RetroMine, or how to provide in-depth retrospective studies from Medline in a glance: the hepcidin use-case.

J Integr Bioinform. 2015;12(3):275

Authors: Ameline de Cadeville B, Loréal O, Moussouni-Marzolf F

Abstract
The rapid expansion of biomedical literature has provoked an increased development of advanced text mining tools to rapidly extract relevant events from the continuously increasing amount of knowledge published periodically in PubMed. However, bioinvestigators are still reluctant to use these tools for two reasons: i) a large volume of events is often extracted upon a query, and this volume is hard to manage, and ii) background events dominate search results and overshadow more pertinent published information, especially for domain experts. In this paper, we propose an approach that incorporates the temporal dimension of published events to the process of information extraction to improve data selection and prioritize more pertinent periodically published knowledge for scientists. Indeed, instead of providing the total knowledge associated with a PubMed query, which is usually a mix of trivial background information and non-background information, we propose a method that incorporates time and selects non background and highly relevant biological entities and events published over time for bioinvestigators. Before excluding background events from the total knowledge extracted, a quantification of their amount is also provided. This work is illustrated by a case study regarding Hepcidin gene publications over a decade, a duration that is sufficiently long enough to generate alternative views on the overall data extracted.

PMID: 26673791 [PubMed - indexed for MEDLINE]

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Repurposing of nitroxoline as a potential anticancer agent against human prostate cancer: a crucial role on AMPK/mTOR signaling pathway and the interplay with Chk2 activation.

Oncotarget. 2015 Nov 24;6(37):39806-20

Authors: Chang WL, Hsu LC, Leu WJ, Chen CS, Guh JH

Abstract
Nitroxoline is an antibiotic by chelating Zn2+ and Fe2+ from biofilm matrix. In this study, nitroxoline induced G1 arrest of cell cycle and subsequent apoptosis in prostate cancer cells through ion chelating-independent pathway. It decreased protein levels of cyclin D1, Cdc25A and phosphorylated Rb, but activated AMP-activated protein kinase (AMPK), a cellular energy sensor and signal transducer, leading to inhibition of downstream mTOR-p70S6K signaling. Knockdown of AMPKα significantly rescued nitroxoline-induced inhibition of cyclin D1-Rb-Cdc25A axis indicating AMPK-dependent mechanism. However, cytoprotective autophagy was simultaneously evoked by nitroxoline. Comet assay and Western blot analysis demonstrated DNA damaging effect and activation of Chk2 other than Chk1 to nitroxoline action. Instead of serving as a DNA repair transducer, nitroxoline-mediated Chk2 activation was identified to function as a pro-apoptotic inducer. In conclusion, the data suggest that nitroxoline induces anticancer activity through AMPK-dependent inhibition of mTOR-p70S6K signaling pathway and cyclin D1-Rb-Cdc25A axis, leading to G1 arrest of cell cycle and apoptosis. AMPK-dependent activation of Chk2, at least partly, contributes to apoptosis. The data suggest the potential role of nitroxoline for therapeutic development against prostate cancers.

PMID: 26447757 [PubMed - indexed for MEDLINE]

Identification of cromolyn sodium as an anti-fibrotic agent targeting both hepatocytes and hepatic stellate cells.

Pharmacol Res. 2015 Dec;102:176-83

Authors: Choi JS, Kim JK, Yang YJ, Kim Y, Kim P, Park SG, Cho EY, Lee DH, Choi JW

Abstract
Liver fibrosis and cirrhosis, the late stage of fibrosis, are threatening diseases that lead to liver failure and patient death. Although aberrantly activated hepatic stellate cells (HSCs) are the main cause of disease initiation, the symptoms are primarily related to damaged hepatocytes. Thus, damaged hepatocytes, as well as HSCs, need to be simultaneously considered as therapeutic targets to develop more efficient treatments. Here, we suggest cromolyn sodium as an anti-fibrotic agent to commonly modulate hepatocytes and hepatic stellate cells. The differentially expressed genes from 6 normal and 40 cirrhotic liver tissues which were collected from GEO data were assessed by pharmacokinetic analysis using a connectivity map to identify agents that commonly revert abnormal hepatocytes and HSCs to normal conditions. Based on a series of analyses, a few candidates were selected. Candidates were tested in vitro to determine their anti-fibrotic efficacy on HSCs and hepatocytes. Cromolyn, which was originally developed as a mast cell stabilizer, showed the potential to ameliorate activated HSCs in vitro. The activation and collagen accumulation for HSC cell lines LX2 and HSC-T6 were reduced by 50% after cromolyn treatment at a low concentration without apoptosis. Furthermore, cromolyn treatment compromised the TGF-β-induced epithelial mesenchyme transition and replicative senescence rate of hepatocytes, which are generally associated with fibrogenesis. Taken together, cromolyn may be the basis for an effective cure for fibrosis and cirrhosis because it targets both HSCs and hepatocytes.

