Change in Sleep Quality of Residents the Night Before High-Fidelity Simulation: Results From a Prospective 1-Year National Survey.

peer reviewed[en] OBJECTIVE: The stress level of participants in high-fidelity simulation stems from various factors but may result in anticipatory anxiety causing sleep disturbances during the night prior to simulation. The objective of this survey was to determine the change in sleep quality of residents during the night prior to the simulation. METHODS: The survey was proposed for 1 year to all residents at the beginning of the simulation, in 10 simulation centres. The questionnaire combined demographics and the Leeds Sleep Evaluation Questionnaire using visual analogue scales divided into 4 sleep qualitative domains. The primary outcome was the prevalence of sleep disturbance (>10 mm on 1 domain). Secondary outcomes were the prevalence of severe sleep disturbance (>25 mm), as well as qualitatively and quantitatively reported explanatory sleep parameters. RESULTS: Among respondents, 66% [95% CI: 63 to 69] of residents had more than 10 mm and 27% [95% CI: 24 to 30] had more than 25 mm of sleep disturbance. Residents with a sleep disturbance of more than 10 mm had fewer hours of sleep (6.4 [standard deviation=1.8] vs 7.3 [standard deviation=1.3], difference: -0.9 [95% CI: -1.1 to -0.7]; P < .0001), with a higher number of night-time awakenings (1.3 [standard deviation=1.5] vs 0.7 [standard deviation=0.9], difference: 0.6 [95% CI: 0.4 to 0.8]; P < .0001). CONCLUSION: Among residents participating in the simulation, a high prevalence of change in sleep quality during the night before the simulation was noted. Strategies to help residents achieve better sleep prior to simulation should be explored

The Human Phenotype Ontology project:linking molecular biology and disease through phenotype data

The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online

Evaluation des pratiques transfusionnelles en chirurgie orthopédique majeure.

STRASBOURG-Medecine (674822101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

INTERET DE L'ANTEHYPOPHYSE COMME MODELE D'ETUDE DES DIALOGUES ENTRE VOIES INTRACELLULAIRES DE SIGNALISATION

CHATENAY M.-PARIS 11-BU Pharma. (920192101) / SudocSudocFranceF

Acquisition numérique des données archéoanthropologiques et spatiales. Le projet « Nécropole numérique »

International audienceCe projet de R & D a pour objectif la mise au point et l’expérimentation d’outils numériques dédiés à l’enregistrement des données de terrain en contexte funéraire. Sa particularité repose sur la dématérialisation intégrale de l’acquisition des données archéologiques, en s’appuyant sur la géomatique pour gérer les données spatiales et attributaires à partir de relevés issus de la photogrammétrie. La saisie et l’interrogation des données archéologiques s’effectuent par l’interface de QGIS dans une base de données archéologiques au format SQLite. Les sépultures sont modélisées et géoréférencées, puis l’édition d’une ortho-image, importée dans QGIS, permet de numériser les vestiges et de mettre instantanément en lien la géométrie et les données attributaires. Ce travail de terrain est ensuite complété et enrichi par les analyses effectuées en laboratoire, notamment les données anthropologiques. L’intégralité des données spatiales et attributaires est ainsi contenue au sein d’un projet unique, éditable et consultable dans QGIS. Les outils du logiciel permettent une grande diversité de représentations cartographiques et d’analyse spatiale des données