Summary: Bioclipse, a graphical workbench for the life sciences, provides functionality for managing and visualizing life science data. We introduce Bioclipse-R, which integrates Bioclipse and the statistical programming language R. The synergy between Bioclipse and R is demonstrated by the construction of a decision support system for anticancer drug screening and mutagenicity prediction, which shows how Bioclipse-R can be used to perform complex tasks from within a single software system.Availability and implementation: Bioclipse-R is implemented as a set of Java plug-ins for Bioclipse based on the R-package rj. Source code and binary packages are available from https://github.com/bioclipse and http://www.bioclipse.net/bioclipse-r, respectively.Contact:martin.eklund@farmbio.uu.seSupplementary information:Supplementary data are available at Bioinformatics online. Bioclipse  and R R are freeThe Bioconductor project  and Tavehttp://bioclipse.net/bioclipse-r for a detailed step-by-step description. The operations can be performed both in the Bioclipse Scripting Language (BSL) and in Bioclipse’ graphical user interface.We demonstrate the synergy between Bioclipse and R by constructing a decision support system for virtual screening and mutagenicity testing of new cancer drugs. Constructing this system without the integration between Bioclipse and R would require extensive efforts of copying data between various software tools and file formats, but is almost trivial with Bioclipse-R. Here, we outline the basic steps of the demonstration and refer to This section describes the workflow for constructing the decision support system for virtual screening and mutagenicity testing of new cancer drugs. It consists of three parts: (i) construction of a quantitative structure-activity relationships (QSAR) model for anticancer activity; (ii) construction of a QSAR model for mutagenicity; and (iii) using the models constructed in (i) and (ii) to perform a virtual screen of the DrugBank approved drugs database for non-mutagenic compounds with anticancer activity.Import and inspect molecules. The chemical structures were downloaded as a Simplified Molecular-Input Line-Entry System (SMILES)-formatted file and imported into a new project in Bioclipse. Bioclipse uses the Chemistry Development Kit (CDK) (it (CDK) a and 2 aGenerate 3D coordinates. We used the Balloon . Using the training data, we built a random forest model to discriminate between compounds with and without anticancer activity [the R-package ‘randomForest’ , which is used for screening of new anticancer drug candidates .Compute signature descriptors and set-up dataset. Signature descriptors . We then used the R-package ‘e1071’  and mutagenicity (using the QSAR model constructed in Section 2.2). Using a BSL script, we generated a table sorted on growth inhibition , tentatively indicating that the growth inhibition screening model does a decent job in ranking molecules.Interpret predictions. To interpret the results, we re-ran the predictions in Bioclipse Decision Support. When using the growth inhibition and mutagenicity models in Bioclipse Decision Support, the substructure that contributed the most to the prediction of a given compound’s bioactivity is highlighted. The highlighted substructure is, thus, the site where structural modification would affect the predicted bioactivity the most (http://pele.farmbio.uu.se/bcr/bcr-vscreen.html).the most a. This iUsing the two QSAR models for growth inhibition and mutagenicity prediction constructed in Sections 2.1 and 2.2, we screened the subset of DrugBank , which in turn uses the R-package rj  from https://github.com/bioclipse and http://www.bioclipse.net/bioclipse-r, respectively.Bioclipse-R is available for the operating systems Windows, Linux and Mac OS X. Source code and binary packages are available under the Eclipse Public License . It also shows how Bioclipse-R allows for easy access to statistical models fitted in R from a graphical user interface as well as from BSL scripts, thus allowing for automation and exchange of complete Bioclipse-R workflows. Bioclipse-R also enriches the bioinformatics functionality in Bioclipse through access to Bioconductor via R. The plug-in-based architecture of Bioclipse makes extensions simple, and future development of Bioclipse-R will include a user-friendly graphical interface for performing statistical analyses.Funding: Support from Uppsala University (KoF 07), Swedish VR-2011-6129, and the Swedish strategic research program eSSENCE.Conflict of Interest: none declared.


Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides.In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation.TransportTP is the most effective tool for eukaryotic transporter characterization up to date. Membrane transporter proteins, or simply transporters, play crucial roles in living cells, such as importing essential nutrients and exporting toxic cellular metabolites, mediating signal transduction, and maintaining ionic and osmotic homeostasis.A handful of in-vitro or in-vivo experimental methods have been developed and applied to study transporter mechanisms, such as the patch-clamp techniques for analyzing ion channels  and heteHomology methods may reveal new putative transporters, but they can also generate many false positive assignments, due to systematic errors arising from homology inference, since nontrivial functional variations may be induced during gene duplication or domain shuffling ,12. For Recently, some integrative methods have been reported that incorporate at least two data sources or methods. We proposed a nearest neighbor approach previously which integrated BLAST, Hidden Markov model (HMM) and topology analysis . AnotherHere, we present an automatic transporter prediction and characterization system called TransportTP which has significantly increased predictive performance than existing systems, including our previous work , requiriTransportTP was implemented as both Linux command lines and a web server. Only basic information, such as nucleotide/amino acid sequences, predictive options like training organisms and initial e-value threshold, are required by TransportTP for prediction. The predictive results are presented in a user-friendly manner through a web interface, enumerating all evidence used in decision making and providing cross-links from the evidence to related databases for further verification. Results are sortable via various criteria selected by the user, to accommodate specific interests or curation emphasis. Implementation of the system is very efficient due to the use of parallel computing algorithms among multiple CPUs in a single computer and/or computers spanning a local network.Escherichia coli O157:H7 EDL933, Saccharomyces cerevisiae S288C, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Oryza sativa and Homo sapiens), and four non-model organisms . Sequences and functional annotations of the eleven organisms were acquired from NCBI, except for S. cerevisiae and O. sativa, which were obtained from SGD ftp://ftp.yeastgenome.org/yeast/data_download/sequence/genomic_sequence/orf_protein/archive/ and JCVI ftp://ftp.jcvi.org/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/pseudomolecules/version_5.0/ respectively. Five plant organims were chosen for case studies, including Sorghum bicolor, Populus trichocarpa, Vitis vinifera, Physcomitrella patens and Chlamydomonas reinhardtii. The genomic sequences of the five plant organisms were downloaded from JGI Microbial Genomics ftp://ftp.jgi-psf.org/pub/JGI_data, except for Vitis vinifera, which was downloaded from Genoscope http://www.genoscope.cns.fr/externe/Download/Projets/Projet_ML/data/annotation/Vitis_vinifera_peptide_v1.fa.The databases for implementation and testing of TransportTP were downloaded in September 2008. The TCDB  for the Three measurements were applied in the performance assessment of TransportTP. The first was recall, defined as the proportion of transporters in test sets which were exactly categorized by TransportTP with the same TC family or superfamily. The test sets were either TransportDB  in crossThese three measurements are also known as sensitivity, selectivity, and F-measure, respectively. The average value of each measurement on multiple testing organisms reflects the predictive performance of TransportTP on the specific training organisms(s). Specificity and the Receiver Operating Characteristic curve were not used in our assessment because non-transporters are much more than the transporters, making them less informative.The performance of TransportTP was tested by two cross-validation schemas: (1) Leave-one-in (LOI) cross-validation, i.e. choosing the proteome of one model organism for training and the proteomes of other ten organisms for testing; (2) Leave-multiple-in (LMI) cross-validation, i.e. choosing the proteomes of all seven model organisms for training and only the proteomes of the four non-model organisms for testing. Redundant protein sequences with a similarity of 40% or above were removed during the training to avoid potential overestimation of prediction accuracy. TransportDB ,8 was usS. cerevisiae (yeast) was chosen as an example organism in our analysis with e-value threshold set to 0.1, since the best predictive results were often achieved around this e-value threshold for most training organisms. The cross-validation results of the example are shown in Table bona fide transporters excluded by TransportDB [The leave-one-in cross-validation intended to examine whether the classification rules learned from one model organism can effectively predict transporters in other organisms. nsportDB . The misnsportDB  applied Predictive performance differed significantly between major transporter classes. The best performance was achieved for carriers, followed by channels, and finally primary active transporters. More specifically, TransportTP achieved a balanced accuracy of 87.9% on 74 carrier families, 61.5% on 31 channel families, and 54.6% on 8 primary active transporter families that TransportDB reported on the ten non-yeast testing organisms, at the e-value threshold of 0.1  The performance of a testing organism was greatly influenced by the evolutionary distance to the training organism. The diagonal elements in the figure generally served as the performance peaks since they correspond to zero evolutionary distances between training and testing organism.The complete results of leave-one-in cross-validation tests at the e-value