U.S. Medical Eligibility Criteria for Contraceptive Use, 2016.

MMWR Recomm Rep. 2016;65(3):1-103

Authors: Curtis KM, Tepper NK, Jatlaoui TC, Berry-Bibee E, Horton LG, Zapata LB, Simmons KB, Pagano HP, Jamieson DJ, Whiteman MK

Abstract
The 2016 U.S. Medical Eligibility Criteria for Contraceptive Use (U.S. MEC) comprises recommendations for the use of specific contraceptive methods by women and men who have certain characteristics or medical conditions. These recommendations for health care providers were updated by CDC after review of the scientific evidence and consultation with national experts who met in Atlanta, Georgia, during August 26-28, 2015. The information in this report updates the 2010 U.S. MEC (CDC. U.S. medical eligibility criteria for contraceptive use, 2010. MMWR 2010:59 [No. RR-4]). Notable updates include the addition of recommendations for women with cystic fibrosis, women with multiple sclerosis, and women receiving certain psychotropic drugs or St. John's wort; revisions to the recommendations for emergency contraception, including the addition of ulipristal acetate; and revisions to the recommendations for postpartum women; women who are breastfeeding; women with known dyslipidemias, migraine headaches, superficial venous disease, gestational trophoblastic disease, sexually transmitted diseases, and human immunodeficiency virus; and women who are receiving antiretroviral therapy. The recommendations in this report are intended to assist health care providers when they counsel women, men, and couples about contraceptive method choice. Although these recommendations are meant to serve as a source of clinical guidance, health care providers should always consider the individual clinical circumstances of each person seeking family planning services. This report is not intended to be a substitute for professional medical advice for individual patients. Persons should seek advice from their health care providers when considering family planning options.

PMID: 27467196 [PubMed - as supplied by publisher]

Acquired Cystic Fibrosis Transmembrane Conductance Regulator Deficiency.

Adv Otorhinolaryngol. 2016;79:78-85

Authors: Cho DY, Woodworth BA

Abstract
In the genetic airway disease cystic fibrosis (CF), deficiency or dysfunction of the cystic fibrosis membrane conductance regulator (CFTR) alters anion transport in respiratory epithelium and consequently disrupts mucociliary clearance. An enriched understanding of the role of CFTR in the maintenance of normal epithelial function has revealed that mild and variable CFTR mutations play a causative role in a number of diseases not classically associated with CF. Furthermore, recent evidence indicates that acquired defects in wild-type CFTR protein processing, endocytic recycling and function can contribute to the pathogenesis of airway diseases, such as chronic obstructive pulmonary disease. In this chapter, we discuss emerging findings implicating acquired CFTR dysfunction in the pathogenesis of chronic rhinosinusitis and propose a new and leading edge approach to future CRS therapy using CFTR potentiators.

PMID: 27466849 [PubMed - as supplied by publisher]

Cystic Fibrosis Sinusitis.

Adv Otorhinolaryngol. 2016;79:29-37

Authors: Le C, McCrary HC, Chang E

Abstract
Cystic fibrosis (CF) is an autosomal recessive genetic disorder caused by mutations in the CF transmembrane conductance regulator gene(CFTR) resulting in impaired ion transport. Nearly all people with CF will develop chronic rhino-sinusitis (CRS) and present with the characteristic viscous mucus, impaired mucociliary clearance and chronic inflammation/infection of the sinonasal cavity. While some individuals with CF can appear relatively asymptomatic in terms of their sinus disease, commonly reported symptoms include anosmia, headache, facial pain, nasal obstruction, chronic congestion and nasal discharge. Nasal endoscopy typically reveals mucosal edema, purulent discharge and nasal polyposis. Computed tomography (CT) imaging classically demonstrates the distinguishing findings of sinus hypoplasia or aplasia with generalized opacification, medial bulging of the lateral sinonasal sidewall and a demineralized uncinate process. Current treatment for CF sinusitis includes the use of hypertonic saline, topical and systemic steroids, antibiotics and endoscopic surgery. Research investigating novel therapies designed at targeting the primary defect of CF is showing promise for reversal of CF sinus disease, in addition to potential for disease prevention.

