cBinderDB: a covalent binding agent database.

Bioinformatics. 2016 Dec 23;:

Authors: Du J, Yan X, Liu Z, Cui L, Ding P, Tan X, Li X, Zhou H, Gu Q, Xu J

Abstract
MOTIVATION: Small molecule drug candidates with attractive toxicity profiles that modulate target proteins through noncovalent interactions are usually favored by scientists and pharmaceutical industry. In the past decades, many noncovalent binding agents have been developed for different diseases. However, an increasing attention has been paid to covalent binding agents in pharmaceutical fields during recent years. Many covalent binding agents entered clinical trials and exerted significant advantages for diseases such as infection, cancers, gastrointestinal disorders, central nervous system or cardiovascular diseases. It has been recognized that covalent binding ligands can be attractive drug candidates. But, there is lack of resource to support covalent ligand discovery.
RESULTS: Hence, we initiated a covalent binder database (cBinderDB). To our best knowledge, it is the first online database that provides information on covalent binding compound structures, chemotypes, targets, covalent binding types, and other biological properties. The covalent binding targets are annotated with biological functions, protein family and domains, gene information, modulators, and receptor-ligand complex structure. The data in the database were collected from scientific publications by combining a text mining method and manual inspection processes. cBinderDB covers covalent binder's data up to September 2016.
AVAILABILITY AND IMPLEMENTATION: cBinderDB is freely available at www.rcdd.org.cn/cbinderdb/ CONTACT: junxu@biochemomes.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 28011781 [PubMed - as supplied by publisher]

Disease and pharmacologic risk factors for first and subsequent episodes of equine laminitis: A cohort study of free-text electronic medical records.

Prev Vet Med. 2017 Jan 01;136:11-18

Authors: Welsh CE, Duz M, Parkin TD, Marshall JF

Abstract
Electronic medical records from first opinion equine veterinary practice may represent a unique resource for epidemiologic research. The appropriateness of this resource for risk factor analyses was explored as part of an investigation into clinical and pharmacologic risk factors for laminitis. Amalgamated medical records from seven UK practices were subjected to text mining to identify laminitis episodes, systemic or intra-synovial corticosteroid prescription, diseases known to affect laminitis risk and clinical signs or syndromes likely to lead to corticosteroid use. Cox proportional hazard models and Prentice, Williams, Peterson models for repeated events were used to estimate associations with time to first, or subsequent laminitis episodes, respectively. Over seventy percent of horses that were diagnosed with laminitis suffered at least one recurrence. Risk factors for first and subsequent laminitis episodes were found to vary. Corticosteroid use (prednisolone only) was only significantly associated with subsequent, and not initial laminitis episodes. Electronic medical record use for such analyses is plausible and offers important advantages over more traditional data sources. It does, however, pose challenges and limitations that must be taken into account, and requires a conceptual change to disease diagnosis which should be considered carefully.

PMID: 28010903 [PubMed - in process]

Impact of late presentation of HIV infection on short-, mid- and long-term mortality and causes of death in a multicenter national cohort: 2004-2013.

J Infect. 2016 May;72(5):587-96

Authors: Sobrino-Vegas P, Moreno S, Rubio R, Viciana P, Bernardino JI, Blanco JR, Bernal E, Asensi V, Pulido F, del Amo J, Hernando V, Cohorte de la Red de Investigación en Sida, Spain

