Prioritization of Susceptibility Genes for Ectopic Pregnancy by Gene Network Analysis.

Int J Mol Sci. 2016 Feb 01;17(2):

Authors: Liu JL, Zhao M

Abstract
Ectopic pregnancy is a very dangerous complication of pregnancy, affecting 1%-2% of all reported pregnancies. Due to ethical constraints on human biopsies and the lack of suitable animal models, there has been little success in identifying functionally important genes in the pathogenesis of ectopic pregnancy. In the present study, we developed a random walk-based computational method named TM-rank to prioritize ectopic pregnancy-related genes based on text mining data and gene network information. Using a defined threshold value, we identified five top-ranked genes: VEGFA (vascular endothelial growth factor A), IL8 (interleukin 8), IL6 (interleukin 6), ESR1 (estrogen receptor 1) and EGFR (epidermal growth factor receptor). These genes are promising candidate genes that can serve as useful diagnostic biomarkers and therapeutic targets. Our approach represents a novel strategy for prioritizing disease susceptibility genes.

PMID: 26840308 [PubMed - indexed for MEDLINE]

23 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 2016/10/19

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.

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

Pharmacogenomics[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/10/19

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.

15 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 2016/10/19

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.

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

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/10/19

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.

Drug Repositioning in Inflammatory Bowel Disease Based on Genetic Information.

Inflamm Bowel Dis. 2016 Nov;22(11):2562-2570

Authors: Collij V, Festen EA, Alberts R, Weersma RK

Abstract
BACKGROUND: Currently, 200 genetic risk loci have been identified for inflammatory bowel disease (IBD). Although these findings have significantly advanced our insight into IBD biology, there has been little progress in translating this knowledge toward clinical practice, like more cost-efficient drug development. Our aim was to use genetic knowledge to identify drugs that warrant further investigation in IBD treatment.
METHODS: We hypothesized that proteins encoded by IBD candidate genes are potential IBD drug targets because genetic information can increase successful drug identification. We identified drugs that target the proteins encoded by IBD candidate genes using the DrugBank. We included proteins that are in direct protein-protein interaction with proteins encoded by IBD risk genes. Promising potential IBD drugs were selected based on a manual literature search of all identified drugs (PubMed, ClinicalTrials.gov).
RESULTS: We have identified 113 drugs that could potentially be used in IBD treatment. Fourteen are known IBD drugs, 48 drugs have been, or are being investigated in IBD, 19 are being used or being investigated in other inflammatory disorders treatment, and 32 are investigational new drugs that have not yet been registered for clinical use.
CONCLUSIONS: We confirm that proteins encoded by IBD candidate genes are targeted by approved IBD therapies. Furthermore, we show that Food and Drug Administration-approved drugs could possibly be repositioned for IBD treatment. We also identify investigational new drugs that warrant further investigation for IBD treatment. Incorporating this process in IBD drug development will improve the utilization of genetic data and could lead to the improvement of IBD treatment.

PMID: 27753694 [PubMed - in process]

A novel strategy of profiling the mechanism of herbal medicines by combining network pharmacology with plasma concentration determination and affinity constant measurement.

Mol Biosyst. 2016 Oct 18;12(11):3347-3356

Authors: Chen L, Lv D, Wang D, Chen X, Zhu Z, Cao Y, Chai Y

Abstract
Herbal medicines have long been widely used in the treatment of various complex diseases in China. However, the active constituents and therapeutic mechanisms of many herbal medicines remain undefined. Therefore, the identification of the active components and target proteins in these herbal medicines is a formidable task in herbal medicine research. In this study, we proposed a strategy, which integrates network pharmacology with biomedical analysis and surface plasmon resonance (SPR) to predict the active ingredients and potential targets of herbal medicine Sophora flavescens or Kushen in Chinese, and evaluate its anti-fibrosis activity. First, we applied a virtual HTDocking platform to predict the potential targets of Kushen related to liver fibrosis by selecting five crucial protein targets based on network parameters and text mining. Then, we identified nine components in mice plasma after oral administration of Kushen extract and determined the plasma concentration of each compound. Binding affinities between the nine potential active compounds and five target proteins were detected by SPR assays. Finally, we constructed a multi-parameter network model on the basis of three important parameters to tentatively explain the anti-fibrosis mechanism of Kushen. The results not only provide evidence for the therapeutic mechanism of Kushen but also shed new light on the activity-based analysis of other Chinese herbal medicines.

