Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution

Text Mining the History of Medicine

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform

Extraction of pharmacokinetic evidence of drug-drug interactions from the literature

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F10.93, MCC0.74, iAUC0.99) and sentences (F10.76, MCC0.65, iAUC0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.National Institutes of Health, National Library of Medicine Program, grant 01LM011945-01 "BLR: Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical," a grant from the Indiana University Collaborative Research Program 2013, "Drug-Drug Interaction Prediction from Large-scale Mining of Literature and Patient Records," as well as a grant from the joint program between the Fundação Luso-Americana para o Desenvolvimento (Portugal) and National Science Foundation (USA), 2012-2014, "Network Mining For Gene Regulation And Biochemical Signaling.

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Clase social, factores de riesgo psicosocial en el trabajo y su asociación con la salud autopercibida y mental en Chile

El objetivo fue examinar la relación entre clase social, riesgo psicosocial laboral y la salud autopercibida y mental en Chile. Se trata de un estudio transversal con los datos de la Primera Encuesta Nacional de Condiciones de Empleo, Trabajo, Calidad de Vida y Salud de los Trabajadores y Trabajadoras en Chile (N = 9.503). Las variables dependientes son: salud mental y salud autopercibida. Las variables explicativas son: clase social neo-marxista, factores de riesgo psicosocial y privación material. Se realizaron análisis descriptivos y de regresión logística. Existen desigualdades en la distribución de los factores exposición laboral a riesgos psicosociales, según clase social y sexo. Además, la clase social y los factores de riesgo psicosocial en el trabajo están asociados a una distribución desigual de la salud autopercibida y salud mental entre la población trabajadora en Chile. Las intervenciones en el área de la salud de los trabajadores deben considerar la clase social y los factores de riesgo psicosocial a que están expuestos los trabajadores

Properties of a short questionnaire for assessing Primary Care experiences for children in a population survey

<p>Abstract</p> <p>Background</p> <p>The Primary Care Assessment Tool (PCAT) is an interesting set of tools for primary care research. A very short version could inform policy makers about consumer experiences with primary care (PC) through health surveys. This work aimed to investigate the validity and reliability of a selection of items from the child short edition (CS) of the PCAT.</p> <p>Methods</p> <p>A 24 item questionnaire permitted the identification of a regular source of care and the assessment of the key attributes of first contact, ongoing care over time, coordination, services available and services received (comprehensiveness), and cultural competence. Structural validity, reliability, and construct validity were assessed using responses from 2,200 parents of a representative sample of the population aged 0 to 14 years in Catalonia (Spain) who participated in the 2006 Health Survey. Structural validity was analyzed using exploratory and confirmatory factor analyses and reliability was assessed using Cronbach's alpha. Construct validity was assessed using linear regression analysis between PC experience scores and a measure of overall user satisfaction with healthcare services.</p> <p>Results</p> <p>A total of 2,095 (95.2%) parents provided useable responses on PC. After Confirmatory Factor Analysis (CFA), the best fitting model was a 5-factor model in which the original dimensions of first contact and ongoing care were collapsed into one. The CFA also showed a second order factor onto which all domains except services available loaded (root mean square error of approximation = 0.000; comparative fit index = 1.00). Cronbach's alpha values for one of the original scales (first-contact) was poor (alpha < 0.50), but improved using the modified factor structure (alpha > 0.70). Scores on the scales were correlated with satisfaction with healthcare services (p < 0.01), thereby providing some preliminary evidence of construct validity.</p> <p>Conclusions</p> <p>This very short questionnaire obtained from the PCAT-CE yields information about five attributes of PC and a summary score. It has shown evidence of validity and reliability for judgments about experiences with primary care overall. If space on surveys is at a premium, the instrument could be useful as a measure of PC experiences.</p