threshold of 0.1 are shown in Figure C. elegans, D. melanogaster, H. sapiens and E. coli for training, but comparable to that using S. cerevisiae, A. thaliana and O. sativa for training. For example, the LMI approach achieved comparable balanced accuracy (80.2%) to that yielded by the yeast alone (81.4%) in the LOI approach. Nevertheless, the maximal loss of balanced accuracy using all model organisms for training was 3.2%, compared with any individual organism for training at the same e-value threshold, therefore, it seems to be a good trade off using all model organisms for training if no well-studied model organism exists.The leave-multiple-in (LMI) validation results using all model organisms for training and the four non-model organisms for testing are shown in Table The performance of TransportTP was also studied by comparing it with other approaches, using at a broad range of e-value thresholds between 10 and 1e-50, to reveal the comprehensive characteristics of TransportTP and alternative strategies.S. cerevisiae (yeast) was again chosen as an example training organism for analysis. The comparative results are shown in Figure Firstly, TransportTP was compared with BLAST search, the approach used widely by biologists, and the integration of BLAST and HMM, the approach applied in our initial classifier. The advantages of TransportTP are further demonstrated in Figure TransportTP was also compared with other reported methods. Compared with our previous work , which wSorghum bicolor (sorghum), Populus trichocarpa (poplar) and Vitis vinifera (grape) are important agricultural organisms. Physcomitrella patens (moss) is a simplest plant model for plant functional evolutionary studies, and Chlamydomonas reinhardtii  is a single-cell green alga organism, representing the group from which land plants evolved. These organisms span a large part of the plant family tree, from single-celled to vascular plants, including monocots to dicots, so that the performance on these organisms should reflect the performance of TransportTP on plant transporters in general. Finally, although most of these organisms were recently sequenced, they lack good annotation for membrane transporters [The performance of TransportTP was investigated further using sequences from five model plant oraganisms, based on the availability of whole proteome and their evolutionary divergence. sporters .Arabidopsis thaliana at an e-value threshold of 10, to cover as many transporters as possible. Manual transporter curation was carried out via human review of all transporter-related evidence from candidate proteins, specifically transmembrane proteins and homologs of TCDB transporters. The evidence included all data utilized by TransportTP, such as predicted membrane topology, presence of homologs in TCDB [http://bioinfo3.noble.org/transporter/model.htm.The performance of TransportTP on plant organisms was evaluated via the comparison of the predictive results of the program with the manual curation by our biologists. Automatic transporter prediction was learned from the model plant organisms  in TCDB , conserv in TCDB , Gene On in TCDB . In addi in TCDB , were albona fide transporters.The comparative results for manual curation versus automatic prediction of transporters for the five plant organisms are shown in Table P. patens and C. reinhardtii, TransportTP achieved a recall of 77.6% and a precision of 78.6% on P. patens at an e-value threshold of 1, and achieved a recall of 71.5% and a precision of 72.8% on C. reinhardtii at an e-value threshold of 0.01 . The low recall made by TransportTP for confidence level 3 does not deny the effectiveness of TransportTP, since the manual curation could not reliably assert this group of potential transporters into superfamilies/families, indicating major problems for classification of these proteins.We adopted stringent assessment in the cross-validation of TransportTP, where only predictions matching with curated transporters in TransportDB were counted in correct ones during the training and the testing. However, TransportDB manually excluded some categories of transporters , resultiWe did not adopt the standard k-folds or leave-one-out cross-validations but instead, used leave-one-in strategy, because the underlying transporter mechanisms in different organisms may be distinct although they share very large similarity. For example, the distribution of transporter families and the gating mechanisms of transporters are likely to be different between prokaryotic and eukaryotic organisms . Thus, tWe did not adopt traditional Support Vector Machine (SVM) but used an ensemble and balanced SVMs in the refining classifier, to handle the unbalanced data produced by the initial classifier. The comparative results of using traditional Support Vector Machine (SVM)  without Despite of the effectiveness and efficiency, some potential pitfalls of TransportTP exist. Firstly, although non-homology pieces of evidence contribute significantly to the performance, sequence homology still plays a nontrivial rule in the inference of novel transporters. If some proteins are very similar to transporters but in fact they are not transporters, for example, receptors or sensors, which evolved from transporters but diverged functionally since the evolutionary split , they maSecondly, TransportTP targets on individual transporters rather than multimeric transporters, which are complex functional structures assembled from products of multiple genes. For example, human TAP transporters dimers , while gThirdly, TransportTP generally relies on complete protein sequences for classification, since partial sequences will introduce doubts on functionality of the gene/peptide, thus being confined to lower confidence levels. When handling partial sequences such as some ESTs, the classification will depend on the key regions of the proteins available for classification, such as unique conserved domains and transmembrane segments. In a nutshell, the system is built to rely on whole protein sequences, but can be useful for partial sequences, with loss of robustness.In summary, the effectiveness and the utility of TransportTP has been demonstrated in detail through cross-validation between model and non-model organisms, comparative study with alternative strategies in the cross-validation, and the case studies of plant organisms. TransportTP will be of importance for researchers working on annotation of newly assembled genomes, especially eukaryotic genomes, and will probably be used as an additional step for classification of genes/proteins that cannot be clearly classified as transporters by using existing database resources. The approach of TransportTP may be of interest for improvement of broad classification tasks, showing how new classification rules can be extracted from sequences through combination of homology and non-homology evidence.The framework of TransportTP is shown in Figure The pre-processor constructed transporter-related databases and models for the predictor. A transporter-related Gene Ontology database  was consThe initial classifier of TransportTP started categorization of unknown proteins among hundreds of TC families in the TCDB and an exceptional group, i.e. non-transporter, using sequence homology based search. The BLAST and HMM e-value scores of an unknown protein, corresponding to the pairwise and family similarity with respect to the TCDB database, were combined, since this combination improves the predictive accuracy ,31. Specp is an unknown protein for prediction, s(p) is the weighted score, f(p) is the classification function and λ is the threshold to discriminate transporters from non-transporters; qij is the jth transporter in the ith TC family Fi, blast is the BLAST e-value between the protein p and the transporter qij and the hmm is the HMM score of protein p on the transporter family Fi, where MDLi is the HMM model built for the family, |Fi| is the cardinality of family Fi, κ is the threshold of family cardinality for construction of HMM models and τ is the threshold of BLAST search. The three cases in calculating s(p) for transporter qij corresponds to 1) both BLAST and HMM scores being available, 2) only HMM e-value being available and 3) only BLAST e-value being available.In equation 2, The refining classifier was to distinguish false positives from true positives generated by the initial classifier, because numerous false positives may be generated due to the lack of negative features of non-transporters in the TCDB database. The discriminative rules between false positive and true positives were learned from some well-studied model organisms based on a variety of transporter-related features. The excluded non-transporters by the initial classifier were not further refined because the initial classifier was designed to cover most of true positives, thus the small number of false negatives, i.e. missed transporters, would not influence the overall predictive accuracy while greatly increasing the efficiency. Seven categories of features for initially categorized proteins were extracted from transporter-related databases and the input sequences. The first was the basic homology scores to TCDB database generated by the initial classifier, specifically the BLAST and HMM e-value scores, calculated by Tera-BLASTP  and SAM The third category of features for initially categorized proteins was the number of transmembrane segments (TMS) in the proteins and in the initially categorized TC families. The number of TMS within most TC families are conserved throughout evolution, due to specific functional requirements . Therefotms(p) was the number of TMS of a protein p, taken as the maximum calculated by HMMTOP [π and σ were the mean and standard deviation of TMS of the initially categorized TC family F of the protein p and ξ was a tiny constant to prevent the denominator of being zero.where y HMMTOP  and TMPRy HMMTOP , π and σThe fourth category of features for an initially categorized protein was the consistency of TC families among the top-K homologs  of the The fifth category of features for an initially categorized protein was the occurrence of transporter-related Pfam domains, calculated by HMMER  with useThe sixth category of features for an initially categorized protein was the hits of transporter-related Gene Ontology terms , via theThe classification value of an initially categorized protein for the refining classifier was defined as whether the initial classifier categorized the