12 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/09/02

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

11 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/09/02

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Designing a patient monitoring system for bipolar disorder using Semantic Web technologies.

Conf Proc IEEE Eng Med Biol Soc. 2015;2015:6788-91

Authors: Thermolia C, Bei ES, Petrakis EG, Kritsotakis V, Tsiknakis M, Sakkalis V

Abstract
The new movement to personalize treatment plans and improve prediction capabilities is greatly facilitated by intelligent remote patient monitoring and risk prevention. This paper focuses on patients suffering from bipolar disorder, a mental illness characterized by severe mood swings. We exploit the advantages of Semantic Web and Electronic Health Record Technologies to develop a patient monitoring platform to support clinicians. Relying on intelligently filtering of clinical evidence-based information and individual-specific knowledge, we aim to provide recommendations for treatment and monitoring at appropriate time or concluding into alerts for serious shifts in mood and patients' non response to treatment.

PMID: 26737852 [PubMed - indexed for MEDLINE]

A Comprehensive Systems Biology Approach to Studying Zika Virus.

PLoS One. 2016;11(9):e0161355

Authors: May M, Relich RF

Abstract
Zika virus (ZIKV) is responsible for an ongoing and intensifying epidemic in the Western Hemisphere. We examined the complete predicted proteomes, glycomes, and selectomes of 33 ZIKV strains representing temporally diverse members of the African lineage, the Asian lineage, and the current outbreak in the Americas. Derivation of the complete selectome is an 'omics' approach to identify distinct evolutionary pressures acting on different features of an organism. Employment of the M8 model did not show evidence of global diversifying selection acting on the ZIKV polyprotein; however, a mixed effect model of evolution showed strong evidence (P<0.05) for episodic diversifying selection acting on specific sites. Single nucleotide polymorphisms (SNPs) were predictably frequent across strains relative to the derived consensus sequence. None of the 9 published detection procedures utilize targets that share 100% identity across the 33 strains examined, indicating that ZIKV escape from molecular detection is predictable. The predicted O-linked glycome showed marked diversity across strains; however, the N-linked glycome was highly stable. All Asian and American strains examined were predicted to include glycosylation of E protein ASN154, a modification proposed to mediate neurotropism, whereas the modification was not predicted for African strains. SNP diversity, episodic diversifying selection, and differential glycosylation, particularly of ASN154, may have major biological implications for ZIKV disease. Taken together, the systems biology perspective of ZIKV indicates: a.) The recently emergent Asian/American N-glycotype is mediating the new and emerging neuropathogenic potential of ZIKV; and b.) further divergence at specific sites is predictable as endemnicity is established in the Americas.

PMID: 27584813 [PubMed - as supplied by publisher]

Experimental Design for Stochastic Models of Nonlinear Signaling Pathways Using an Interval-Wise Linear Noise Approximation and State Estimation.

PLoS One. 2016;11(9):e0159902

Authors: Zimmer C

Abstract
BACKGROUND: Computational modeling is a key technique for analyzing models in systems biology. There are well established methods for the estimation of the kinetic parameters in models of ordinary differential equations (ODE). Experimental design techniques aim at devising experiments that maximize the information encoded in the data. For ODE models there are well established approaches for experimental design and even software tools. However, data from single cell experiments on signaling pathways in systems biology often shows intrinsic stochastic effects prompting the development of specialized methods. While simulation methods have been developed for decades and parameter estimation has been targeted for the last years, only very few articles focus on experimental design for stochastic models.
METHODS: The Fisher information matrix is the central measure for experimental design as it evaluates the information an experiment provides for parameter estimation. This article suggest an approach to calculate a Fisher information matrix for models containing intrinsic stochasticity and high nonlinearity. The approach makes use of a recently suggested multiple shooting for stochastic systems (MSS) objective function. The Fisher information matrix is calculated by evaluating pseudo data with the MSS technique.
RESULTS: The performance of the approach is evaluated with simulation studies on an Immigration-Death, a Lotka-Volterra, and a Calcium oscillation model. The Calcium oscillation model is a particularly appropriate case study as it contains the challenges inherent to signaling pathways: high nonlinearity, intrinsic stochasticity, a qualitatively different behavior from an ODE solution, and partial observability. The computational speed of the MSS approach for the Fisher information matrix allows for an application in realistic size models.

PMID: 27583802 [PubMed - as supplied by publisher]

CYR61 and TAZ Upregulation and Focal Epithelial to Mesenchymal Transition May Be Early Predictors of Barrett's Esophagus Malignant Progression.