Abstract
OBJECTIVES: To analyze the impact of late presentation (LP) on overall mortality and causes of death and describe LP trends and risk factors (2004-2013).
METHODS: Cox models and logistic regression were used to analyze data from a nation-wide cohort in Spain. LP is defined as being diagnosed when CD4 < 350 cells/ml or AIDS.
RESULTS: Of 7165 new HIV diagnoses, 46.9% (CI95%:45.7-48.0) were LP, 240 patients died. First-year mortality was the highest (aHRLP.vs.nLP = 10.3[CI95%:5.5-19.3]); between 1 and 4 years post-diagnosis, aHRLP.vs.nLP = 1.9(1.2-3.0); and >4 years, aHRLP.vs.nLP = 1.5(0.7-3.1). First-year's main cause of death was HIV/AIDS (73%); and malignancies among those surviving >4 years (32%). HIV/AIDS-related deaths were more likely in LP (59.2% vs. 25.0%; p < 0.001). LP declined from 55.9% (2004-05) to 39.4% (2012-13), and reduced in 46.1% in men who have sex with men (MSM) and 37.6% in heterosexual men, but increased in 22.6% in heterosexual women. Factors associated with LP: sex (ORMEN.vs.WOMEN = 1.4[1.2-1.7]); age (OR31-40.vs.<30 = 1.6[1.4-1.8], OR41-50.vs.<30 = 2.2[1.8-2.6], OR>50.vs.<30 = 3.6[2.9-4.4]); behavior (ORInjectedDrugUse.vs.MSM = 2.8[2.0-3.8]; ORHeterosexual.vs.MSM = 2.2[1.7-3.0]); education (ORPrimaryEducation.vs.University = 1.5[1.1-2.0], ORLowerSecondary.vs.University = 1.3[1.1-1.5]); and geographical origin (ORSub-Saharan.vs.Spain = 1.6[1.3-2.0], ORLatin-American.vs.Spain = 1.4[1.2-1.8]).
CONCLUSIONS: LP is associated with higher mortality, especially short-term- and HIV/AIDS-related mortality. Mid-term-, but not long-term mortality, remained also higher in LP than nLP. LP decreased in MSM and heterosexual men, not in heterosexual women. The groups most affected by LP are low educated, non-Spanish and heterosexual women.

PMID: 26920789 [PubMed - indexed for MEDLINE]

FLP-4 neuropeptide and its receptor in a neuronal circuit regulate preference choice through functions of ASH-2 trithorax complex in Caenorhabditis elegans.

Sci Rep. 2016 Feb 18;6:21485

Authors: Yu Y, Zhi L, Guan X, Wang D, Wang D

Abstract
Preference choice on food is an important response strategy for animals living in the environment. Using assay system of preference choice on bacterial foods, OP50 and PA14, we identified the involvement of ADL sensory neurons in the control of preference choice in Caenorhabditis elegans. Both genetically silencing and ChR2-mediated activation of ADL sensory neurons significantly affected preference choice. ADL regulated preference choice by inhibiting function of G protein-coupled receptor (GPCR)/SRH-220. ADL sensory neurons might regulate preference choice through peptidergic signals of FLP-4 and NLP-10, and function of FLP-4 or NLP-10 in regulating preference choice was regulated by SRH-220. FLP-4 released from ADL sensory neurons further regulated preference choice through its receptor of NPR-4 in AIB interneurons. In AIB interneurons, NPR-4 was involved in the control of preference choice by activating the functions of ASH-2 trithorax complex consisting of SET-2, ASH-2, and WDR-5, implying the crucial role of molecular machinery of trimethylation of histone H3K4 in the preference choice control. The identified novel neuronal circuit and the underlying molecular mechanisms will strengthen our understanding neuronal basis of preference choice in animals.

PMID: 26887501 [PubMed - indexed for MEDLINE]

Building an Evaluation Scale using Item Response Theory.

Proc Conf Empir Methods Nat Lang Process. 2016 Nov;2016:648-657

Authors: Lalor JP, Wu H, Yu H

Abstract
Evaluation of NLP methods requires testing against a previously vetted gold-standard test set and reporting standard metrics (accuracy/precision/recall/F1). The current assumption is that all items in a given test set are equal with regards to difficulty and discriminating power. We propose Item Response Theory (IRT) from psychometrics as an alternative means for gold-standard test-set generation and NLP system evaluation. IRT is able to describe characteristics of individual items - their difficulty and discriminating power - and can account for these characteristics in its estimation of human intelligence or ability for an NLP task. In this paper, we demonstrate IRT by generating a gold-standard test set for Recognizing Textual Entailment. By collecting a large number of human responses and fitting our IRT model, we show that our IRT model compares NLP systems with the performance in a human population and is able to provide more insight into system performance than standard evaluation metrics. We show that a high accuracy score does not always imply a high IRT score, which depends on the item characteristics and the response pattern.

PMID: 28004039 [PubMed]

Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU.