PMID: 26453959 [PubMed - indexed for MEDLINE]

Phenotypic shift in Pseudomonas aeruginosa populations from cystic fibrosis lungs after 2-week antipseudomonal treatment.

J Cyst Fibros. 2016 Sep 16;

Authors: Fernández-Barat L, Ciofu O, Kragh KN, Pressler T, Johansen U, Motos A, Torres A, Hoiby N

Abstract
BACKGROUND: The influence of suppressive therapy on the different P. aeruginosa phenotypes harbored in the lungs of cystic fibrosis (CF) patients remains unclear. Our aim was to investigate the phenotypic changes (mucoidy, hypermutability, antibiotic resistance, transcriptomic profiles and biofilm) in P. aeruginosa populations before and after a 2-week course of suppressive antimicrobial therapy in chronically infected CF patients in Denmark.
MATERIAL AND METHODS: Prospective observational clinical study. Sputum samples were assessed before and after treatment for P. aeruginosa, with regard to: a) colony-forming units (CFU/mL), b) frequency of mucoids and non-mucoids, c) resistance pattern to anti-pseudomonal drugs, d) hypermutability, e) transcriptomic profiles, and f) presence of biofilms.
RESULTS: We collected 23 sputum samples (12 before antibiotic treatment and 11 after) and 77 P. aeruginosa from different CF patients. After treatment, the P. aeruginosa burden diminished but antimicrobial resistance to aztreonam, tobramycin and ceftazidime rose; non-mucoid phenotypes presented increased resistance to colistin, tobramycin, meropenem, and ciprofloxacin, and hypermutable phenotypes to ciprofloxacin. In spite of biofilm persistence, a down-regulation of genes involved in denitrification was detected.
CONCLUSION: A 2-week course of suppressive therapy reduces P. aeruginosa lung colonization and influences nitrogen metabolism genes, but also promotes antimicrobial resistance while P. aeruginosa persists in biofilms.

PMID: 27651273 [PubMed - as supplied by publisher]

Transcription-associated mutation of lasR in Pseudomonas aeruginosa.

DNA Repair (Amst). 2016 Sep 13;

Authors: Wang C, McPherson JR, Zhang LH, Rozen S, Sabapathy K

Abstract
Pseudomonas aeruginosa is an opportunistic pathogen which infects cystic fibrosis and cancer patients with compromised immune systems. LasR is a master regulator which controls the virulence of P. aeruginosa in response to bacterial cell-density and host signals. During infection, lasR is frequently mutated, conferring P. aeruginosa a growth advantage in hosts and enhances resistance to widely used antibiotics. However, the mechanistic basis of lasR mutation is not well understood. We have tested here the hypothesis that transcription strength is a contributory determinant of lasR mutagenesis. P. aeruginosa strains with different lasR transcription strengths were therefore engineered and the lasR mutations were monitored unbiasedly using next-generation sequencing technology. Our results suggest that the strength of transcription could be one of the deterministic factors that drive the mutagenesis of lasR in P. aeruginosa, shedding new insights into bacterial infection and antibiotic resistance.

PMID: 27650847 [PubMed - as supplied by publisher]

Rhinovirus infections and cystic fibrosis.

J Paediatr Child Health. 2016 Sep;52(9):911

Authors: Isaacs D

PMID: 27650155 [PubMed - as supplied by publisher]

Clinical value of pulmonary hyperinflation as a treatment outcome in cystic fibrosis.

Respirology. 2016 Sep 20;

Authors: Stevens D

PMID: 27649936 [PubMed - as supplied by publisher]

Using the same cut-off for sulfur hexafluoride and nitrogen multiple-breath washout may not be appropriate.

J Appl Physiol (1985). 2015 Dec 15;119(12):1510-2

Authors: Yammine S, Lenherr N, Nyilas S, Singer F, Latzin P

PMID: 26159760 [PubMed - indexed for MEDLINE]

Systemic therapies in neuroendocrine tumors and novel approaches towards personalised medicine.