protein with a correct TC family. A false classification value meant that the protein should be removed from the final predictions. A true classification value of a training protein in the refining classifier was determined by the match with a curated transporter at family or superfamily level in TransportDB ,8. TransSupport Vector Machine (SVM) in the Weka package  was adopNmajor and Nminor were the number of instances in the majority and minority classes, respectively, and β was a constant. If β was large enough, being 10 in our implementation, most instances in the majority class would have a chance to be explored. The ensemble of balanced SVMs vote by their confidences to decide the final predicted classification value for an unknown protein. The stochastic and voting strategies have been similarly applied in handling imbalanced data and proved to be effective [where ffective ,38.Project name: A two-phase transporter categorization systemhttp://bioinfo3.noble.org/transporter.Project home page: Operating system: Platform independent.Program language: C++, Perl and JSP.Other requirement: Java application server.Any restrictions to use by non-academics: Licence needed for non-academic use and source code available for academic purpose upon request.HL designed and implemented the algorithms. VB and MU curated transporters in plant organisms. PZ conceived and directed the project. All authors read and approved the final manuscript.Comparative performance of TransportTP on non-model and model organisms. PDF displaying relative balanced accuracy, recall and precision of TransportTP on non-model organisms subtracted that on model organisms in leave-one-in cross-validations.Click here for filePerformance of TransportTP on transporter families covered by TransportDB. This PDF table displays recall, precision and balanced accuracy of TransportTP on transporter families covered by TransportDB, using the proteome of yeast for training, the proteomes of ten other organisms for testing, and 0.1 as e-value threshold.Click here for fileComplete validation results of TransportTP on seven training model-organisms. PDF displaying complete balanced accuracy, recall and precision of TransportTP in leave-on-in cross-validations at e-value threshold of 0.1, where one of the proteomes of the seven model organisms was used for training and the proteomes of ten other organisms were used for testing.Click here for fileComparative results of TransportTP on non-model organisms using leave-multiple-in versus leave-one-in cross-validations. PDF displaying relative balanced accuracy, recall and precision of TransportTP on non-model organisms yielded by leave-multiple-in cross-validations subtracted that of leave-one-in cross-validations.Click here for fileValidation results of TransportTP on P. patens and C. reinhardtii using TransportDB as the benchmark database. This PDF displays validation results of TransportTP on P. patens and C. reinhardtii using arabidopsis for training and e-value thresholds between 10 and 0.00001 for homology search, and TransportDB for the benchmark database.Click here for fileComparative performance of TransportTP with four alternative refining classifiers. This PDF displays the comparative recall versus precision, and balanced accuracy versus e-value thresholds among TransportTP with ensemble of balanced SVMs, balanced random forest, traditional SVM, and decision tree J48.Click here for fileMapping of Pfam domains to Transporter Classification (TC) families/superfamilies. This PDF table displays the mapping of Pfam domains to Transporter Classification families or superfamilies, using 1) cross-links in Pfam database, 2) automatic mapping between Pfam domains and TC families/superfamilies and 3) manual curation.Click here for fileThe keywords adopted in the text-mining program to detect non-transporter functions in Swiss-Prot database. This PDF displays the keywords adopted in the text-mining program to detect non-transporter functions hit by unknown proteins in Swiss-prot Database.Click here for file


Uncovering complex relationships, however, remains the most challenging task as it requires compiling and efficiently querying data from various sources. Here, we describe the Stress Response Array Profiler (StRAP), an open-source, web-based resource for storage, profiling, visualization, and sharing of cancer genomic data. StRAP houses multi-cancer microarray data with major emphasis on radiotherapy studies, and takes a systems biology approach towards the integration, comparison, and cross-validation of multiple cancer profiling studies. The database is a comprehensive platform for comparative analysis of gene expression data. For effective use of arrays, we provide user-friendly and interactive visualization tools that can display the data and query results. StRAP is web-based, platform-independent, and freely accessible at  DNA microarrays are successfully being used to classify tumors and identify novel biomarkers associated with cancer  We develop StRAP, a free web-accessible resource to address the need to query, compare, profile, and visualize results from different microarray experiments. StRAP hosts data from diverse cancer studies (currently from 12 different tissue types), and will be further extended in the future. We used Z scoring method to standardize data, since the internally normalized values do not change with subsequent addition of new datasets. All data are mapped to Entrez Gene identifiers for consistency in comparison. The user-friendly interface facilitates exploration by a wide-range of researchers, including those with little expertise in bioinformatics.In the remainder of this paper we briefly describe StRAP’s construction and core features.The runtime architecture of StRAP is described in http://www.ncbi.nlm.nih.gov/), (2) Pre-processed gene expression microarray molecular profile data (including pre-computed statistics), (3) Metadata on cell lines, and (4) Metadata on platform-associated information.Four main data repositories reside at the backend of StRAP: (1) Gene associated annotation information derived from the National Center for Biotechnology Information .The repositories are stored as a MySQL relational database .Pre-processing and computation of statistical tests are performed in the R environment (http://www.php.net/) and Python (http://www.python.org/). The application is deployed on an Apache HTTP server (http://httpd.apache.org/) at the National Cancer Institute (NCI).The front end interface is a web-based application implemented using R, PHP (Data access and presentation is organized around three main concepts or modules: (1) Genes, (2) Cell lines, and (3) Arrays. Flexible user-defined data queries can be initiated from any of the modules; the data visualization options for the results are displayed in integrated views and may, depending on the query, involve cross-talk between modules. Several links to external resources promote a systems biology approach. The genes module enables gene-centric queries of the StRAP microarray studies. Queries can be based on gene or protein identifiers, synonyms, gene descriptions, or chromosome location. The results include associated arrays and studies, and a compilation of gene-annotation information, spatial localization within the genome visualized in the UCSC Genome browser A typical gene-centric query  to another .The cell lines module provides metadata on available cell lines and associated studies. Queries in this module are tailored to allow selection of complete studies, by tissue of origin, or individual cell line. Comparisons can be made for samples within a study or across studies. .Currently because of the limitation of the number of studies available, it may have limited biological significance. However, the framework of the database is flexible and would allow extensions with data from other types of cancer studies that will help in novel findings.The database will be periodically updated with new studies and features. We plan, for example, to enable construction of interaction networks using literature text-mining, and information from the Human Protein Reference Database (HPRD)


Use of nanovectors has also opened up novel avenues for non-invasive imaging of tumor angiogenesis. Herein, we review the types of nanovector and therapeutic/diagnostic agent combinations used in targeting tumor angiogenesis.Angiogenesis is disregulated in many diseased states, most notably in cancer. An emerging strategy for the development of therapies targeting tumor-associated angiogenesis is to harness the potential of nanotechnology to improve the pharmacology of chemotherapeutics, including anti-angiogenic agents. Nanoparticles confer several advantages over that of free drugs, including their capability to carry high payloads of therapeutic agents, confer increased half-life and reduced toxicity to the drugs, and provide means for selective targeting of the tumor tissue and vasculature. The plethora of nanovectors available, in addition to the various methods available to combine them with anti-angiogenic drugs, allows researchers to fine-tune the pharmacological profile of the drugs  Since Judah Folkman emphasized the 'angiogenic switch' hypothesis for tumor progression in 1991, there has been a tremendous surge in targeting angiogenesis for cancer therapeutics -4. Deregvβ3 is the best-characterized heterodimer that is upregulated in most cancer settings, both on the vasculature and on the tumor cells themselves [It is now well-established that without angiogenesis, tumors cannot grow more than 2 mm in diameter -7. Studiemselves ,14. It iAs such, the first U.S. Food and Drug Administration (FDA) approved anti-angiogenic therapy was the monoclonal antibody Bevacizumab (Avastin), that targets VEGF proteins overexpressed on colorectal cancer cells and their vasculature ,16. In sDevelopment of anti-angiogenesis therapy is based on either drugs that prevent the formation of new blood vessels supplying to the tumor , or drugs that damage existing blood vessels (e.g. combretastatin) . The und50), and allow for potent selective targeting due to their high surface density [Despite these advantages of anti-angiogenic therapy over conventional chemotherapeutic methods, it still suffers from certain limitations. For instance, as a result of systemic administration, most angiogenic inhibitors often fail to reach the targeted tumor vessels, thus exhibiting a poor biodistribution and pharmacokinetic profile, with