Appl Clin Inform. 2016;7(1):101-15

Authors: Temple MW, Lehmann CU, Fabbri D

Abstract
OBJECTIVES: Discharging patients from the Neonatal Intensive Care Unit (NICU) can be delayed for non-medical reasons including the procurement of home medical equipment, parental education, and the need for children's services. We previously created a model to identify patients that will be medically ready for discharge in the subsequent 2-10 days. In this study we use Natural Language Processing to improve upon that model and discern why the model performed poorly on certain patients.
METHODS: We retrospectively examined the text of the Assessment and Plan section from daily progress notes of 4,693 patients (103,206 patient-days) from the NICU of a large, academic children's hospital. A matrix was constructed using words from NICU notes (single words and bigrams) to train a supervised machine learning algorithm to determine the most important words differentiating poorly performing patients compared to well performing patients in our original discharge prediction model.
RESULTS: NLP using a bag of words (BOW) analysis revealed several cohorts that performed poorly in our original model. These included patients with surgical diagnoses, pulmonary hypertension, retinopathy of prematurity, and psychosocial issues.
DISCUSSION: The BOW approach aided in cohort discovery and will allow further refinement of our original discharge model prediction. Adequately identifying patients discharged home on g-tube feeds alone could improve the AUC of our original model by 0.02. Additionally, this approach identified social issues as a major cause for delayed discharge.
CONCLUSION: A BOW analysis provides a method to improve and refine our NICU discharge prediction model and could potentially avoid over 900 (0.9%) hospital days.

PMID: 27081410 [PubMed - indexed for MEDLINE]

Classical Challenges in the Physical Chemistry of Polymer Networks and the Design of New Materials.

Acc Chem Res. 2016 Dec 20;49(12):2786-2795

Authors: Wang R, Sing MK, Avery RK, Souza BS, Kim M, Olsen BD

Abstract
Polymer networks are widely used from commodity to biomedical materials. The space-spanning, net-like structure gives polymer networks their advantageous mechanical and dynamic properties, the most essential factor that governs their responses to external electrical, thermal, and chemical stimuli. Despite the ubiquity of applications and a century of active research on these materials, the way that chemistry and processing interact to yield the final structure and the material properties of polymer networks is not fully understood, which leads to a number of classical challenges in the physical chemistry of gels. Fundamentally, it is not yet possible to quantitatively predict the mechanical response of a polymer network based on its chemical design, limiting our ability to understand and characterize the nanostructure of gels and rationally design new materials. In this Account, we summarize our recent theoretical and experimental approaches to study the physical chemistry of polymer networks. First, our understanding of the impact of molecular defects on topology and elasticity of polymer networks is discussed. By systematically incorporating the effects of different orders of loop structure, we develop a kinetic graph theory and real elastic network theory that bridge the chemical design, the network topology, and the mechanical properties of the gel. These theories show good agreement with the recent experimental data without any fitting parameters. Next, associative polymer gel dynamics is discussed, focusing on our evolving understanding of the effect of transient bonds on the mechanical response. Using forced Rayleigh scattering (FRS), we are able to probe diffusivity across a wide range of length and time scales in gels. A superdiffusive region is observed in different associative network systems, which can be captured by a two-state kinetic model. Further, the effects of the architecture and chemistry of polymer chains on gel nanostructure are studied. By incorporating shear-thinning coiled-coil protein motifs into the midblock of a micelle-forming block copolymer, we are able to responsively adjust the gel toughness through controlling the nanostructure. Finally, we review the development of novel application-oriented materials that emerge from our enhanced understanding of gel physical chemistry, including injectable gel hemostats designed to treat internal wounds and engineered nucleoporin-like polypeptide (NLP) hydrogels that act as biologically selective filters. We believe that the fundamental physical chemistry questions articulated in this Account will provide inspiration to fully understand the design of polymer networks, a group of mysterious yet critically important materials.

PMID: 27993006 [PubMed - in process]

Comparison of Presentation and Outcome in 100 Pediatric Hodgkin Lymphoma Patients Treated at Children Hospital, Lahore, Pakistan and Royal Marsden Hospital, UK.