Endocr Relat Cancer. 2016 Sep 20;

Authors: Pavel M, Sers C

Abstract
Neuroendocrine tumors (NET) are a group of heterogenous neoplasms. Evidence-based treatment options for antiproliferative therapy include somatostatin analogs, the mTOR inhibitor everolimus, the multiple tyrosine kinase inhibitor sunitinib and peptide receptor radionuclide therapy with 177-Lu-octreotate. In the absence of definite predictive markers therapeutic decision making follows clinical and pathological criteria. Since objective reponse rates with targeted drugs are rather low, and response duration is limited in most patients, numerous combination therapies targeting multiple pathways have been explored in the field. Upfront combination of drugs, however, is associated with increasing toxicity and has shown little benefit. Major advancements in the molecular understanding of NET based on genomic, epigenomic, and transcriptomic analysis have been achieved with prognostic and therapeutic impact. New insight into molecular alterations has paved the way to biomarker-driven clinical trials and may facilitate treatment stratification towards personalized medicine in the near future. However, an improved understandiing of the complexity of pathway interactions is required for successful treatment. A systems biology approach is one of the tools that may help to achieve this endeavour.

PMID: 27649723 [PubMed - as supplied by publisher]

iTAP: integrated transcriptomics and phenotype database for stress response of Escherichia coli and Saccharomyces cerevisiae.

BMC Res Notes. 2015;8:771

Authors: Sundararaman N, Ash C, Guo W, Button R, Singh J, Feng X

Abstract
BACKGROUND: Organisms are subject to various stress conditions, which affect both the organism's gene expression and phenotype. It is critical to understand microbial responses to stress conditions and uncover the underlying molecular mechanisms. To this end, it is necessary to build a database that collects transcriptomics and phenotypic data of microbes growing under various stress factors for in-depth systems biology analysis. Despite of numerous databases that collect gene expression profiles, to our best knowledge, there are few, if any, databases that collect both transcriptomics and phenotype data simultaneously. In light of this, we have developed an open source, web-based database, namely integrated transcriptomics and phenotype (iTAP) database, that records and links the transcriptomics and phenotype data for two model microorganisms, Escherichia coli and Saccharomyces cerevisiae in response to exposure of various stress conditions.
RESULTS: To collect the data, we chose relevant research papers from the PubMed database containing all the necessary information for data curation including experimental conditions, transcriptomics data, and phenotype data. The transcriptomics data, including the p value and fold change, were obtained through the comparison of test strains against control strains using Gene Expression Omnibus's GEO2R analyzer. The phenotype data, including the cell growth rate and the productivity, volumetric rate, and mass-based yield of byproducts, were calculated independently from charts or graphs within the reference papers. Since the phenotype data was never reported in a standardized format, the curation of correlated transcriptomics-phenotype datasets became extremely tedious and time-consuming. Despite the challenges, till now, we successfully correlated 57 and 143 datasets of transcriptomics and phenotype for E. coli and S. cerevisiae, respectively, and applied a regression model within the iTAP database to accurately predict over 93 and 73 % of the growth rates of E. coli and S. cerevisiae, respectively, directly from the transcriptomics data.
CONCLUSION: This is the first time that transcriptomics and phenotype data are categorized and correlated in an open-source database. This allows biologists to access the database and utilize it to predict the phenotype of microorganisms from their transcriptomics data. The iTAP database is freely available at https://sites.google.com/a/vt.edu/biomolecular-engineering-lab/software .

PMID: 26653323 [PubMed - indexed for MEDLINE]

Reproducible quantitative proteotype data matrices for systems biology.

Mol Biol Cell. 2015 Nov 5;26(22):3926-31

Authors: Röst HL, Malmström L, Aebersold R

Abstract
Historically, many mass spectrometry-based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.

PMID: 26543201 [PubMed - indexed for MEDLINE]

A comprehensive model to predict mitotic division in budding yeasts.