associated side effects and low efficacy. A great advance towards this end has come from harnessing the advantages of nanotechnology to more efficiently target and kill tumor-associated vasculature. These advantages are made possible by several parameters, including the size of these nanoparticles that allows them to intrinsically home in to metastasized tumors through the enhanced permeability and retention effect (EPR), their ability to evade the immune system and improve the drug's half-life significantly thus lowering its effective dose 50 . Furthermore, poor lymphatic drainage mechanisms in tumors allow the nanoparticles to be retained in the vicinity of the tumor cells and allow them to release their cargo in a sustained manner [The leaky vasculature associated with tumors contributes towards the phenomenon of passive targeting by nanoparticles. The tumor vessels have increased permeability due to aberrant angiogenesis, thus allowing nanoparticles with diameters less than 200 nm to passively extravasate into the tumor sites through the EPR effect. These nanoparticles are subsequently cleared by the liver ,23. Althd manner . For exad manner ,25. In sd manner ,27 and ad manner  can prefνβ3 integrins, and other angiogenic factors, as discussed briefly in the following section and in more details in each nanovector category later in this review.It should be noted that EPR alone is not always sufficient in targeting the tumor sites and hence is often used in conjunction with active targeting. This combination ensures that nanoparticles are retained in the tumor tissues following their extravasation from leaky vessels. Active targeting of tumor tissues is achieved by chemically arraying ligands on the surface of nanoparticles that can recognize and selectively bind to receptors specifically expressed on tumor cells and vessels. The high surface area to volume ratio of the nanoparticles leads to high local density of ligands for targeting. Nanoparticle-mediated active targeting of the tumor vasculature in anti-angiogenic therapy has been achieved by targeting the VEGF receptors (VEGFRs), αvβ3 has been the most widely used as a targeting moiety on nanovectors due to its pleitropic upregulation in a variety of tumor settings [The most prominent modification of nanovectors entails covalently conjugating 'tags' at their surface, in order to increase their targeting potential towards tumorogenic cancer cells and/or their associated vasculature. The main 'tag' used thus far for chemotherapy involves proteins that target the integrin family. As previously mentioned, integrins are key players in the angiogenesis process, and moreover, their upregulation is known to promote survival, growth, and invasion of both tumor and endothelial cells . Integrisettings -32, somesettings ,33. Howe19F) have successfully been linked to an αvβ3 integrin antibody and then visualized by magnetic resonance imaging (MRI) in rabbit and mouse models of tumor angiogenesis [Nanotechnology-based approaches could remedy this limitation, due to their prolonged half-life and increased targeting efficiency. For instance, perfluorocarbon nanoparticles conjugated to various contrasting agents  . AlthougIt becomes apparent that targeting-based approaches for tumor therapies are only as good as the selectivity and specificity of the targeting moiety used. This, in turn, implies that using disease-selective markers is crucial in order to obtain maximum selectivity without deleterious side-effects. Since targeted nanotechnology is often coupled with a chemotherapeutic agent entrapped in the nanoparticle, proper targeting to the diseased tissue is crucial to minimize systemic side-effects . As mostet al. in 1978 as novel drug-delivery systems [Since nanoparticles were first proposed by Marty JJ.  systems , their u systems . SeveralA diverse family of polymers has been studied for the engineering of nanoparticle-based drug delivery agents since one of the earliest reports in 1979 describing their use in cancer therapy . PolymerHPMA copolymers are hydrophilic substances that have been extensively studied for their anti-angiogenesis potential. An HPMA copolymer conjugated to the angiogenesis inhibitor TNP-470 (Caplostatin), was the first polymer-conjugated angiogenesis inhibitor reported . It was νβ3 integrin in tumor-associated vasculature. Radionuclide-labeled, cyclized RGD peptide-tagged HPMA copolymer-based nanoconjugates have been designed that provide the potential for targeted delivery of radionuclides and drugs to solid tumors for diagnostic and therapeutic applications [νβ3 targeting using RGD-bearing conjugates, as this could provide a promising strategy for selective delivery of angiogenesis inhibitors and imaging agents to tumor vasculature and tumor sites expressing the ανβ3 integrin.Specific peptide sequences have been conjugated to HPMA copolymers for active targeting of the αications ,45. The et al. have reported the use of PLGA copolymer for chemically conjugating PD98059, a selective MAPK inhibitor [et al. encapsulated an inhibitor of the phosphatidylinositol-3-kinase (PI3K) pathway in a PLGA copolymer, and used novel zebrafish melanoma and breast adenocarcinoma tumor xenograft models to demonstrate its anti-angiogenic effect [PLGA copolymers have been extensively used in the field of cancer research, owing to their biodegradability and biocompatibility, resulting in their FDA approval. PLGA is synthesized by the co-polymerization of two different monomers, lactic acid and glycolic acid, and can be further modified chemically for conjugation or simple encapsulation of drugs in a nanoparticle formulation, as shown in Figure nhibitor . The resc effect .et al., temporal targeting of tumor cells and the tumor vasculature was achieved using a nanoscale delivery system, described as a 'nanocell', that comprised of a core PLGA nanoparticle encapsulated within a polyethylene glycol (PEG)-linked lipid envelop [PLGA-based nanoparticles have also been used to engineer complex nanosystems. In a novel strategy reported by Sengupta  envelop . PEGylat envelop . In thisIn our ongoing studies using a Zebrafish mutant, named Casper, which allows the visualization of cancer cells in real-time even when they are unlabeled , we demoPLGA nanoparticles have not only been used as delivery agents of synthetic drugs, but have also recently been described for delivering natural products thought to have anti-cancer effects. Curcumin (yellow pigment in the spice turmeric)-loaded PLGA nanoparticles were reported to successfully suppress tumor necrosis factor (TNF)-regulated expression of VEGF, culminating in reduced tumor metastasis . This apet al. have described the construction of VEGF121-containing, boronated polyamidoamine dendrimer that can be used to target VEGF receptors on tumor neovasculature [Polysaccharides (e.g. chitosan) and dendrimers can also be considered as polymeric systems, and have been studied as carriers of anti-angiogenic agents for therapeutic applications. Chitosan is a commercially available cationic linear polysaccharide, which has found a variety of applications in pharmaceuticals and biomedicine. Dendrimers, on the other hand, possess a branched structure that provides it with certain unique properties like ease of chemical conjugation, biocompatibility, high water-solubility and easy renal clearance due to their small size ,50. Diffculature . Near-IRet al. reported that conjugation of the angiogenesis inhibitor TNP-470 to monomethoxy-polyethyleneglycol-polylactic acid copolymer resulted in the formation of nanopolymeric micelles, named Lodamin [et al. designed a cyclic RGD pentapeptide conjugated, doxorubicin-loaded poly(ε-caprolactone)-polyethyleneglycol (PCL-PEG) nanopolymeric micelles [in vitro. Since most chemotherapeutics are delivered in highly invasive manners, such as by systemic injection, this method of targeted delivery has the crucial capacity of immensely reducing discomfort and cytotoxicity in the patient.Lipid-based nanocarriers, such as liposomes and micelles, possess attractive biological properties, including biocompatibility, biodegradability, and the ability to entrap both hydrophobic and hydrophilic drugs . Liposom Lodamin . On oralmicelles . The micAdding to the library of peptide-tags for targeting is a pentapeptide sequence consisting of the amino acids Alanine-Proline-Arginine-Proline-Glycine (APRPG), that has been isolated from a phage-displayed peptide library and has been found to specifically bind to tumor angiogenic vasculature . APRPG-cet al. investigated the potential anti-angiogenic efficacy of two polymerized liposomal nanoparticles radiolabeled with Yttrium isotope 90 (90Y), one conjugated to a small molecule integrin antagonist targeting the ανβ3 integrin and the other loaded with a monoclonal antibody against murine VEGFR-2 receptor Flk-1 [et al. designed Fab' fragments of anti-human MT1-MMP monoclonal antibody conjugated to PEG-modified doxorubicin-encapsulating liposomes that showed significant decrease in tumor volume in vivo as compared to the non-targeted liposomes [Due to their ease of synthesis, liposomal systems have been used to target a plethora of angiogenic factors, including VEGFRs, ανβ3 integrins and matrix metalloproteinases (MMPs). Li or Flk-1 . Membraniposomes .Carbon-based nanostructures, such as carbon nanofibers, nanotubes and fullerenes, have received considerable attention for cancer research in the past. This is due to several advantages, including: (1) high mechanical strength and surface area, (2) numerous sites for chemical or physical conjugation, (3) light weight properties, and (4) ease of scalability and manufacturing . Howeveret al. have reported the efficacy of multiwalled carbon nanotubes, C60 fullerenes and graphite in inhibiting VEGF- and bFGF-induced angiogenesis in chick chorioallantoic membrane (CAM) [et al. reported that the shape of carbon-based nanostructures markedly affects the chemotherapeutic potential of doxorubicin [in vitro and in vivo. Mechanistic studies implicated differential activation of ανβ3 integrins and downstream PI3K signaling between CNT and fullerenol in endothelial cells, which implicates the role of nanovector shapes in mediating drug fate, and underlies the importance of choosing the right vector to obtain optimal therapeutic index.Murugesan ne (CAM) . These corubicin . The autInorganic nanoparticles, such as the ones derived from gold, silver and iron-oxide, possess unique optical, electrical, magnetic, and photothermal