J Coll Physicians Surg Pak. 2016 Nov;26(11):904-907

Authors: Faizan M, Taj MM, Anwar S, Asghar N, Ahmad A, Lancaster D, Atra A, Ali AS

Abstract
OBJECTIVE: To compare differences in demographics and outcomes in childhood Hodgkin lymphoma (HL) presenting at the Children's Hospital Lahore (CHL), and Royal Marsden Hospital (RMH), UK.
STUDY DESIGN: An observational comparative study.
PLACE AND DURATION OF STUDY: From January 2011 to February 2012 at CH, Lahore and from October 2008 to February 2012 at RMH, UK.
METHODOLOGY: Consecutive HL patients (50 from each hospital) were inducted. Data regarding age, gender, staging, histopathology and outcome were analysed. Clinical and pathological staging done according to Ann-Arbor and World Health Organization classification. Treatment duration was 6-8 months. They were followed for 6 months post-treatment. Frequencies of variables were noted and compared. Chi-square test was used for determining significance.
RESULTS: Patients from Children's Hospital, Lahore were younger (mean 7.9 years) with male predominance (n=42, 84%). Histopathology showed Mixed Cellularity (MC) in 32 (64%), Nodular Sclerosis (NS) in 5 (10%), Lymphocyte Rich in 4 (8%) and lymphocyte depleted in 1 (2%), nodular lymphocyte predominant (NLP) in 1 (2%) each. Majority presented in stage IV (n=25,50%), or stage III (n=20,40%). Constitutional B symptoms were present in 37 (74%). Bone marrow involvement observed in 23 (46%). Remission was achieved in 42 (84%) patients; 2 (4%) relapsed, 4 (8%) expired and 2 (4%) left against medical advice. In contrast, RMH patients were older (mean 11.8 years.) and 30 (60%) were males. NS (n=40,80%) and NLP (n=6,12%) types were predominant. Two (4%) patients were in stage I, 27 (54%) in stage II, 12 (24%) in stage III and 9 (18%) presented in stage IV. Fourteen (28%) had B-symptoms. None had bone marrow disease. Event free survival was 46 (92%). Four (8%) patients relapsed. Three responded to second line therapy and one relapsed postautologous transplant.
CONCLUSION: Significant differences were observed in age at presentation, stage, histopathology and extent of bone marrow involvement between the groups. Of interest is the bone marrow involvement in stage IV patients in Pakistan. Delayed diagnosis account for advanced stage but difference in pathological subtype needs further study.

PMID: 27981925 [PubMed - in process]

Comparison of Three Information Sources for Smoking Information in Electronic Health Records.

Cancer Inform. 2016;15:237-242

Authors: Wang L, Ruan X, Yang P, Liu H

Abstract
OBJECTIVE: The primary aim was to compare independent and joint performance of retrieving smoking status through different sources, including narrative text processed by natural language processing (NLP), patient-provided information (PPI), and diagnosis codes (ie, International Classification of Diseases, Ninth Revision [ICD-9]). We also compared the performance of retrieving smoking strength information (ie, heavy/light smoker) from narrative text and PPI.
MATERIALS AND METHODS: Our study leveraged an existing lung cancer cohort for smoking status, amount, and strength information, which was manually chart-reviewed. On the NLP side, smoking-related electronic medical record (EMR) data were retrieved first. A pattern-based smoking information extraction module was then implemented to extract smoking-related information. After that, heuristic rules were used to obtain smoking status-related information. Smoking information was also obtained from structured data sources based on diagnosis codes and PPI. Sensitivity, specificity, and accuracy were measured using patients with coverage (ie, the proportion of patients whose smoking status/strength can be effectively determined).
RESULTS: NLP alone has the best overall performance for smoking status extraction (patient coverage: 0.88; sensitivity: 0.97; specificity: 0.70; accuracy: 0.88); combining PPI with NLP further improved patient coverage to 0.96. ICD-9 does not provide additional improvement to NLP and its combination with PPI. For smoking strength, combining NLP with PPI has slight improvement over NLP alone.
CONCLUSION: These findings suggest that narrative text could serve as a more reliable and comprehensive source for obtaining smoking-related information than structured data sources. PPI, the readily available structured data, could be used as a complementary source for more comprehensive patient coverage.

PMID: 27980387 [PubMed]

Elementary, My Dear Watson - the era of natural language processing in transplantation.