PMID: 27466844 [PubMed - as supplied by publisher]

Differential Diagnosis of Chronic Rhinosinusitis with Nasal Polyps.

Adv Otorhinolaryngol. 2016;79:1-12

Authors: London NR, Reh DD

Abstract
Nasal polyps are semi-translucent mucosal outgrowths of the paranasal sinuses which typically arise in the setting of chronic rhinosinusitis (CRS). Nasal polyps are also associated with asthma, aspirin sensitivity, cystic fibrosis and allergic fungal rhinosinusitis (AFS). The majority of nasal polyps are bilateral and characterized by tissue edema and eosinophil infiltration. Patients with nasal polyps often present with complaints including nasal obstruction, congestion, rhinorrhea or altered sense of smell. The differential diagnosis ranges from benign masses such as schneiderian papilloma, antrochoanal polyp, angiofibroma and encephalocele to malignant neoplasms such as squamous cell carcinoma (SCC), esthesioneuroblastoma, nasal lymphoma and rhabdomyosarcoma. These lesions may have a similar appearance as nasal polyps and particular attention to an alternative diagnosis for nasal polyps should be entertained if the mass is unilateral or congenital in nature. Workup for patients with a unilateral mass should include radiographic imaging, possible biopsy and careful follow-up when appropriate. Here, we review the disease etiology of nasal polyps and describe the approach to the patient with nasal polyps with emphasis on differential diagnosis and workup.

PMID: 27466841 [PubMed - as supplied by publisher]

Lichen secondary metabolite evernic acid as potential quorum sensing inhibitor against Pseudomonas aeruginosa.

World J Microbiol Biotechnol. 2016 Sep;32(9):150

Authors: Gökalsın B, Sesal NC

Abstract
Cystic Fibrosis is a genetic disease and it affects the respiratory and digestive systems. Pseudomonas aeruginosa infections in Cystic Fibrosis are presented as the main cause for high mortality and morbidity rates. Pseudomonas aeruginosa populations can regulate their virulence gene expressions via the bacterial communication system: quorum sensing. Inhibition of quorum sensing by employing quorum sensing inhibitors can leave the bacteria vulnerable. Therefore, determining natural sources to obtain potential quorum sensing inhibitors is essential. Lichens have ethnobotanical value for their medicinal properties and it is possible that their secondary metabolites have quorum sensing inhibitor properties. This study aims to investigate an alternative treatment approach by utilizing lichen secondary metabolite evernic acid to reduce the expressions of Pseudomonas aeruginosa virulence factors by inhibiting quorum sensing. For this purpose, fluorescent monitor strains were utilized for quorum sensing inhibitor screens and quantitative reverse-transcriptase PCR analyses were conducted for comparison. Results indicate that evernic acid is capable of inhibiting Pseudomonas aeruginosa quorum sensing systems.

PMID: 27465850 [PubMed - in process]

Principles of Systems Biology, No. 7.

Cell Syst. 2016 Jul 27;3(1):3-6

Authors:

Abstract
With applications of CRISPR-Cas proteins for probing chromatin dynamics and recording information in a genome, this month's Cell Systems call (Cell Systems 1, 307) highlights a plethora of new techniques.

PMID: 27467241 [PubMed - as supplied by publisher]

Altered protein phosphorylation as a resource for potential AD biomarkers.

Sci Rep. 2016;6:30319

Authors: Henriques AG, Müller T, Oliveira JM, Cova M, da Cruz E Silva CB, da Cruz E Silva OA

Abstract
The amyloidogenic peptide, Aβ, provokes a series of events affecting distinct cellular pathways regulated by protein phosphorylation. Aβ inhibits protein phosphatases in a dose-dependent manner, thus it is expected that the phosphorylation state of specific proteins would be altered in response to Aβ. In fact several Alzheimer's disease related proteins, such as APP and TAU, exhibit pathology associated hyperphosphorylated states. A systems biology approach was adopted and the phosphoproteome, of primary cortical neuronal cells exposed to Aβ, was evaluated. Phosphorylated proteins were recovered and those whose recovery increased or decreased, upon Aβ exposure across experimental sets, were identified. Significant differences were evident for 141 proteins and investigation of their interactors revealed key protein clusters responsive to Aβ treatment. Of these, 73 phosphorylated proteins increased and 68 decreased upon Aβ addition. These phosphorylated proteins represent an important resource of potential AD phospho biomarkers that should be further pursued.