properties, which have been harnessed in numerous biomedical applications. Recently, they have been reported for use in anti-angiogenic therapy also.et al. observed that gold nanoparticles bind specifically with heparin-binding growth factors, such as VEGF165 and bFGF, resulting in inhibition of endothelial cell proliferation in vitro, and VEGF-induced permeability and angiogenesis in vivo, but failed to inhibit the activity of non-heparin binding growth factor VEGF121 [et al. reported on the anti-angiogenic properties of silver nanoparticles, and demonstrated that these agents could inhibit VEGF-induced cell proliferation, migration and formation of new blood microvessels in vivo [Mukherjee  VEGF121 . Gurunat in vivo . Furtheret al. have reported that dextran-coated iron-oxide nanoparticles conjugated to radiolabeled  anti-VEGF monoclonal antibody significantly increased imaging resolution as well as destruction of liver cancer in mice [et al. have developed doxorubicin-loaded, folate-receptor targeted superparamagnetic iron oxide nanoparticles that significantly inhibited tumor growth, yet surprisingly, did not increase systemic cytotoxicity often associated with heavy metals, most likely due to their selective localization in tumors [The paramagnetic properties of iron-oxide nanoparticles have been harnessed for therapeutic and imaging applications . Chen et in mice . Maeng en tumors .Medical imaging has undergone tremendous research and development over the past few decades, with the introduction of techniques such as magnetic resonance imaging (MRI), computed tomography (CT), ultrasonography, nuclear medicine scanning, and optical fluorescence imaging ,68. The MRI has been found to correlate more directly with tumor angiogenesis as compared to other imaging techniques . Drevs ein vitro significantly improving the uptake of these nanoparticles as compared to those modified for single ligand targeting.A combination of two or more modalities, either in imaging or in therapeutic applications, or both, can function synergistically to provide complementary informations. A bimodal approach to imaging tumor angiogenesis, using MRI and fluorescence, was reported by Mulder et al . In a foApart from MRI and optical imaging, liposomes have also been used to encapsulate contrast agents for CT imaging. For example, Samei et al. have reported the use of a long-circulating liposomal system encapsulating traditional iodinated contrast agent for micro-CT imaging of rats implanted with R3230AC mammary carcinoma .in vitro, showed increased uptake into the cells as compared to QDs that were conjugated to a control RAD (Arginine-Alanine-Aspartic acid) peptide (negative control) , which are fluorescent nanocrystals made of inorganic semiconductor materials, have gained prominence in biomedical imaging due to their unique photostable and fluorescence properties. We observed that QD complexes, conjugated to ανβ3 integrin-binding cyclic RGD peptide for endothelial cell targeting ) Figure .in vitro [in vivo [Paramagnetic QDs have been engineered as a bimodal (MRI and fluorescence) imaging probe. Such QDs functionalized by ανβ3-specific RGD peptide have been used for successful targeting of human endothelial cells in vitro . In anot[in vivo .et al. have recently reported the mechanism of receptor-mediated endocytosis of transferrin-decorated bacteriophage Qβ [in vivo [Natural nanoparticles, such as viruses and ferritin, have several advantages over synthetic nanoparticles, including precise dimensions, possible evasion by the immune system, biocompatibility and biodegradability . A numbephage Qβ . Multivaphage Qβ . In a di[in vivo .in vivo by targeting the neural cell adhesion molecules expressed on tumor endothelium with a highly sensitive gadolinium-containing apoferritin probe [Apart from viral nanoparticles, protein cages such as the iron-containing ferritin have also been studied in medical imaging . Crich ein probe .Apart from the more conventional approaches of arraying small molecule chemotherapeutic drugs or antibodies on different synthetic or natural nanovectors to achieve anti-angiogenic effects, new research reports are emerging that target the molecular mechanism of angiogenesis by using approaches such as gene silencing and others. In the following sections, we will review some of these emerging new strategies.et al. showed that the stable expression of sFlt-1 by endothelial cell targeted gene delivery inhibited angiogenesis [g-PEG-RGD, and complexed with the therapeutic gene encoding sFlt-1. The PEI-g-PEG-RGD/pCMV-sFlt-1 nanoconjugate system successfully inhibited the proliferation of cultured endothelial cells in vitro by blocking the binding of VEGF to the membrane-bound full length Flt-1 receptor. In a further study, the authors showed that this polymeric gene delivery system reduced tumor burden in mice while increasing prognosis [An interesting strategy to target tumor vasculature is by systemic delivery of an anti-angiogenic gene using a nanoconjugate system. The soluble fragment of VEGF receptor Flt-1 (sFlt-1) is a potent and selective inhibitor of VEGF. As such, Kim ogenesis . For thirognosis .et al. have reported the engineering of RGD-sequence bearing PEGylated self-assembling polymeric nanoparticles that can be used to deliver siRNAs specifically targeted to inhibit VEGFR-2 expression, thereby inhibiting tumor angiogenesis [The use of small interfering RNA (siRNA) and short-hairpin RNA (shRNA) have tremendously helped in our understanding of the molecular mechanisms underlying angiogenesis and tumor developments. The high specificity of these small RNA sequences in binding target proteins post-transcriptionally can prove crucial to our efforts in designing new generation of therapeutics . In one ogenesis . IntraveLipid-based nanosystems have also been reported for targeted gene and siRNA delivery. Protease-activated receptor-1 (PAR-1) siRNA incorporated into neutral liposomes was used to target the thrombin receptor PAR-1, which is involved in adhesion, invasion and angiogenesis . Systemiet al. have reported the use of a chitosan-coated polyisohexylcyanoacrylate nanoparticle for encapsulating anti-RhoA siRNA, resulting in inhibition of tumor growth and angiogenesis in an aggressive breast cancer mouse xenograft model [Apart from polymeric and lipid-based nanosystems, chitosan nanoparticles loaded with siRNA have also been designed to provide a novel therapeutic tool for selectively knocking down angiogenesis genes. For instance, Pillé ft model .Aptamer-based nanotherapeutics have emerged as a novel strategy due to their recognition, and hence targeting, of an endless list of moieties including oncogenes, viruses, bacteria and inflammatory proteins. Aptamers are short, three-dimensional synthetic RNA or DNA oligonucleotides (15-40 nucleotides long) or peptides (10-20 amino acids long) that bind their target with high affinity and specificity, hence their nickname of 'chemical antibodies' -91. Depesn-glycero-3-phosphatidylcholine (DSPC) and cholesterol nanoliposomes, resulting in potent and selective inhibition of angiogenesis in vitro and in vivo for prolonged time periods [Oligonucleotide aptamers have recently gained much popularity in the field of cancer in general. For instance, they have played a crucial role in increasing targeting and imaging of cancer cell and their associated vasculature by being coupled to metallic nanoparticles such as magnetic, gold and ruby-eye doped nanoparticles, as well as quantum dots ,92-94. T periods ,96. In a periods . As the  periods .Oligonucleotide aptamers have preferentially been used, as compared with their peptide counterparts, mainly due to their higher binding affinities and lower costs and ease of synthesis and purification. This has translated into very few reports combining peptide aptamer- and nanoparticle-based technologies, with most studies instead focusing on the advantages of these aptamers for forward and reverse genetic experiments . Hence, In a novel approach, embryonic stem cells have been used in conjunction with nanoparticles for either tumor imaging  or for pThe tumor neovasculature is an attractive target for anti-angiogenic therapy as well as non-invasive imaging studies. Nanotechnology has emerged as an exciting field in this area of research due to multiple advantages, including the capacity of nanoparticles to carry multiple moities of therapeutic and imaging agents, offer longer circulation time and increase the therapeutic index of chemotherapeutcs, to name a few. Moreover, with the various types of nanovectors available, many of which are FDA-approved, along with the various methods for coupling them to drugs and diagnostic agents, there is an endless opportunity to fine-tune nanotherapeutics depending on the task needed. Clearly, the advent of nanothechnology provides a huge potential for devising increasingly novel anti-angiogenic therapeutics that can eventually be translated from bench to bed-side.The authors declare that they have no competing interests.DB and RH collected the references and compiled the manuscript; DB, RH and SS edited the manuscript. All authors read and approved the final manuscript.


Severe sepsis is the main cause of death in the ICU. Relatives are at risk for post-traumatic stress disorder (PTSD) or anxiety and depression . The objProspective observational study in four ICUs of one university hospital, including all patients with severe sepsis and end-of-life-decisions. At 90 days, the main relative was interviewed with the Impact of Event Scale (to measure PTSD), the Hospital Anxiety and Depression Scale and self-developed items on satisfaction with the ICU experience, including medical care and communication in general as well as specifically in the end-of-life context, and decision-making. Three multiple linear regression models were calculated to predict anxiety, depression and post-traumatic stress each.P = 0.025).Eighty-four relatives were included. They were mostly female (74%), spouse (42%) or child (42%), median age was 57 years. Seventy-seven percent acted as proxies. After 90 days, 51% relatives were at risk for PTSD, 48% for anxiety and 33% for depression. Overall satisfaction with the ICU experience was high. Relatives' satisfaction with medical care and communication in general predicted lower anxiety (Relatives of patients with sepsis have a high psychological burden. Improving communication between ICU staff and relatives may reduce their symptoms of anxiety.