Am J Transplant. 2016 Dec 15;:

Authors: Ho B, Skaro A, Montag S, Zhao L

Abstract
The use of modern data acquisition and analytics commonly in use in the technology sector by Google, IBM, and others have long been the envy of the health services researcher. In this issue, Srinivas et al. have demonstrated feasibility in the use of data mining and natural language processing (NLP) in data abstraction.(1) In their study they developed predictive models for graft loss and patient survival in kidney transplantation using single-center retrospective data. This article is protected by copyright. All rights reserved.

PMID: 27977903 [PubMed - as supplied by publisher]

The Outcome of Endoscopic Transethmosphenoid Optic Canal Decompression for Indirect Traumatic Optic Neuropathy with No-Light-Perception.

J Ophthalmol. 2016;2016:6492858

Authors: Yu B, Ma Y, Tu Y, Wu W

Abstract
Purpose. To present the safety and effect of endoscopic transethmosphenoid optic canal decompression (ETOCD) for indirect traumatic optic neuropathy (ITON) patients with no-light-perception (NLP). Methods. A retrospective study performed on 96 patients (96 eyes) with NLP after ITON between June 1, 2010, and June 1, 2015, who underwent ETOCD, was reviewed. Visual outcome before and after treatment was taken into comparison. Results. The overall visual acuity improvement rate after surgery was 46.9%. The improvement rates of visual acuity of patients who received treatment within 3 days of injury, 3-7 days after injury, and later than 7 days were 63.6%, 42.9%, and 35.7%, respectively. Statistically significant difference was detected between the effective rates of within-3-day group and later-than-7-day group (χ(2) = 5.772, P = 0.016). The effective rate of atrophy group and nonatrophy group was 25.0% and 51.3%, respectively. The effective rate was significantly higher in nonatrophy group (χ(2) = 4.417, P = 0.036). Conclusion. For patients suffering from ITON with NLP, time to medical treatment within 3 days is an influential factor for visual prognosis. Optic nerve atrophy is an important predictor for visual prognosis. Treatment should still be recommended even for cases of delayed presentation to hospital.

PMID: 27965891 [PubMed - in process]

Simplifying EHR Overview of Critically Ill Patients Through Vital Signs Monitoring.

IEEE J Biomed Health Inform. 2016 Dec 09;

Authors: Vilic A, Hoppe K, Petersen J, Kjaer T, Sorensen H

Abstract
This paper presents a novel data-driven approach to graphical presentation of text-based electronic health records (EHR) while maintaining all textual information. We have developed the Patient Condition Timeline (PCT) tool, which creates a timeline representation of a patients' physiological condition during admission. PCT is based on electronical monitoring of vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on existing EHR to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see drastic changes in the patients' health, and thereby enabling staff to see where in the EHR critical events have taken place.

PMID: 27959834 [PubMed - as supplied by publisher]

E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter.

JMIR Public Health Surveill. 2016 Dec 12;2(2):e171

Authors: Lazard AJ, Saffer AJ, Wilcox GB, Chung AD, Mackert MS, Bernhardt JM

Abstract
BACKGROUND: As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product.
OBJECTIVE: This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users.
METHODS: Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptive text mining to reveal the primary topics from tweets collected from March 24, 2015, to July 3, 2015, using a Python script in conjunction with Twitter's streaming application programming interface. A total of 18 keywords related to e-cigarettes were used and resulted in a total of 872,544 tweets that were sorted into overarching themes through a text topic node for tweets (126,127) and retweets (114,451) that represented more than 1% of the conversation.
RESULTS: While some of the final themes were marketing-focused, many topics represented diverse proponent and user conversations that included discussion of policies, personal experiences, and the differentiation of e-cigarettes from traditional tobacco, often by pointing to the lack of evidence for the harm or risks of e-cigarettes or taking the position that e-cigarettes should be promoted as smoking cessation devices.
CONCLUSIONS: These findings reveal that unique, large-scale public conversations are occurring on Twitter alongside e-cigarette advertising and promotion. Proponents and users are turning to social media to share knowledge, experience, and questions about e-cigarette use. Future research should focus on these unique conversations to understand how they influence attitudes towards and use of e-cigarettes.

PMID: 27956376 [PubMed - in process]

Thematic issue of the Second combined Bio-ontologies and Phenotypes Workshop.

