10 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 2016/06/10

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.

6 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 2016/06/10

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.

Pharmacological exploitation of the phenothiazine antipsychotics to develop novel antitumor agents-A drug repurposing strategy.

Sci Rep. 2016;6:27540

Authors: Wu CH, Bai LY, Tsai MH, Chu PC, Chiu CF, Chen MY, Chiu SJ, Chiang JH, Weng JR

Abstract
Phenothiazines (PTZs) have been used for the antipsychotic drugs for centuries. However, some of these PTZs have been reported to exhibit antitumor effects by targeting various signaling pathways in vitro and in vivo. Thus, this study was aimed at exploiting trifluoperazine, one of PTZs, to develop potent antitumor agents. This effort culminated in A4 [10-(3-(piperazin-1-yl)propyl)-2-(trifluoromethyl)-10H-phenothiazine] which exhibited multi-fold higher apoptosis-inducing activity than the parent compound in oral cancer cells. Compared to trifluoperazine, A4 demonstrated similar regulation on the phosphorylation or expression of multiple molecular targets including Akt, p38, and ERK. In addition, A4 induced autophagy, as evidenced by increased expression of the autophagy biomarkers LC3B-II and Atg5, and autophagosomes formation. The antitumor activity of A4 also related to production of reactive oxygen species and adenosine monophosphate-activated protein kinase. Importantly, the antitumor utility of A4 was extended in vivo as it, administrated at 10 and 20 mg/kg intraperitoneally, suppressed the growth of Ca922 xenograft tumors. In conclusion, the ability of A4 to target diverse aspects of cancer cell growth suggests its value in oral cancer therapy.

PMID: 27277973 [PubMed - in process]

Association of colonic regulatory T cells with hepatitis C virus pathogenesis and liver pathology.

J Gastroenterol Hepatol. 2015 Oct;30(10):1543-51

Authors: Hetta HF, Mekky MA, Khalil NK, Mohamed WA, El-Feky MA, Ahmed SH, Daef EA, Nassar MI, Medhat A, Sherman KE, Shata MT

Abstract
BACKGROUND AND AIM: Forkhead box protein P3 (FoxP3)(+) regulatory T (Treg ) cells play a fundamental role in maintaining the balance between the tissue-damaging and protective immune response to chronic hepatitis C (CHC) infection. Herein, we investigated the frequency of Treg cells in the colon and their potential relationship to the various CHC outcomes and hepatic histopathology.
METHODS: Colonic biopsies were collected from three groups with CHC: treatment naïve (TN; n = 20), non-responders (NR; n = 20), sustained virologic response (SVR; n = 20), and a fourth healthy control group (n = 10). The plasma viral loads and cytokines levels were determined by quantitative real-time polymerase chain reaction, and ELISA, respectively. Liver biopsies were examined to assess inflammatory score and fibrosis stage. Colonic Treg frequency was estimated by immunohistochemistry using confocal microscopy.
RESULTS: A significant increase in the frequency of colonic Treg was found in TN, and NR groups compared with the control and SVR group. The frequency of colonic Treg , plasma interleukin (IL)-10 and IL-4 levels were significantly positively correlated with viral load and negatively correlated with METAVIR inflammatory score, and fibrosis stages.
CONCLUSION: Colonic Treg cells are negatively correlated with liver inflammation and hepatitis C virus (HCV) viral load, which suggests a strong linkage between gut-derived Treg cell populations and HCV infection.

PMID: 25708446 [PubMed - indexed for MEDLINE]

Effects of CYP2B6 and CYP1A2 Genetic Variation on Nevirapine Plasma Concentration and Pharmacodynamics as Measured by CD4 Cell Count in Zimbabwean HIV-Infected Patients.