MicroRNAs (miRNAs) have been known to play an important role in several biological processes in both animals and plants. Although several tools for miRNA and target identification are available, the number of tools tailored towards plants is limited, and those that are available have specific functionality, lack graphical user interfaces, and restrict the number of input sequences. Large-scale computational identifications of miRNAs and/or targets of several plants have been also reported. Their methods, however, are only described as flow diagrams, which require programming skills and the understanding of input and output of the connected programs to reproduce.To overcome these limitations and programming complexities, we proposed C-mii as a ready-made software package for both plant miRNA and target identification. C-mii was designed and implemented based on established computational steps and criteria derived from previous literature with the following distinguishing features. First, software is easy to install with all-in-one programs and packaged databases. Second, it comes with graphical user interfaces (GUIs) for ease of use. Users can identify plant miRNAs and targets via step-by-step execution, explore the detailed results from each step, filter the results according to proposed constraints in plant miRNA and target biogenesis, and export sequences and structures of interest. Third, it supplies bird's eye views of the identification results with infographics and grouping information. Fourth, in terms of functionality, it extends the standard computational steps of miRNA target identification with miRNA-target folding and GO annotation. Fifth, it provides helper functions for the update of pre-installed databases and automatic recovery. Finally, it supports multi-project and multi-thread management.http://www.biotec.or.th/isl/c-mii for both Windows and Ubuntu Linux platforms.C-mii constitutes the first complete software package with graphical user interfaces enabling computational identification of both plant miRNA genes and miRNA targets. With the provided functionalities, it can help accelerate the study of plant miRNAs and targets, especially for small and medium plant molecular labs without bioinformaticians. C-mii is freely available at  MicroRNAs (miRNAs) are a class of small, non-coding, single-stranded RNA molecules of 18-22 nucleotides. In various species, they play roles in gene regulation by targeting mRNAs at the post-transcriptional level . AlthougTaking into account all these computational steps and criteria for plant miRNA and target identification -38, we dFigure The target identification pipeline consists of four consecutive modules: sequence loading and validation, target scanning, miRNA-target folding, and target annotation. The sequence loading module is the same as that for miRNA identification, but accepts input sequences of longer lengths . The target scanning module determines if input sequences contain the complementary sites to mature miRNAs of interest. Implementation of this module is similar to the homolog search except for the use of reverse-complement mature miRNAs as queries for BLAST. The miRNA-target folding module utilizes UNAFold to predict the secondary structure and free energy of miRNA:target duplexes. This module was introduced to C-mii to help refine the target identification result. The last module, target annotation, supplies function and gene ontology (GO) for the potential target sequences. Along the computational steps of both pipelines, users can customize parameters for their execution, select databases of interest, and explore and filter the results.The pre-installed databases of C-mii are divided into three categories. First, the mature miRNA database, miRBase release 16, was incorporated for homolog search. Second, the non-coding RNA database, Rfam 10 with removed miRNAs, was introduced for removing other types of RNAs. Third, the protein databases, the UniProtKB/Swiss-Prot release 2010_12 and UniProtKB/TrEMBL release 2011_01, were incorporated for removing protein-coding sequences in the primary and precursor miRNA folding steps and for identifying gene functions during target annotation. These databases were pre-processed using the formatdb program in the BLAST package. Users can update these databases from our web site via a menu in C-mii. Furthermore, users can integrate their own protein and non-coding RNA databases into the system by following the pre-processing steps documented on the C-mii web site.To ease its installation and usage, C-mii was designed as a complete package containing all required software, including BLAST, Java Development Kit (JDK), Perl, Python, UNAFold, and Ghostscript , which cC-mii is composed of two pipelines for plant miRNA and target identifications, which could be used autonomously or consecutively. The functionalities of these pipelines have been described with biological rationales and a running example .Taking into account the computational steps and criteria for plant miRNA identification as described previously ,29,31, tArabidopsis mature miRNAs from miRBase with the 30,707 TAIR10 cDNA sequences with default parameter settings. Using the plus strand only, 1286 Arabidopsis cDNAs of 129 miRNA families and 231 members were identified. We selected all these sequences as input for the primary miRNA folding module.The homolog search module helps users identify input sequences that contain mature miRNA sequences from miRBase. In this step, users can select mature miRNAs of multiple plants from miRBase to be used as source mature miRNAs for the identification process. The identification methods include sequence scans with and without BLAST. The sequence scan with BLAST is much faster, but with the trade-off of possibly missing matches due to the word size limitation of BLASTN, which needs to be 4 or greater. Users can also customize the E-value (≤ 10 by default) of BLASTN, the number of allowed mismatches (≤ 4 by default) between a source mature miRNA and its homolog in an input sequence, and the number of processors automatically detected by C-mii for running the homolog search. Figure An miRNA gene needs to be non-coding and have a stem-loop precursor in its secondary structure . The priAuto mode. The Manual mode allows users to specify the start and stop cleavage positions. From the results of our running example using the plus strand only, 567 Arabidopsis cDNAs in 124 miRNA families and 224 members remained. All these sequences were selected with Auto mode for the precursor miRNA folding module.Figure Auto mode setting from the previous step, the precursor miRNA folding module will cleave a pri-miRNA structure from the start position of the found homologous miRNA to the end position of its duplex miRNA* with two-nucleotide 3' overhangs. The extracted stem-loop sequences are screened against the protein and non-coding RNA databases again. UNAFold is then reapplied to the remaining sequences to predict the secondary structure folding of precursor miRNAs (pre-miRNAs). Users can customize the same set of parameters as in the primary miRNA folding step.The precursor miRNA folding module helps users (1) extract the stem-loop structures from the secondary structures of pri-miRNAs, (2) remove stem-loop sequences that hit protein-coding sequences and other types of non-coding RNAs, (3) re-predict the secondary structure folding of the extracted stem-loop sequences, and (4) verify the predicted structures with previously suggested criteria. With the Arabidopsis cDNAs in 103 miRNA families and 197 members were finalized as potential miRNAs from 30,707 TAIR10 cDNAs .Figure Arabidopsis were uploaded to a new project and all mature Arabidopsis miRNAs from miRBase were selected for target scanning.C-mii's target prediction menu consists of four submenus: sequence loading, target scanning, miRNA-target folding, and target annotation. Users may build a new project for target identification only or continue the project from the miRNA identification pipeline. Users may also upload a new set of nucleotide sequences in FASTA format or reuse the uploaded sequences from the miRNA identification process. However, the acceptable length of an input sequence for target identification is extended to 20,000 nt. Besides sequence uploading, users also need to select mature miRNAs of interest from miRBase and/or from predicted mature miRNAs saved from the miRNA identification pipeline. In our running example, 434 sequences previously reported as miRNA-specific targets in Based on plant-miRNA target binding through perfect or nearly-perfect complementarities ,34, the The miRNA-target folding module helps users refine their scanning results. It uses UNAFold to predict the secondary structures of miRNA:target duplexes, MFEs for hybridization, and binding positions of miRNA-target pairs. Users can specify the temperature that might affect miRNA:target duplex formation. Figure The target annotation module supplies function and gene ontology (GO) for potential targets selected from the previous step. Users can choose a protein database and customize the E-value and number of hits for BLASTX. Figure The project summary view provides users with the overall number of identified miRNA families and targets, which can be exported as a report and linked back to the detailed identification results. The miRNA prediction view Figure  shows thArabidopsis precursor miRNAs from miRBase 16 (213 sequences), and (iv) plant RNAs that are not miRNAs from Rfam 10   TAIR10 cDNAs , (ii) TAIR10 miRNAs (176 sequences), (iii) Arabidopsis precursor miRNAs from miRBase, 195 out of 213 miRNAs were correctly identified while the remaining 18 miRNAs were missing and considered as FN  were used for benchmarking the target identification . Table The previously reported 434 sequences of miRNA-associated targets of We measured the efficiency of multi-thread management on Ubuntu 9.10 (karmic) machine with four Intel(R) Core(TM)2 Quad CPU Q6600 at 2.4 GHz, 8GB RAM. The average speed of the miRNA and target identifications on TAIR10 cDNAs with default parameter settings was improved by 30 % and 46 % from single to two and four threads  Project home page: http://www.biotec.or.th/isl/c-mii2) Operating system(s): Windows and Ubuntu Linux 9.10 or higher3) Programming language: Java and Python4) Other requirements: -5) License: GNU GPL6) Any restrictions to use by non-academics: license needed7) The authors declare that they have no competing interests.SN, WM, SI, and DW together designed software architecture and graphical user interfaces. SN developed Java-based interfaces and modules. WM implemented python scripts for results extractions. DW oversaw the software development. All authors helped test the overall functionalities of the system, and performed benchmarking and validation. SI helped draft the manuscript and DW wrote this manuscript. All co-authors read and approved the final manuscript.FASTA (can be viewed with a text editor) - TAIR10 cDNA sequences. This file is used as input for benchmarking the miRNA identification pipeline. It is available under the Documentation & Benchmarking menu at http://www.biotec.or.th/isl/c-mii.Click here for fileFASTA (can be viewed with a text editor) - TAIR10 miRNA sequences. This file is used as input for benchmarking the miRNA identification pipeline.Click here for fileArabidopsis precursor miRNA sequences from miRBase 16FASTA (can be viewed with a text editor) - . This file is used as input for benchmarking the miRNA identification pipeline.Click here for file(FASTA) - All plant RNA sequences that are not miRNAs from Rfam 10. This file is used as input for benchmarking the miRNA identification pipeline.Click here for file(Microsoft Excel) - List of true positives (TP), false positives (FP), and false negatives (FN) of the miRNA identification on TAIR10 cDNA dataset.Click here for fileArabidopsis precursor miRNAs from miRBase 16 dataset(Microsoft Excel) - List of false negatives (FN) of the miRNA identification on .Click here for fileArabidopsis known miRNA target sequences(FASTA) - . This file is used as input for benchmarking the target identification pipeline.Click here for file


Head and neck paragangliomas are tumors associated with the parasympathetic nerve system and typically show an indolent growth pattern. Therefore a conservative management strategy is considered in selected cases.succinate dehydrogenase -sub-unit-D (SDHD). She was operated on the right carotid body tumor and the tympanic tumor. Thereafter the follow-up was performed with MR examinations at 2-year intervals. After an initial stable phase, over the last 3 years a spontaneous near-total regression of the contralateral carotid body tumor was observed, with only subtle rest-abnormalities visible in 2011.We present a case of a female patient who presented in 2003 with bilateral carotid body tumors and a tympanic tumor, associated with a mutation in the The present case underlines the indolent growth pattern of head and neck paragangliomas and for the first time describes a rare manifestation of spontaneous regression of a carotid body tumor. The literature was reviewed to discuss this phenomenon. Common sites include the carotid body, the temporal bone, and the vagal body. The maj-D (SDHD). In the -D (SDHD). Diagnos-D (SDHD),7. Treat-D (SDHD).In 2003 a 32-year-old female patient was referred to our institution after magnetic resonance imaging (MRI) examination in a regional hospital had revealed bilateral carotid body tumors. She had discovered a swelling in the right neck since 6 months before, and only after questioning mentioned a long-existing pulsatile tinnitus on the right side. The family history for paragangliomas was positive: her father and uncle were affected.D (P-Asp92Tyr).Physical examination revealed a small reddish tumor in the right middle ear, with positive Brown’s sign and a tumor in the right neck at the level of the hyoid bone; on the left side there was no palpable lesion. The lower cranial nerves were intact and the hearing was normal. In general examination, the patient was all-over obese , blood pressure was 130/70 mm Hg, pulse rate 80/min. There were no other abnormalities. Urinary excretion (24-h sample) of catecholamines was repeatedly normal. Germline mutation analysis in DNA from a blood sample demonstrated a mutation in SDHMRI including a gadolinium-enhanced 3D time-of-flight MR angiography sequence was acquThe right carotid body tumor was surgically resected in 2004, followed by extirpation of the tympanic tumor in 2005. No radiotherapy was applied. There were no complications from the surgical procedures. In the postoperative phase a severe obstructive sleep apnea syndrome (OSAS) was diagnosed, for which she started continuous positive airway pressure (CPAP) therapy. In 2005 she started thyroid hormone suppletion for Hashimoto disease. Between 2005 and 2008, the patient intentionally lost weight from 136 kg to 93 kg, because of a pregnancy wish which was eventually unsuccessful. During follow-up the patient regained bodyweight in 2010 to 129 kg. In 2011 she suffered anamnestically from a transient ischemic attack , after which she started statin treatment and therapy with carbaselate calcium and dipyridamole.Follow-up MRI was performed with 2-year intervals , hormonal (such as contraceptive use), vascular , and operative mechanisms.SDHD mutation in the present case (p.Asp92Tyr), however, is relatively common - as it is found in almost 70% of 690 Dutch SDH-gene mutation carriers[Biological mechanisms related to tumor regression include genetic instability (telomerase inhibition) and programmed cell death, which has been described for neuroblastomas,17. The carriers and has Although changes in body weight may go with changes in catecholamine levels, we found no evidence for such changes in our patient, neither after the start of CPAP treatment for OSAS. Therefore a hormonal mechanism for tumor regression seems unlikely.Vascular mechanisms may be due to changes in tumor angiostructure, and have been reported in spontaneous regressed  neuroblastomas. Such vaOperative mechanisms include regression of a tumor after biopsy, which has been described in patients with Merkel cell tumors,20, and Although the present case describes spontaneous involution, surgical resection is the favored treatment in carotid body tumors. Nevertheless, resection is associated with complications such as cranial nerve impairment, stroke, or partial scarification of the carotid arteries.In conclusion, the present case underlines the indolent growth pattern of head and neck paragangliomas and for the first time describes a rare manifestation of involution of a carotid body tumor.Written informed consent was obtained from the patient for publication of this case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.Succinate dehydrogenase-sub-unit-D.CPAP: Continuous positive airway pressure; HNPGL: Head and neck paragangliomas; MRI: Magnetic resonance imaging; OSAS: Obstructive sleep apnea syndrome; SDHD: All authors declare no competing interest.SH and MK carried out the MR examinations and drafted the manuscript. FH performed genetic analysis and drafted the manuscript. JJ and EC performed physical examinations and patient follow-up and drafted the manuscript. All authors read and approved the final manuscript.


