct metal laser sintering.

PubMed

Barucca, G; Santecchia, E; Majni, G; Girardin, E; Bassoli, E; Denti, L; Gatto, A; Iuliano, L; Moskalewicz, T; Mengucci, P

2015-03-01

Direct metal laser sintering (DMLS) is a technique to manufacture complex functional mechanical parts from a computer-aided design (CAD) model. Usually, the mechanical components produced by this procedure show higher residual porosity and poorer mechanical properties than those obtained by conventional manufacturing techniques. In this work, a Co-Cr-Mo alloy produced by DMLS with a composition suitable for biomedical applications was submitted to hardness measurements and structural characterization. The alloy showed a hardness value remarkably higher than those commonly obtained for the same cast or wrought alloys. In order to clarify the origin of this unexpected result, the sample microstructure was investigated by X-ray diffraction (XRD), electron microscopy (SEM and TEM) and energy dispersive microanalysis (EDX). For the first time, a homogeneous microstructure comprised of an intricate network of thin ε (hcp)-lamellae distributed inside a γ (fcc) phase was observed. The ε-lamellae grown on the {111}γ planes limit the dislocation slip inside the γ (fcc) phase, causing the measured hardness increase. The results suggest possible innovative applications of the DMLS technique to the production of mechanical parts in the medical and dental fields. Copyright © 2014 Elsevier B.V. All rights reserved.

"Whatever average is:" understanding African-American mothers' perceptions of infant weight, growth, and health.

PubMed

Thompson, Amanda L; Adair, Linda; Bentley, Margaret E

2014-06-01

Biomedical researchers have raised concerns that mothers' inability to recognize infant and toddler overweight poses a barrier to stemming increasing rates of overweight and obesity, particularly among low-income or minority mothers. Little anthropological research has examined the sociocultural, economic or structural factors shaping maternal perceptions of infant and toddler size or addressed biomedical depictions of maternal misperception as a "socio-cultural problem." We use qualitative and quantitative data from 237 low-income, African-American mothers to explore how they define 'normal' infant growth and infant overweight. Our quantitative results document that mothers' perceptions of infant size change with infant age, are sensitive to the size of other infants in the community, and are associated with concerns over health and appetite. Qualitative analysis documents that mothers are concerned with their children's weight status and assess size in relation to their infants' cues, local and societal norms of appropriate size, interactions with biomedicine, and concerns about infant health and sufficiency. These findings suggest that mothers use multiple models to interpret and respond to child weight. An anthropological focus on the complex social and structural factors shaping what is considered 'normal' and 'abnormal' infant weight is critical for shaping appropriate and successful interventions.

Beyond medical pluralism: characterising health-care delivery of biomedicine and traditional medicine in rural Guatemala.

PubMed

Hoyler, Elizabeth; Martinez, Roxana; Mehta, Kurren; Nisonoff, Hunter; Boyd, David

2018-04-01

Although approximately one half of Guatemalans are indigenous, the Guatemalan Maya account for 72% of the extremely poor within the country. While some biomedical services are available in these communities, many Maya utilise traditional medicine as a significant, if not primary, source of health care. While existing medical anthropological research characterises these modes of medicine as medically dichotomous or pluralistic, our research in a Maya community of the Western Highlands, Concepción Huista, builds on previous studies and finds instead a syncretistic, imbricated local health system. We find significant overlap and interpenetration of the biomedical and traditional medical models that are described best as a framework where practitioners in both settings employ elements of the other in order to best meet community needs. By focusing on the practitioner's perspective, we demonstrate that in addition to patients' willingness to seek care across health systems, practitioners converse across seemingly distinct systems via incorporation of certain elements of the 'other'. Interventions to date have not accounted for this imbrication. Guatemalan governmental policies to support local healers have led to little practical change in the health-care landscape of the country. Therefore, understanding this complex imbrication is crucial for interventions and policy changes.

Venom gland transcriptome analyses of two freshwater stingrays (Myliobatiformes: Potamotrygonidae) from Brazil.

PubMed

de Oliveira Júnior, Nelson Gomes; Fernandes, Gabriel da Rocha; Cardoso, Marlon Henrique; Costa, Fabrício F; Cândido, Elizabete de Souza; Garrone Neto, Domingos; Mortari, Márcia Renata; Schwartz, Elisabeth Ferroni; Franco, Octávio Luiz; de Alencar, Sérgio Amorim

2016-02-26

Stingrays commonly cause human envenoming related accidents in populations of the sea, near rivers and lakes. Transcriptomic profiles have been used to elucidate components of animal venom, since they are capable of providing molecular information on the biology of the animal and could have biomedical applications. In this study, we elucidated the transcriptomic profile of the venom glands from two different freshwater stingray species that are endemic to the Paraná-Paraguay basin in Brazil, Potamotrygon amandae and Potamotrygon falkneri. Using RNA-Seq, we identified species-specific transcripts and overlapping proteins in the venom gland of both species. Among the transcripts related with envenoming, high abundance of hyaluronidases was observed in both species. In addition, we built three-dimensional homology models based on several venom transcripts identified. Our study represents a significant improvement in the information about the venoms employed by these two species and their molecular characteristics. Moreover, the information generated by our group helps in a better understanding of the biology of freshwater cartilaginous fishes and offers clues for the development of clinical treatments for stingray envenoming in Brazil and around the world. Finally, our results might have biomedical implications in developing treatments for complex diseases.

Improved Noncoherent UWB Receiver for Implantable Biomedical Devices.

PubMed

Nagaraj, Santosh; Rassam, Faris G

2016-10-01

The purpose of this paper is to describe a novel noncoherent receiver architecture to improve the error performance of impulse-radio ultrawideband (IR-UWB) in bioimplanted devices. IR-UWB receivers based on energy detection are popular in biomedical applications owing to the low implementation cost/complexity and the high data rates that UWB can potentially support. Implanted devices suffer from severe frequency-dependent attenuation due to human blood and tissues, while most receivers in the literature are designed based on commonly used indoor wireless channel models. We propose a novel receiver design that is based on judiciously combining the energies in different bands of the signal spectrum with a weighted linear combiner. We derive the optimum coefficients of the combiner. The receiver retains almost all of the advantages of a conventional noncoherent detector, but can also compensate for attenuation properties of blood/tissue. The receiver design can be adapted to different implantation depths by simply varying the combiner weights. The receiver can also be considered to be a simple form of equalizer for noncoherent reception. Our simulations show about 2-dB improvement over other commonly used receivers. This receiver design is significant in that it can enhance critical battery life of implanted transmitters.

Venom gland transcriptome analyses of two freshwater stingrays (Myliobatiformes: Potamotrygonidae) from Brazil

PubMed Central

Júnior, Nelson Gomes de Oliveira; Fernandes, Gabriel da Rocha; Cardoso, Marlon Henrique; Costa, Fabrício F.; Cândido, Elizabete de Souza; Neto, Domingos Garrone; Mortari, Márcia Renata; Schwartz, Elisabeth Ferroni; Franco, Octávio Luiz; de Alencar, Sérgio Amorim

2016-01-01

Stingrays commonly cause human envenoming related accidents in populations of the sea, near rivers and lakes. Transcriptomic profiles have been used to elucidate components of animal venom, since they are capable of providing molecular information on the biology of the animal and could have biomedical applications. In this study, we elucidated the transcriptomic profile of the venom glands from two different freshwater stingray species that are endemic to the Paraná-Paraguay basin in Brazil, Potamotrygon amandae and Potamotrygon falkneri. Using RNA-Seq, we identified species-specific transcripts and overlapping proteins in the venom gland of both species. Among the transcripts related with envenoming, high abundance of hyaluronidases was observed in both species. In addition, we built three-dimensional homology models based on several venom transcripts identified. Our study represents a significant improvement in the information about the venoms employed by these two species and their molecular characteristics. Moreover, the information generated by our group helps in a better understanding of the biology of freshwater cartilaginous fishes and offers clues for the development of clinical treatments for stingray envenoming in Brazil and around the world. Finally, our results might have biomedical implications in developing treatments for complex diseases. PMID:26916342

A comparison of traditional anti-inflammation and anti-infection medicinal plants with current evidence from biomedical research: Results from a regional study

PubMed Central

Vieira, A.

2010-01-01

Background: In relation to pharmacognosy, an objective of many ethnobotanical studies is to identify plant species to be further investigated, for example, tested in disease models related to the ethnomedicinal application. To further warrant such testing, research evidence for medicinal applications of these plants (or of their major phytochemical constituents and metabolic derivatives) is typically analyzed in biomedical databases. Methods: As a model of this process, the current report presents novel information regarding traditional anti-inflammation and anti-infection medicinal plant use. This information was obtained from an interview-based ethnobotanical study; and was compared with current biomedical evidence using the Medline® database. Results: Of the 8 anti-infection plant species identified in the ethnobotanical study, 7 have related activities reported in the database; and of the 6 anti-inflammation plants, 4 have related activities in the database. Conclusion: Based on novel and complimentary results from the ethnobotanical and biomedical database analyses, it is suggested that some of these plants warrant additional investigation of potential anti-inflammatory or anti-infection activities in related disease models, and also additional studies in other population groups. PMID:21589754

100 Metrics to Assess and Communicate the Value of Biomedical Research: An Ideas Book.

PubMed

Guthrie, Susan; Krapels, Joachim; Lichten, Catherine A; Wooding, Steven

2017-01-01

Biomedical research affects society in many ways. It has been shown to improve health, create jobs, add to our knowledge, and foster new collaborations. Despite the complexity of modern research, many of the metrics used to evaluate the impacts of research still focus on the traditional, often academic, part of the research pathway, covering areas such as the amount of grant funding received and the number of peer-reviewed publications. In response to increasing expectations of accountability and transparency, the Association of American Medical Colleges (AAMC), in collaboration with RAND Europe, undertook a project to help communicate the wider value of biomedical research. The initiative developed resources to support academic medical centers in evaluating the outcomes and impacts of their research using approaches relevant to various stakeholders, including patients, providers, administrators, and legislators. This study presents 100 ideas for metrics that can be used to assess and communicate the value of biomedical research. The list is not comprehensive, and the metrics are not fully developed, but they should serve to stimulate and broaden thinking about how academic medical centers can communicate the value of their research to a broad range of stakeholders.

100 Metrics to Assess and Communicate the Value of Biomedical Research

PubMed Central

Guthrie, Susan; Krapels, Joachim; Lichten, Catherine A.; Wooding, Steven

2017-01-01

Abstract Biomedical research affects society in many ways. It has been shown to improve health, create jobs, add to our knowledge, and foster new collaborations. Despite the complexity of modern research, many of the metrics used to evaluate the impacts of research still focus on the traditional, often academic, part of the research pathway, covering areas such as the amount of grant funding received and the number of peer-reviewed publications. In response to increasing expectations of accountability and transparency, the Association of American Medical Colleges (AAMC), in collaboration with RAND Europe, undertook a project to help communicate the wider value of biomedical research. The initiative developed resources to support academic medical centers in evaluating the outcomes and impacts of their research using approaches relevant to various stakeholders, including patients, providers, administrators, and legislators. This study presents 100 ideas for metrics that can be used to assess and communicate the value of biomedical research. The list is not comprehensive, and the metrics are not fully developed, but they should serve to stimulate and broaden thinking about how academic medical centers can communicate the value of their research to a broad range of stakeholders. PMID:28983437

On analyzing ordinal data when responses and covariates are both missing at random.

PubMed

Rana, Subrata; Roy, Surupa; Das, Kalyan

2016-08-01

In many occasions, particularly in biomedical studies, data are unavailable for some responses and covariates. This leads to biased inference in the analysis when a substantial proportion of responses or a covariate or both are missing. Except a few situations, methods for missing data have earlier been considered either for missing response or for missing covariates, but comparatively little attention has been directed to account for both missing responses and missing covariates, which is partly attributable to complexity in modeling and computation. This seems to be important as the precise impact of substantial missing data depends on the association between two missing data processes as well. The real difficulty arises when the responses are ordinal by nature. We develop a joint model to take into account simultaneously the association between the ordinal response variable and covariates and also that between the missing data indicators. Such a complex model has been analyzed here by using the Markov chain Monte Carlo approach and also by the Monte Carlo relative likelihood approach. Their performance on estimating the model parameters in finite samples have been looked into. We illustrate the application of these two methods using data from an orthodontic study. Analysis of such data provides some interesting information on human habit. © The Author(s) 2013.

Research evaluation support services in biomedical libraries.

PubMed

Gutzman, Karen Elizabeth; Bales, Michael E; Belter, Christopher W; Chambers, Thane; Chan, Liza; Holmes, Kristi L; Lu, Ya-Ling; Palmer, Lisa A; Reznik-Zellen, Rebecca C; Sarli, Cathy C; Suiter, Amy M; Wheeler, Terrie R

2018-01-01

The paper provides a review of current practices related to evaluation support services reported by seven biomedical and research libraries. A group of seven libraries from the United States and Canada described their experiences with establishing evaluation support services at their libraries. A questionnaire was distributed among the libraries to elicit information as to program development, service and staffing models, campus partnerships, training, products such as tools and reports, and resources used for evaluation support services. The libraries also reported interesting projects, lessons learned, and future plans. The seven libraries profiled in this paper report a variety of service models in providing evaluation support services to meet the needs of campus stakeholders. The service models range from research center cores, partnerships with research groups, and library programs with staff dedicated to evaluation support services. A variety of products and services were described such as an automated tool to develop rank-based metrics, consultation on appropriate metrics to use for evaluation, customized publication and citation reports, resource guides, classes and training, and others. Implementing these services has allowed the libraries to expand their roles on campus and to contribute more directly to the research missions of their institutions. Libraries can leverage a variety of evaluation support services as an opportunity to successfully meet an array of challenges confronting the biomedical research community, including robust efforts to report and demonstrate tangible and meaningful outcomes of biomedical research and clinical care. These services represent a transformative direction that can be emulated by other biomedical and research libraries.

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

PubMed Central

2012-01-01

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. PMID:22759614

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

PubMed

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Towards a 21st-century roadmap for biomedical research and drug discovery: consensus report and recommendations.

PubMed

Langley, Gillian R; Adcock, Ian M; Busquet, François; Crofton, Kevin M; Csernok, Elena; Giese, Christoph; Heinonen, Tuula; Herrmann, Kathrin; Hofmann-Apitius, Martin; Landesmann, Brigitte; Marshall, Lindsay J; McIvor, Emily; Muotri, Alysson R; Noor, Fozia; Schutte, Katrin; Seidle, Troy; van de Stolpe, Anja; Van Esch, Hilde; Willett, Catherine; Woszczek, Grzegorz

2017-02-01

Decades of costly failures in translating drug candidates from preclinical disease models to human therapeutic use warrant reconsideration of the priority placed on animal models in biomedical research. Following an international workshop attended by experts from academia, government institutions, research funding bodies, and the corporate and non-governmental organisation (NGO) sectors, in this consensus report, we analyse, as case studies, five disease areas with major unmet needs for new treatments. In view of the scientifically driven transition towards a human pathways-based paradigm in toxicology, a similar paradigm shift appears to be justified in biomedical research. There is a pressing need for an approach that strategically implements advanced, human biology-based models and tools to understand disease pathways at multiple biological scales. We present recommendations to help achieve this. Copyright © 2016 Elsevier Ltd. All rights reserved.

Organs-on-a-chip: a focus on compartmentalized microdevices.

PubMed

Moraes, Christopher; Mehta, Geeta; Lesher-Perez, Sasha Cai; Takayama, Shuichi

2012-06-01

Advances in microengineering technologies have enabled a variety of insights into biomedical sciences that would not have been possible with conventional techniques. Engineering microenvironments that simulate in vivo organ systems may provide critical insight into the cellular basis for pathophysiologies, development, and homeostasis in various organs, while curtailing the high experimental costs and complexities associated with in vivo studies. In this article, we aim to survey recent attempts to extend tissue-engineered platforms toward simulating organ structure and function, and discuss the various approaches and technologies utilized in these systems. We specifically focus on microtechnologies that exploit phenomena associated with compartmentalization to create model culture systems that better represent the in vivo organ microenvironment.

Genomics and transcriptomics in drug discovery.

PubMed

Dopazo, Joaquin

2014-02-01

The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein-drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery. Copyright © 2013 Elsevier Ltd. All rights reserved.

[Ethics and prevention: environmental and individual disparities].

PubMed

Ricciardi, Claudio

2004-01-01

The complex interactions which exist between environmental variabilities, genetic susceptibility of population subgroups, and high individual variability for age, sex, gender, ethnicity and general status of health, acquire an ever-increasing bioethical significance. Different risk conditions caused by toxic environmental agents and environmental inequities and inequalities are increasingly evident. "Social determinants" of health increase the probability of health effects and an effective intervention of prevision and prevention for environmental pathologies is needed. The debate on environmental inequalities caused by cultural, social and economic factors and the uncertainty about possible prevention emphasize the limits of the "bio-medical model". Ethics with its further anthropological and philosophical considerations may strongly help to understand the relationship between environmental pollution and health.

Tomographic phase microscopy: principles and applications in bioimaging [Invited

PubMed Central

Jin, Di; Zhou, Renjie; Yaqoob, Zahid; So, Peter T. C.