Mol Biol Cell. 2015 Nov 5;26(22):3954-65

Authors: Sutradhar S, Yadav V, Sridhar S, Sreekumar L, Bhattacharyya D, Ghosh SK, Paul R, Sanyal K

Abstract
High-fidelity chromosome segregation during cell division depends on a series of concerted interdependent interactions. Using a systems biology approach, we built a robust minimal computational model to comprehend mitotic events in dividing budding yeasts of two major phyla: Ascomycota and Basidiomycota. This model accurately reproduces experimental observations related to spindle alignment, nuclear migration, and microtubule (MT) dynamics during cell division in these yeasts. The model converges to the conclusion that biased nucleation of cytoplasmic microtubules (cMTs) is essential for directional nuclear migration. Two distinct pathways, based on the population of cMTs and cortical dyneins, differentiate nuclear migration and spindle orientation in these two phyla. In addition, the model accurately predicts the contribution of specific classes of MTs in chromosome segregation. Thus we present a model that offers a wider applicability to simulate the effects of perturbation of an event on the concerted process of the mitotic cell division.

PMID: 26310442 [PubMed - indexed for MEDLINE]

A corpus for plant-chemical relationships in the biomedical domain.

BMC Bioinformatics. 2016;17(1):386

Authors: Choi W, Kim B, Cho H, Lee D, Lee H

Abstract
BACKGROUND: Plants are natural products that humans consume in various ways including food and medicine. They have a long empirical history of treating diseases with relatively few side effects. Based on these strengths, many studies have been performed to verify the effectiveness of plants in treating diseases. It is crucial to understand the chemicals contained in plants because these chemicals can regulate activities of proteins that are key factors in causing diseases. With the accumulation of a large volume of biomedical literature in various databases such as PubMed, it is possible to automatically extract relationships between plants and chemicals in a large-scale way if we apply a text mining approach. A cornerstone of achieving this task is a corpus of relationships between plants and chemicals.
RESULTS: In this study, we first constructed a corpus for plant and chemical entities and for the relationships between them. The corpus contains 267 plant entities, 475 chemical entities, and 1,007 plant-chemical relationships (550 and 457 positive and negative relationships, respectively), which are drawn from 377 sentences in 245 PubMed abstracts. Inter-annotator agreement scores for the corpus among three annotators were measured. The simple percent agreement scores for entities and trigger words for the relationships were 99.6 and 94.8 %, respectively, and the overall kappa score for the classification of positive and negative relationships was 79.8 %. We also developed a rule-based model to automatically extract such plant-chemical relationships. When we evaluated the rule-based model using the corpus and randomly selected biomedical articles, overall F-scores of 68.0 and 61.8 % were achieved, respectively.
CONCLUSION: We expect that the corpus for plant-chemical relationships will be a useful resource for enhancing plant research. The corpus is available at http://combio.gist.ac.kr/plantchemicalcorpus .

PMID: 27650402 [PubMed - as supplied by publisher]

A hybrid model for automatic identification of risk factors for heart disease.

J Biomed Inform. 2015 Dec;58 Suppl:S171-82

Authors: Yang H, Garibaldi JM

Abstract
Coronary artery disease (CAD) is the leading cause of death in both the UK and worldwide. The detection of related risk factors and tracking their progress over time is of great importance for early prevention and treatment of CAD. This paper describes an information extraction system that was developed to automatically identify risk factors for heart disease in medical records while the authors participated in the 2014 i2b2/UTHealth NLP Challenge. Our approaches rely on several nature language processing (NLP) techniques such as machine learning, rule-based methods, and dictionary-based keyword spotting to cope with complicated clinical contexts inherent in a wide variety of risk factors. Our system achieved encouraging performance on the challenge test data with an overall micro-averaged F-measure of 0.915, which was competitive to the best system (F-measure of 0.927) of this challenge task.

PMID: 26375492 [PubMed - indexed for MEDLINE]

Coronary artery disease risk assessment from unstructured electronic health records using text mining.

J Biomed Inform. 2015 Dec;58 Suppl:S203-10

Authors: Jonnagaddala J, Liaw ST, Ray P, Kumar M, Chang NW, Dai HJ

Abstract
Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstructured clinical narratives if the data is not collected specifically for risk assessment purposes. Clinical text mining can be used to extract data related to risk factors from unstructured clinical notes. This study presents methods to extract Framingham risk factors from unstructured electronic health records using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients. We developed a rule-based system to extract risk factors: age, gender, total cholesterol, HDL-C, blood pressure, diabetes history and smoking history. The results showed that the output from the text mining system was reliable, but there was a significant amount of missing data to calculate the Framingham risk score. A systematic approach for understanding missing data was followed by implementation of imputation strategies. An analysis of the 10-year Framingham risk scores for coronary artery disease in this cohort has shown that the majority of the diabetic patients are at moderate risk of CAD.