PLoS One. 2016;11(9):e0161967

Authors: Cardoso J, Mesquita M, Dias Pereira A, Bettencourt-Dias M, Chaves P, Pereira-Leal JB

Abstract
Barrett's esophagus is the major risk factor for esophageal adenocarcinoma. It has a low but non-neglectable risk, high surveillance costs and no reliable risk stratification markers. We sought to identify early biomarkers, predictive of Barrett's malignant progression, using a meta-analysis approach on gene expression data. This in silico strategy was followed by experimental validation in a cohort of patients with extended follow up from the Instituto Português de Oncologia de Lisboa de Francisco Gentil EPE (Portugal). Bioinformatics and systems biology approaches singled out two candidate predictive markers for Barrett's progression, CYR61 and TAZ. Although previously implicated in other malignancies and in epithelial-to-mesenchymal transition phenotypes, our experimental validation shows for the first time that CYR61 and TAZ have the potential to be predictive biomarkers for cancer progression. Experimental validation by reverse transcriptase quantitative PCR and immunohistochemistry confirmed the up-regulation of both genes in Barrett's samples associated with high-grade dysplasia/adenocarcinoma. In our cohort CYR61 and TAZ up-regulation ranged from one to ten years prior to progression to adenocarcinoma in Barrett's esophagus index samples. Finally, we found that CYR61 and TAZ over-expression is correlated with early focal signs of epithelial to mesenchymal transition. Our results highlight both CYR61 and TAZ genes as potential predictive biomarkers for stratification of the risk for development of adenocarcinoma and suggest a potential mechanistic route for Barrett's esophagus neoplastic progression.

PMID: 27583562 [PubMed - as supplied by publisher]

An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study.

F1000Res. 2016;5:1574

Authors: Wang Z, Ma'ayan A

Abstract
RNA-seq analysis is becoming a standard method for global gene expression profiling. However, open and standard pipelines to perform RNA-seq analysis by non-experts remain challenging due to the large size of the raw data files and the hardware requirements for running the alignment step. Here we introduce a reproducible open source RNA-seq pipeline delivered as an IPython notebook and a Docker image. The pipeline uses state-of-the-art tools and can run on various platforms with minimal configuration overhead. The pipeline enables the extraction of knowledge from typical RNA-seq studies by generating interactive principal component analysis (PCA) and hierarchical clustering (HC) plots, performing enrichment analyses against over 90 gene set libraries, and obtaining lists of small molecules that are predicted to either mimic or reverse the observed changes in mRNA expression. We apply the pipeline to a recently published RNA-seq dataset collected from human neuronal progenitors infected with the Zika virus (ZIKV). In addition to confirming the presence of cell cycle genes among the genes that are downregulated by ZIKV, our analysis uncovers significant overlap with upregulated genes that when knocked out in mice induce defects in brain morphology. This result potentially points to the molecular processes associated with the microcephaly phenotype observed in newborns from pregnant mothers infected with the virus. In addition, our analysis predicts small molecules that can either mimic or reverse the expression changes induced by ZIKV. The IPython notebook and Docker image are freely available at:  http://nbviewer.jupyter.org/github/maayanlab/Zika-RNAseq-Pipeline/blob/master/Zika.ipynb and  https://hub.docker.com/r/maayanlab/zika/.

PMID: 27583132 [PubMed]

Dynamic optimization of biological networks under parametric uncertainty.