Contrast-enhanced radiographic examinations carry the risk of contrast-associated acute kidney injury (CA-AKI). While CA-AKI is a well-known complication outside the intensive care unit (ICU) setting, data on CA-AKI in ICU patients are scarce. Our aim was to assess the incidence and short-term outcome of CA-AKI in a mixed medical-surgical ICU population.We conducted a single-center retrospective analysis between September 2006 and December 2008 on adult patients who underwent a contrast-enhanced computed tomography for urgent diagnostic purposes. CA-AKI was defined as either a relative increment in serum creatinine of ≥ 25% or an absolute increment in serum creatinine of ≥ 0.3 mg/dL within 48 hrs after contrast administration. ICU mortality rates of patients with and without CA-AKI were compared in univariate and multivariate analyses. The need for renal replacement therapy (RRT) was also recorded.CA-AKI occurred in 24/143 (16.8%) patients. Coexisting risk factors for kidney injury, such as sepsis, nephrotoxic drugs and hemodynamic failure were commonly observed in patients who developed CA-AKI. ICU mortality was significantly higher in patients with than in those without CA-AKI . In multivariate logistic regression, CA-AKI remained associated with ICU mortality . RRT was required in 7 (29.2%) patients with CA-AKI.In our cohort, CA-AKI was a frequent complication. It was associated with a poor short-term outcome and seemed to occur mainly when multiple risk factors for kidney injury were present. Administration of ICM should be considered as a potential high-risk procedure and not as a routine innocuous practice in ICU patients. Acute kidney injury (AKI) is a frequent complication in both general and intensive care unit (ICU) patients, that significantly contributes to morbidity and mortality -5.Among the multiple causes of hospital-acquired AKI, administration of iodinated contrast media (ICM) has been found to be the third commonest one , and conIn the specific ICU setting, administration of ICM to patients with otherwise multiple risk factors for AKI  may be even more deleterious. Paradoxically, few studies -14 have Despite CA-AKI being a well-known complication outside the ICU, the paucity and the discrepancy of data in the ICU justifies additional data. The aim of this study was to evaluate the incidence and short-term outcome of CA-AKI in a mixed medical-surgical ICU population.This retrospective study was carried out in the medical-surgical ICU of the Avicenne teaching hospital . All adult patients who required a contrast-enhanced CT for urgent diagnostic purposes between September 2006 and December 2008 were eligible for inclusion. Patients with coronary or non coronary angiography were not considered because they do not correspond to our usual patient population. Exclusion criteria were: history of chronic kidney disease, AKI with or without renal replacement therapy (RRT) before administration of ICM, and multiple administrations of ICM. For patients who were admitted more than once to the ICU, only the first ICU stay was included in the analysis.The decision to perform a contrast-enhanced CT was left at the attending physicians’ discretion. During the whole study period, iso-osmolar media only were used. There were no written protocols for either CA-AKI prevention or dosing of ICM. Indications for and modalities of RRT were also left at the attending physicians’ discretion. No patient received “prophylactic” RRT after administration of ICM.The study was approved by the local ethics committee  and conducted in accordance with the declaration of Helsinki. Since the study implied no change in patients’ management and data were anonymously processed, the need for informed consent was waived.In the absence of consensual definition criteria, CA-AKI was defined as either a relative increment in serum creatinine of ≥ 25%, as in many prior reports , or as aThe term “contrast-associated” instead of “contrast-induced” AKI was preferred because the development of AKI in the ICU setting is often of multiple origins, thus making hazardous to consider the administration of ICM as the unique cause for AKI. Rather, the nephrotoxicity of ICM may be cumulative .For each patient, the following variables were collected:– Baseline characteristics: age, gender, SAPS II score, Mc Cabe class , admission category , and comorbidities ,– Serum creatinine values measured on the CT day before administration of ICM, and 24 hours and 48 hours thereafter,– Additional risk factors for AKI occurring within 48 hours before and after administration of ICM: sepsis, hemodynamic failure and prescription of potential nephrotoxic drugs ,N-acetylcysteine or isotonic crystalloids), and– Presence or absence of preventive measures .The secondary endpoints were the length of ICU stay, and persistent need for renal support on ICU discharge.Comparisons between patients with and those without CA-AKI were based on the Fischer’s exact test for categorical variables and on the Wilcoxon’s test for continuous variables.c statistic (area under the curve), respectively. Results are shown as adjusted odds ratios with their 95% confidence intervals.The effect of CA-AKI on ICU mortality was assessed through a logistic regression model. Specifically, we adjusted for patients’ baseline severity reflected by the SAPS II score and for organ dysfunctions before administration of ICM reflected by the non-renal SOFA score . The goodness of fit and the discrimination of the model were determined by the Hosmer-Lemeshow statistic and the p values were two-tailed, and a p value < .05 was considered significant.All Statistical analyses were performed using a statistical software package .Over the study period, 1639 patients were admitted to our unit. Two hundred fifty-six (15.6%) CTs were required for urgent diagnostic purposes, of whom 194 (75.8%) were contrast-enhanced. Forty patients were excluded. In 11 cases, the patient’s data file was incomplete. Finally, 143 patients were considered for analysis  patients  patients with CA-AKI with a median time of 1 day (interquartile range: 0–2) after CA-AKI onset  Figure .Patients with and those without CA-AKI had similar lengths of ICU stay . Among survivors, patients with CA-AKI had a trend towards increased lengths of ICU stay .While CA-AKI has been extensively investigated outside the ICU setting, few studies on CA-AKI in ICU patients have been carried out so far. Moreover, these studies reported discrepant results regarding the incidence and outcome of CA-AKI according to case mix and definitions of AKI [8–14].In this work, we did not only assess the incidence of CA-AKI but we also assessed the outcome of patients in terms of need for RRT and ICU mortality. We found CA-AKI to be a frequent event in our mixed medical-surgical population. It was associated with a high risk of ICU death, and tended to increase the length of ICU stay. These results are consistent with the ones of Hoste et al . RemarkaAlthough emphasizing the significant burden of CA-AKI in ICU patients, our study has potential limitations. First, we were not able to accurately assess the benefit/risk ratio of contrast-enhanced CT since precise indications of CT and subsequent therapeutic changes were hazardous to extract retrospectively from patients’ files. This important issue deserves further investigation. Second, we did not examine the influence of the volume of ICM and application of preventive measures on the incidence of CA-AKI. The volume of ICM, which has been shown to play a role in the development of CA-AKI,  could noIn our mixed medical-surgical ICU population requiring contrast-enhanced CTs for urgent diagnostic purposes, CA-AKI was a frequent and severe complication. In keeping with the few available retrospective data, our study indicates that the administration of ICM in patients with otherwise multiple risk factors for AKI should be considered with caution, all the more as CA-AKI may not only impair short-term but also long-term prognosis ,19.Large prospective trials on ICU patients are warranted to more accurately assess the benefit/risk ratio of contrast-enhanced CTs, the role of preventive measures, and the impact of repeated administrations of ICM.AKI: Acute kidney injury; ICU: Intensive care unit; CA-AKI: Contrast-associated acute kidney injury; ICM: Iodinated contrast media; CT: Computed tomography.The authors declare that they have no competing interests.CC designed the study and wrote the manuscript; CC performed the statistical analyses; DR, ML, FC and YC participated in the collection of data and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2369/14/31/prepub