2017-01-01

Tomographic phase microscopy (TPM) is an emerging optical microscopic technique for bioimaging. TPM uses digital holographic measurements of complex scattered fields to reconstruct three-dimensional refractive index (RI) maps of cells with diffraction-limited resolution by solving inverse scattering problems. In this paper, we review the developments of TPM from the fundamental physics to its applications in bioimaging. We first provide a comprehensive description of the tomographic reconstruction physical models used in TPM. The RI map reconstruction algorithms and various regularization methods are discussed. Selected TPM applications for cellular imaging, particularly in hematology, are reviewed. Finally, we examine the limitations of current TPM systems, propose future solutions, and envision promising directions in biomedical research. PMID:29386746

Teaching ethical aptitude to graduate student researchers.

PubMed

Weyrich, Laura S; Harvill, Eric T

2013-01-01

Limited time dedicated to each training areas, irrelevant case-studies, and ethics "checklists" have resulted in bare-bones Responsible Conduct of Research (RCR) training for present biomedical graduate student researchers. Here, we argue that science graduate students be taught classical ethical theory, such as virtue ethics, consequentialist theory, and deontological theory, to provide a basic framework to guide researchers through ethically complex situations and examine the applicability, implications, and societal ramifications of their research. Using a relevant biomedical research example to illustrate this point, we argue that proper ethics training for graduate student researchers not only will enhance current RCR training, but train more creative, responsible scientists.

3D Bioprinting for Engineering Complex Tissues

PubMed Central

Mandrycky, Christian; Wang, Zongjie; Kim, Keekyoung; Kim, Deok-Ho

2016-01-01

Bioprinting is a 3D fabrication technology used to precisely dispense cell-laden biomaterials for the construction of complex 3D functional living tissues or artificial organs. While still in its early stages, bioprinting strategies have demonstrated their potential use in regenerative medicine to generate a variety of transplantable tissues, including skin, cartilage, and bone. However, current bioprinting approaches still have technical challenges in terms of high-resolution cell deposition, controlled cell distributions, vascularization, and innervation within complex 3D tissues. While no one-size-fits-all approach to bioprinting has emerged, it remains an on-demand, versatile fabrication technique that may address the growing organ shortage as well as provide a high-throughput method for cell patterning at the micrometer scale for broad biomedical engineering applications. In this review, we introduce the basic principles, materials, integration strategies and applications of bioprinting. We also discuss the recent developments, current challenges and future prospects of 3D bioprinting for engineering complex tissues. Combined with recent advances in human pluripotent stem cell technologies, 3D-bioprinted tissue models could serve as an enabling platform for high-throughput predictive drug screening and more effective regenerative therapies. PMID:26724184

3D bioprinting for engineering complex tissues.

PubMed

Mandrycky, Christian; Wang, Zongjie; Kim, Keekyoung; Kim, Deok-Ho

2016-01-01

Bioprinting is a 3D fabrication technology used to precisely dispense cell-laden biomaterials for the construction of complex 3D functional living tissues or artificial organs. While still in its early stages, bioprinting strategies have demonstrated their potential use in regenerative medicine to generate a variety of transplantable tissues, including skin, cartilage, and bone. However, current bioprinting approaches still have technical challenges in terms of high-resolution cell deposition, controlled cell distributions, vascularization, and innervation within complex 3D tissues. While no one-size-fits-all approach to bioprinting has emerged, it remains an on-demand, versatile fabrication technique that may address the growing organ shortage as well as provide a high-throughput method for cell patterning at the micrometer scale for broad biomedical engineering applications. In this review, we introduce the basic principles, materials, integration strategies and applications of bioprinting. We also discuss the recent developments, current challenges and future prospects of 3D bioprinting for engineering complex tissues. Combined with recent advances in human pluripotent stem cell technologies, 3D-bioprinted tissue models could serve as an enabling platform for high-throughput predictive drug screening and more effective regenerative therapies. Copyright © 2015 Elsevier Inc. All rights reserved.

Technology-induced errors. The current use of frameworks and models from the biomedical and life sciences literatures.

PubMed

Borycki, E M; Kushniruk, A W; Bellwood, P; Brender, J

2012-01-01

The objective of this paper is to examine the extent, range and scope to which frameworks, models and theories dealing with technology-induced error have arisen in the biomedical and life sciences literature as indexed by Medline®. To better understand the state of work in the area of technology-induced error involving frameworks, models and theories, the authors conducted a search of Medline® using selected key words identified from seminal articles in this research area. Articles were reviewed and those pertaining to frameworks, models or theories dealing with technology-induced error were further reviewed by two researchers. All articles from Medline® from its inception to April of 2011 were searched using the above outlined strategy. 239 citations were returned. Each of the abstracts for the 239 citations were reviewed by two researchers. Eleven articles met the criteria based on abstract review. These 11 articles were downloaded for further in-depth review. The majority of the articles obtained describe frameworks and models with reference to theories developed in other literatures outside of healthcare. The papers were grouped into several areas. It was found that articles drew mainly from three literatures: 1) the human factors literature (including human-computer interaction and cognition), 2) the organizational behavior/sociotechnical literature, and 3) the software engineering literature. A variety of frameworks and models were found in the biomedical and life sciences literatures. These frameworks and models drew upon and extended frameworks, models and theoretical perspectives that have emerged in other literatures. These frameworks and models are informing an emerging line of research in health and biomedical informatics involving technology-induced errors in healthcare.

The academic-industrial complex: navigating the translational and cultural divide.

PubMed

Freedman, Stephen; Mullane, Kevin

2017-07-01

In general, the fruits of academic discoveries can only be realized through joint efforts with industry. However, the poor reproducibility of much academic research has damaged credibility and jeopardized translational efforts that could benefit patients. Meanwhile, journals are rife with articles bemoaning the limited productivity and increasing costs of the biopharmaceutical industry and its resultant predilection for mergers and reorganizations while decreasing internal research efforts. The ensuing disarray and uncertainty has created tremendous opportunities for academia and industry to form even closer ties, and to embrace new operational and financial models to their joint benefit. This review article offers a personal perspective on the opportunities, models and approaches that harness the increased interface and growing interdependency between biomedical research institutes, the biopharmaceutical industry and the technological world. Copyright © 2017 Elsevier Ltd. All rights reserved.

Complex-based OCT angiography algorithm recovers microvascular information better than amplitude- or phase-based algorithms in phase-stable systems

NASA Astrophysics Data System (ADS)

Xu, Jingjiang; Song, Shaozhen; Li, Yuandong; Wang, Ruikang K.

2018-01-01

Optical coherence tomography angiography (OCTA) is increasingly becoming a popular inspection tool for biomedical imaging applications. By exploring the amplitude, phase and complex information available in OCT signals, numerous algorithms have been proposed that contrast functional vessel networks within microcirculatory tissue beds. However, it is not clear which algorithm delivers optimal imaging performance. Here, we investigate systematically how amplitude and phase information have an impact on the OCTA imaging performance, to establish the relationship of amplitude and phase stability with OCT signal-to-noise ratio (SNR), time interval and particle dynamics. With either repeated A-scan or repeated B-scan imaging protocols, the amplitude noise increases with the increase of OCT SNR; however, the phase noise does the opposite, i.e. it increases with the decrease of OCT SNR. Coupled with experimental measurements, we utilize a simple Monte Carlo (MC) model to simulate the performance of amplitude-, phase- and complex-based algorithms for OCTA imaging, the results of which suggest that complex-based algorithms deliver the best performance when the phase noise is  <  ~40 mrad. We also conduct a series of in vivo vascular imaging in animal models and human retina to verify the findings from the MC model through assessing the OCTA performance metrics of vessel connectivity, image SNR and contrast-to-noise ratio. We show that for all the metrics assessed, the complex-based algorithm delivers better performance than either the amplitude- or phase-based algorithms for both the repeated A-scan and the B-scan imaging protocols, which agrees well with the conclusion drawn from the MC simulations.

Complex-based OCT angiography algorithm recovers microvascular information better than amplitude- or phase-based algorithms in phase-stable systems.

PubMed

Xu, Jingjiang; Song, Shaozhen; Li, Yuandong; Wang, Ruikang K

2017-12-19

Optical coherence tomography angiography (OCTA) is increasingly becoming a popular inspection tool for biomedical imaging applications. By exploring the amplitude, phase and complex information available in OCT signals, numerous algorithms have been proposed that contrast functional vessel networks within microcirculatory tissue beds. However, it is not clear which algorithm delivers optimal imaging performance. Here, we investigate systematically how amplitude and phase information have an impact on the OCTA imaging performance, to establish the relationship of amplitude and phase stability with OCT signal-to-noise ratio (SNR), time interval and particle dynamics. With either repeated A-scan or repeated B-scan imaging protocols, the amplitude noise increases with the increase of OCT SNR; however, the phase noise does the opposite, i.e. it increases with the decrease of OCT SNR. Coupled with experimental measurements, we utilize a simple Monte Carlo (MC) model to simulate the performance of amplitude-, phase- and complex-based algorithms for OCTA imaging, the results of which suggest that complex-based algorithms deliver the best performance when the phase noise is  <  ~40 mrad. We also conduct a series of in vivo vascular imaging in animal models and human retina to verify the findings from the MC model through assessing the OCTA performance metrics of vessel connectivity, image SNR and contrast-to-noise ratio. We show that for all the metrics assessed, the complex-based algorithm delivers better performance than either the amplitude- or phase-based algorithms for both the repeated A-scan and the B-scan imaging protocols, which agrees well with the conclusion drawn from the MC simulations.

Biomedical databases: protecting privacy and promoting research.

PubMed

Wylie, Jean E; Mineau, Geraldine P

2003-03-01

When combined with medical information, large electronic databases of information that identify individuals provide superlative resources for genetic, epidemiology and other biomedical research. Such research resources increasingly need to balance the protection of privacy and confidentiality with the promotion of research. Models that do not allow the use of such individual-identifying information constrain research; models that involve commercial interests raise concerns about what type of access is acceptable. Researchers, individuals representing the public interest and those developing regulatory guidelines must be involved in an ongoing dialogue to identify practical models.

Prediction of lung cells oncogenic transformation for induced radon progeny alpha particles using sugarscape cellular automata.

PubMed

Baradaran, Samaneh; Maleknasr, Niaz; Setayeshi, Saeed; Akbari, Mohammad Esmaeil

2014-01-01

Alpha particle irradiation from radon progeny is one of the major natural sources of effective dose in the public population. Oncogenic transformation is a biological effectiveness of radon progeny alpha particle hits. The biological effects which has caused by exposure to radon, were the main result of a complex series of physical, chemical, biological and physiological interactions. The cellular and molecular mechanisms for radon-induced carcinogenesis have not been clear yet. Various biological models, including cultured cells and animals, have been found useful for studying the carcinogenesis effects of radon progeny alpha particles. In this paper, sugars cape cellular automata have been presented for computational study of complex biological effect of radon progeny alpha particles in lung bronchial airways. The model has included mechanism of DNA damage, which has been induced alpha particles hits, and then formation of transformation in the lung cells. Biomarkers were an objective measure or evaluation of normal or abnormal biological processes. In the model, the metabolism rate of infected cell has been induced alpha particles traversals, as a biomarker, has been followed to reach oncogenic transformation. The model results have successfully validated in comparison with "in vitro oncogenic transformation data" for C3H 10T1/2 cells. This model has provided an opportunity to study the cellular and molecular changes, at the various stages in radiation carcinogenesis, involving human cells. It has become well known that simulation could be used to investigate complex biomedical systems, in situations where traditional methodologies were difficult or too costly to employ.

A comprehensive SWOT audit of the role of the biomedical physicist in the education of healthcare professionals in Europe.

PubMed

Caruana, C J; Wasilewska-Radwanska, M; Aurengo, A; Dendy, P P; Karenauskaite, V; Malisan, M R; Meijer, J H; Mihov, D; Mornstein, V; Rokita, E; Vano, E; Weckstrom, M; Wucherer, M

2010-04-01

Although biomedical physicists provide educational services to the healthcare professions in the majority of universities in Europe, their precise role with respect to the education of the healthcare professions has not been studied systematically. To address this issue we are conducting a research project to produce a strategic development model for the role using the well-established SWOT (Strengths, Weaknesses, Opportunities, Threats) methodology. SWOT based strategic planning is a two-step process: one first carries out a SWOT position audit and then uses the identified SWOT themes to construct the strategic development model. This paper reports the results of a SWOT audit for the role of the biomedical physicist in the education of the healthcare professions in Europe. Internal Strengths and Weaknesses of the role were identified through a qualitative survey of biomedical physics departments and biomedical physics curricula delivered to healthcare professionals across Europe. External environmental Opportunities and Threats were identified through a systematic survey of the healthcare, healthcare professional education and higher education literature and categorized under standard PEST (Political, Economic, Social-Psychological, Technological-Scientific) categories. The paper includes an appendix of terminology. Defined terms are marked with an asterisk in the text. Copyright 2009 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Hot soup! Correlating the severity of liquid scald burns to fluid and biomedical properties.

PubMed

Loller, Cameron; Buxton, Gavin A; Kerzmann, Tony L

2016-05-01

Burns caused by hot drinks and soups can be both debilitating and costly, especially to pediatric and geriatric patients. This research is aimed at better understanding the fluid properties that can influence the severity of skin burns. We use a standard model which combines heat transfer and biomedical equations to predict burn severity. In particular, experimental data from a physical model serves as the input to our numerical model to determine the severity of scald burns as a consequence of actual fluid flows. This technique enables us to numerically predict the heat transfer from the hot soup into the skin, without the need to numerically estimate the complex fluid mechanics and thermodynamics of the potentially highly viscous and heterogeneous soup. While the temperature of the soup is obviously is the most important fact in determining the degree of burn, we also find that more viscous fluids result in more severe burns, as the slower flowing thicker fluids remain in contact with the skin for longer. Furthermore, other factors can also increase the severity of burn such as a higher initial fluid temperature, a greater fluid thermal conductivity, or a higher thermal capacity of the fluid. Our combined experimental and numerical investigation finds that for average skin properties a very viscous fluid at 100°C, the fluid must be in contact with the skin for around 15-20s to cause second degree burns, and more than 80s to cause a third degree burn. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Model-based Bayesian filtering of cardiac contaminants from biomedical recordings.

PubMed

Sameni, R; Shamsollahi, M B; Jutten, C

2008-05-01

Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals.

Reprogramming cellular identity for regenerative medicine

PubMed Central

Cherry, Anne B.C.; Daley, George Q.

2012-01-01

The choreographed development of over 200 distinct differentiated cell types from a single zygote is a complex and poorly understood process. Whereas development leads unidirectionally towards more restricted cell fates, recent work in cellular reprogramming has proven that striking conversions of one cellular identity into another can be engineered, promising countless applications in biomedical research and paving the way for modeling disease with patient-derived stem cells. To date, there has been little discussion of which disease models are likely to be most informative. We here review evidence demonstrating that because environmental influences and epigenetic signatures are largely erased during reprogramming, patient-specific models of diseases with strong genetic bases and high penetrance are likely to prove most informative in the near term. However, manipulating in vitro culture conditions may ultimately enable cell-based models to recapitulate gene-environment interactions. Here, we discuss the implications of the new reprogramming paradigm in biomedicine and outline how reprogramming of cell identities is enhancing our understanding of cell differentiation and prospects for cellular therapies and in vivo regeneration. PMID:22424223

A Computational Model for Thrombus Formation in Response to Cardiovascular Implantable Devices

NASA Astrophysics Data System (ADS)

Horn, John; Ortega, Jason; Maitland, Duncan

2014-11-01

Cardiovascular implantable devices elicit complex physiological responses within blood. Notably, alterations in blood flow dynamics and interactions between blood proteins and biomaterial surface chemistry may lead to the formation of thrombus. For some devices, such as stents and heart valves, this is an adverse outcome. For other devices, such as embolic aneurysm treatments, efficient blood clot formation is desired. Thus a method to study how biomedical devices induce thrombosis is paramount to device development and optimization. A multiscale, multiphysics computational model is developed to predict thrombus formation within the vasculature. The model consists of a set of convection-diffusion-reaction partial differential equations for blood protein constituents involved in the progression of the clotting cascades. This model is used to study thrombus production from endovascular devices with the goal of optimizing the device design to generate the desired clotting response. This work was performed in part under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Particle image velocimetry measurements in an anatomical vascular model fabricated using inkjet 3D printing

NASA Astrophysics Data System (ADS)

Aycock, Kenneth I.; Hariharan, Prasanna; Craven, Brent A.

2017-11-01

For decades, the study of biomedical fluid dynamics using optical flow visualization and measurement techniques has been limited by the inability to fabricate transparent physical models that realistically replicate the complex morphology of biological lumens. In this study, we present an approach for producing optically transparent anatomical models that are suitable for particle image velocimetry (PIV) using a common 3D inkjet printing process (PolyJet) and stock resin (VeroClear). By matching the index of refraction of the VeroClear material using a room-temperature mixture of water, sodium iodide, and glycerol, and by printing the part in an orientation such that the flat, optical surfaces are at an approximately 45° angle to the build plane, we overcome the challenges associated with using this 3D printing technique for PIV. Here, we summarize our methodology and demonstrate the process and the resultant PIV measurements of flow in an optically transparent anatomical model of the human inferior vena cava.