Translational Selection Is Ubiquitous in Prokaryotes

Codon usage bias in prokaryotic genomes is largely a consequence of background substitution patterns in DNA, but highly expressed genes may show a preference towards codons that enable more efficient and/or accurate translation. We introduce a novel approach based on supervised machine learning that detects effects of translational selection on genes, while controlling for local variation in nucleotide substitution patterns represented as sequence composition of intergenic DNA. A cornerstone of our method is a Random Forest classifier that outperformed previous distance measure-based approaches, such as the codon adaptation index, in the task of discerning the (highly expressed) ribosomal protein genes by their codon frequencies. Unlike previous reports, we show evidence that translational selection in prokaryotes is practically universal: in 460 of 461 examined microbial genomes, we find that a subset of genes shows a higher codon usage similarity to the ribosomal proteins than would be expected from the local sequence composition. These genes constitute a substantial part of the genome—between 5% and 33%, depending on genome size—while also exhibiting higher experimentally measured mRNA abundances and tending toward codons that match tRNA anticodons by canonical base pairing. Certain gene functional categories are generally enriched with, or depleted of codon-optimized genes, the trends of enrichment/depletion being conserved between Archaea and Bacteria. Prominent exceptions from these trends might indicate genes with alternative physiological roles; we speculate on specific examples related to detoxication of oxygen radicals and ammonia and to possible misannotations of asparaginyl–tRNA synthetases. Since the presence of codon optimizations on genes is a valid proxy for expression levels in fully sequenced genomes, we provide an example of an “adaptome” by highlighting gene functions with expression levels elevated specifically in thermophilic Bacteria and Archaea

Evaluating Psychometric Properties of an Instrument Addressing Comprehensiveness of Care Among Dentists

Universidade Federal de Minas Gerais. Departamento de Odontologia Comunitária e Preventiva. Belo Horizonte, MG, Brazil.Fundação Oswaldo Cruz. Instituto René Rachou. Belo Horizonte, MG, Brazil.Division of Population and Patient Health. King’s College London Dental Institute at Guy’s. King’s College and St Thomas’ Hospitals. London, UK.Universidade Federal de Minas Gerais. Departamento de Ortodontia e Odontopediatria Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Departamento de Odontologia Comunitária e Preventiva. Belo Horizonte, MG, Brazil.Objetivou-se avaliar as propriedades psicométricas de um instrumento para avaliar a integralidade do cuidado por dentistas, usando uma combinação da teoria clássica e teoria da resposta ao item. Um instrumento com 46 itens foi desenvolvido e testado por um painel de experts, seguido por um teste piloto e aplicação em 187 dentistas da atenção primária de uma grande cidade brasileira. Os seguintes critérios foram utilizados para avaliar os 46 itens: aceitabilidade, consistência interna, estabilidade temporal, correlação inter-item e correlação tetracórica. Essa avaliação resultou em uma versão reduzida de 11 itens que preencheram todos os critérios descritos acima. A estabilidade temporal foi medida utilizando o Cohen Kappa. Os 11 itens apresentaram valores maiores que 0,5. O alfa de Cronbach foi de 0,72. Nenhum dos 11 itens apresentou perda na distribuição das respostas, e o modelo que considera a discriminação como variante se adequa melhor aos dados do que o modelo que considera a discriminação como um parâmetro constante (p <0,001). Curvas características mostraram que 54,5% dos itens pode ser considerado difícil, ou seja, apenas os dentistas com uma boa compreensão da integralidade respondeu favoravelmente. O instrumento de 11 itens para avaliar a integralidade do cuidado por dentistas é considerado como tendo boas propriedades psicométricas.This study aimed to evaluate the psychometric properties of an instrument to assess comprehensiveness of care from dentists using a combination of classical test theory and item response theory. A 46-item instrument was developed and tested by a panel of experts, followed by a pilot test and administration to 187 primary care dentists in a large Brazilian city. The 46 items were evaluated using the following criteria: acceptability, internal consistency, temporal stability, inter-item correlation, and tetrachoric correlation. This evaluation led to a shortened version consisting of 11 items that met all the criteria previously described. The temporal stability was measured using Cohen’s kappa, and all 11 items presented values greater than 0.5. The Cronbach’s alpha value was 0.72. None of the 11 items had missing data on the distribution of responses, and the model considering the discrimination as varying fit the data better than the model considering discrimination as a constant parameter (p<0.001). Item characteristic curves showed that 54.5% of items could be considered difficult, i.e., only dentists with a good understanding of comprehensiveness responded favorably. The 11-item instrument to assess comprehensiveness of care by dentists is considered to have good psychometric properties