PMID: 27754507 [PubMed - in process]

13 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 2016/10/18

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.

17 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 2016/10/18

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.

PopHR: a knowledge-based platform to support integration, analysis, and visualization of population health data.

Ann N Y Acad Sci. 2016 Oct 17;:

Authors: Shaban-Nejad A, Lavigne M, Okhmatovskaia A, Buckeridge DL

Abstract
Population health decision makers must consider complex relationships between multiple concepts measured with differential accuracy from heterogeneous data sources. Population health information systems are currently limited in their ability to integrate data and present a coherent portrait of population health. Consequentially, these systems can provide only basic support for decision makers. The Population Health Record (PopHR) is a semantic web application that automates the integration and extraction of massive amounts of heterogeneous data from multiple distributed sources (e.g., administrative data, clinical records, and survey responses) to support the measurement and monitoring of population health and health system performance for a defined population. The design of the PopHR draws on the theories of the determinants of health and evidence-based public health to harmonize and explicitly link information about a population with evidence about the epidemiology and control of chronic diseases. Organizing information in this manner and linking it explicitly to evidence is expected to improve decision making related to the planning, implementation, and evaluation of population health and health system interventions. In this paper, we describe the PopHR platform and discuss the architecture, design, key modules, and its implementation and use.

PMID: 27750378 [PubMed - as supplied by publisher]

Using Pharmacogenomic Databases for Discovering Patient-Target Genes and Small Molecule Candidates to Cancer Therapy.

Front Pharmacol. 2016;7:312

Authors: Belizário JE, Sangiuliano BA, Perez-Sosa M, Neyra JM, Moreira DF

Abstract
With multiple omics strategies being applied to several cancer genomics projects, researchers have the opportunity to develop a rational planning of targeted cancer therapy. The investigation of such numerous and diverse pharmacogenomic datasets is a complex task. It requires biological knowledge and skills on a set of tools to accurately predict signaling network and clinical outcomes. Herein, we describe Web-based in silico approaches user friendly for exploring integrative studies on cancer biology and pharmacogenomics. We briefly explain how to submit a query to cancer genome databases to predict which genes are significantly altered across several types of cancers using CBioPortal. Moreover, we describe how to identify clinically available drugs and potential small molecules for gene targeting using CellMiner. We also show how to generate a gene signature and compare gene expression profiles to investigate the complex biology behind drug response using Connectivity Map. Furthermore, we discuss on-going challenges, limitations and new directions to integrate molecular, biological and epidemiological information from oncogenomics platforms to create hypothesis-driven projects. Finally, we discuss the use of Patient-Derived Xenografts models (PDXs) for drug profiling in vivo assay. These platforms and approaches are a rational way to predict patient-targeted therapy response and to develop clinically relevant small molecules drugs.

PMID: 27746730 [PubMed - in process]

Metformin Targets Central Carbon Metabolism and Reveals Mitochondrial Requirements in Human Cancers.

Cell Metab. 2016 Oct 12;:

Authors: Liu X, Romero IL, Litchfield LM, Lengyel E, Locasale JW

Abstract
Repurposing metformin for cancer therapy is attractive due to its safety profile, epidemiological evidence, and encouraging data from human clinical trials. Although it is known to systemically affect glucose metabolism in liver, muscle, gut, and other tissues, the molecular determinants that predict a patient response in cancer remain unknown. Here, we carry out an integrative metabolomics analysis of metformin action in ovarian cancer. Metformin accumulated in patient biopsies, and pathways involving nucleotide metabolism, redox, and energy status, all related to mitochondrial metabolism, were affected in treated tumors. Strikingly, a metabolic signature obtained from a patient with an exceptional clinical outcome mirrored that of a responsive animal tumor. Mechanistically, we demonstrate with stable isotope tracing that these metabolic signatures are due to an inability to adapt nutrient utilization in the mitochondria. This analysis provides new insights into mitochondrial metabolism and may lead to more precise indications of metformin in cancer.

PMID: 27746051 [PubMed - as supplied by publisher]

Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision.