Analysis and Design of Novel Nanophotonic Structures

NASA Astrophysics Data System (ADS)

Shugayev, Roman

Nanophotonic devices hold promise to revolutionize the fields of optical communications, quantum computing and bioimaging. Designing viable solutions to these pressing problems require developing accurate models of the relevant systems. While a great deal of work has been performed in terms of developing individual models with varying levels of fidelity, some of these more complex systems still require improved links between scales to allow for accurate design and optimization within a reasonable amount of computing time. For instance, color centers in nanocrystals appear to be a promising platform for room-temperature scalable quantum information science, but questions still remain about the optimal structures to control single-photon emitter rates, coupling fidelity, and suitable scaling architectures. In this work, a method for efficient optical access and readout of nanocrystal states via magnetic transitions was demonstrated. Separately novel Mie resonant devices that guarantee on-demand enhancement of emission from the single vacancy sources were shown. To improve addressability of the crystal-based impurities, a new approach for realization of single photon electro-optical devices is also proposed in this work. Furthermore, this work on color centers in nanocrystals has been shown to be sensitive to the local refractive index environment. This allows this system to be adapted to biomedical applications, such as sensitive, minimally invasive cancer detection. In this work, a novel scheme for propagation loss-free sensing of local refractive index using nanocrystal probes with broken symmetry is carefully investigated. In conclusion, this thesis develops several novel simulation and optimization techniques that combine existing nanophotonic modeling tools into a unique multi-scale modeling tool. It has been successfully applied to nanophotonically-tuned color vacancy centers. Potential applications span optical communications, quantum information processing, and biomedical sensing.

Functionalization of Platinum Complexes for Biomedical Applications.

PubMed

Wang, Xiaoyong; Wang, Xiaohui; Guo, Zijian

2015-09-15

Platinum-based anticancer drugs are the mainstay of chemotherapy regimens in clinic. Nevertheless, the efficacy of platinum drugs is badly affected by serious systemic toxicities and drug resistance, and the pharmacokinetics of most platinum drugs is largely unknown. In recent years, a keen interest in functionalizing platinum complexes with bioactive molecules, targeting groups, photosensitizers, fluorophores, or nanomaterials has been sparked among chemical and biomedical researchers. The motivation for functionalization comes from some of the following demands: to improve the tumor selectivity or minimize the systemic toxicity of the drugs, to enhance the cellular accumulation of the drugs, to overcome the tumor resistance to the drugs, to visualize the drug molecules in vitro or in vivo, to achieve a synergistic anticancer effect between different therapeutic modalities, or to add extra functionality to the drugs. In this Account, we present different strategies being used for functionalizing platinum complexes, including conjugation with bisphosphonates, peptides, receptor-specific ligands, polymers, nanoparticles, magnetic resonance imaging contrast agents, metal chelators, or photosensitizers. Among them, bisphosphonates, peptides, and receptor-specific ligands are used for actively targeted drug delivery, polymers and nanoparticles are for passively targeted drug delivery, magnetic resonance imaging contrast agents are for theranostic purposes, metal chelators are for the treatment or prevention of Alzheimer's disease (AD), and photosensitizers are for photodynamic therapy of cancers. The rationales behind these designs are explained and justified at the molecular or cellular level, associating with the requirements for diagnosis, therapy, and visualization of biological processes. To illustrate the wide range of opportunities and challenges that are emerging in this realm, representative examples of targeted drug delivery systems, anticancer conjugates, anticancer theranostic agents, and anti-AD compounds relevant to functionalized platinum complexes are provided. All the examples exhibit new potential of platinum complexes for future applications in biomedical areas. The emphases of this Account are placed on the functionalization for targeted drug delivery and theranostic agents. In the end, a general assessment of various strategies has been made according to their major shortcomings and defects. The original information in this Account comes entirely from literature appearing since 2010.

Biomedical systems analysis program

NASA Technical Reports Server (NTRS)

1979-01-01

Biomedical monitoring programs which were developed to provide a system analysis context for a unified hypothesis for adaptation to space flight are presented and discussed. A real-time system of data analysis and decision making to assure the greatest possible crew safety and mission success is described. Information about man's abilities, limitations, and characteristic reactions to weightless space flight was analyzed and simulation models were developed. The predictive capabilities of simulation models for fluid-electrolyte regulation, erythropoiesis regulation, and calcium regulation are discussed.

Biomedical technology prosperity game{trademark}

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berman, M.; Boyack, K.W.; Wesenberg, D.L.

1996-07-01

Prosperity Games{trademark} are an outgrowth and adaptation of move/countermove and seminar War Games. Prosperity Games{trademark} are simulations that explore complex issues in a variety of areas including economics, politics, sociology, environment, education and research. These issues can be examined from a variety of perspectives ranging from a global, macroeconomic and geopolitical viewpoint down to the details of customer/supplier/market interactions in specific industries. All Prosperity Games{trademark} are unique in that both the game format and the player contributions vary from game to game. This report documents the Biomedical Technology Prosperity Game{trademark} conducted under the sponsorship of Sandia National Laboratories, the Defensemore » Advanced Research Projects Agency, and the Koop Foundation, Inc. Players were drawn from all stakeholders involved in biomedical technologies including patients, hospitals, doctors, insurance companies, legislators, suppliers/manufacturers, regulators, funding organizations, universities/laboratories, and the legal profession. The primary objectives of this game were to: (1) Identify advanced/critical technology issues that affect the cost and quality of health care. (2) Explore the development, patenting, manufacturing and licensing of needed technologies that would decrease costs while maintaining or improving quality. (3) Identify policy and regulatory changes that would reduce costs and improve quality and timeliness of health care delivery. (4) Identify and apply existing resources and facilities to develop and implement improved technologies and policies. (5) Begin to develop Biomedical Technology Roadmaps for industry and government cooperation. The deliberations and recommendations of these players provided valuable insights as to the views of this diverse group of decision makers concerning biomedical issues. Significant progress was made in the roadmapping of key areas in the biomedical technology field.« less

Therapy or human right? The meaning of recreation for children and youth with disabilities in the "Krembo Wings" youth movement.

PubMed

Soffer, Michal; Almog-Bar, Michal

2016-07-01

Research shows that leisure or recreation promotes health, quality of life and wellbeing. Participation in leisure is also a fundamental right of people with disabilities. Studies report disparities in leisure participation between children and youth with and without disabilities. Youth movements are a form of leisure activity, and are of particular importance in Israeli society. In this study we set out to explore how the youth movement Krembo Wings (KW) outlines the meanings of recreation for children and youth with disabilities. Our theoretical framework centers on the critical perspective of a disability study committed to disability rights. We conducted a qualitative study of KW. Data were drawn from multiple sources: published and unpublished documents, website materials, and semi-structured interviews with various key people in the movement. Data were analyzed through directed content analysis and were categorized into either the biomedical model or the social model of disability. Most of our findings show that KW adopts a biomedical understanding of disability. Nonetheless, indicators of the social model, though few, were also evident. Although the biomedical model was found to be dominant in Israel, there are promising indicators of change. Our somewhat mixed findings might suggest that KW is at a transitional phase between biomedical thinking and a more rights-based approach. Copyright © 2016 Elsevier Inc. All rights reserved.

Rapid production of hollow SS316 profiles by extrusion based additive manufacturing

NASA Astrophysics Data System (ADS)

Rane, Kedarnath; Cataldo, Salvatore; Parenti, Paolo; Sbaglia, Luca; Mussi, Valerio; Annoni, Massimiliano; Giberti, Hermes; Strano, Matteo

2018-05-01

Complex shaped stainless steel tubes are often required for special purpose biomedical equipment. Nevertheless, traditional manufacturing technologies, such as extrusion, lack the ability to compete in a market of customized complex components because of associated expenses towards tooling and extrusion presses. To rapid manufacture few of such components with low cost and high precision, a new Extrusion based Additive Manufacturing (EAM) process, is proposed in this paper, and as an example, short stainless steel 316L complex shaped and sectioned tubes were prepared by EAM. Several sample parts were produced using this process; the dimensional stability, surface roughness and chemical composition of sintered samples were investigated to prove process competence. The results indicate that feedstock with a 316L particle content of 92.5 wt. % can be prepared with a sigma blade mixing, whose rheological behavior is fit for EAM. The green samples have sufficient strength to handle them for subsequent treatments. The sintered samples considerably shrunk to designed dimensions and have a homogeneous microstructure to impart mechanical strength. Whereas, maintaining comparable dimensional accuracy and chemical composition which are required for biomedical equipment still need iterations, a kinematic correction and modification in debinding cycle was proposed.

I'll take that to go: Big data bags and minimal identifiers for exchange of large, complex datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chard, Kyle; D'Arcy, Mike; Heavner, Benjamin D.

Big data workflows often require the assembly and exchange of complex, multi-element datasets. For example, in biomedical applications, the input to an analytic pipeline can be a dataset consisting thousands of images and genome sequences assembled from diverse repositories, requiring a description of the contents of the dataset in a concise and unambiguous form. Typical approaches to creating datasets for big data workflows assume that all data reside in a single location, requiring costly data marshaling and permitting errors of omission and commission because dataset members are not explicitly specified. We address these issues by proposing simple methods and toolsmore » for assembling, sharing, and analyzing large and complex datasets that scientists can easily integrate into their daily workflows. These tools combine a simple and robust method for describing data collections (BDBags), data descriptions (Research Objects), and simple persistent identifiers (Minids) to create a powerful ecosystem of tools and services for big data analysis and sharing. We present these tools and use biomedical case studies to illustrate their use for the rapid assembly, sharing, and analysis of large datasets.« less

In vitro platelet activation, aggregation and platelet-granulocyte complex formation induced by surface modified single-walled carbon nanotubes.

PubMed

Fent, János; Bihari, Péter; Vippola, Minnamari; Sarlin, Essi; Lakatos, Susan

2015-08-01

Surface modification of single-walled carbon nanotubes (SWCNTs) such as carboxylation, amidation, hydroxylation and pegylation is used to reduce the nanotube toxicity and render them more suitable for biomedical applications than their pristine counterparts. Toxicity can be manifested in platelet activation as it has been shown for SWCNTs. However, the effect of various surface modifications on the platelet activating potential of SWCNTs has not been tested yet. In vitro platelet activation (CD62P) as well as the platelet-granulocyte complex formation (CD15/CD41 double positivity) in human whole blood were measured by flow cytometry in the presence of 0.1mg/ml of pristine or various surface modified SWCNTs. The effect of various SWCNTs was tested by whole blood impedance aggregometry, too. All tested SWCNTs but the hydroxylated ones activate platelets and promote platelet-granulocyte complex formation in vitro. Carboxylated, pegylated and pristine SWCNTs induce whole blood aggregation as well. Although pegylation is preferred from biomedical point of view, among the samples tested by us pegylated SWCNTs induced far the most prominent activation and a well detectable aggregation of platelets in whole blood. Copyright © 2015 Elsevier Ltd. All rights reserved.

A novel biomedical image indexing and retrieval system via deep preference learning.

PubMed

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications. Copyright © 2018 Elsevier B.V. All rights reserved.

You'll be a clinician-scientist, my son.

PubMed

Smeesters, Pierre R

2015-11-04

Opinion-based commentary about the complex reality of being a clinician-scientist in today's modern biomedical environment. The essay uses the beautiful, but old, poem "If" from Rudyard Kipling to draw a parallel with the ambitions, dreams and limits of being a clinical-scientist today.

Integrating Epigenomics into the Understanding of Biomedical Insight.

PubMed

Han, Yixing; He, Ximiao

2016-01-01

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics.

Integrating Epigenomics into the Understanding of Biomedical Insight

PubMed Central

Han, Yixing; He, Ximiao

2016-01-01

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics. PMID:27980397

A general path for large-scale solubilization of cellular proteins: From membrane receptors to multiprotein complexes

PubMed Central

Pullara, Filippo; Guerrero-Santoro, Jennifer; Calero, Monica; Zhang, Qiangmin; Peng, Ye; Spåhr, Henrik; Kornberg, Guy L.; Cusimano, Antonella; Stevenson, Hilary P.; Santamaria-Suarez, Hugo; Reynolds, Shelley L.; Brown, Ian S.; Monga, Satdarshan P.S.; Van Houten, Bennett; Rapić-Otrin, Vesna; Calero, Guillermo; Levine, Arthur S.

2014-01-01

Expression of recombinant proteins in bacterial or eukaryotic systems often results in aggregation rendering them unavailable for biochemical or structural studies. Protein aggregation is a costly problem for biomedical research. It forces research laboratories and the biomedical industry to search for alternative, more soluble, non-human proteins and limits the number of potential “druggable” targets. In this study we present a highly reproducible protocol that introduces the systematic use of an extensive number of detergents to solubilize aggregated proteins expressed in bacterial and eukaryotic systems. We validate the usefulness of this protocol by solubilizing traditionally difficult human protein targets to milligram quantities and confirm their biological activity. We use this method to solubilize monomeric or multimeric components of multi-protein complexes and demonstrate its efficacy to reconstitute large cellular machines. This protocol works equally well on cytosolic, nuclear and membrane proteins and can be easily adapted to a high throughput format. PMID:23137940

Synthetic biology: programming cells for biomedical applications.

PubMed

Hörner, Maximilian; Reischmann, Nadine; Weber, Wilfried

2012-01-01

The emerging field of synthetic biology is a novel biological discipline at the interface between traditional biology, chemistry, and engineering sciences. Synthetic biology aims at the rational design of complex synthetic biological devices and systems with desired properties by combining compatible, modular biological parts in a systematic manner. While the first engineered systems were mainly proof-of-principle studies to demonstrate the power of the modular engineering approach of synthetic biology, subsequent systems focus on applications in the health, environmental, and energy sectors. This review describes recent approaches for biomedical applications that were developed along the synthetic biology design hierarchy, at the level of individual parts, of devices, and of complex multicellular systems. It describes how synthetic biological parts can be used for the synthesis of drug-delivery tools, how synthetic biological devices can facilitate the discovery of novel drugs, and how multicellular synthetic ecosystems can give insight into population dynamics of parasites and hosts. These examples demonstrate how this new discipline could contribute to novel solutions in the biopharmaceutical industry.

Efficient solvers for coupled models in respiratory mechanics.

PubMed

Verdugo, Francesc; Roth, Christian J; Yoshihara, Lena; Wall, Wolfgang A

2017-02-01

We present efficient preconditioners for one of the most physiologically relevant pulmonary models currently available. Our underlying motivation is to enable the efficient simulation of such a lung model on high-performance computing platforms in order to assess mechanical ventilation strategies and contributing to design more protective patient-specific ventilation treatments. The system of linear equations to be solved using the proposed preconditioners is essentially the monolithic system arising in fluid-structure interaction (FSI) extended by additional algebraic constraints. The introduction of these constraints leads to a saddle point problem that cannot be solved with usual FSI preconditioners available in the literature. The key ingredient in this work is to use the idea of the semi-implicit method for pressure-linked equations (SIMPLE) for getting rid of the saddle point structure, resulting in a standard FSI problem that can be treated with available techniques. The numerical examples show that the resulting preconditioners approach the optimal performance of multigrid methods, even though the lung model is a complex multiphysics problem. Moreover, the preconditioners are robust enough to deal with physiologically relevant simulations involving complex real-world patient-specific lung geometries. The same approach is applicable to other challenging biomedical applications where coupling between flow and tissue deformations is modeled with additional algebraic constraints. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Dynamics of blood flow in a microfluidic ladder network

NASA Astrophysics Data System (ADS)

Maddala, Jeevan; Zilberman-Rudenko, Jevgenia; McCarty, Owen

The dynamics of a complex mixture of cells and proteins, such as blood, in perturbed shear flow remains ill-defined. Microfluidics is a promising technology for improving the understanding of blood flow under complex conditions of shear; as found in stent implants and in tortuous blood vessels. We model the fluid dynamics of blood flow in a microfluidic ladder network with dimensions mimicking venules. Interaction of blood cells was modeled using multiagent framework, where cells of different diameters were treated as spheres. This model served as the basis for predicting transition regions, collision pathways, re-circulation zones and residence times of cells dependent on their diameters and device architecture. Based on these insights from the model, we were able to predict the clot formation configurations at various locations in the device. These predictions were supported by the experiments using whole blood. To facilitate platelet aggregation, the devices were coated with fibrillar collagen and tissue factor. Blood was perfused through the microfluidic device for 9 min at a physiologically relevant venous shear rate of 600 s-1. Using fluorescent microscopy, we observed flow transitions near the channel intersections and at the areas of blood flow obstruction, which promoted larger thrombus formation. This study of integrating model predictions with experimental design, aids in defining the dynamics of blood flow in microvasculature and in development of novel biomedical devices.

Testing an integrated behavioural and biomedical model of disability in N-of-1 studies with chronic pain.