PMID: 27466139 [PubMed - in process]

PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast.

BMC Bioinformatics. 2015;16 Suppl 18:S2

Authors: Lai FJ, Chang HT, Wu WS

Abstract
BACKGROUND: Computational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface.
RESULTS: The developed tool is called PCTFPeval (Predicted Cooperative TF Pair evaluator), written in PHP and Python programming languages. The friendly web interface allows users to input a list of predicted cooperative TF pairs from their algorithm and select (i) the compared algorithms among the 15 existing algorithms, (ii) the performance indices among the eight existing indices, and (iii) the overall performance scores from two possible choices. The comprehensive performance comparison results are then generated in tens of seconds and shown as both bar charts and tables. The original comparison results of each compared algorithm and each selected performance index can be downloaded as text files for further analyses.
CONCLUSIONS: Allowing users to select eight existing performance indices and 15 existing algorithms for comparison, our web tool benefits researchers who are eager to comprehensively and objectively evaluate the performance of their newly developed algorithm. Thus, our tool greatly expedites the progress in the research of computational identification of cooperative TF pairs.

PMID: 26677932 [PubMed - indexed for MEDLINE]

Sialomes and Mialomes: A Systems-Biology View of Tick Tissues and Tick-Host Interactions.

Trends Parasitol. 2016 Mar;32(3):242-54

Authors: Chmelař J, Kotál J, Karim S, Kopacek P, Francischetti IM, Pedra JH, Kotsyfakis M

Abstract
Tick saliva facilitates tick feeding and infection of the host. Gene expression analysis of tick salivary glands and other tissues involved in host-pathogen interactions has revealed a wide range of bioactive tick proteins. Transcriptomic analysis has been a milestone in the field and has recently been enhanced by next-generation sequencing (NGS). Furthermore, the application of quantitative proteomics to ticks with unknown genomes has provided deeper insights into the molecular mechanisms underlying tick hematophagy, pathogen transmission, and tick-host-pathogen interactions. We review current knowledge on the transcriptomics and proteomics of tick tissues from a systems-biology perspective and discuss future challenges in the field.

PMID: 26520005 [PubMed - indexed for MEDLINE]

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability.

OMICS. 2015 Oct;19(10):581-601

Authors: Kumar A, Pathak RK, Gupta SM, Gaur VS, Pandey D

Abstract
In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use well-organized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes.

PMID: 26484978 [PubMed - indexed for MEDLINE]

Editorial overview: Cell signalling and gene regulation-communication and control as the twin pillars of systems biology.

Curr Opin Plant Biol. 2015 Oct;27:v-viii

Authors: Cao X, Meyers BC

PMID: 26433830 [PubMed - indexed for MEDLINE]

Food metabolomics: from farm to human.

Curr Opin Biotechnol. 2016 Feb;37:16-23

Authors: Kim S, Kim J, Yun EJ, Kim KH

Abstract
Metabolomics, one of the latest components in the suite of systems biology, has been used to understand the metabolism and physiology of living systems, including microorganisms, plants, animals and humans. Food metabolomics can be defined as the application of metabolomics in food systems, including food resources, food processing and diet for humans. The study of food metabolomics has increased gradually in the recent years, because food systems are directly related to nutrition and human health. This review describes the recent trends and applications of metabolomics to food systems, from farm to human, including food resource production, industrial food processing and food intake by humans.

PMID: 26426959 [PubMed - indexed for MEDLINE]

Drug Drug Interaction Extraction from Biomedical Literature Using Syntax Convolutional Neural Network.