J Biomed Semantics. 2016 Dec 12;7(1):66

Authors: Verspoor K, Oellrich A, Collier N, Groza T, Rocca-Serra P, Soldatova L, Dumontier M, Shah N

Abstract
This special issue covers selected papers from the 18th Bio-Ontologies Special Interest Group meeting and Phenotype Day, which took place at the Intelligent Systems for Molecular Biology (ISMB) conference in Dublin in 2015. The papers presented in this collection range from descriptions of software tools supporting ontology development and annotation of objects with ontology terms, to applications of text mining for structured relation extraction involving diseases and phenotypes, to detailed proposals for new ontologies and mapping of existing ontologies. Together, the papers consider a range of representational issues in bio-ontology development, and demonstrate the applicability of bio-ontologies to support biological and clinical knowledge-based decision making and analysis.The full set of papers in the Thematic Issue is available at http://www.biomedcentral.com/collections/sig .

PMID: 27955708 [PubMed - in process]

Beyond Academia - Interrogating Research Impact in the Research Excellence Framework.

PLoS One. 2016;11(12):e0168533

Authors: Terämä E, Smallman M, Lock SJ, Johnson C, Austwick MZ

Abstract
Big changes to the way in which research funding is allocated to UK universities were brought about in the Research Excellence Framework (REF), overseen by the Higher Education Funding Council, England. Replacing the earlier Research Assessment Exercise, the purpose of the REF was to assess the quality and reach of research in UK universities-and allocate funding accordingly. For the first time, this included an assessment of research 'impact', accounting for 20% of the funding allocation. In this article we use a text mining technique to investigate the interpretations of impact put forward via impact case studies in the REF process. We find that institutions have developed a diverse interpretation of impact, ranging from commercial applications to public and cultural engagement activities. These interpretations of impact vary from discipline to discipline and between institutions, with more broad-based institutions depicting a greater variety of impacts. Comparing the interpretations with the score given by REF, we found no evidence of one particular interpretation being more highly rewarded than another. Importantly, we also found a positive correlation between impact score and [overall research] quality score, suggesting that impact is not being achieved at the expense of research excellence.

PMID: 27997599 [PubMed - in process]

Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends.

BMC Res Notes. 2016 Apr 26;9:236

Authors: Jurca G, Addam O, Aksac A, Gao S, Özyer T, Demetrick D, Alhajj R

Abstract
BACKGROUND: Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer.
RESULTS: We utilized PubMed for the testing. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries.
CONCLUSIONS: Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene-gene relations and gene functions.

PMID: 27112211 [PubMed - indexed for MEDLINE]

ARN: Analysis and Visualization System for Adipogenic Regulation Network Information.

Sci Rep. 2016 Dec 16;6:39347

Authors: Huang Y, Wang L, Zan LS

Abstract
Adipogenesis is the process of cell differentiation through which preadipocytes become adipocytes. Lots of research is currently ongoing to identify genes, including their gene products and microRNAs, that correlate with fat cell development. However, information fragmentation hampers the identification of key regulatory genes and pathways. Here, we present a database of literature-curated adipogenesis-related regulatory interactions, designated the Adipogenesis Regulation Network (ARN, http://210.27.80.93/arn/), which currently contains 3101 nodes (genes and microRNAs), 1863 regulatory interactions, and 33,969 expression records associated with adipogenesis, based on 1619 papers. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 37,000 PubMed abstracts. Additionally, we further determined 13,103 possible node relationships by searching miRGate, BioGRID, PAZAR and TRRUST. ARN also has several useful features: i) regulatory map information; ii) tests to examine the impact of a query node on adipogenesis; iii) tests for the interactions and modes of a query node; iv) prediction of interactions of a query node; and v) analysis of experimental data or the construction of hypotheses related to adipogenesis. In summary, ARN can store, retrieve and analyze adipogenesis-related information as well as support ongoing adipogenesis research and contribute to the discovery of key regulatory genes and pathways.

PMID: 27982098 [PubMed - in process]

Utilization and Costs of Compounded Medications for Commercially Insured Patients, 2012-2013.