PubMed

Quinn, Francis; Johnston, Marie; Johnston, Derek W

2013-01-01

Previous research has supported an integrated biomedical and behavioural model explaining activity limitations. However, further tests of this model are required at the within-person level, because while it proposes that the constructs are related within individuals, it has primarily been tested between individuals in large group studies. We aimed to test the integrated model at the within-person level. Six correlational N-of-1 studies in participants with arthritis, chronic pain and walking limitations were carried out. Daily measures of theoretical constructs were collected using a hand-held computer (PDA), the activity was assessed by self-report and accelerometer and the data were analysed using time-series analysis. The biomedical model was not supported as pain impairment did not predict activity, so the integrated model was supported partially. Impairment predicted intention to move around, while perceived behavioural control (PBC) and intention predicted activity. PBC did not predict activity limitation in the expected direction. The integrated model of disability was partially supported within individuals, especially the behavioural elements. However, results suggest that different elements of the model may drive activity (limitations) for different individuals. The integrated model provides a useful framework for understanding disability and suggests interventions, and the utility of N-of-1 methodology for testing theory is illustrated.

Modelling of Argon Cold Atmospheric Plasmas for Biomedical Applications

NASA Astrophysics Data System (ADS)

Atanasova, M.; Benova, E.; Degrez, G.; van der Mullen, J. A. M.

2018-02-01

Plasmas for biomedical applications are one of the newest fields of plasma utilization. Especially high is the interest toward plasma usage in medicine. Promising results are achieved in blood coagulation, wound healing, treatment of some forms of cancer, diabetic complications, etc. However, the investigations of the biomedical applications from biological and medical viewpoint are much more advanced than the studies on the dynamics of the plasma. In this work we aim to address some specific challenges in the field of plasma modelling, arising from biomedical applications - what are the plasma reactive species’ and electrical fields’ spatial distributions as well as their production mechanisms; what are the fluxes and energies of the various components of the plasma delivers to the treated surfaces; what is the gas flow pattern? The focus is on two devices, namely the capacitive coupled plasma jet and the microwave surface wave sustained discharge. The devices are representatives of the so called cold atmospheric plasmas (CAPs). These are discharges characterized by low gas temperature - less than 40°C at the point of application - and non-equilibrium chemistry.

Research evaluation support services in biomedical libraries

PubMed Central

Gutzman, Karen Elizabeth; Bales, Michael E.; Belter, Christopher W.; Chambers, Thane; Chan, Liza; Holmes, Kristi L.; Lu, Ya-Ling; Palmer, Lisa A.; Reznik-Zellen, Rebecca C.; Sarli, Cathy C.; Suiter, Amy M.; Wheeler, Terrie R.

2018-01-01

Objective The paper provides a review of current practices related to evaluation support services reported by seven biomedical and research libraries. Methods A group of seven libraries from the United States and Canada described their experiences with establishing evaluation support services at their libraries. A questionnaire was distributed among the libraries to elicit information as to program development, service and staffing models, campus partnerships, training, products such as tools and reports, and resources used for evaluation support services. The libraries also reported interesting projects, lessons learned, and future plans. Results The seven libraries profiled in this paper report a variety of service models in providing evaluation support services to meet the needs of campus stakeholders. The service models range from research center cores, partnerships with research groups, and library programs with staff dedicated to evaluation support services. A variety of products and services were described such as an automated tool to develop rank-based metrics, consultation on appropriate metrics to use for evaluation, customized publication and citation reports, resource guides, classes and training, and others. Implementing these services has allowed the libraries to expand their roles on campus and to contribute more directly to the research missions of their institutions. Conclusions Libraries can leverage a variety of evaluation support services as an opportunity to successfully meet an array of challenges confronting the biomedical research community, including robust efforts to report and demonstrate tangible and meaningful outcomes of biomedical research and clinical care. These services represent a transformative direction that can be emulated by other biomedical and research libraries. PMID:29339930

Facilitating biomedical researchers' interrogation of electronic health record data: Ideas from outside of biomedical informatics.

PubMed

Hruby, Gregory W; Matsoukas, Konstantina; Cimino, James J; Weng, Chunhua

2016-04-01

Electronic health records (EHR) are a vital data resource for research uses, including cohort identification, phenotyping, pharmacovigilance, and public health surveillance. To realize the promise of EHR data for accelerating clinical research, it is imperative to enable efficient and autonomous EHR data interrogation by end users such as biomedical researchers. This paper surveys state-of-art approaches and key methodological considerations to this purpose. We adapted a previously published conceptual framework for interactive information retrieval, which defines three entities: user, channel, and source, by elaborating on channels for query formulation in the context of facilitating end users to interrogate EHR data. We show the current progress in biomedical informatics mainly lies in support for query execution and information modeling, primarily due to emphases on infrastructure development for data integration and data access via self-service query tools, but has neglected user support needed during iteratively query formulation processes, which can be costly and error-prone. In contrast, the information science literature has offered elaborate theories and methods for user modeling and query formulation support. The two bodies of literature are complementary, implying opportunities for cross-disciplinary idea exchange. On this basis, we outline the directions for future informatics research to improve our understanding of user needs and requirements for facilitating autonomous interrogation of EHR data by biomedical researchers. We suggest that cross-disciplinary translational research between biomedical informatics and information science can benefit our research in facilitating efficient data access in life sciences. Copyright © 2016 Elsevier Inc. All rights reserved.

Using uncertainty to link and rank evidence from biomedical literature for model curation

PubMed Central

Zerva, Chrysoula; Batista-Navarro, Riza; Day, Philip; Ananiadou, Sophia

2017-01-01

Abstract Motivation In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models. Results We present a novel method for extracting uncertainty from the literature using a hybrid approach that combines rule induction and machine learning. Variations of this hybrid approach are then discussed, alongside their advantages and disadvantages. We use subjective logic theory to combine multiple uncertainty values extracted from different sources for the same interaction. Our approach achieves F-scores of 0.76 and 0.88 based on the BioNLP-ST and Genia-MK corpora, respectively, making considerable improvements over previously published work. Moreover, we evaluate our proposed system on pathways related to two different areas, namely leukemia and melanoma cancer research. Availability and implementation The leukemia pathway model used is available in Pathway Studio while the Ras model is available via PathwayCommons. Online demonstration of the uncertainty extraction system is available for research purposes at http://argo.nactem.ac.uk/test. The related code is available on https://github.com/c-zrv/uncertainty_components.git. Details on the above are available in the Supplementary Material. Contact sophia.ananiadou@manchester.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:29036627

Using uncertainty to link and rank evidence from biomedical literature for model curation.

PubMed

Zerva, Chrysoula; Batista-Navarro, Riza; Day, Philip; Ananiadou, Sophia

2017-12-01

In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models. We present a novel method for extracting uncertainty from the literature using a hybrid approach that combines rule induction and machine learning. Variations of this hybrid approach are then discussed, alongside their advantages and disadvantages. We use subjective logic theory to combine multiple uncertainty values extracted from different sources for the same interaction. Our approach achieves F-scores of 0.76 and 0.88 based on the BioNLP-ST and Genia-MK corpora, respectively, making considerable improvements over previously published work. Moreover, we evaluate our proposed system on pathways related to two different areas, namely leukemia and melanoma cancer research. The leukemia pathway model used is available in Pathway Studio while the Ras model is available via PathwayCommons. Online demonstration of the uncertainty extraction system is available for research purposes at http://argo.nactem.ac.uk/test. The related code is available on https://github.com/c-zrv/uncertainty_components.git. Details on the above are available in the Supplementary Material. sophia.ananiadou@manchester.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

The Faculty Costs to Educate a Biomedical Sciences Graduate Student

PubMed Central

Smolka, Adam J.; Halushka, Perry V.; Garrett-Mayer, Elizabeth

2015-01-01

Academic medical centers nationwide face numerous fiscal challenges resulting from implementation of restructured healthcare delivery models, contracting state support for higher education, and increased competition for federal and other sources of biomedical research funding. In pursuing greater accountability and transparency in its fiscal operations, the Medical University of South Carolina (MUSC) has implemented a responsibility centers management budgetary model, which requires all MUSC colleges to be eventually self-sustaining financially. Graduate schools in the biomedical sciences are particularly vulnerable in the face of these challenges, depending traditionally as they do on financial support from training grant tuition, occasional medical school tuition and medical practice plan revenues, graduate college–based revenue-generating programs, and faculty payment of PhD tuition. The revenue streams are often insufficient to support PhD training programs, and supplemental financial support is required from the institution. In the context of a college of graduate studies, estimates of the cost of educating a graduate student become a significant necessity. This study presents a readily applicable model of empirically estimating the faculty salary costs that may provide a basis for budgetary planning that will help to sustain a biomedical sciences graduate school’s commitment to its teaching, research, and service mission goals. PMID:25673355

National Heart, Lung, and Blood Institute Workshop Summary: Enhancing Opportunities for Training and Retention of a Diverse Biomedical Workforce.

PubMed

Duncan, Gregg A; Lockett, Angelia; Villegas, Leah R; Almodovar, Sharilyn; Gomez, Jose L; Flores, Sonia C; Wilkes, David S; Tigno, Xenia T

2016-04-01

Committed to its mission of conducting and supporting research that addresses the health needs of all sectors of the nation's population, the Division of Lung Diseases, National Heart, Lung, and Blood Institute of the National Institutes of Health (NHLBI/NIH) seeks to identify issues that impact the training and retention of underrepresented individuals in the biomedical research workforce. Early-stage investigators who received grant support through the NIH Research Supplements to Promote Diversity in Health Related Research Program were invited to a workshop held in Bethesda, Maryland in June, 2015, in order to (1) assess the effectiveness of the current NHLBI diversity program, (2) improve its strategies towards achieving its goal, and (3) provide guidance to assist the transition of diversity supplement recipients to independent NIH grant support. Workshop participants participated in five independent focus groups to discuss specific topics affecting underrepresented individuals in the biomedical sciences: (1) Socioeconomic barriers to success for diverse research scientists; (2) role of the academic research community in promoting diversity; (3) life beyond a research project grant: non-primary investigator career paths in research; (4) facilitating career development of diverse independent research scientists through NHLBI diversity programs; and (5) effectiveness of current NHLBI programs for promoting diversity of the biomedical workforce. Several key issues experienced by young, underrepresented biomedical scientists were identified, and solutions were proposed to improve on training and career development for diverse students, from the high school to postdoctoral trainee level, and address limitations of currently available diversity programs. Although some of the challenges mentioned, such as cost of living, limited parental leave, and insecure extramural funding, are also likely faced by nonminority scientists, these issues are magnified among diversity scientists and are complicated by unique circumstances in this group, such as limited exposure to science at a young age, absence of role models and mentors from underrepresented backgrounds, and social norms that relegate their career endeavors, particularly among women, to being subordinate to their expected cultural role. The factors influencing the participation of underrepresented minorities in the biomedical workforce are complex and span several continuous or overlapping stages in the professional development of scientists from these groups. Therefore, a multipronged approach is needed to enable the professional development and retention of underrepresented minorities in biomedical research. This approach should address both individual and social factors and should involve funding agencies, academic institutions, mentoring teams, professional societies, and peer collaboration. Implementation of some of the recommendations, such as access to child care, institutional support and health benefits for trainees, teaching and entrepreneurial opportunities, grant-writing webinars, and pre-NIH career development (Pre-K) pilot programs would not only benefit biomedical scientists from underrepresented groups but also improve the situation of nondiverse junior scientists. However, other issues, such as opportunities for early exposure to science of disadvantaged/minority groups, and identifying mentors/life coaches/peer mentors who come from similar cultural backgrounds and vantage points, are unique to this group.

National Heart, Lung, and Blood Institute Workshop Summary: Enhancing Opportunities for Training and Retention of a Diverse Biomedical Workforce

PubMed Central

Duncan, Gregg A.; Lockett, Angelia; Villegas, Leah R.; Almodovar, Sharilyn; Gomez, Jose L.; Flores, Sonia C.; Tigno, Xenia T.

2016-01-01

Rationale: Committed to its mission of conducting and supporting research that addresses the health needs of all sectors of the nation's population, the Division of Lung Diseases, National Heart, Lung, and Blood Institute of the National Institutes of Health (NHLBI/NIH) seeks to identify issues that impact the training and retention of underrepresented individuals in the biomedical research workforce. Objectives: Early-stage investigators who received grant support through the NIH Research Supplements to Promote Diversity in Health Related Research Program were invited to a workshop held in Bethesda, Maryland in June, 2015, in order to (1) assess the effectiveness of the current NHLBI diversity program, (2) improve its strategies towards achieving its goal, and (3) provide guidance to assist the transition of diversity supplement recipients to independent NIH grant support. Methods: Workshop participants participated in five independent focus groups to discuss specific topics affecting underrepresented individuals in the biomedical sciences: (1) Socioeconomic barriers to success for diverse research scientists; (2) role of the academic research community in promoting diversity; (3) life beyond a research project grant: non–primary investigator career paths in research; (4) facilitating career development of diverse independent research scientists through NHLBI diversity programs; and (5) effectiveness of current NHLBI programs for promoting diversity of the biomedical workforce. Measurements and Main Results: Several key issues experienced by young, underrepresented biomedical scientists were identified, and solutions were proposed to improve on training and career development for diverse students, from the high school to postdoctoral trainee level, and address limitations of currently available diversity programs. Although some of the challenges mentioned, such as cost of living, limited parental leave, and insecure extramural funding, are also likely faced by nonminority scientists, these issues are magnified among diversity scientists and are complicated by unique circumstances in this group, such as limited exposure to science at a young age, absence of role models and mentors from underrepresented backgrounds, and social norms that relegate their career endeavors, particularly among women, to being subordinate to their expected cultural role. Conclusions: The factors influencing the participation of underrepresented minorities in the biomedical workforce are complex and span several continuous or overlapping stages in the professional development of scientists from these groups. Therefore, a multipronged approach is needed to enable the professional development and retention of underrepresented minorities in biomedical research. This approach should address both individual and social factors and should involve funding agencies, academic institutions, mentoring teams, professional societies, and peer collaboration. Implementation of some of the recommendations, such as access to child care, institutional support and health benefits for trainees, teaching and entrepreneurial opportunities, grant-writing webinars, and pre-NIH career development (Pre-K) pilot programs would not only benefit biomedical scientists from underrepresented groups but also improve the situation of nondiverse junior scientists. However, other issues, such as opportunities for early exposure to science of disadvantaged/minority groups, and identifying mentors/life coaches/peer mentors who come from similar cultural backgrounds and vantage points, are unique to this group. PMID:27058184

Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network.

PubMed

Ceylan, Murat; Ceylan, Rahime; Ozbay, Yüksel; Kara, Sadik

2008-09-01

In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 males and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (lower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 males and 12 females (mean age, 23 years; range, 19-27 years). Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim.

A simplified constitutive model for predicting shape memory polymers deformation behavior

NASA Astrophysics Data System (ADS)

Li, Yunxin; Guo, Siu-Siu; He, Yuhao; Liu, Zishun

2015-12-01

Shape memory polymers (SMPs) can keep a temporary shape after pre-deformation at a higher temperature and subsequent cooling. When they are reheated, their original shapes can be recovered. Such special characteristics of SMPs make them widely used in aerospace structures, biomedical devices, functional textiles and other devices. Increasing usefulness of SMPs motivates us to further understand their thermomechanical properties and deformation behavior, of which the development of appropriate constitutive models for SMPs is imperative. There is much work in literatures that address constitutive models of the thermo-mechanical coupling in SMPs. However, due to their complex forms, it is difficult to apply these constitutive models in the real world. In this paper, a three-element model with simple form is proposed to investigate the thermo-mechanical small strain (within 10%) behavior of polyurethane under uniaxial tension. Two different cases of heated recovery are considered: (1) unconstrained free strain recovery and (2) stress recovery under full constraint at a strain level fixed during low temperature unloading. To validate the model, simulated and predicted results are compared with Tobushi's experimental results and good agreement can be observed.

Optimal Management of the Critically Ill: Anaesthesia, Monitoring, Data Capture, and Point-of-Care Technological Practices in Ovine Models of Critical Care

PubMed Central

Shekar, Kiran; Tung, John-Paul; Dunster, Kimble R.; Platts, David; Watts, Ryan P.; Gregory, Shaun D.; Simonova, Gabriela; McDonald, Charles; Hayes, Rylan; Bellpart, Judith; Timms, Daniel; Fung, Yoke L.; Toon, Michael; Maybauer, Marc O.; Fraser, John F.

2014-01-01

Animal models of critical illness are vital in biomedical research. They provide possibilities for the investigation of pathophysiological processes that may not otherwise be possible in humans. In order to be clinically applicable, the model should simulate the critical care situation realistically, including anaesthesia, monitoring, sampling, utilising appropriate personnel skill mix, and therapeutic interventions. There are limited data documenting the constitution of ideal technologically advanced large animal critical care practices and all the processes of the animal model. In this paper, we describe the procedure of animal preparation, anaesthesia induction and maintenance, physiologic monitoring, data capture, point-of-care technology, and animal aftercare that has been successfully used to study several novel ovine models of critical illness. The relevant investigations are on respiratory failure due to smoke inhalation, transfusion related acute lung injury, endotoxin-induced proteogenomic alterations, haemorrhagic shock, septic shock, brain death, cerebral microcirculation, and artificial heart studies. We have demonstrated the functionality of monitoring practices during anaesthesia required to provide a platform for undertaking systematic investigations in complex ovine models of critical illness. PMID:24783206

Single-molecule FRET-Rosetta reveals RNA structural rearrangements during human telomerase catalysis

PubMed Central

Parks, Joseph W.; Kappel, Kalli; Das, Rhiju; Stone, Michael D.