BMC Syst Biol. 2016;10(1):86

Authors: Nimmegeers P, Telen D, Logist F, Impe JV

Abstract
BACKGROUND: Micro-organisms play an important role in various industrial sectors (including biochemical, food and pharmaceutical industries). A profound insight in the biochemical reactions inside micro-organisms enables an improved biochemical process control. Biological networks are an important tool in systems biology for incorporating microscopic level knowledge. Biochemical processes are typically dynamic and the cells have often more than one objective which are typically conflicting, e.g., minimizing the energy consumption while maximizing the production of a specific metabolite. Therefore multi-objective optimization is needed to compute trade-offs between those conflicting objectives. In model-based optimization, one of the inherent problems is the presence of uncertainty. In biological processes, this uncertainty can be present due to, e.g., inherent biological variability. Not taking this uncertainty into account, possibly leads to the violation of constraints and erroneous estimates of the actual objective function(s). To account for the variance in model predictions and compute a prediction interval, this uncertainty should be taken into account during process optimization. This leads to a challenging optimization problem under uncertainty, which requires a robustified solution.
RESULTS: Three techniques for uncertainty propagation: linearization, sigma points and polynomial chaos expansion, are compared for the dynamic optimization of biological networks under parametric uncertainty. These approaches are compared in two case studies: (i) a three-step linear pathway model in which the accumulation of intermediate metabolites has to be minimized and (ii) a glycolysis inspired network model in which a multi-objective optimization problem is considered, being the minimization of the enzymatic cost and the minimization of the end time before reaching a minimum extracellular metabolite concentration. A Monte Carlo simulation procedure has been applied for the assessment of the constraint violations. For the multi-objective case study one Pareto point has been considered for the assessment of the constraint violations. However, this analysis can be performed for any Pareto point.
CONCLUSIONS: The different uncertainty propagation strategies each offer a robustified solution under parametric uncertainty. When making the trade-off between computation time and the robustness of the obtained profiles, the sigma points and polynomial chaos expansion strategies score better in reducing the percentage of constraint violations. This has been investigated for a normal and a uniform parametric uncertainty distribution. The polynomial chaos expansion approach allows to directly take prior knowledge of the parametric uncertainty distribution into account.

PMID: 27580913 [PubMed - in process]

Meta-generalis: A novel method for structuring information from radiology reports.

Appl Clin Inform. 2016;7(3):803-16

Authors: Barbosa F, Traina AJ, Muglia VF

Abstract
BACKGROUND: A structured report for imaging exams aims at increasing the precision in information retrieval and communication between physicians. However, it is more concise than free text and may limit specialists' descriptions of important findings not covered by pre-defined structures. A computational ontological structure derived from free texts designed by specialists may be a solution for this problem. Therefore, the goal of our study was to develop a methodology for structuring information in radiology reports covering specifications required for the Brazilian Portuguese language, including the terminology to be used.
METHODS: We gathered 1,701 radiological reports of magnetic resonance imaging (MRI) studies of the lumbosacral spine from three different institutions. Techniques of text mining and ontological conceptualization of lexical units extracted were used to structure information. Ten radiologists, specialists in lumbosacral MRI, evaluated the textual superstructure and terminology extracted using an electronic questionnaire.
RESULTS: The established methodology consists of six steps: 1) collection of radiology reports of a specific MRI examination; 2) textual decomposition; 3) normalization of lexical units; 4) identification of textual superstructures; 5) conceptualization of candidate-terms; and 6) evaluation of superstructures and extracted terminology by experts using an electronic questionnaire. Three different textual superstructures were identified, with terminological variations in the names of their textual categories. The number of candidate-terms conceptualized was 4,183, yielding 727 concepts. There were a total of 13,963 relationships between candidate-terms and concepts and 789 relationships among concepts.
CONCLUSIONS: The proposed methodology allowed structuring information in a more intuitive and practical way. Indications of three textual superstructures, extraction of lexicon units and the normalization and ontologically conceptualization were achieved while maintaining references to their respective categories and free text radiology reports.

PMID: 27580980 [PubMed - in process]

15 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/09/01

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

8 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/09/01

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Expression of DRD2 is Increased in Human Pancreatic Ductal Adenocarcinoma and Inhibitors Slow Tumor Growth in Mice.

Gastroenterology. 2016 Aug 28;

Authors: Jandaghi P, Najafabadi HS, Bauer AS, Papadakis AI, Fassan M, Hall A, Monast A, von Knebel Doeberitz M, Neoptolemos JP, Costello E, Greenhalf W, Scarpa A, Sipos B, Auld D, Lathrop M, Park M, Büchler MW, Strobel O, Hackert T, Giese NA, Zogopoulos G, Sangwan V, Huang S, Riazalhosseini Y, Hoheisel JD