OMICS. 2015 Sep;19(9):553-62

Authors: Mhandire D, Lacerda M, Castel S, Mhandire K, Zhou D, Swart M, Shamu T, Smith P, Musingwini T, Wiesner L, Stray-Pedersen B, Dandara C

Abstract
The extremely high prevalence of HIV/AIDS in sub-Saharan Africa and limitations of current antiretroviral medicines demand new tools to optimize therapy such as pharmacogenomics for person-to-person variations. African populations exhibit greater genetic diversity than other world populations, thus making it difficult to extrapolate findings from one population to another. Nevirapine, an antiretroviral medicine, displays large plasma concentration variability which adversely impacts therapeutic virological response. This study, therefore, aimed to identify sources of variability in nevirapine pharmacokinetics and pharmacodynamics, focusing on genetic variation in CYP2B6 and CYP1A2. Using a cross-sectional study design, 118 HIV-infected adult Zimbabwean patients on nevirapine-containing highly active antiretroviral therapy (HAART) were characterized for three key functional single nucleotide polymorphisms (SNPs), CYP2B6 c.516G>T (rs3745274), CYP2B6 c.983T>C (rs28399499), and CYP1A2 g.-163C>A (rs762551). We investigated whether genotypes at these loci were associated with nevirapine plasma concentration, a therapeutic biomarker, and CD4 cell count, a biomarker of disease progression. CYP2B6 and CYP1A2 were chosen as the candidate genes based on reports in literature, as well as their prominence in the metabolism of efavirenz, a drug in the same class with nevirapine. Nevirapine plasma concentration was determined using LC-MS/MS. The mean nevirapine concentration for CYP2B6 c.516T/T genotype differed significantly from that of 516G/G (p < 0.001) and 516G/T (p < 0.01) genotypes, respectively. There were also significant differences in mean nevirapine concentration between CYP2B6 c.983T > C genotypes (p = 0.04). Importantly, the CYP1A2 g.-163C>A SNP was significantly associated with the pharmacodynamics endpoint, the CD4 cell count (p = 0.012). Variant allele frequencies for the three SNPs observed in this Zimbabwean group were similar to other African population groups but different to observations among Caucasian and Asian populations. We conclude that CYP2B6 c.516G>T and CYP2B6 c.983T>C could be important sources of nevirapine pharmacokinetic variability that could be considered for dosage optimization, while CYP1A2 g.-163C>A seems to be associated with HIV disease progression. These inter- and intra-population pharmacokinetic and pharmacodynamics differences suggest that a single prescribed dosage may not be appropriate for the treatment of disease. Further research into a personalized nevirapine regimen is required.

PMID: 26348712 [PubMed - indexed for MEDLINE]

Genetic polymorphism of pharmacogenomic VIP variants in the Deng people from the Himalayas in Southeast Tibet.

Biomarkers. 2015;20(5):275-86

Authors: Shi X, Wang L, Du S, Wang H, Feng T, Jin T, Kang L

Abstract
Little is known about polymorphic distribution of pharmacogenes among ethnicities, including the Deng people. In this study, we recruited 100 unrelated, healthy Deng people and genotyped them with respect to 76 different single-nucleotide polymorphisms by the PharmGKB database. Our results first indicated that the polymorphic distribution of pharmacogenes of the Deng people is most similar to CHD, suggesting that Deng people have a closest genetic relationship with CHD. Our data will enrich the database of pharmacogenomics and provide a theoretical basis for safer drug administration and individualized treatment plans, promoting the development of personalized medicine.

PMID: 26329523 [PubMed - indexed for MEDLINE]

BIG: A large-scale data integration tool for renal physiology.

Am J Physiol Renal Physiol. 2016 Jun 8;:ajprenal.00249.2016

Authors: Zhao Y, Yang CR, Raghuram V, Parulekar J, Knepper MA

Abstract
Due to recent advances in high throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: "How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?" This is the type of problem that has motivated the "Big-Data" revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.

PMID: 27279488 [PubMed - as supplied by publisher]

A systems biology and proteomics-based approach identifies SRC and VEGFA as biomarkers in risk factor mediated coronary heart disease.

Mol Biosyst. 2016 Jun 9;