2017-01-01

Maintenance of telomeres by telomerase permits continuous proliferation of rapidly dividing cells, including the majority of human cancers. Despite its direct biomedical significance, the architecture of the human telomerase complex remains unknown. Generating homogeneous telomerase samples has presented a significant barrier to developing improved structural models. Here we pair single-molecule Förster resonance energy transfer (smFRET) measurements with Rosetta modeling to map the conformations of the essential telomerase RNA core domain within the active ribonucleoprotein. FRET-guided modeling places the essential pseudoknot fold distal to the active site on a protein surface comprising the C-terminal element, a domain that shares structural homology with canonical polymerase thumb domains. An independently solved medium-resolution structure of Tetrahymena telomerase provides a blind test of our modeling methodology and sheds light on the structural homology of this domain across diverse organisms. Our smFRET-Rosetta models reveal nanometer-scale rearrangements within the RNA core domain during catalysis. Taken together, our FRET data and pseudoatomic molecular models permit us to propose a possible mechanism for how RNA core domain rearrangement is coupled to template hybrid elongation. PMID:28096444

Communicating with Investigators about Financial Compensation for Statistical Collaboration

ERIC Educational Resources Information Center

Ittenbach, Richard F.; DeAngelis, Francis W.; Altaye, Mekibib

2013-01-01

Communicating with investigators about financial compensation in the area of statistical collaboration represents an important but often underemphasized component of biomedical research. The more complex the area, the greater the need for sound and effective communication strategies. Ittenbach and DeAngelis (2012) recently compared two…

Leveraging biomedical ontologies and annotation services to organize microbiome data from Mammalian hosts.

PubMed

Sarkar, Indra Neil

2010-11-13

A better understanding of commensal microbiotic communities ("microbiomes") may provide valuable insights to human health. Towards this goal, an essential step may be the development of approaches to organize data that can enable comparative hypotheses across mammalian microbiomes. The present study explores the feasibility of using existing biomedical informatics resources - especially focusing on those available at the National Center for Biomedical Ontology - to organize microbiome data contained within large sequence repositories, such as GenBank. The results indicate that the Foundational Model of Anatomy and SNOMED CT can be used to organize greater than 90% of the bacterial organisms associated with 10 domesticated mammalian species. The promising findings suggest that the current biomedical informatics infrastructure may be used towards the organizing of microbiome data beyond humans. Furthermore, the results identify key concepts that might be organized into a semantic structure for incorporation into subsequent annotations that could facilitate comparative biomedical hypotheses pertaining to human health.

Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization.

PubMed

Qiu, Jiaheng; Chen, Ray-Bing; Wang, Weichung; Wong, Weng Kee

2014-10-01

Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.

Biomedical effects of low-power laser controlled by electroacupuncture

NASA Astrophysics Data System (ADS)

Kalenchits, Nadezhda I.; Nicolaenko, Andrej A.; Shpilevoj, Boris N.

1997-12-01

The methods and technical facilities of testing the biomedical effects caused by the influence of low-power laser radiation in the process of laser therapy are presented. Described studies have been conducted by means of the complex of fireware facilities consisting of the system of electroacupuncture diagnostics (EA) and a system of laser therapy on the basis of multichannel laser and magneto-laser devices. The task of laser therapy was concluded in undertaking acupuncture anaesthetization, achievement of antioedemic and dispersional actions, raising tone of musculus and nervous system, normalization of immunity factors under the control of system EA. The 82 percent to 95 percent agreement of the result of an electroacupuncture diagnostics with clinical diagnoses were achieved.

BioTCM-SE: a semantic search engine for the information retrieval of modern biology and traditional Chinese medicine.

PubMed

Chen, Xi; Chen, Huajun; Bi, Xuan; Gu, Peiqin; Chen, Jiaoyan; Wu, Zhaohui

2014-01-01

Understanding the functional mechanisms of the complex biological system as a whole is drawing more and more attention in global health care management. Traditional Chinese Medicine (TCM), essentially different from Western Medicine (WM), is gaining increasing attention due to its emphasis on individual wellness and natural herbal medicine, which satisfies the goal of integrative medicine. However, with the explosive growth of biomedical data on the Web, biomedical researchers are now confronted with the problem of large-scale data analysis and data query. Besides that, biomedical data also has a wide coverage which usually comes from multiple heterogeneous data sources and has different taxonomies, making it hard to integrate and query the big biomedical data. Embedded with domain knowledge from different disciplines all regarding human biological systems, the heterogeneous data repositories are implicitly connected by human expert knowledge. Traditional search engines cannot provide accurate and comprehensive search results for the semantically associated knowledge since they only support keywords-based searches. In this paper, we present BioTCM-SE, a semantic search engine for the information retrieval of modern biology and TCM, which provides biologists with a comprehensive and accurate associated knowledge query platform to greatly facilitate the implicit knowledge discovery between WM and TCM.

BioTCM-SE: A Semantic Search Engine for the Information Retrieval of Modern Biology and Traditional Chinese Medicine

PubMed Central

Chen, Xi; Chen, Huajun; Bi, Xuan; Gu, Peiqin; Chen, Jiaoyan; Wu, Zhaohui

2014-01-01

Understanding the functional mechanisms of the complex biological system as a whole is drawing more and more attention in global health care management. Traditional Chinese Medicine (TCM), essentially different from Western Medicine (WM), is gaining increasing attention due to its emphasis on individual wellness and natural herbal medicine, which satisfies the goal of integrative medicine. However, with the explosive growth of biomedical data on the Web, biomedical researchers are now confronted with the problem of large-scale data analysis and data query. Besides that, biomedical data also has a wide coverage which usually comes from multiple heterogeneous data sources and has different taxonomies, making it hard to integrate and query the big biomedical data. Embedded with domain knowledge from different disciplines all regarding human biological systems, the heterogeneous data repositories are implicitly connected by human expert knowledge. Traditional search engines cannot provide accurate and comprehensive search results for the semantically associated knowledge since they only support keywords-based searches. In this paper, we present BioTCM-SE, a semantic search engine for the information retrieval of modern biology and TCM, which provides biologists with a comprehensive and accurate associated knowledge query platform to greatly facilitate the implicit knowledge discovery between WM and TCM. PMID:24772189

Management and analysis of genomic functional and phenotypic controlled annotations to support biomedical investigation and practice.

PubMed

Masseroli, Marco

2007-07-01

The growing available genomic information provides new opportunities for novel research approaches and original biomedical applications that can provide effective data management and analysis support. In fact, integration and comprehensive evaluation of available controlled data can highlight information patterns leading to unveil new biomedical knowledge. Here, we describe Genome Function INtegrated Discover (GFINDer), a Web-accessible three-tier multidatabase system we developed to automatically enrich lists of user-classified genes with several functional and phenotypic controlled annotations, and to statistically evaluate them in order to identify annotation categories significantly over- or underrepresented in each considered gene class. Genomic controlled annotations from Gene Ontology (GO), KEGG, Pfam, InterPro, and Online Mendelian Inheritance in Man (OMIM) were integrated in GFINDer and several categorical tests were implemented for their analysis. A controlled vocabulary of inherited disorder phenotypes was obtained by normalizing and hierarchically structuring disease accompanying signs and symptoms from OMIM Clinical Synopsis sections. GFINDer modular architecture is well suited for further system expansion and for sustaining increasing workload. Testing results showed that GFINDer analyses can highlight gene functional and phenotypic characteristics and differences, demonstrating its value in supporting genomic biomedical approaches aiming at understanding the complex biomolecular mechanisms underlying patho-physiological phenotypes, and in helping the transfer of genomic results to medical practice.

Approaches to the development of biomedical support systems for piloted exploration missions

NASA Astrophysics Data System (ADS)

Grigoriev, A. I.; Potapov, A. N.

2014-01-01

Many aspects of the biomedical systems developed and realized aboard orbital stations, the International space station in the first place, deserve to be regarded as predecessors of the systems for health monitoring and maintenance of future exploration crews. At the same time, there are issues and tasks which have not been yet fully resolved. Specifically, these are prevention of the adverse changes in body systems and organs due to microgravity, reliable protection from the spectrum of space radiation, and elucidation of possible effects of hypomagnetic environment. We should not walk away from search and development of key biomedical technologies such as a system of automated fitness evaluation and a psychodiagnostic complex for testing and optimization of operator‧s efficiency, and others. We have to address a large number of issues related to designing the composite life support systems of the utmost autonomy, closure and ecological safety of the human environment that will provide transformation of all kinds of waste. Another crucial task is to define a concept of the onboard medical center and dataware including the telemedicine technology. All the above developments should assimilate the most recent achievements in physiology, molecular biology, genetics, and advanced medical technologies. Biomedical researches on biosatellites also do not lose topicality.

Scattering and Diffraction of Elastodynamic Waves in a Concentric Cylindrical Phantom for MR Elastography

PubMed Central

Schwartz, Benjamin L.; Yin, Ziying; Yaşar, Temel K.; Liu, Yifei; Khan, Altaf A.; Ye, Allen Q.; Royston, Thomas J.; Magin, Richard L.

2016-01-01

Aim The focus of this paper is to report on the design and construction of a multiply connected phantom for use in magnetic resonance elasography (MRE)–an imaging technique that allows for the non-invasive visualization of the displacement field throughout an object from externally driven harmonic motion–as well as its inverse modeling with a closed-form analytic solution which is derived herein from first principles. Methods Mathematically, the phantom is described as two infinite concentric circular cylinders with unequal complex shear moduli, harmonically vibrated at the exterior surface in a direction along their common axis. Each concentric cylinder is made of a hydrocolloid with its own specific solute concentration. They are assembled in a multi-step process for which custom scaffolding was designed and built. A customized spin-echo based MR elastography sequence with a sinusoidal motion-sensitizing gradient was used for data acquisition on a 9.4 T Agilent small-animal MR scanner. Complex moduli obtained from the inverse model are used to solve the forward problem with a finite element method. Results Both complex shear moduli show a significant frequency dependence (p < 0.001) in keeping with previous work. Conclusion The novel multiply connected phantom and mathematical model are validated as a viable tool for MRE studies. Significance On a small enough scale much of physiology can be mathematically modeled with basic geometric shapes, e.g. a cylinder representing a blood vessel. This work demonstrates the possibility of elegant mathematical analysis of phantoms specifically designed and carefully constructed for biomedical MRE studies. PMID:26886963

Supporting undergraduate biomedical entrepreneurship.

PubMed

Patterson, P E

2004-01-01

As biomedical innovations become more sophisticated and expensive to bring to market, an approach is needed to ensure the survival of the best ideas. The tactic used by Iowa State University to provide entrepreneurship opportunities for undergraduate students in biomedical areas is a model that has proven to be both distinctive and effective. Iowa State supports and fosters undergraduate student entrepreneurship efforts through the Pappajohn Center for Entrepreneurship. This unique partnership encourages ISU faculty, researchers, and students to become involved in the world of entrepreneurship, while allowing Iowa's business communities to gain access to a wide array of available resources, skills, and information from Iowa State University.

Adapt or Perish – Updating the Pre-doctoral Training Model

PubMed Central

Chabowski, Dawid; Kadlec, Andrew; Dellostritto, Daniel; Gutterman, David

2017-01-01

The fate of biomedical research lies in the hands of future generations of scientists. In recent decades the diversity of scientific career opportunities has exploded multidimensionally. However, the educational system for maintaining a pipeline of talented biomedical trainees remains unidimensional and has become outdated. This Viewpoint identifies inadequacies in training and offers potential solutions and implementation strategies to stimulate interest in science at a younger age and to better align individualized training pathways with career opportunities (“precision training”). Both interventions support of the ultimate goal of attracting the best possible future leaders in biomedical science. PMID:28360347

Inclusion of policies on ethical standards in animal experiments in biomedical science journals.

PubMed

Rands, Sean A

2011-11-01

Most published biomedical research involving animal models is evaluated carefully to ensure that appropriate ethical standards are met. In the current study, 500 journals randomly selected from MedLine were assessed for whether they presented animal research. Of the 138 journals that did, the instructions to authors of 85 (61.6%) included a requirement for author assurance of adherence to ethical standards during experiments involving animals. In comparison to a wider range of biologic journals, biomedical science journals were more likely to have some sort of ethical policy concerning the reporting and presentation of animal experiments.

A UML profile for the OBO relation ontology.

PubMed

Guardia, Gabriela D A; Vêncio, Ricardo Z N; de Farias, Cléver R G

2012-01-01

Ontologies have increasingly been used in the biomedical domain, which has prompted the emergence of different initiatives to facilitate their development and integration. The Open Biological and Biomedical Ontologies (OBO) Foundry consortium provides a repository of life-science ontologies, which are developed according to a set of shared principles. This consortium has developed an ontology called OBO Relation Ontology aiming at standardizing the different types of biological entity classes and associated relationships. Since ontologies are primarily intended to be used by humans, the use of graphical notations for ontology development facilitates the capture, comprehension and communication of knowledge between its users. However, OBO Foundry ontologies are captured and represented basically using text-based notations. The Unified Modeling Language (UML) provides a standard and widely-used graphical notation for modeling computer systems. UML provides a well-defined set of modeling elements, which can be extended using a built-in extension mechanism named Profile. Thus, this work aims at developing a UML profile for the OBO Relation Ontology to provide a domain-specific set of modeling elements that can be used to create standard UML-based ontologies in the biomedical domain.

Do medical models of mental illness relate to increased or decreased stigmatization of mental illness among orthodox Jews?

PubMed

Pirutinsky, Steven; Rosen, Daniel D; Shapiro Safran, Rachel; Rosmarin, David H

2010-07-01

Research suggests that attributing mental illness to moral causes and perceiving it as dangerous relates to greater stigma, whereas belief in biomedical factors is associated with less. Within the family-centric Orthodox Jewish community, mental illness is perceived as a risk to family functioning and future generations, and is therefore stigmatizing of the individual and their family. Since biomedical models may exacerbate these concerns, we hypothesized that unlike within the general population, biological causal attributions would relate to increased stigma among Orthodox Jews. Consequently, we also examined the attitudinal correlates of stigmatization of obsessive-compulsive disorder within the Orthodox community, as measured by both social distance and family/marriage concerns. Results indicated that, unlike previous research, biological models were associated with greater marriage/family stigma, and did not predict less social distance. This suggests that biomedical approaches may increase salient aspects of stigma within the Orthodox community, and clinical practice should be sensitive to these concerns.

Current Progress of Genetically Engineered Pig Models for Biomedical Research

PubMed Central

Gün, Gökhan

2014-01-01

Abstract The first transgenic pigs were generated for agricultural purposes about three decades ago. Since then, the micromanipulation techniques of pig oocytes and embryos expanded from pronuclear injection of foreign DNA to somatic cell nuclear transfer, intracytoplasmic sperm injection-mediated gene transfer, lentiviral transduction, and cytoplasmic injection. Mechanistically, the passive transgenesis approach based on random integration of foreign DNA was developed to active genetic engineering techniques based on the transient activity of ectopic enzymes, such as transposases, recombinases, and programmable nucleases. Whole-genome sequencing and annotation of advanced genome maps of the pig complemented these developments. The full implementation of these tools promises to immensely increase the efficiency and, in parallel, to reduce the costs for the generation of genetically engineered pigs. Today, the major application of genetically engineered pigs is found in the field of biomedical disease modeling. It is anticipated that genetically engineered pigs will increasingly be used in biomedical research, since this model shows several similarities to humans with regard to physiology, metabolism, genome organization, pathology, and aging. PMID:25469311

Judging The Efficacy of Anthrax Fumigations

DTIC Science & Technology

2003-11-20

FUMIGATIONS IN RESPONSE TO 2001 ANTHRAX ATTACKS Most fumigations modeled after biomedical sterilization processes, with established ranges for process...exposure to VHP All BIs recovered aseptically negative for growth of B. stearothermophilus Positive control BIs (5% of BIs) demonstrate growth Negative...of 10 zones was re-fumigated; second fumigation met all requirements HISTORICAL CRITERIA FOR SUCCESSFUL TREATMENT Biomedical sterilizations – FDA

NASA/NSF Antarctic Science Working Group

NASA Technical Reports Server (NTRS)

Stoklosa, Janis H.

1990-01-01

A collection of viewgraphs on NASA's Life Sciences Biomedical Programs is presented. They show the structure of the Life Sciences Division; the tentative space exploration schedule from the present to 2018; the biomedical programs with their objectives, research elements, and methodological approaches; validation models; proposed Antarctic research as an analog for space exploration; and the Science Working Group's schedule of events.

Recontextualizing and Delivering the Biomedical Model as a Physical Education Curriculum

ERIC Educational Resources Information Center

Johns, David P.

2005-01-01

This paper examines the problem of delivering a body of knowledge based on biomedical research as a school physical education discourse. The paper attempts to deconstruct the ideology of healthism upon which the discourse is based in order to show how ascetic practices in school physical education are promoted as a way of combating the hedonistic…

Endovascular Device Testing with Particle Image Velocimetry Enhances Undergraduate Biomedical Engineering Education

ERIC Educational Resources Information Center

Nair, Priya; Ankeny, Casey J.; Ryan, Justin; Okcay, Murat; Frakes, David H.

2016-01-01

We investigated the use of a new system, HemoFlow™, which utilizes state of the art technologies such as particle image velocimetry to test endovascular devices as part of an undergraduate biomedical engineering curriculum. Students deployed an endovascular stent into an anatomical model of a cerebral aneurysm and measured intra-aneurysmal flow…

Complex systems approach to scientific publication and peer-review system: development of an agent-based model calibrated with empirical journal data.