PMID: 26319542 [PubMed - indexed for MEDLINE]

Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes.

J Biomed Inform. 2015 Dec;58 Suppl:S128-32

Authors: Khalifa A, Meystre S

Abstract
The 2014 i2b2 natural language processing shared task focused on identifying cardiovascular risk factors such as high blood pressure, high cholesterol levels, obesity and smoking status among other factors found in health records of diabetic patients. In addition, the task involved detecting medications, and time information associated with the extracted data. This paper presents the development and evaluation of a natural language processing (NLP) application conceived for this i2b2 shared task. For increased efficiency, the application main components were adapted from two existing NLP tools implemented in the Apache UIMA framework: Textractor (for dictionary-based lookup) and cTAKES (for preprocessing and smoking status detection). The application achieved a final (micro-averaged) F1-measure of 87.5% on the final evaluation test set. Our attempt was mostly based on existing tools adapted with minimal changes and allowed for satisfying performance with limited development efforts.

PMID: 26318122 [PubMed - indexed for MEDLINE]

Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.

J Biomed Inform. 2015 Dec;58 Suppl:S143-9

Authors: Urbain J

Abstract
We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic patients over time. The system was originally developed for the 2014 i2b2 Challenges in Natural Language in Clinical Data. The system's strengths included a high level of accuracy for identifying named entities associated with heart disease risk factor events. The system's primary weakness was due to inaccuracies when characterizing the attributes of some events. For example, determining the relative time of an event with respect to the record date, whether an event is attributable to the patient's history or the patient's family history, and differentiating between current and prior smoking status. We believe these inaccuracies were due in large part to the lack of an effective approach for integrating context into our event detection model. To address these inaccuracies, we explore the addition of a distributional semantic model for characterizing contextual evidence of heart disease risk factor events. Using this semantic model, we raise our initial 2014 i2b2 Challenges in Natural Language of Clinical data F1 score of 0.838 to 0.890 and increased precision by 10.3% without use of any lexicons that might bias our results.

PMID: 26305514 [PubMed - indexed for MEDLINE]

Automatic detection of protected health information from clinic narratives.

J Biomed Inform. 2015 Dec;58 Suppl:S30-8

Authors: Yang H, Garibaldi JM

Abstract
This paper presents a natural language processing (NLP) system that was designed to participate in the 2014 i2b2 de-identification challenge. The challenge task aims to identify and classify seven main Protected Health Information (PHI) categories and 25 associated sub-categories. A hybrid model was proposed which combines machine learning techniques with keyword-based and rule-based approaches to deal with the complexity inherent in PHI categories. Our proposed approaches exploit a rich set of linguistic features, both syntactic and word surface-oriented, which are further enriched by task-specific features and regular expression template patterns to characterize the semantics of various PHI categories. Our system achieved promising accuracy on the challenge test data with an overall micro-averaged F-measure of 93.6%, which was the winner of this de-identification challenge.

PMID: 26231070 [PubMed - indexed for MEDLINE]

Combining knowledge- and data-driven methods for de-identification of clinical narratives.

J Biomed Inform. 2015 Dec;58 Suppl:S53-9

Authors: Dehghan A, Kovacevic A, Karystianis G, Keane JA, Nenadic G

Abstract
A recent promise to access unstructured clinical data from electronic health records on large-scale has revitalized the interest in automated de-identification of clinical notes, which includes the identification of mentions of Protected Health Information (PHI). We describe the methods developed and evaluated as part of the i2b2/UTHealth 2014 challenge to identify PHI defined by 25 entity types in longitudinal clinical narratives. Our approach combines knowledge-driven (dictionaries and rules) and data-driven (machine learning) methods with a large range of features to address de-identification of specific named entities. In addition, we have devised a two-pass recognition approach that creates a patient-specific run-time dictionary from the PHI entities identified in the first step with high confidence, which is then used in the second pass to identify mentions that lack specific clues. The proposed method achieved the overall micro F1-measures of 91% on strict and 95% on token-level evaluation on the test dataset (514 narratives). Whilst most PHI entities can be reliably identified, particularly challenging were mentions of Organizations and Professions. Still, the overall results suggest that automated text mining methods can be used to reliably process clinical notes to identify personal information and thus providing a crucial step in large-scale de-identification of unstructured data for further clinical and epidemiological studies.

PMID: 26210359 [PubMed - indexed for MEDLINE]