Authors: V A, Nayar PG, Murugesan R, S S, Krishnan J, Ahmed SS

Abstract
Coronary heart disease (CHD) is the most common cause of death worldwide. The burden of CHD increases with risk factors such as smoking, hypertension, obesity and diabetes. Several studies have demonstrated the association of these classical risk factors with CHD. However, the mechanisms of these associations remain largely unclear due to the complexity of disease pathophysiology and the lack of an integrative approach that fails to provide a definite understanding of molecular linkage. To overcome these problems, we propose a novel systems biology approach that relates causative genes, interactomes and pathways to elucidate the risk factors mediating the molecular mechanisms and biomarkers for feasible diagnosis. The literature was mined to retrieve the causative genes of each risk factor and CHD to construct protein interactomes. The interactomes were examined to identify 298 common molecular signatures. The common signatures were mapped to the tissue network to synthesize a sub-network consisting of 82 proteins. Further, the dissection of the sub-network provides functional modules representing a diverse range of molecular functions, including the AKT/p13k, MAPK and wnt pathways. Also, the prioritization of functional modules identifies SRC, VEGFA and HIF1A as potential candidate markers. Further, we validate these candidates with the existing markers CRP, NOS3 and VCAM1 in the serum of 63 individuals, 33 with CHD and 30 controls, using ELISA. SRC, VEGFA, H1F1A, CRP and NOS3 were significantly altered in patients compared to controls. These results support the utility of these candidate markers for the diagnosis of CHD. Overall, our molecular observations indicate the influence of risk factors in the pathophysiology of CHD and identify serum markers for diagnosis.

PMID: 27279347 [PubMed - as supplied by publisher]

Global, quantitative and dynamic mapping of protein subcellular localization.

Elife. 2016 Jun 9;5

Authors: Itzhak DN, Tyanova S, Cox J, Borner GH

Abstract
Subcellular localization critically influences protein function, and cells control protein localization to regulate biological processes. We have developed and applied Dynamic Organellar Maps, a proteomic method that allows global mapping of protein translocation events. We initially used maps statically to generate a database with localization and absolute copy number information for over 8,700 proteins from HeLa cells, approaching comprehensive coverage. All major organelles were resolved, with exceptional prediction accuracy (estimated at >92%). Combining spatial and abundance information yielded an unprecedented quantitative view of HeLa cell anatomy and organellar composition, at the protein level. We subsequently demonstrated the dynamic capabilities of the approach by capturing translocation events following EGF stimulation, which we integrated into a quantitative model. Dynamic Organellar Maps enable the proteome-wide analysis of physiological protein movements, without requiring any reagents specific to the investigated process, and will thus be widely applicable in cell biology.

PMID: 27278775 [PubMed - as supplied by publisher]

Applying NGS Data to Find Evolutionary Network Biomarkers from the Early and Late Stages of Hepatocellular Carcinoma.

Biomed Res Int. 2015;2015:391475

Authors: Wong YH, Wu CC, Lin CL, Chen TS, Chang TH, Chen BS

Abstract
Hepatocellular carcinoma (HCC) is a major liver tumor (~80%), besides hepatoblastomas, angiosarcomas, and cholangiocarcinomas. In this study, we used a systems biology approach to construct protein-protein interaction networks (PPINs) for early-stage and late-stage liver cancer. By comparing the networks of these two stages, we found that the two networks showed some common mechanisms and some significantly different mechanisms. To obtain differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two stages of liver cancer by systems biology method using NGS data from cancer cells and adjacent noncancer cells. Using carcinogenesis relevance values (CRVs), we identified 43 and 80 significant proteins and their PPINs (network markers) for early-stage and late-stage liver cancer. To investigate the evolution of network biomarkers in the carcinogenesis process, a primary pathway analysis showed that common pathways of the early and late stages were those related to ordinary cancer mechanisms. A pathway specific to the early stage was the mismatch repair pathway, while pathways specific to the late stage were the spliceosome pathway, lysine degradation pathway, and progesterone-mediated oocyte maturation pathway. This study provides a new direction for cancer-targeted therapies at different stages.

PMID: 26366411 [PubMed - indexed for MEDLINE]

Mining clinical attributes of genomic variants through assisted literature curation in Egas.

Database (Oxford). 2016;2016

Authors: Matos S, Campos D, Pinho R, Silva RM, Mort M, Cooper DN, Oliveira JL

Abstract
The veritable deluge of biological data over recent years has led to the establishment of a considerable number of knowledge resources that compile curated information extracted from the literature and store it in structured form, facilitating its use and exploitation. In this article, we focus on the curation of inherited genetic variants and associated clinical attributes, such as zygosity, penetrance or inheritance mode, and describe the use of Egas for this task. Egas is a web-based platform for text-mining assisted literature curation that focuses on usability through modern design solutions and simple user interactions. Egas offers a flexible and customizable tool that allows defining the concept types and relations of interest for a given annotation task, as well as the ontologies used for normalizing each concept type. Further, annotations may be performed on raw documents or on the results of automated concept identification and relation extraction tools. Users can inspect, correct or remove automatic text-mining results, manually add new annotations, and export the results to standard formats. Egas is compatible with the most recent versions of Google Chrome, Mozilla Firefox, Internet Explorer and Safari and is available for use at https://demo.bmd-software.com/egas/Database URL: https://demo.bmd-software.com/egas/.