PubMed

Kovanis, Michail; Porcher, Raphaël; Ravaud, Philippe; Trinquart, Ludovic

Scientific peer-review and publication systems incur a huge burden in terms of costs and time. Innovative alternatives have been proposed to improve the systems, but assessing their impact in experimental studies is not feasible at a systemic level. We developed an agent-based model by adopting a unified view of peer review and publication systems and calibrating it with empirical journal data in the biomedical and life sciences. We modeled researchers, research manuscripts and scientific journals as agents. Researchers were characterized by their scientific level and resources, manuscripts by their scientific value, and journals by their reputation and acceptance or rejection thresholds. These state variables were used in submodels for various processes such as production of articles, submissions to target journals, in-house and external peer review, and resubmissions. We collected data for a sample of biomedical and life sciences journals regarding acceptance rates, resubmission patterns and total number of published articles. We adjusted submodel parameters so that the agent-based model outputs fit these empirical data. We simulated 105 journals, 25,000 researchers and 410,000 manuscripts over 10 years. A mean of 33,600 articles were published per year; 19 % of submitted manuscripts remained unpublished. The mean acceptance rate was 21 % after external peer review and rejection rate 32 % after in-house review; 15 % publications resulted from the first submission, 47 % the second submission and 20 % the third submission. All decisions in the model were mainly driven by the scientific value, whereas journal targeting and persistence in resubmission defined whether a manuscript would be published or abandoned after one or many rejections. This agent-based model may help in better understanding the determinants of the scientific publication and peer-review systems. It may also help in assessing and identifying the most promising alternative systems of peer review.

Boosting drug named entity recognition using an aggregate classifier.

PubMed

Korkontzelos, Ioannis; Piliouras, Dimitrios; Dowsey, Andrew W; Ananiadou, Sophia

2015-10-01

Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations. We perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition. Our approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers. Aggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names. We conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or comparable classification performance with that of the best performing model trained on gold-standard annotations. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

The Atlas of Physiology and Pathophysiology: Web-based multimedia enabled interactive simulations.

PubMed

Kofranek, Jiri; Matousek, Stanislav; Rusz, Jan; Stodulka, Petr; Privitzer, Pavol; Matejak, Marek; Tribula, Martin

2011-11-01

The paper is a presentation of the current state of development for the Atlas of Physiology and Pathophysiology (Atlas). Our main aim is to provide a novel interactive multimedia application that can be used for biomedical education where (a) simulations are combined with tutorials and (b) the presentation layer is simplified while the underlying complexity of the model is retained. The development of the Atlas required the cooperation of many professionals including teachers, system analysts, artists, and programmers. During the design of the Atlas, tools were developed that allow for component-based creation of simulation models, creation of interactive multimedia and their final coordination into a compact unit based on the given design. The Atlas is a freely available online application, which can help to explain the function of individual physiological systems and the causes and symptoms of their disorders. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Interpreter of maladies: redescription mining applied to biomedical data analysis.

PubMed

Waltman, Peter; Pearlman, Alex; Mishra, Bud

2006-04-01

Comprehensive, systematic and integrated data-centric statistical approaches to disease modeling can provide powerful frameworks for understanding disease etiology. Here, one such computational framework based on redescription mining in both its incarnations, static and dynamic, is discussed. The static framework provides bioinformatic tools applicable to multifaceted datasets, containing genetic, transcriptomic, proteomic, and clinical data for diseased patients and normal subjects. The dynamic redescription framework provides systems biology tools to model complex sets of regulatory, metabolic and signaling pathways in the initiation and progression of a disease. As an example, the case of chronic fatigue syndrome (CFS) is considered, which has so far remained intractable and unpredictable in its etiology and nosology. The redescription mining approaches can be applied to the Centers for Disease Control and Prevention's Wichita (KS, USA) dataset, integrating transcriptomic, epidemiological and clinical data, and can also be used to study how pathways in the hypothalamic-pituitary-adrenal axis affect CFS patients.

Titanate-silica mesostructured nanocables: synthesis, structural analysis and biomedical applications

NASA Astrophysics Data System (ADS)

Su, Yonghua; Qiao, Shizhang; Yang, Huagui; Yang, Chen; Jin, Yonggang; Stahr, Frances; Sheng, Jiayu; Cheng, Lina; Ling, Changquan; Qing Lu, Gao

2010-02-01

1D hierarchical composite mesostructures of titanate and silica were synthesized via an interfacial surfactant templating approach. Such mesostructures have complex core-shell architectures consisting of single-crystalline H2Ti3O7 nanobelts inside the ordered mesoporous SiO2 shell, which are nontoxic and highly biocompatible. The overall diameter of as-prepared 1D hierarchical composite mesostructures is only approx. 34.2 nm with a length over 500 nm on average. A model to explain the formation mechanism of these mesostructures has been proposed; the negatively charged surface of H2Ti3O7 nanobelts controls the formation of the octadecyltrimethylammonium bromide (C18TAB) bilayer, which in turn regulates the cooperative self-assembly of silica and C18TAB complex micelles on the interface to produce a mesoporous silica shell. More importantly, the application of synthesized mesostructured nanocables as anticancer drug reservoirs has also been explored, which indicates that the membranes containing these mesoporous nanocables have a great potential to be used as transdermal drug delivery systems.

3D printing of soft robotic systems

NASA Astrophysics Data System (ADS)

Wallin, T. J.; Pikul, J.; Shepherd, R. F.

2018-06-01

Soft robots are capable of mimicking the complex motion of animals. Soft robotic systems are defined by their compliance, which allows for continuous and often responsive localized deformation. These features make soft robots especially interesting for integration with human tissues, for example, the implementation of biomedical devices, and for robotic performance in harsh or uncertain environments, for example, exploration in confined spaces or locomotion on uneven terrain. Advances in soft materials and additive manufacturing technologies have enabled the design of soft robots with sophisticated capabilities, such as jumping, complex 3D movements, gripping and releasing. In this Review, we examine the essential soft material properties for different elements of soft robots, highlighting the most relevant polymer systems. Advantages and limitations of different additive manufacturing processes, including 3D printing, fused deposition modelling, direct ink writing, selective laser sintering, inkjet printing and stereolithography, are discussed, and the different techniques are investigated for their application in soft robotic fabrication. Finally, we explore integrated robotic systems and give an outlook for the future of the field and remaining challenges.

Using CamiTK for rapid prototyping of interactive computer assisted medical intervention applications.

PubMed

Promayon, Emmanuel; Fouard, Céline; Bailet, Mathieu; Deram, Aurélien; Fiard, Gaëlle; Hungr, Nikolai; Luboz, Vincent; Payan, Yohan; Sarrazin, Johan; Saubat, Nicolas; Selmi, Sonia Yuki; Voros, Sandrine; Cinquin, Philippe; Troccaz, Jocelyne

2013-01-01

Computer Assisted Medical Intervention (CAMI hereafter) is a complex multi-disciplinary field. CAMI research requires the collaboration of experts in several fields as diverse as medicine, computer science, mathematics, instrumentation, signal processing, mechanics, modeling, automatics, optics, etc. CamiTK is a modular framework that helps researchers and clinicians to collaborate together in order to prototype CAMI applications by regrouping the knowledge and expertise from each discipline. It is an open-source, cross-platform generic and modular tool written in C++ which can handle medical images, surgical navigation, biomedicals simulations and robot control. This paper presents the Computer Assisted Medical Intervention ToolKit (CamiTK) and how it is used in various applications in our research team.

Occupational therapy in India: focus on functional recovery and need for empowerment

PubMed Central

Samuel, Reema; Jacob, K. S.

2017-01-01

While there have been significant advances in treatments for mental disorders over the past century, cure for many mental disorders remains elusive. The complex problems of mental illness require a multi-sectoral, multi-disciplinary and multi-dimensional approach to care. The need for focus on biopsychosocial model rather than on biomedical practise, client-centred rather than physician-oriented care, personal rather than clinical recovery, are often preached but rarely practiced. The lack of emphasis on functioning and the limited workforce and evidence base complicate issues related to the care of people with chronic mental illness in India. The role of occupational therapy in bridging the gap between symptomatic improvement and functional recovery is discussed. PMID:28827877

A survey of quality assurance practices in biomedical open source software projects.

PubMed

Koru, Günes; El Emam, Khaled; Neisa, Angelica; Umarji, Medha

2007-05-07

Open source (OS) software is continuously gaining recognition and use in the biomedical domain, for example, in health informatics and bioinformatics. Given the mission critical nature of applications in this domain and their potential impact on patient safety, it is important to understand to what degree and how effectively biomedical OS developers perform standard quality assurance (QA) activities such as peer reviews and testing. This would allow the users of biomedical OS software to better understand the quality risks, if any, and the developers to identify process improvement opportunities to produce higher quality software. A survey of developers working on biomedical OS projects was conducted to examine the QA activities that are performed. We took a descriptive approach to summarize the implementation of QA activities and then examined some of the factors that may be related to the implementation of such practices. Our descriptive results show that 63% (95% CI, 54-72) of projects did not include peer reviews in their development process, while 82% (95% CI, 75-89) did include testing. Approximately 74% (95% CI, 67-81) of developers did not have a background in computing, 80% (95% CI, 74-87) were paid for their contributions to the project, and 52% (95% CI, 43-60) had PhDs. A multivariate logistic regression model to predict the implementation of peer reviews was not significant (likelihood ratio test = 16.86, 9 df, P = .051) and neither was a model to predict the implementation of testing (likelihood ratio test = 3.34, 9 df, P = .95). Less attention is paid to peer review than testing. However, the former is a complementary, and necessary, QA practice rather than an alternative. Therefore, one can argue that there are quality risks, at least at this point in time, in transitioning biomedical OS software into any critical settings that may have operational, financial, or safety implications. Developers of biomedical OS applications should invest more effort in implementing systemic peer review practices throughout the development and maintenance processes.

Reprint 1987: Research Administration in a Time of Change

ERIC Educational Resources Information Center

Brandt, Edward N.

2017-01-01

The field of biomedical research has undergone several changes in recent years. These include increased funding, the rapid development in scientific knowledge which speeds up the obsolescence of equipment, facilities and knowledge and the growing complexity of scientific problems. Research administrators can take steps to address these changes…

Prioritising lexical patterns to increase axiomatisation in biomedical ontologies. The role of localisation and modularity.

PubMed

Quesada-Martínez, M; Fernández-Breis, J T; Stevens, R; Mikroyannidi, E

2015-01-01

This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". In previous work, we have defined methods for the extraction of lexical patterns from labels as an initial step towards semi-automatic ontology enrichment methods. Our previous findings revealed that many biomedical ontologies could benefit from enrichment methods using lexical patterns as a starting point.Here, we aim to identify which lexical patterns are appropriate for ontology enrichment, driving its analysis by metrics to prioritised the patterns. We propose metrics for suggesting which lexical regularities should be the starting point to enrich complex ontologies. Our method determines the relevance of a lexical pattern by measuring its locality in the ontology, that is, the distance between the classes associated with the pattern, and the distribution of the pattern in a certain module of the ontology. The methods have been applied to four significant biomedical ontologies including the Gene Ontology and SNOMED CT. The metrics provide information about the engineering of the ontologies and the relevance of the patterns. Our method enables the suggestion of links between classes that are not made explicit in the ontology. We propose a prioritisation of the lexical patterns found in the analysed ontologies. The locality and distribution of lexical patterns offer insights into the further engineering of the ontology. Developers can use this information to improve the axiomatisation of their ontologies.

Transforming patient experience: health web science meets medicine 2.0.

PubMed

McHattie, Lynn-Sayers; Cumming, Grant; French, Tara

2014-01-01

Until recently, the Western biomedical paradigm has been effective in delivering health care, however this model is not positioned to tackle complex societal challenges or solve the current problems facing health care and delivery. The future of medicine requires a shift to a patient-centric model and in so doing the Internet has a significant role to play. The disciplines of Health Web Science and Medicine 2.0 are pivotal to this approach. This viewpoint paper argues that these disciplines, together with the field of design, can tackle these challenges. Drawing together ideas from design practice and research, complexity theory, and participatory action research we depict design as an approach that is fundamentally social and linked to concepts of person-centered care. We discuss the role of design, specifically co-design, in understanding the social, psychological, and behavioral dimensions of illness and the implications for the design of future care towards transforming the patient experience. This paper builds on the presentations and subsequent interdisciplinary dialogue that developed from the panel session "Transforming Patient Experience: Health Web Science Meets Web 2.0" at the 2013 Medicine 2.0 conference in London.

Transforming Patient Experience: Health Web Science Meets Medicine 2.0

PubMed Central

2014-01-01

Until recently, the Western biomedical paradigm has been effective in delivering health care, however this model is not positioned to tackle complex societal challenges or solve the current problems facing health care and delivery. The future of medicine requires a shift to a patient-centric model and in so doing the Internet has a significant role to play. The disciplines of Health Web Science and Medicine 2.0 are pivotal to this approach. This viewpoint paper argues that these disciplines, together with the field of design, can tackle these challenges. Drawing together ideas from design practice and research, complexity theory, and participatory action research we depict design as an approach that is fundamentally social and linked to concepts of person-centered care. We discuss the role of design, specifically co-design, in understanding the social, psychological, and behavioral dimensions of illness and the implications for the design of future care towards transforming the patient experience. This paper builds on the presentations and subsequent interdisciplinary dialogue that developed from the panel session "Transforming Patient Experience: Health Web Science Meets Web 2.0" at the 2013 Medicine 2.0 conference in London. PMID:25075246

Zebrafish (Danio rerio): A Potential Model for Toxinological Studies.

PubMed

Vargas, Rafael Antonio; Sarmiento, Karen; Vásquez, Isabel Cristina

2015-10-01

Zebrafish are an emerging basic biomedical research model that has multiple advantages compared with other research models. Given that biotoxins, such as toxins, poisons, and venoms, represent health hazards to animals and humans, a low-cost biological model that is highly sensitive to biotoxins is useful to understand the damage caused by such agents and to develop biological tests to prevent and reduce the risk of poisoning in potential cases of bioterrorism or food contamination. In this article, a narrative review of the general aspects of zebrafish as a model in basic biomedical research and various studies in the field of toxinology that have used zebrafish as a biological model are presented. This information will provide useful material to beginner students and researchers who are interested in developing toxinological studies with the zebrafish model.

Knowledge and Theme Discovery across Very Large Biological Data Sets Using Distributed Queries: A Prototype Combining Unstructured and Structured Data

PubMed Central

Repetski, Stephen; Venkataraman, Girish; Che, Anney; Luke, Brian T.; Girard, F. Pascal; Stephens, Robert M.

2013-01-01

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework. PMID:24312478

Nanoscale electrode arrays produced with microscale lithographic techniques for use in biomedical sensing applications.

PubMed

Terry, Jonathan G; Schmüser, Ilka; Underwood, Ian; Corrigan, Damion K; Freeman, Neville J; Bunting, Andrew S; Mount, Andrew R; Walton, Anthony J

2013-12-01

A novel technique for the production of nanoscale electrode arrays that uses standard microfabrication processes and micron-scale photolithography is reported here in detail. These microsquare nanoband edge electrode (MNEE) arrays have been fabricated with highly reproducible control of the key array dimensions, including the size and pitch of the individual elements and, most importantly, the width of the nanoband electrodes. The definition of lateral features to nanoscale dimensions typically requires expensive patterning techniques that are complex and low-throughput. However, the fabrication methodology used here relies on the fact that vertical dimensions (i.e. layer thicknesses) have long been manufacturable at the nanoscale using thin film deposition techniques that are well established in mainstream microelectronics. The authors report for the first time two aspects that highlight the particular suitability of these MNEE array systems for probe monolayer biosensing. The first is simulation, which shows the enhanced sensitivity to the redox reaction of the solution redox couple. The second is the enhancement of probe film functionalisation observed for the probe film model molecule, 6-mercapto-1-hexanol compared with microsquare electrodes. Such surface modification for specific probe layer biosensing and detection is of significance for a wide range of biomedical and other sensing and analytical applications.

Knowledge and theme discovery across very large biological data sets using distributed queries: a prototype combining unstructured and structured data.

PubMed

Mudunuri, Uma S; Khouja, Mohamad; Repetski, Stephen; Venkataraman, Girish; Che, Anney; Luke, Brian T; Girard, F Pascal; Stephens, Robert M

2013-01-01

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework.

Causality in Psychiatry: A Hybrid Symptom Network Construct Model

PubMed Central

Young, Gerald

2015-01-01

Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved) that inform approaches to nosology, or classification, such as in the DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (1)]. However, network approaches to symptom interaction [i.e., symptoms are formative of the construct; e.g., (2), for posttraumatic stress disorder (PTSD)] are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth non-linear dynamical systems theory (NLDST). The article applies the concept of emergent circular causality (3) to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning) and universal (e.g., causal) processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments. PMID:26635639

Insight, psychopathology, explanatory models and outcome of schizophrenia in India: a prospective 5-year cohort study.