Abstract
BACKGROUND & AIMS: Incidence of and mortality from pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer, are almost equivalent, so better treatments are needed. We studied gene expression profiles of PDACs and the functions of genes with altered expression to identify new therapeutic targets.
METHODS: We performed microarray analysis to analyze gene expression profiles of 195 PDAC and 41 non-tumor pancreatic tissue samples. We undertook an extensive analysis of PDAC transcriptome by superimposing interaction networks of proteins encoded by aberrantly expressed genes over signaling pathways associated with PDAC development to identify factors that might alter regulation of these pathways during tumor progression. We performed tissue microarray analysis to verify changes in expression of candidate protein using an independent set of 152 samples (40 non-tumor pancreatic tissues, 63 PDAC sections, and 49 chronic pancreatitis samples). We validated the functional relevance of the candidate molecule using RNA interference (RNAi) or pharmacologic inhibitors in pancreatic cancer cell lines and analyses of xenograft tumors in mice.
RESULTS: In an analysis of 38,276 human genes and loci, we identified 1676 genes that were significantly upregulated and 1166 genes that were significantly downregulated in PDAC, compared with non-tumor pancreatic tissues. One gene that was upregulated and associated with multiple signaling pathways that are dysregulated in PDAC was G protein subunit alpha i2 (GNAI2), which has not been previously associated with PDAC. GNAI2 mediates the effects of dopamine receptor D2 (DRD2) on cAMP-signaling; PDAC tissues had a slight but significant increase in DRD2 mRNA. Levels of DRD2 protein were substantially increased in PDACs, compared with non-tumor tissues, in tissue microarray analyses. RNAi knockdown of DRD2 or inhibition with pharmacologic antagonists (pimozide and haloperidol) reduced proliferation of pancreatic cancer cells, induced endoplasmic reticulum stress and apoptosis, and reduced cell migration. RNAi knockdown of DRD2 in pancreatic tumor cells reduced growth of xenograft tumors in mice, and administration of the DRD2 inhibitor haloperidol to mice with orthotopic xenograft tumors reduced final tumor size and metastasis.
CONCLUSIONS: In gene expression profile analysis of PDAC samples, we found the DRD2 signaling pathway to be activated. Inhibition of DRD2 in pancreatic cancer cells reduced proliferation and migration, and slowed growth of xenograft tumors in mice. DRD2 antagonists routinely used for management of schizophrenia might tested in patients with pancreatic cancer.

PMID: 27578530 [PubMed - as supplied by publisher]

Fusing literature and full network data improves disease similarity computation.

BMC Bioinformatics. 2016;17(1):326

Authors: Li P, Nie Y, Yu J

Abstract
BACKGROUND: Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature.
RESULTS: Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively.
CONCLUSIONS: Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http:// www.digintelli.com:8000/ .

PMID: 27578323 [PubMed - in process]

Clinically Available Medicines Demonstrating Anti-Toxoplasma Activity.

Antimicrob Agents Chemother. 2015 Dec;59(12):7161-9

Authors: Neville AJ, Zach SJ, Wang X, Larson JJ, Judge AK, Davis LA, Vennerstrom JL, Davis PH

Abstract
Toxoplasma gondii is an apicomplexan parasite of humans and other mammals, including livestock and companion animals. While chemotherapeutic regimens, including pyrimethamine and sulfadiazine regimens, ameliorate acute or recrudescent disease such as toxoplasmic encephalitis or ocular toxoplasmosis, these drugs are often toxic to the host. Moreover, no approved options are available to treat infected women who are pregnant. Lastly, no drug regimen has shown the ability to eradicate the chronic stage of infection, which is characterized by chemoresistant intracellular cysts that persist for the life of the host. In an effort to promote additional chemotherapeutic options, we now evaluate clinically available drugs that have shown efficacy in disease models but which lack clinical case reports. Ideally, less-toxic treatments for the acute disease can be identified and developed, with an additional goal of cyst clearance from human and animal hosts.

PMID: 26392504 [PubMed - indexed for MEDLINE]

Dynamic User Interfaces for Service Oriented Architectures in Healthcare.

Stud Health Technol Inform. 2016;228:795-7

Authors: Schweitzer M, Hoerbst A

Abstract
Electronic Health Records (EHRs) play a crucial role in healthcare today. Considering a data-centric view, EHRs are very advanced as they provide and share healthcare data in a cross-institutional and patient-centered way adhering to high syntactic and semantic interoperability. However, the EHR functionalities available for the end users are rare and hence often limited to basic document query functions. Future EHR use necessitates the ability to let the users define their needed data according to a certain situation and how this data should be processed. Workflow and semantic modelling approaches as well as Web services provide means to fulfil such a goal. This thesis develops concepts for dynamic interfaces between EHR end users and a service oriented eHealth infrastructure, which allow the users to design their flexible EHR needs, modeled in a dynamic and formal way. These are used to discover, compose and execute the right Semantic Web services.

PMID: 27577496 [PubMed - in process]

A Digital Health System to Assist Family Physicians to Safely Prescribe NOAC Medications.