PMID: 27278817 [PubMed - in process]

Overlap in drug-disease associations between clinical practice guidelines and drug structured product label indications.

J Biomed Semantics. 2016;7:37

Authors: Leung TI, Dumontier M

Abstract
BACKGROUND: Clinical practice guidelines (CPGs) recommend pharmacologic treatments for clinical conditions, and drug structured product labels (SPLs) summarize approved treatment indications. Both resources are intended to promote evidence-based medical practices and guide clinicians' prescribing decisions. However, it is unclear how well CPG recommendations about pharmacologic therapies match SPL indications for recommended drugs. In this study, we perform text mining of CPG summaries to examine drug-disease associations in CPG recommendations and in SPL treatment indications for 15 common chronic conditions.
METHODS: We constructed an initial text corpus of guideline summaries from the National Guideline Clearinghouse (NGC) from a set of manually selected ICD-9 codes for each of the 15 conditions. We obtained 377 relevant guideline summaries and their Major Recommendations section, which excludes guidelines for pediatric patients, pregnant or breastfeeding women, or for medical diagnoses not meeting inclusion criteria. A vocabulary of drug terms was derived from five medical taxonomies. We used named entity recognition, in combination with dictionary-based and ontology-based methods, to identify drug term occurrences in the text corpus and construct drug-disease associations. The ATC (Anatomical Therapeutic Chemical Classification) was utilized to perform drug name and drug class matching to construct the drug-disease associations from CPGs. We then obtained drug-disease associations from SPLs using conditions mentioned in their Indications section in SIDER. The primary outcomes were the frequency of drug-disease associations in CPGs and SPLs, and the frequency of overlap between the two sets of drug-disease associations, with and without using taxonomic information from ATC.
RESULTS: Without taxonomic information, we identified 1444 drug-disease associations across CPGs and SPLs for 15 common chronic conditions. Of these, 195 drug-disease associations overlapped between CPGs and SPLs, 917 associations occurred in CPGs only and 332 associations occurred in SPLs only. With taxonomic information, 859 unique drug-disease associations were identified, of which 152 of these drug-disease associations overlapped between CPGs and SPLs, 541 associations occurred in CPGs only, and 166 associations occurred in SPLs only.
CONCLUSIONS: Our results suggest that CPG-recommended pharmacologic therapies and SPL indications do not overlap frequently when identifying drug-disease associations using named entity recognition, although incorporating taxonomic relationships between drug names and drug classes into the approach improves the overlap. This has important implications in practice because conflicting or inconsistent evidence may complicate clinical decision making and implementation or measurement of best practices.

PMID: 27277160 [PubMed - in process]

Systematic Analysis of Endometrial Cancer-Associated Hub Proteins Based on Text Mining.

Biomed Res Int. 2015;2015:615825

Authors: Gao H, Zhang Z

Abstract
OBJECTIVE: The aim of this study was to systematically characterize the expression of endometrial cancer- (EC-) associated genes and to analysis the functions, pathways, and networks of EC-associated hub proteins.
METHODS: Gene data for EC were extracted from the PubMed (MEDLINE) database using text mining based on NLP. PPI networks and pathways were integrated and obtained from the KEGG and other databases. Proteins that interacted with at least 10 other proteins were identified as the hub proteins of the EC-related genes network.
RESULTS: A total of 489 genes were identified as EC-related with P < 0.05, and 32 pathways were identified as significant (P < 0.05, FDR < 0.05). A network of EC-related proteins that included 271 interactions was constructed. The 17 proteins that interact with 10 or more other proteins (P < 0.05, FDR < 0.05) were identified as the hub proteins of this PPI network of EC-related genes. These 17 proteins are EGFR, MET, PDGFRB, CCND1, JUN, FGFR2, MYC, PIK3CA, PIK3R1, PIK3R2, KRAS, MAPK3, CTNNB1, RELA, JAK2, AKT1, and AKT2.
CONCLUSION: Our data may help to reveal the molecular mechanisms of EC development and provide implications for targeted therapy for EC. However, corrections between certain proteins and EC continue to require additional exploration.