PubMed

Johnson, Shanthi; Sathyaseelan, Manoranjitham; Charles, Helen; Jeyaseelan, Visalakshi; Jacob, Kuruthukulangara Sebastian

2012-09-27

The sole focus of models of insight on bio-medical perspectives to the complete exclusion of local, non-medical and cultural constructs mandates review. This study attempted to investigate the impact of insight, psychopathology, explanatory models of illness on outcome of first episode schizophrenia. Patients diagnosed to have DSM IV schizophrenia (n = 131) were assessed prospectively for insight, psychopathology, explanatory models of illness at baseline, 6, 12 and 60 months using standard instruments. Multiple linear and logistic regression and generalized estimating equations (GEE) were employed to assess predictors of outcome. We could follow up 95 (72.5%) patients. Sixty-five of these patients (68.4%) achieved remission. There was a negative relationship between psychosis rating and insight scores. Urban residence, fluctuating course of the initial illness, and improvement in global functioning at 6 months and lower psychosis rating at 12 months were significantly related to remission at 5 years. Insight scores, number of non-medical explanatory models and individual explanatory models held during the later course of the illness were significantly associated with outcome. Analysis of longitudinal data using GEE showed that women, rural residence, insight scores and number of non-medical explanatory models of illness held were significantly associated with BPRS scores during the study period. Insight, the disease model and the number of non-medical model positively correlated with improvement in psychosis arguing for a complex interaction between the culture, context and illness variables. These finding argue that insight and explanatory models are secondary to psychopathology, course and outcome of the illness. The awareness of mental illness is a narrative act in which people make personal sense of the many challenges they face. The course and outcome of the illness, cultural context, acceptable cultural explanations and the prevalent social stigma interact to produce a complex and multifaceted understanding of the issues. This complexity calls for a nuanced framing of insight.

Machine-learned cluster identification in high-dimensional data.

PubMed

Ultsch, Alfred; Lötsch, Jörn

2017-02-01

High-dimensional biomedical data are frequently clustered to identify subgroup structures pointing at distinct disease subtypes. It is crucial that the used cluster algorithm works correctly. However, by imposing a predefined shape on the clusters, classical algorithms occasionally suggest a cluster structure in homogenously distributed data or assign data points to incorrect clusters. We analyzed whether this can be avoided by using emergent self-organizing feature maps (ESOM). Data sets with different degrees of complexity were submitted to ESOM analysis with large numbers of neurons, using an interactive R-based bioinformatics tool. On top of the trained ESOM the distance structure in the high dimensional feature space was visualized in the form of a so-called U-matrix. Clustering results were compared with those provided by classical common cluster algorithms including single linkage, Ward and k-means. Ward clustering imposed cluster structures on cluster-less "golf ball", "cuboid" and "S-shaped" data sets that contained no structure at all (random data). Ward clustering also imposed structures on permuted real world data sets. By contrast, the ESOM/U-matrix approach correctly found that these data contain no cluster structure. However, ESOM/U-matrix was correct in identifying clusters in biomedical data truly containing subgroups. It was always correct in cluster structure identification in further canonical artificial data. Using intentionally simple data sets, it is shown that popular clustering algorithms typically used for biomedical data sets may fail to cluster data correctly, suggesting that they are also likely to perform erroneously on high dimensional biomedical data. The present analyses emphasized that generally established classical hierarchical clustering algorithms carry a considerable tendency to produce erroneous results. By contrast, unsupervised machine-learned analysis of cluster structures, applied using the ESOM/U-matrix method, is a viable, unbiased method to identify true clusters in the high-dimensional space of complex data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

BioSig: The Free and Open Source Software Library for Biomedical Signal Processing

PubMed Central

Vidaurre, Carmen; Sander, Tilmann H.; Schlögl, Alois

2011-01-01

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals. PMID:21437227

BioSig: the free and open source software library for biomedical signal processing.

PubMed

Vidaurre, Carmen; Sander, Tilmann H; Schlögl, Alois

2011-01-01

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

A data-driven modeling approach to stochastic computation for low-energy biomedical devices.

PubMed

Lee, Kyong Ho; Jang, Kuk Jin; Shoeb, Ali; Verma, Naveen

2011-01-01

Low-power devices that can detect clinically relevant correlations in physiologically-complex patient signals can enable systems capable of closed-loop response (e.g., controlled actuation of therapeutic stimulators, continuous recording of disease states, etc.). In ultra-low-power platforms, however, hardware error sources are becoming increasingly limiting. In this paper, we present how data-driven methods, which allow us to accurately model physiological signals, also allow us to effectively model and overcome prominent hardware error sources with nearly no additional overhead. Two applications, EEG-based seizure detection and ECG-based arrhythmia-beat classification, are synthesized to a logic-gate implementation, and two prominent error sources are introduced: (1) SRAM bit-cell errors and (2) logic-gate switching errors ('stuck-at' faults). Using patient data from the CHB-MIT and MIT-BIH databases, performance similar to error-free hardware is achieved even for very high fault rates (up to 0.5 for SRAMs and 7 × 10(-2) for logic) that cause computational bit error rates as high as 50%.

Penalized Nonlinear Least Squares Estimation of Time-Varying Parameters in Ordinary Differential Equations

PubMed Central

Cao, Jiguo; Huang, Jianhua Z.; Wu, Hulin

2012-01-01

Ordinary differential equations (ODEs) are widely used in biomedical research and other scientific areas to model complex dynamic systems. It is an important statistical problem to estimate parameters in ODEs from noisy observations. In this article we propose a method for estimating the time-varying coefficients in an ODE. Our method is a variation of the nonlinear least squares where penalized splines are used to model the functional parameters and the ODE solutions are approximated also using splines. We resort to the implicit function theorem to deal with the nonlinear least squares objective function that is only defined implicitly. The proposed penalized nonlinear least squares method is applied to estimate a HIV dynamic model from a real dataset. Monte Carlo simulations show that the new method can provide much more accurate estimates of functional parameters than the existing two-step local polynomial method which relies on estimation of the derivatives of the state function. Supplemental materials for the article are available online. PMID:23155351

Modelling studies in aqueous solution of lanthanide (III) chelates designed for nuclear magnetic resonance biomedical applications

NASA Astrophysics Data System (ADS)

Henriques, E. S.; Geraldes, C. F. G. C.; Ramos, M. J.

Molecular dynamics simulations and complementary modelling studies have been carried out for the [Gd(DOTA)·(H2O)]- and [Tm(DOTP)]5- chelates in aqueous media, to provide a better understanding of several structural and dynamical properties of these versatile nuclear magnetic resonance (NMR) probes, including coordination shells and corresponding water exchange mechanisms, and interactions of these complexes with alkali metal ions. This knowledge is of key importance in the areas of 1H relaxation and shift reagents for NMR applications in medical diagnosis. A new refinement of our own previously developed set of parameters for these Ln(III) chelates has been used, and is reported here. Calculations of water mean residence times suggest a reassessment of the characterization of the chelates' second coordination shell, one where the simple spherical distribution model is discarded in favour of a more detailed approach. Na+ probe interaction maps are in good agreement with the available site location predictions derived from 23Na NMR shifts.

Tumour-on-a-chip provides an optical window into nanoparticle tissue transport

NASA Astrophysics Data System (ADS)

Albanese, Alexandre; Lam, Alan K.; Sykes, Edward A.; Rocheleau, Jonathan V.; Chan, Warren C. W.

2013-10-01

Nanomaterials are used for numerous biomedical applications, but the selection of optimal properties for maximum delivery remains challenging. Thus, there is a significant interest in elucidating the nano-bio interactions underlying tissue accumulation. To date, researchers have relied on cell culture or animal models to study nano-bio interactions. However, cell cultures lack the complexity of biological tissues and animal models are prohibitively slow and expensive. Here we report a tumour-on-a-chip system where incorporation of tumour-like spheroids into a microfluidic channel permits real-time analysis of nanoparticle (NP) accumulation at physiological flow conditions. We show that penetration of NPs into the tissue is limited by their diameter and that retention can be improved by receptor targeting. NP transport is predominantly diffusion-limited with convection improving accumulation mostly at the tissue perimeter. A murine tumour model confirms these findings and demonstrates that the tumour-on-a-chip can be useful for screening optimal NP designs prior to in vivo studies.

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text.

PubMed

Zhu, Qile; Li, Xiaolin; Conesa, Ana; Pereira, Cécile

2018-05-01

Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. andyli@ece.ufl.edu or aconesa@ufl.edu. Supplementary data are available at Bioinformatics online.

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text

PubMed Central

Zhu, Qile; Li, Xiaolin; Conesa, Ana; Pereira, Cécile

2018-01-01

Abstract Motivation Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. Results We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. Availability and implementation The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. Contact andyli@ece.ufl.edu or aconesa@ufl.edu Supplementary information Supplementary data are available at Bioinformatics online. PMID:29272325

Rules and management of biomedical waste at Vivekananda Polyclinic: a case study.

PubMed

Gupta, Saurabh; Boojh, Ram; Mishra, Ajai; Chandra, Hem

2009-02-01

Hospitals and other healthcare establishments have a "duty of care" for the environment and for public health, and have particular responsibilities in relation to the waste they produce (i.e., biomedical waste). Negligence, in terms of biomedical waste management, significantly contributes to polluting the environment, affects the health of human beings, and depletes natural and financial resources. In India, in view of the serious situation of biomedical waste management, the Ministry of Environment and Forests, within the Government of India, ratified the Biomedical Waste (Management and Handling) Rules, in July 1998. The present paper provides a brief description of the biomedical waste (Management and Handling) Rules 1998, and the current biomedical waste management practices in one of the premier healthcare establishments of Lucknow, the Vivekananda Polyclinic. The objective in undertaking this study was to analyse the biomedical waste management system, including policy, practice (i.e., storage, collection, transportation and disposal), and compliance with the standards prescribed under the regulatory framework. The analysis consisted of interviews with medical authorities, doctors, and paramedical staff involved in the management of the biomedical wastes in the Polyclinic. Other important stakeholders that were consulted and interviewed included environmental engineers (looking after the Biomedical Waste Cell) of the State Pollution Control Board, and randomly selected patients and visitors to the Polyclinic. A general survey of the facilities of the Polyclinic was undertaken to ascertain the efficacy of the implemented measures. The waste was quantified based on random samples collected from each ward. It was found that, although the Polyclinic in general abides by the prescribed regulations for the treatment and disposal of biomedical waste, there is a need to further build the capacity of the Polyclinic and its staff in terms of providing state-of-the-art facilities and on-going training in order to develop a model biomedical waste management system in the Polyclinic. There is also a need to create awareness among all other stakeholders about the importance of biomedical waste management and related regulations. Furthermore, healthcare waste management should go beyond data compilation, enforcement of regulations, and acquisition of better equipment. It should be supported through appropriate education, training, and the commitment of the healthcare staff and management and healthcare managers within an effective policy and legislative framework.

Biomedical equipment and medical services in India.

PubMed

Sahay, K B; Saxena, R K

Varieties of Biomedical Equipment (BME) are now used for quick diagnosis, flawless surgery and therapeutics etc. Use of a malfunctioning BME could result in faulty diagnosis and wrong treatment and can lead to damaging or even devastating aftermath. Modern Biomedical Equipments inevitably employ highly sophisticated technology and use complex systems and instrumentation for best results. To the best of our knowledge the medical education in India does not impart any knowledge on the theory and design of BME and it is perhaps not possible also. Hence there is need for a permanent mechanism which can maintain and repair the biomedical equipments routinely before use and this can be done only with the help of qualified Clinical Engineers. Thus there is a genuine need for well organized cadre of Clinical Engineers who would be persons with engineering background with specialization in medical instrumentation. These Clinical engineers should be made responsible for the maintenance and proper functioning of BME. Every hospital or group of hospitals in the advanced countries has a clinical engineering unit that takes care of the biomedical equipments and systems in the hospital by undertaking routine and preventive maintenance, regular calibration of equipments and their timely repairs. Clinical engineers should be thus made an essential part of modern health care system and services. Unfortunately such facilities and mechanism do not exist in India. To make BME maintenance efficient and flawless in India, study suggests following measures and remedies: (i) design and development of comprehensive computerized database for BME (ii) cadre of Clinical engineers (iii) online maintenance facility and (iv) farsighted managerial skill to maximize accuracy, functioning and cost effectiveness.

Are graduate students rational? Evidence from the market for biomedical scientists.

PubMed

Blume-Kohout, Margaret E; Clack, John W

2013-01-01

The U.S. National Institutes of Health (NIH) budget expansion from 1998 through 2003 increased demand for biomedical research, raising relative wages and total employment in the market for biomedical scientists. However, because research doctorates in biomedical sciences can often take six years or more to complete, the full labor supply response to such changes in market conditions is not immediate, but rather is observed over a period of several years. Economic rational expectations models assume that prospective students anticipate these future changes, and also that students take into account the opportunity costs of their pursuing graduate training. Prior empirical research on student enrollment and degree completions in science and engineering (S&E) fields indicates that "cobweb" expectations prevail: that is, at least in theory, prospective graduate students respond to contemporaneous changes in market wages and employment, but do not forecast further changes that will arise by the time they complete their degrees and enter the labor market. In this article, we analyze time-series data on wages and employment of biomedical scientists versus alternative careers, on completions of S&E bachelor's degrees and biomedical sciences PhDs, and on research expenditures funded both by NIH and by biopharmaceutical firms, to examine the responsiveness of the biomedical sciences labor supply to changes in market conditions. Consistent with previous studies, we find that enrollments and completions in biomedical sciences PhD programs are responsive to market conditions at the time of students' enrollment. More striking, however, is the close correspondence between graduate student enrollments and completions, and changes in availability of NIH-funded traineeships, fellowships, and research assistantships.

Feasibility study of complex wavefield retrieval in off-axis acoustic holography employing an acousto-optic sensor

PubMed Central

Rodríguez, Guillermo López; Weber, Joshua; Sandhu, Jaswinder Singh; Anastasio, Mark A.

2011-01-01

We propose and experimentally demonstrate a new method for complex-valued wavefield retrieval in off-axis acoustic holography. The method involves use of an intensity-sensitive acousto-optic (AO) sensor, optimized for use at 3.3 MHz, to record the acoustic hologram and a computational method for reconstruction of the object wavefield. The proposed method may circumvent limitations of conventional implementations of acoustic holography and may facilitate the development of acoustic-holography-based biomedical imaging methods. PMID:21669451

Complex Systems

PubMed Central

Goldberger, Ary L.

2006-01-01

Physiologic systems in health and disease display an extraordinary range of temporal behaviors and structural patterns that defy understanding based on linear constructs, reductionist strategies, and classical homeostasis. Application of concepts and computational tools derived from the contemporary study of complex systems, including nonlinear dynamics, fractals and “chaos theory,” is having an increasing impact on biology and medicine. This presentation provides a brief overview of an emerging area of biomedical research, including recent applications to cardiopulmonary medicine and chronic obstructive lung disease. PMID:16921107

TU-F-BRD-01: Biomedical Informatics for Medical Physicists

DOE Office of Scientific and Technical Information (OSTI.GOV)

Phillips, M; Kalet, I; McNutt, T

Biomedical informatics encompasses a very large domain of knowledge and applications. This broad and loosely defined field can make it difficult to navigate. Physicists often are called upon to provide informatics services and/or to take part in projects involving principles of the field. The purpose of the presentations in this symposium is to help medical physicists gain some knowledge about the breadth of the field and how, in the current clinical and research environment, they can participate and contribute. Three talks have been designed to give an overview from the perspective of physicists and to provide a more in-depth discussionmore » in two areas. One of the primary purposes, and the main subject of the first talk, is to help physicists achieve a perspective about the range of the topics and concepts that fall under the heading of 'informatics'. The approach is to de-mystify topics and jargon and to help physicists find resources in the field should they need them. The other talks explore two areas of biomedical informatics in more depth. The goal is to highlight two domains of intense current interest--databases and models--in enough depth into current approaches so that an adequate background for independent inquiry is achieved. These two areas will serve as good examples of how physicists, using informatics principles, can contribute to oncology practice and research. Learning Objectives: To understand how the principles of biomedical informatics are used by medical physicists. To put the relevant informatics concepts in perspective with regard to biomedicine in general. To use clinical database design as an example of biomedical informatics. To provide a solid background into the problems and issues of the design and use of data and databases in radiation oncology. To use modeling in the service of decision support systems as an example of modeling methods and data use. To provide a background into how uncertainty in our data and knowledge can be incorporated into modeling methods.« less

Computational approaches for predicting biomedical research collaborations.

PubMed

Zhang, Qing; Yu, Hong

2014-01-01

Biomedical research is increasingly collaborative, and successful collaborations often produce high impact work. Computational approaches can be developed for automatically predicting biomedical research collaborations. Previous works of collaboration prediction mainly explored the topological structures of research collaboration networks, leaving out rich semantic information from the publications themselves. In this paper, we propose supervised machine learning approaches to predict research collaborations in the biomedical field. We explored both the semantic features extracted from author research interest profile and the author network topological features. We found that the most informative semantic features for author collaborations are related to research interest, including similarity of out-citing citations, similarity of abstracts. Of the four supervised machine learning models (naïve Bayes, naïve Bayes multinomial, SVMs, and logistic regression), the best performing model is logistic regression with an ROC ranging from 0.766 to 0.980 on different datasets. To our knowledge we are the first to study in depth how research interest and productivities can be used for collaboration prediction. Our approach is computationally efficient, scalable and yet simple to implement. The datasets of this study are available at https://github.com/qingzhanggithub/medline-collaboration-datasets.