J Manag Care Spec Pharm. 2016 Feb;22(2):172-81

Authors: McPherson T, Fontane P, Iyengar R, Henderson R

Abstract
BACKGROUND: Although compounding has a long-standing tradition in clinical practice, insurers and pharmacy benefit managers have instituted policies to decrease claims for compounded medications, citing questions about their safety, efficacy, high costs, and lack of FDA approval. There are no reliable published data on the extent of compounding by community pharmacists nor on the fraction of patients who use compounded medications. Prior research suggests that compounded medications represent a relatively small proportion of prescription medications, but those surveys were limited by small sample sizes, subjective data collection methods, and low response rates.
OBJECTIVE: To determine the number of claims for compounded medications on a per user per year (PUPY) basis and the average ingredient cost of these claims among commercially insured patients in the United States for 2012 and 2013.
METHODS: This study used prescription claims data from a nationally representative sample of commercially insured members whose pharmacy benefits were managed by a large pharmacy benefit management company. A retrospective claims analysis was conducted from January 1, 2012, through December 31, 2013. Annualized prevalence, cost, and utilization estimates were drawn from the data. All prescription claims were adjusted to 30-day equivalents. Data-mining techniques (association rule mining) were employed in order to identify the most commonly combined ingredients in compounded medications.
RESULTS: The prevalence of compound users was 1.1% (245,285) of eligible members in 2012 and 1.4% (323,501) in 2013, an increase of 27.3%. Approximately 66% of compound users were female, and the average age of a compound user was approximately 42 years throughout the study period. The geographic distribution of compound user prevalence was consistent across the United States. Compound users' prescription claims increased 36.6% from 2012 to 2013, from approximately 7.1 million to approximately 9.7 million prescriptions. The number of claims for compounded medications increased by 34.2% during the same period, from 486,886 to 653,360. PUPY utilization remained unchanged at 2 prescriptions from 2012 to 2013. The most commonly compounded drugs were similar for all adult age groups and represented therapies typically indicated for chronic pain or hormone replacement therapy. The average ingredient cost for compounded medications increased by 130.3% from 2012 to 2013, from $308.49 to $710.36. The average ingredient cost for these users' non-compounded prescriptions increased only 7.7%, from $148.75 to $160.20. For comparison, the average ingredient cost for all prescription users' claims was $81.50 in 2012 and increased by 3.8% to $84.57 in 2013.
CONCLUSIONS: Compound users represented 1.4% of eligible members in 2013. The average ingredient cost for compound users' compounded prescriptions ($710.36) was greater than for noncompounded prescriptions ($160.20). The 1-year increase in average compounded prescription costs (130.3%) was also greater than for noncompounded prescriptions (7.7%). Although prevalence of compound users and the PUPY utilization for compounded prescriptions increased only slightly between 2012 and 2013, the mean and median cost of compounded medications increased dramatically during this time. Text mining revealed that drug combinations characteristic of topical pain formulations were among the most frequently compounded medications for adults.

PMID: 27015256 [PubMed - indexed for MEDLINE]

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OGEE v2: an update of the online gene essentiality database with special focus on differentially essential genes in human cancer cell lines.

Nucleic Acids Res. 2016 Oct 30;:

Authors: Chen WH, Lu G, Chen X, Zhao XM, Bork P

Abstract
OGEE is an Online GEne Essentiality database. To enhance our understanding of the essentiality of genes, in OGEE we collected experimentally tested essential and non-essential genes, as well as associated gene properties known to contribute to gene essentiality. We focus on large-scale experiments, and complement our data with text-mining results. We organized tested genes into data sets according to their sources, and tagged those with variable essentiality statuses across data sets as conditionally essential genes, intending to highlight the complex interplay between gene functions and environments/experimental perturbations. Developments since the last public release include increased numbers of species and gene essentiality data sets, inclusion of non-coding essential sequences and genes with intermediate essentiality statuses. In addition, we included 16 essentiality data sets from cancer cell lines, corresponding to 9 human cancers; with OGEE, users can easily explore the shared and differentially essential genes within and between cancer types. These genes, especially those derived from cell lines that are similar to tumor samples, could reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer types, and can be further screened to identify targets for cancer therapy and/or new drug development. OGEE is freely available at http://ogee.medgenius.info.

PMID: 27799467 [PubMed - as supplied by publisher]