PMID: 26366417 [PubMed - indexed for MEDLINE]

31 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 2016/06/09

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.

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

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

These pubmed results were generated on 2016/06/09

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.

21 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 2016/06/09

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

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

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

These pubmed results were generated on 2016/06/09

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.

The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.

Brief Bioinform. 2016 Jun 5;

Authors: Vilar S, Hripcsak G

Abstract
Explosion of the availability of big data sources along with the development in computational methods provides a useful framework to study drugs' actions, such as interactions with pharmacological targets and off-targets. Databases related to protein interactions, adverse effects and genomic profiles are available to be used for the construction of computational models. In this article, we focus on the description of biological profiles for drugs that can be used as a system to compare similarity and create methods to predict and analyze drugs' actions. We highlight profiles constructed with different biological data, such as target-protein interactions, gene expression measurements, adverse effects and disease profiles. We focus on the discovery of new targets or pathways for drugs already in the pharmaceutical market, also called drug repurposing, in the interaction with off-targets responsible for adverse reactions and in drug-drug interaction analysis. The current and future applications, strengths and challenges facing all these methods are also discussed. Biological profiles or signatures are an important source of data generation to deeply analyze biological actions with important implications in drug-related studies.

PMID: 27273288 [PubMed - as supplied by publisher]

A Platform to Enable the Pharmacological Profiling of Small Molecules in Gel-Based Electrophoretic Mobility Shift Assays.

J Biomol Screen. 2016 Jun 6;

Authors: Foley TL, Dorjsuren D, Dexheimer TS, Burkart MD, Wight WC, Simeonov A

Abstract
We describe a polyacrylamide gel casting cassette that overcomes limitations of commercially available gel electrophoresis equipment. This apparatus molds a single polyacrylamide gel that can evaluate more than 200 samples in parallel, is loaded with a multichannel pipettor, and is flexible with respect to composition of the separating matrix. We demonstrate its use to characterize inhibitors of enzymes that modify protein and nucleic acid substrates. Throughputs of greater than 1000 samples per day were achieved when this system was paired with a quantitative laser-based imaging system, yielding data of remarkable quality.

PMID: 27269812 [PubMed - as supplied by publisher]

Treatment of Disseminated Leishmaniasis With Liposomal Amphotericin B.

Clin Infect Dis. 2015 Sep 15;61(6):945-9

Authors: Machado PR, Rosa ME, Guimarães LH, Prates FV, Queiroz A, Schriefer A, Carvalho EM

Abstract
BACKGROUND: Disseminated leishmaniasis (DL) is a severe and emerging form of American tegumentary leishmaniasis, associated primarily with infection by Leishmania brasiliensis. DL is defined by the presence of ≥10 mixed-type lesions such as inflammatory papules and ulcers, located in ≥2 body parts. Most patients have hundreds of lesions all over the body, and mucosal involvement is detected in up to 44% of cases. DL is a difficult to cure disease and pentavalent antimony (Sb(v)) is used as standard treatment, its highest dosage being 20 mg/kg/day, for 30 days. However, less than 25% of DL cases will be cured after standard therapy, and the majority of cases will require more than one course of Sb(v) for a cure. In this context, new therapies are needed that offer a higher cure rate and a better safety profile, with convenience in drug administration.
METHODS: We have evaluated liposomal amphotericin B in 20 patients with DL in an open clinical trial. The total dose ranged from 17 to 37 mg/kg, used in 7 to 14 days of treatment.
RESULTS: Cure rate at 3 months after therapy was 70%. One relapse was documented 4 months after treatment, producing a final cure rate of 65%. Although liposomal amphotericin B was considered well tolerated, mild adverse events were documented in 75% of the patients.
CONCLUSIONS: Liposomal amphotericin B is an effective therapy for DL, with a higher final cure rate of 75% observed when used in a total dose above 30 mg/kg.
CLINICAL TRIALS REGISTRATION: NCT02025491.

PMID: 26048961 [PubMed - indexed for MEDLINE]