Allergic bronchopulmonary aspergillosis (ABPA) is classified radiologically based on the findings of central bronchiectasis (CB) and other radiologic features (ORF). However, the long-term clinical significance of these classifications remains unknown. We hypothesized that the immunological activity and outcomes of ABPA could be predicted on HRCT chest finding of high-attenuation mucus (HAM), a marker of inflammatory activity. In this study, we evaluate the severity and clinical outcomes of ABPA with different radiological classifications.A fumigatus specific IgE levels, eosinophil count) severity of the disease and clinical outcomes in various classifications were analyzed.Patients were classified based on CT chest findings as: (a) serologic ABPA (ABPA-S) and ABPA-CB; (b) ABPA-S, ABPA-CB, and ABPA-CB-ORF; and, (c) ABPA-S, ABPA-CB and ABPA-CB-HAM. The clinical, spirometric and serological  patients, 55 (23.5%) had normal HRCT, 179 (76.5%) had CB, 49 (20.9%) had HAM, and 27 (11.5%) had ORF. All immunological markers were consistently higher in the HAM classification, while in other classifications these findings were inconsistent. On multivariate analysis, the factors predicting frequent relapses were presence of HAM  and CB  after adjusting for ORF.The classification scheme based on HAM most consistently predicts immunological severity in ABPA. Central bronchiectasis and HAM are independent predictors of recurrent relapses in ABPA. Hence, HAM should be employed in the radiological classification of ABPA. Aspergillus fumigatus, an ubiquitous fungi that colonizes the tracheobronchial tree in these patients.Allergic bronchopulmonary aspergillosis (ABPA) is a complex immunological disorder that commonly complicates the course of patients with asthma and cystic fibrosis. The disease occurs secondary to antigens released by There are patients of ABPA who otherwise fulfill all diagnostic criteria, but lack demonstrable abnormalities on CT chest. They are labeled as seropositive ABPA (ABPA-S) compared with the more common presentation with CB (ABPA-CB).Over the last two decades there is an increased awareness that CT findings can predict outcomes in many pulmonary disorders including CF.The present study is a post hoc analysis of prospectively collected data, and includes 234 consecutive patients of ABPA diagnosed between January 2004 and December 2008 and followed till December 2009. The clinical characteristics of 205 patients have been previously described. Aspergillus sensitization using an intradermal skin test. Patients who demonstrate immediate cutaneous hyperreactivity to Aspergillus antigen are further investigated for ABPA with IgE levels , eosinophil count, Aspergillus precipitins and HRCT chest. The detailed methodology has been previously described.A fumigatus specific IgE levels >0.35 kUA/L; and, two of the following criteria: (a) presence of serum precipitins against A fumigatus; (b) radiographic pulmonary opacities (fixed/transient); (c) absolute eosinophil count >1000 cells/µL; (d) central bronchiectasis on HRCT.In our Chest Clinic, all patients with asthma (except those with glucocorticoid intake more than three weeks in the preceding six months) are screened for The HRCT of the chest was categorized for the presence and extent of bronchiectasis.(a) Greenberger classification: ABPA-S and ABPA-CB(b) Kumar classification: ABPA-S, ABPA-CB and ABPA-CB-ORF(c) Classification based on HAM: ABPA-S, ABPA-CB and ABPA-CB-HAMremission, if the IgE levels declined by >35% and there was clinical/radiological improvement after three months of glucocorticoids or relapse if there was a doubling of baseline IgE levels with clinicoradiological worsening. Patients with two or more relapses were classified as frequent relapses whereas those with no or one relapse were categorized as infrequent relapses. For the purpose of this study, we evaluated the clinical and serological severity in the three radiological classifications, and the factors predicting frequent relapses. To ascertain the clinical significance of various classifications, we systematically excluded patients with HAM, CB and ORF from each of the classifications and then re-analyzed the severity.The patients were treated with glucocorticoids and followed up with history and physical examination, chest radiograph and total IgE levels every six weeks.Data are presented as mean (SD), median (IQR) or number (percentage). Statistical significance was assumed at a p-value of less than 0.05. The differences between continuous variables were analyzed using student's t-test, Mann-Whitney U test, ANOVA  or Kruskal-Waalis test  as appropriate. Categorical variables were compared using the chi-square test. A multivariate logistic regression analysis was performed to determine the CT findings predicting frequent relapses.There were 123 males and 111 females with a mean (SD) age of 34.1 (12.5) years. The baseline characteristics of these patients are shown in A fumigatus specific IgE levels and eosinophil counts but not the total IgE levels were higher in ABPA-CB compared to ABPA-S. However, the serological severity varied once patients with HAM and ORF were excluded from the analysis. On removal of ORF, both A fumigatus specific IgE levels and eosinophil counts remained significant, while on removal of HAM only the eosinophil counts retained significance . Once patients with HAM were excluded, only the eosinophil counts retained significance in patients with ABPA-CB compared to ABPA-S and ABPA-CB-ORF. The number of relapses were higher in both ABPA-CB and ABPA-CB-ORF compared to ABPA-S, but was not different between ABPA-CB and ABPA-CB-ORF.On classifying the patients according to the Kumar classification , the cliA fumigatus specific) were higher in ABPA-CB-HAM compared to both ABPA-S and ABPA-CB, and remained significant even after exclusion of patients with ORF  duration of follow-up was 31.1 (17.6) months. 138 (59%) patients experienced relapse of ABPA with the median (range) relapses being 1 (0–7). There were 97 (41.5%) patients with frequent relapses. On multivariate logistic regression analysis, the factors predicting frequent relapses were presence of HAM and CB after adjusting for other CT finding of ORF .The results of this study suggest that HRCT chest findings correlate with immunological severity in ABPA, and the most consistent marker of serological severity was HAM. The classification of ABPA based on other criteria was inconsistent with some features being severe in one classification but not in the other. The CT findings of HAM and CB at diagnosis were also independent predictors of recurrent relapses.A fumigatus antigen is the most reliable method for screening for ABPA.A fumigatus specific IgG/IgE levels greater than twice the pooled serum samples from patients with Aspergillus hypersensitive asthma is paramount in the differential diagnosis of ABPA from Aspergillus sensitized asthma.A fumigatus although present in 69–90% of patients with ABPA,The Rosenberg-Patterson criteria  are mostA fumigatus specific IgG levels and precipitins were higher in patients with ABPA-CB.A fumigatus specific IgE were similar in the two groups.Similarly, there is no agreement on the severity classification, and they continue to be modified and updated.A fumigatus specific IgE levels) but not in others . Moreover, on excluding patients with HAM, the immunological severity was restricted only to eosinophil counts. Interestingly, in the Kumar classification, the immunological markers were most severe in patients with ABPA-CB and not ABPA-CB-ORF. This suggests that ORF does not determine serological severity and probably represents the fibrotic, burnt out phase of the disease. In addition to evaluating these two classifications, we proposed a new classification based on HAM.In this study, we evaluated the clinical and serological severity of both these classifications using data from a large set of patients. The classification scheme by Greenberger et al. showed immunological severity in some parameters (eosinophil count and High attenuation mucous plugging is described as the most characteristic finding of ABPA.We also assessed the clinical significance of all classifications in terms of relapse. The presence of CB was a consistent marker of relapse in all classifications irrespective of whether patients with HAM or ORF were included or not. Moreover, in the Kumar classification, there was no statistical significance in the relapse rates between ABPA-CB and ABPA-CB-ORF, suggesting that it is the CB and not ORF that predicts relapse. However, in the HAM classification, the relapse rates were higher in ABPA-CB-HAM compared to ABPA-CB even after excluding patients with ORF suggesting that HAM independently predicts relapses. Finally, this was also confirmed in the multivariate model, wherein CB and HAM were independent predictors of frequent relapses.Few studies have described factors predicting outcome in patients with ABPA. In two earlier studies, we had observed that HAM and CB are independent markers of relapse.The current study is different from our previous reports in numerous aspects, although it does contain majority of patients from these previous studies.What are the clinical implications of this study? Based on the results of this study, a patient with ABPA and hyperattenuated mucus at diagnosis represents an immunologically severe disease with increased propensity of recurrent relapses. Whether these group of patients require more intensive treatment protocols or closer monitoring remains to be answered. Hence, HAM should be incorporated in the classification of ABPA. These conclusions are valid and particularly relevant, not only because of the large sample size and long duration of follow-up, but also due to the fact that we analyzed the clinical, serological and long-term outcomes in terms of relapses.In conclusion, the classification scheme based on high attenuation mucus most consistently predicts immunological severity in ABPA. CB and HAM are independent predictors of frequent relapses in ABPA. Hence, HAM should be employed in the radiological classification of ABPA.