Bioinformatics. 2016 Jul 27;

Authors: Zhao Z, Yang Z, Luo L, Lin H, Wang J

Abstract
MOTIVATION: Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to improve.
RESULTS: In this paper, we present a syntax convolutional neural network (SCNN) based DDI extraction method. In this method, a novel word embedding, syntax word embedding, is proposed to employ the syntactic information of a sentence. Then the position and part of speech (POS) features are introduced to extend the embedding of each word. Later, auto-encoder is introduced to encode the traditional bag-of-words feature (sparse 0-1 vector) as the dense real value vector. Finally, a combination of embedding-based convolutional features and traditional features are fed to the softmax classifier to extract DDIs from biomedical literature. Experimental results on the DDIExtraction 2013 corpus show that SCNN obtains a better performance (an F-score of 0.686) than other state-of-the-art methods.
AVAILABILITY: The source code is available for academic use at http://202.118.75.18:8080/DDI/SCNN-DDI.zip CONTACT: yangzh@dlut.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 27466626 [PubMed - as supplied by publisher]

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

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

These pubmed results were generated on 2016/07/28

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

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

"Cystic Fibrosis"

These pubmed results were generated on 2016/07/28

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

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

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

These pubmed results were generated on 2016/07/28

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

Strategies in the discovery of novel antifungal scaffolds.

Future Med Chem. 2016 Jul 27;

Authors: Liu N, Wang C, Su H, Zhang W, Sheng C

Abstract
The development of next-generation antifungal agents with novel chemical scaffolds and new mechanisms of action is vital due to increased incidence and mortality of invasive fungal infections and severe drug resistance. This review will summarize current strategies to discover novel antifungal scaffolds. In particular, high-throughput screening, drug repurposing, antifungal natural products and new antifungal targets are focused on. New scaffolds with validated antifungal activity, their discovery and optimization process as well as structure-activity relationships are discussed in detail. Perspectives that could inspire future antifungal drug discovery are provided.

PMID: 27463376 [PubMed - as supplied by publisher]

Potential Reuse of Oncology Drugs in the Treatment of Rare Diseases.

Trends Pharmacol Sci. 2016 Jul 22;

Authors: Liu Z, Fang H, Slikker W, Tong W

Abstract
Cancer research has made remarkable progress with the help of advancing genomics techniques, resulting in more precise clinical application and many new anticancer drugs on the market. By contrast, very few treatment options are available for rare diseases that are often progressive, severe, and life-threatening. In this opinion we elaborate on the possible association between cancers and rare diseases across three different levels including clinical observation, crosstalk between germline mutation and somatic mutation, and shared biological pathways. Consequently, by utilizing systematic drug-repositioning approaches, and taking safety issues into consideration, we suggest that oncology drugs have great potential for reuse in the treatment of rare diseases.

PMID: 27461952 [PubMed - as supplied by publisher]

Anti-inflammatory effects of dabrafenib on polyphosphate-mediated vascular disruption.

Chem Biol Interact. 2016 Jul 22;

Authors: Lee S, Ku SK, Bae JS

Abstract
The screening of bioactive compound libraries can be an effective approach for repositioning FDA-approved drugs or discovering new treatments for human diseases. Previous studies have reported polyphosphate (PolyP)-mediated vascular inflammatory responses such as disruption of vascular integrity. Dabrafenib is a B-Raf inhibitor and initially used for the treatment of metastatic melanoma therapy. This study illustrates drug repositioning with dabrafenib (DAB) for the modulation of PolyP-mediated vascular inflammatory responses in human umbilical vein endothelial cells (HUVECs) and mice. The survival rates, septic biomarker levels, behavior of human neutrophils, and vascular permeability were determined in PolyP-activated HUVECs and mice. Dabrafenib suppressed the PolyP-mediated vascular barrier permeability, upregulation of inflammatory biomarkers, adhesion/migration of leukocytes, and activation and/or production of nuclear factor-κB, tumor necrosis factor-α, and interleukin-6. Furthermore, dabrafenib demonstrated protective effects on PolyP-mediated lethal death and the levels of the related septic biomarkers. Therefore, these results indicated the therapeutic potential of dabrafenib on various systemic inflammatory diseases, such as sepsis or septic shock.

PMID: 27458080 [PubMed - as supplied by publisher]

Scoring multiple features to predict drug disease associations using information fusion and aggregation.

SAR QSAR Environ Res. 2016 Jul 25;:1-20

Authors: Moghadam H, Rahgozar M, Gharaghani S

Abstract
Prediction of drug-disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug-disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug-drug level and the drug-disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug-disease interactions. The method was validated against a well-established drug-disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.

PMID: 27455069 [PubMed - as supplied by publisher]