J Mach Learn Res. 2016;17:

Authors: Wallace BC, Kuiper J, Sharma A, Zhu MB, Marshall IJ

Abstract
Systematic reviews underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a PICO criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full-texts of trial reports is thus a critical yet time-consuming step in the systematic review process. We seek to expedite evidence synthesis by developing machine learning models to automatically extract sentences from articles relevant to PICO elements. Collecting a large corpus of training data for this task would be prohibitively expensive. Therefore, we derive distant supervision (DS) with which to train models using previously conducted reviews. DS entails heuristically deriving 'soft' labels from an available structured resource. However, we have access only to unstructured, free-text summaries of PICO elements for corresponding articles; we must derive from these the desired sentence-level annotations. To this end, we propose a novel method - supervised distant supervision (SDS) - that uses a small amount of direct supervision to better exploit a large corpus of distantly labeled instances by learning to pseudo-annotate articles using the available DS. We show that this approach tends to outperform existing methods with respect to automated PICO extraction.

PMID: 27746703 [PubMed - in process]

A PubMed-wide study of endometriosis.

Genomics. 2016 Oct 13;:

Authors: Liu JL, Zhao M

Abstract
Endometriosis affects 5-10% of women in reproductive age, leading to dysmenorrhea, pelvic pain and infertility; however, our understanding on the pathogenesis of this disease remains incomplete. In the present study, we performed a systematic analysis of endometriosis-related genes using text mining. Taking text mining results as input, we subsequently generated a filtered gene set by computing the likelihood of finding more than expected occurrences for every gene across the disease-centered subset of the PubMed database. Characterization of this filtered gene set by gene ontology, pathway and network analysis provides clues to the multiple mechanisms hypothesized to be responsible for the establishment of ectopic endometrial tissues, including the migration, implantation, survival and proliferation of ectopic endometrial cells. Finally, using this gene set as "seed", we scanned human genome to predict novel candidate genes based on gene annotations from multiple databases. Our study provides in-depth insights into the pathogenesis of endometriosis.

PMID: 27746014 [PubMed - as supplied by publisher]

Parental Reflections on the Diagnostic Process for Duchenne Muscular Dystrophy: A Qualitative Study.

J Pediatr Health Care. 2016 Oct 12;:

Authors: Bendixen RM, Houtrow A

Abstract
PURPOSE: Duchenne muscular dystrophy (DMD) is a rare neuromuscular disease with no known cure. We sought to update over 30 years of research reporting on the diagnostic delays in DMD.
METHODS: Through personal interviews, this study qualitatively explored parents' experiences regarding receipt of the DMD diagnosis and the guidance for care provided. Thematic analysis identified themes and provided answers to the research questions being addressed.
RESULTS: Four themes emerged: (a) Dismissive illustrates little consideration of parent concern in the diagnostic process; (b) Limited Knowledge describes misunderstandings about clinical signs, recommended screenings, and testing to achieve a diagnosis of DMD; (c) Careless Delivery reports on the manner in which the diagnosis was given; and (d) Lack of Guidance describes the follow-up that occurred after the diagnosis.
CONCLUSION: Despite marked medical progress over the past several decades, substantial barriers to arriving at the diagnosis of DMD and the provision of care guidance remain.

PMID: 27743907 [PubMed - as supplied by publisher]

Lupus anticoagulant-hypoprothrombinemia syndrome presenting with co-existing cerebral venous thrombosis and subdural hemorrhage.

J Mal Vasc. 2016 Oct 12;:

Authors: Bel Feki N, Zayet S, Ben Ghorbel I, Houman MH

Abstract
The lupus anticoagulant-hypoprothrombinemia syndrome (LA-HPS) - the association of acquired factor II deficiency and lupus anticoagulant - is a rare disease that may cause a predisposition not only to thrombosis but also to severe bleeding. We are reporting on a 36-year-old female patient presenting with co-existing cerebral venous thrombosis and subdural hemorrhage. The coagulation screening showed a prolonged prothrombin time (PT), activated partial thromboplastin time (aPTT), and a normal fibrinogen level and platelet count. Evaluation of the clotting factors revealed decreased levels of factors II (37%). Factors V, VIII, IX and XI were normal. Lupus anticoagulant (LA) was demonstrated by the Dilute Russell's Viper Venom Test (DRVVT). Immunological work-up was positive for IgG type anticardiolipines antibodies (aCL). Successful management consisted first of oral prednisone (60mg/d). Thus, anticoagulation was introduced once factor II had stabilized.