Stud Health Technol Inform. 2016;228:519-23

Authors: Abidi SR, Cox J, Abusharekh A, Hashemian N, Abidi SS

Abstract
Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Generally, the therapeutic options for managing AF include the use of anticoagulant drugs that prevent the coagulation of blood. New Oral Anticoagulants (NOACs) are not optimally prescribed to patients, despite their efficacy. In Canada, NOAC medications are not directly available to patients who belong to provincial benefits programs, rather a NOAC special authorization process establishes the eligibility of a patient to receive a NOAC and be paid by the provincial Pharmacare program. This special authorization process is tedious and paper-based which inhibits physicians to prescribe NOAC leading to suboptimal AF care to patients. In this paper, we present a computerized NOAC Authorization Decision Support System (NOAC-ADSS), accessible to physicians to help them (a) determine a patient eligibility for NOAC based on Canadian AF clinical guidelines, and (b) complete the special authorization form. We present a semantic web based system to ontologically model the NOAC eligibility criteria and execute the knowledge to determine a patient NOAC eligibility and dosage.

PMID: 27577437 [PubMed - in process]

Thesaurus-Based Hierarchical Semantic Grouping of Medical Terms in Information Extraction.

Stud Health Technol Inform. 2016;228:446-50

Authors: Lassoued Y, Deleris L

Abstract
In this paper we describe a semantic approach for grouping medical terms into a hierarchy of concepts based on the UMLS meta-thesaurus. The context of this work is Medical Recap, a Web system that automatically extracts risk information from PubMed abstracts, and then aggregates this knowledge into dependence graphs or Bayesian networks.

PMID: 27577422 [PubMed - in process]

An Ontological Model of Behaviour Theory to Generate Personalized Action Plans to Modify Behaviours.

Stud Health Technol Inform. 2016;228:399-403

Authors: Baig W, Abidi S, Abidi SS

Abstract
Behavior change approaches aim to assist patients in achieving self-efficacy in managing their condition. Social cognitive theory (SCT) stipulates self-efficacy as a central element to behavior change and provides constructs to achieve self-efficacy guided by person-specific action plans. In our work, to administer behaviour change in patient with chronic conditions, our approach entails the computerization of SCT-based self-efficacy constructs in order to generate personalized action plans that are suitable to an individual's current care scenario. We have taken a knowledge management approach, whereby we have computerized the SCT-based self-efficacy constructs in terms of a high-level SCT knowledge model that can be operationalized to generate personalized behaviour change action plans. We have collected and computerized behavior change content targeting healthy living and physical activity. Semantic web technologies have been used to develop the SCT knowledge model, represented in terms of an ontology and SWRL rules. The ontological SCT model can inferred to generate personalized self-management action plans for a given patient profile. We present formative evaluation of the clinical correctness and relevance of the generated personalized action plans for a range of test patient profiles.

PMID: 27577412 [PubMed - in process]

Building a Semantic Model to Enhance the User's Perceived Functionality of the EHR.

Stud Health Technol Inform. 2016;228:137-41

Authors: Lasierra N, Schweitzer M, Gorfer T, Toma I, Hoerbst A

Abstract
In order to facilitate and increase the usability of Electronic Health Records (EHRs) for healthcare professional's daily work, we have designed a system that enables functional and flexible EHR access, based on the execution of clinical workflows and the composition of Semantic Web Services (SWS). The backbone of this system is based on an ontology. In this paper we present the strategy that we have followed for its design, and an overview of the resulting model. The designed ontology enables to model patient-centred clinical EHR workflows, the involved sequence of tasks and its associated functionality and managed clinical data. This semantic model constitutes also the main pillar for enabling dynamic service selection in our system.

PMID: 27577358 [PubMed - in process]

A National Medical Information System for Senegal: Architecture and Services.

Stud Health Technol Inform. 2016;228:43-7

Authors: Camara G, Diallo AH, Lo M, Tendeng JN, Lo S

Abstract
In Senegal, great amounts of data are daily generated by medical activities such as consultation, hospitalization, blood test, x-ray, birth, death, etc. These data are still recorded in register, printed images, audios and movies which are manually processed. However, some medical organizations have their own software for non-standardized patient record management, appointment, wages, etc. without any possibility of sharing these data or communicating with other medical structures. This leads to lots of limitations in reusing or sharing these data because of their possible structural and semantic heterogeneity. To overcome these problems we have proposed a National Medical Information System for Senegal (SIMENS). As an integrated platform, SIMENS provides an EHR system that supports healthcare activities, a mobile version and a web portal. The SIMENS architecture proposes also a data and application integration services for supporting interoperability and decision making.

PMID: 27577338 [PubMed - in process]