Systems Biology of the Vervet Monkey

PubMed Central

Jasinska, Anna J.; Schmitt, Christopher A.; Service, Susan K.; Cantor, Rita M.; Dewar, Ken; Jentsch, James D.; Kaplan, Jay R.; Turner, Trudy R.; Warren, Wesley C.; Weinstock, George M.; Woods, Roger P.; Freimer, Nelson B.

2013-01-01

Nonhuman primates (NHP) provide crucial biomedical model systems intermediate between rodents and humans. The vervet monkey (also called the African green monkey) is a widely used NHP model that has unique value for genetic and genomic investigations of traits relevant to human diseases. This article describes the phylogeny and population history of the vervet monkey and summarizes the use of both captive and wild vervet monkeys in biomedical research. It also discusses the effort of an international collaboration to develop the vervet monkey as the most comprehensively phenotypically and genomically characterized NHP, a process that will enable the scientific community to employ this model for systems biology investigations. PMID:24174437

Dynamic tables: an architecture for managing evolving, heterogeneous biomedical data in relational database management systems.

PubMed

Corwin, John; Silberschatz, Avi; Miller, Perry L; Marenco, Luis

2007-01-01

Data sparsity and schema evolution issues affecting clinical informatics and bioinformatics communities have led to the adoption of vertical or object-attribute-value-based database schemas to overcome limitations posed when using conventional relational database technology. This paper explores these issues and discusses why biomedical data are difficult to model using conventional relational techniques. The authors propose a solution to these obstacles based on a relational database engine using a sparse, column-store architecture. The authors provide benchmarks comparing the performance of queries and schema-modification operations using three different strategies: (1) the standard conventional relational design; (2) past approaches used by biomedical informatics researchers; and (3) their sparse, column-store architecture. The performance results show that their architecture is a promising technique for storing and processing many types of data that are not handled well by the other two semantic data models.

Applications of Ontology Design Patterns in Biomedical Ontologies

PubMed Central

Mortensen, Jonathan M.; Horridge, Matthew; Musen, Mark A.; Noy, Natalya F.

2012-01-01

Ontology design patterns (ODPs) are a proposed solution to facilitate ontology development, and to help users avoid some of the most frequent modeling mistakes. ODPs originate from similar approaches in software engineering, where software design patterns have become a critical aspect of software development. There is little empirical evidence for ODP prevalence or effectiveness thus far. In this work, we determine the use and applicability of ODPs in a case study of biomedical ontologies. We encoded ontology design patterns from two ODP catalogs. We then searched for these patterns in a set of eight ontologies. We found five patterns of the 69 patterns. Two of the eight ontologies contained these patterns. While ontology design patterns provide a vehicle for capturing formally reoccurring models and best practices in ontology design, we show that today their use in a case study of widely used biomedical ontologies is limited. PMID:23304337

Modeling a description logic vocabulary for cancer research.

PubMed

Hartel, Frank W; de Coronado, Sherri; Dionne, Robert; Fragoso, Gilberto; Golbeck, Jennifer

2005-04-01

The National Cancer Institute has developed the NCI Thesaurus, a biomedical vocabulary for cancer research, covering terminology across a wide range of cancer research domains. A major design goal of the NCI Thesaurus is to facilitate translational research. We describe: the features of Ontylog, a description logic used to build NCI Thesaurus; our methodology for enhancing the terminology through collaboration between ontologists and domain experts, and for addressing certain real world challenges arising in modeling the Thesaurus; and finally, we describe the conversion of NCI Thesaurus from Ontylog into Web Ontology Language Lite. Ontylog has proven well suited for constructing big biomedical vocabularies. We have capitalized on the Ontylog constructs Kind and Role in the collaboration process described in this paper to facilitate communication between ontologists and domain experts. The artifacts and processes developed by NCI for collaboration may be useful in other biomedical terminology development efforts.

Synthesis, characterization and applications of carboxylated and polyethylene-glycolated bifunctionalized InP/ZnS quantum dots in cellular internalization mediated by cell-penetrating peptides.

PubMed

Liu, Betty R; Winiarz, Jeffrey G; Moon, Jong-Sik; Lo, Shih-Yen; Huang, Yue-Wern; Aronstam, Robert S; Lee, Han-Jung

2013-11-01

Semiconductor nanoparticles, also known as quantum dots (QDs), are widely used in biomedical imaging studies and pharmaceutical research. Cell-penetrating peptides (CPPs) are a group of small peptides that are able to traverse cell membrane and deliver a variety of cargoes into living cells. CPPs deliver QDs into cells with minimal nonspecific absorption and toxic effect. In this study, water-soluble, monodisperse, carboxyl-functionalized indium phosphide (InP)/zinc sulfide (ZnS) QDs coated with polyethylene glycol lipids (designated QInP) were synthesized for the first time. The physicochemical properties (optical absorption, fluorescence and charging state) and cellular internalization of QInP and CPP/QInP complexes were characterized. CPPs noncovalently interact with QInP in vitro to form stable CPP/QInP complexes, which can then efficiently deliver QInP into human A549 cells. The introduction of 500nM of CPP/QInP complexes and QInP at concentrations of less than 1μM did not reduce cell viability. These results indicate that carboxylated and polyethylene-glycolylated (PEGylated) bifunctionalized QInP are biocompatible nanoparticles with potential for use in biomedical imaging studies and drug delivery applications. Copyright © 2013 Elsevier B.V. All rights reserved.

Modeling exposure–lag–response associations with distributed lag non-linear models

PubMed Central

Gasparrini, Antonio

2014-01-01

In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure–lag–response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:24027094

Academic program models for undergraduate biomedical engineering.

PubMed

Krishnan, Shankar M

2014-01-01

There is a proliferation of medical devices across the globe for the diagnosis and therapy of diseases. Biomedical engineering (BME) plays a significant role in healthcare and advancing medical technologies thus creating a substantial demand for biomedical engineers at undergraduate and graduate levels. There has been a surge in undergraduate programs due to increasing demands from the biomedical industries to cover many of their segments from bench to bedside. With the requirement of multidisciplinary training within allottable duration, it is indeed a challenge to design a comprehensive standardized undergraduate BME program to suit the needs of educators across the globe. This paper's objective is to describe three major models of undergraduate BME programs and their curricular requirements, with relevant recommendations to be applicable in institutions of higher education located in varied resource settings. Model 1 is based on programs to be offered in large research-intensive universities with multiple focus areas. The focus areas depend on the institution's research expertise and training mission. Model 2 has basic segments similar to those of Model 1, but the focus areas are limited due to resource constraints. In this model, co-op/internship in hospitals or medical companies is included which prepares the graduates for the work place. In Model 3, students are trained to earn an Associate Degree in the initial two years and they are trained for two more years to be BME's or BME Technologists. This model is well suited for the resource-poor countries. All three models must be designed to meet applicable accreditation requirements. The challenges in designing undergraduate BME programs include manpower, facility and funding resource requirements and time constraints. Each academic institution has to carefully analyze its short term and long term requirements. In conclusion, three models for BME programs are described based on large universities, colleges, and community colleges. Model 1 is suitable for research-intensive universities. Models 2 and 3 can be successfully implemented in higher education institutions with low and limited resources with appropriate guidance and support from international organizations. The models will continually evolve mainly to meet the industry needs.

Probing the modulated formation of gold nanoparticles-beta-lactoglobulin corona complexes and their applications.

PubMed

Yang, Jiang; Wang, Bo; You, Youngsang; Chang, Woo-Jin; Tang, Ke; Wang, Yi-Cheng; Zhang, Wenzhao; Ding, Feng; Gunasekaran, Sundaram

2017-11-23

Understanding the interactions between proteins and nanoparticles (NPs) along with the underlying structural and dynamic information is of utmost importance to exploit nanotechnology for biomedical applications. Upon adsorption onto a NP surface, proteins form a well-organized layer, termed the corona, that dictates the identity of the NP-protein complex and governs its biological pathways. Given its high biological relevance, in-depth molecular investigations and applications of NPs-protein corona complexes are still scarce, especially since different proteins form unique corona patterns, making identification of the biomolecular motifs at the interface critical. In this work, we provide molecular insights and structural characterizations of the bio-nano interface of a popular food-based protein, namely bovine beta-lactoglobulin (β-LG), with gold nanoparticles (AuNPs) and report on our investigations of the formation of corona complexes by combined molecular simulations and complementary experiments. Two major binding sites in β-LG were identified as being driven by citrate-mediated electrostatic interactions, while the associated binding kinetics and conformational changes in the secondary structures were also characterized. More importantly, the superior stability of the corona led us to further explore its biomedical applications, such as in the smartphone-based point-of-care biosensing of Escherichia coli (E. coli) and in the computed tomography (CT) of the gastrointestinal (GI) tract through oral administration to probe GI tolerance and functions. Considering their biocompatibility, edible nature, and efficient excretion through defecation, AuNPs-β-LG corona complexes have shown promising perspectives for future in vitro and in vivo clinical settings.

A bioinformatics roadmap for the human vaccines project.

PubMed

Scheuermann, Richard H; Sinkovits, Robert S; Schenkelberg, Theodore; Koff, Wayne C

2017-06-01

Biomedical research has become a data intensive science in which high throughput experimentation is producing comprehensive data about biological systems at an ever-increasing pace. The Human Vaccines Project is a new public-private partnership, with the goal of accelerating development of improved vaccines and immunotherapies for global infectious diseases and cancers by decoding the human immune system. To achieve its mission, the Project is developing a Bioinformatics Hub as an open-source, multidisciplinary effort with the overarching goal of providing an enabling infrastructure to support the data processing, analysis and knowledge extraction procedures required to translate high throughput, high complexity human immunology research data into biomedical knowledge, to determine the core principles driving specific and durable protective immune responses.

Closing the loops in biomedical informatics from theory to daily practice.

PubMed

Gaudinat, A

2009-01-01

This article presents the 2009 selection of the best papers in the special section dedicated to biomedical informatics and cybernetics. Synopsis of the articles selected for the IMIA yearbook 2009 Five papers from international peer reviewed journals where selected for this section. Most of the papers have a strong practical orientation in clinical care. And this selection gives a good overview of what is done with "closing loop" approach, particularly during the year 2008. While quite mature for some clinical applications such as mechanical ventilation, it remains a challenge where rules for the decision system could be difficult to identify due to the number of variables. More complex systems with greater Artificial Intelligence approaches will certainly be the next trend for closed-loop applications.

The theory and design of piezoelectric/pyroelectric polymer film sensors for biomedical engineering applications.

PubMed

Brown, L F

1989-01-01

The unique properties of piezoelectric/pyroelectric polymers offer many new opportunities for biomedical engineering sensor applications. Since their discovery nearly 20 years ago, the polymer films have been used for many novel switching and sensor applications. Despite the prodigious exposure from many recent publications describing piezo film applications, methods of sensor fabrication and circuit interfacing still elude most engineers. This paper is presented as a tutorial guide to applying piezo polymers to biomedical engineering applications. A review of the fundamentals of piezoelectricity/pyroelectricity in piezo polymers is first presented. Their material properties are contrasted with piezoelectric ceramic materials. Some advantages and disadvantages of the films for biomedical sensors are discussed. Specific details on the fabrication of piezo film sensors are presented. Methods are described for forming, cutting, and mounting film sensors, and making lead connections. A brief discussion of equivalent circuit models for the design and simulation of piezoelectric/pyroelectric sensors is included, as well as common circuit interface techniques. Finally, several sources are recommended for further information on a variety of biomedical sensor applications.

SOCR "Motion Charts": An Efficient, Open-Source, Interactive and Dynamic Applet for Visualizing Longitudinal Multivariate Data

ERIC Educational Resources Information Center

Al-Aziz, Jameel; Christou, Nicolas; Dinov, Ivo D.

2010-01-01

The amount, complexity and provenance of data have dramatically increased in the past five years. Visualization of observed and simulated data is a critical component of any social, environmental, biomedical or scientific quest. Dynamic, exploratory and interactive visualization of multivariate data, without preprocessing by dimensionality…

Cell Biology and Microbiology: A Continuous Cross-Feeding.

PubMed

Pizarro-Cerdá, Javier; Cossart, Pascale

2016-07-01

Microbiology and cell biology both involve the study of cells, albeit at different levels of complexity and scale. Interactions between both fields during the past 25 years have led to major conceptual and technological advances that have reshaped the whole biology landscape and its biomedical applications. Copyright © 2016 Elsevier Ltd. All rights reserved.

Cytotoxicity Test and Mass Spectrometry of IPMC

NASA Astrophysics Data System (ADS)

Takashima, Kazuto; Kamamichi, Norihiro; Yagi, Tohru; Asaka, Kinji; Mukai, Toshiharu

Ionic polymer-metal composite (IPMC) is a promising material in biomedical actuators and sensors because IPMC is soft and flexible, leading to the safety of the device itself. The purpose of this study is to investigate the biocompatibility of IPMC by in vitro experiments, in order to evaluate the applicability in biomedical fields. In addition to an IPMC specimen prepared by the conventional “impregnation-reduction method” using cationic gold complexes and reducing agents, two specimens were prepared by processes in addition to that used for the conventional IPMC specimen. One specimen was reduced in Na2SO3 solution and another specimen was cleaned in H2O2 solution. Colony-forming test using Chinese hamster V79 cells shows high cytotoxicity of all IPMC specimens. Examination of direct inlet mass spectrometry (DI-MS) revealed that the peak intensity of gold complex (particularly, m/z=180) was different from that of Nafion film. Monitoring the peak at m/z=180 showed a remnant with the structure of phenanthroline in IPMC specimens which were not cleaned in H2O2 solution.

Neural prostheses in clinical applications--trends from precision mechanics towards biomedical microsystems in neurological rehabilitation.

PubMed

Stieglitz, T; Schuettler, M; Koch, K P

2004-04-01

Neural prostheses partially restore body functions by technical nerve excitation after trauma or neurological diseases. External devices and implants have been developed since the early 1960s for many applications. Several systems have reached nowadays clinical practice: Cochlea implants help the deaf to hear, micturition is induced by bladder stimulators in paralyzed persons and deep brain stimulation helps patients with Parkinson's disease to participate in daily life again. So far, clinical neural prostheses are fabricated with means of precision mechanics. Since microsystem technology opens the opportunity to design and develop complex systems with a high number of electrodes to interface with the nervous systems, the opportunity for selective stimulation and complex implant scenarios seems to be feasible in the near future. The potentials and limitations with regard to biomedical microdevices are introduced and discussed in this paper. Target specifications are derived from existing implants and are discussed on selected applications that has been investigated in experimental research: a micromachined implant to interface a nerve stump with a sieve electrode, cuff electrodes with integrated electronics, and an epiretinal vision prosthesis.

Faith-based perspectives on the use of chimeric organisms for medical research.

PubMed

Degeling, Chris; Irvine, Rob; Kerridge, Ian

2014-04-01

Efforts to advance our understanding of neurodegenerative diseases involve the creation chimeric organisms from human neural stem cells and primate embryos--known as prenatal chimeras. The existence of potential mentally complex beings with human and non-human neural apparatus raises fundamental questions as to the ethical permissibility of chimeric research and the moral status of the creatures it creates. Even as bioethicists find fewer reasons to be troubled by most types of chimeric organisms, social attitudes towards the non-human world are often influenced by religious beliefs. In this paper scholars representing eight major religious traditions provide a brief commentary on a hypothetical case concerning the development and use of prenatal human-animal chimeric primates in medical research. These commentaries reflect the plurality and complexity within and between religious discourses of our relationships with other species. Views on the moral status and permissibility of research on neural human animal chimeras vary. The authors provide an introduction to those who seek a better understanding of how faith-based perspectives might enter into biomedical ethics and public discourse towards forms of biomedical research that involves chimeric organisms.

A LDA-based approach to promoting ranking diversity for genomics information retrieval.

PubMed

Chen, Yan; Yin, Xiaoshi; Li, Zhoujun; Hu, Xiaohua; Huang, Jimmy Xiangji

2012-06-11

In the biomedical domain, there are immense data and tremendous increase of genomics and biomedical relevant publications. The wealth of information has led to an increasing amount of interest in and need for applying information retrieval techniques to access the scientific literature in genomics and related biomedical disciplines. In many cases, the desired information of a query asked by biologists is a list of a certain type of entities covering different aspects that are related to the question, such as cells, genes, diseases, proteins, mutations, etc. Hence, it is important of a biomedical IR system to be able to provide relevant and diverse answers to fulfill biologists' information needs. However traditional IR model only concerns with the relevance between retrieved documents and user query, but does not take redundancy between retrieved documents into account. This will lead to high redundancy and low diversity in the retrieval ranked lists. In this paper, we propose an approach which employs a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. We perform our approach on TREC 2007 Genomics collection and two distinctive IR baseline runs, which can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track. The proposed method is the first study of adopting topic model to genomics information retrieval, and demonstrates its effectiveness in promoting ranking diversity as well as in improving relevance of ranked lists of genomics search. Moreover, we proposes a distance measure to quantify how much a passage can increase topical diversity by considering both topical importance and topical coefficient by LDA, and the distance measure is a modified Euclidean distance.