PMID: 27743753 [PubMed - as supplied by publisher]

Optimization of Ciprofloxacin Complex Loaded PLGA Nanoparticles for Pulmonary Treatment of Cystic Fibrosis Infections: Design of Experiments Approach.

Int J Pharm. 2016 Oct 12;:

Authors: Türeli NG, Türeli AE, Schneider M

Abstract
Design of Experiments (DoE) is a powerful tool for systematic evaluation of process parameters' effect on nanoparticle (NP) quality with minimum number of experiments. DoE was employed for optimization of ciprofloxacin loaded PLGA NPs for pulmonary delivery against Pseudomonas aeruginosa infections in cystic fibrosis (CF) lungs. Since the biofilm produced by bacteria was shown to be a complicated 3D barrier with heterogeneous meshes ranging from 100nm to 500 nm, nanoformulations small enough to travel through those channels were assigned as target quality. Nanoprecipitation was realized utilizing MicroJet Reactor (MJR) technology based on impinging jets principle. Effect of MJR parameters flow rate, temperature and gas pressure on particle size and PDI was investigated using Box-Behnken design. The relationship between process parameters and particle quality was demonstrated by constructed fit functions (R(2)=0.9934 p<0.0001 and R(2)=0.9983 p < 0.0001, for particle size and PDI, respectively). Prepared nanoformulations varied between 145.2 and 979.8nm with PDI ranging from 0.050 to 1.00 and showed encapsulation efficiencies > 65%. Response surface plots provided experimental data-based understanding of MJR parameters' effect, thus NP quality. Presented work enables ciprofloxacin loaded PLGA nanoparticle preparations with pre-defined quality to fulfill the requirements of local drug delivery under CF disease conditions.

PMID: 27744035 [PubMed - as supplied by publisher]

Cystic fibrosis - Comparison between patients in paediatric and adult age.

Rev Port Pneumol (2006). 2016 Oct 13;:

Authors: Santos V, Cardoso AV, Lopes C, Azevedo P, Gamboa F, Amorim A

Abstract
Cystic fibrosis (CF) is the most common autosomal recessive disease in Caucasians. Although most cases are diagnosed in childhood, diagnosis in adults is apparently increasing.
OBJECTIVE: Evaluate the adult population with CF, comparing patients who were diagnosed before and after 18 years of age.
METHODS: Retrospective analysis of patients followed in three main medical centres in Portugal in 2012. Comparison of two groups: G1 - patients diagnosed at <18 years and G2 - patients diagnosed at ≥18 years.
RESULTS: 89 adults were identified: 61.8% in G1, 38.2% in G2. Gender distribution was similar in both groups. Average age in G2 was higher (38.3±8.4 vs. 26.8±6.1 years, p<0.001). Respiratory symptoms most frequently led to CF diagnosis in all patients, mainly in adulthood. There was a greater percentage of patients homozygous for the mutation delF508 in G1 (43.6 vs. 8.8%, p=0.02). Respiratory and pancreatic function, and body mass index (BMI) showed a higher severity in G1 (G1 vs. G2: FEV1: 54.6±27.3 vs. 29.9±64.6%, p=0.177; pancreatic insufficiency 72.7 vs. 26.5%, p<0.001; BMI 20.2±3.4 vs. 22.2±4.8, p=0.018). Pseudomonas aeruginosa and methicillin-sensitive Staphylococcus aureus were the most frequently isolated microorganisms. Lung transplantation rate was higher in G2 (20.6 vs. 10.9%, p=0.231) while mortality rate was higher in G1 (0 vs. 3.6%, p=0.261). Hospital admission rate was higher in G1 as well as mortality rate.
CONCLUSION: The results suggest that patients with CF diagnosed in childhood have characteristics that distinguish them from those diagnosed in adulthood, and these differences may have implications for diagnosis, prognosis and life expectancy.

PMID: 27743767 [PubMed - as supplied by publisher]

Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis.

Comput Biol Chem. 2016 Sep 30;65:37-44

Authors: Vyas R, Bapat S, Jain E, Karthikeyan M, Tambe S, Kulkarni BD

Abstract
In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein-protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies.

PMID: 27744173 [PubMed - as supplied by publisher]

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 2016/10/16

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.