Suchergebnisse

La tecnología y gamificación se presenta como una alternativa a los métodos y estrategias tradicionales que se presentan actualmente en la Educación Superior. El desarrollo de matemática y estadística aplicadas a la administración en modalidad en línea y presencial a nivel universitario considera que los estudiantes requieren un dominio general; sin embargo, se puede observar que presentan dificultades en el razonamiento lógico y abstracto, Según la prueba del Programa para la Evaluación Internacional de Alumnos (PISA), en el 2019 los estudiantes que rindieron esta evaluación, más del 70% no alcanzaron el nivel básico en la resolución de problemas matemáticos. En este artículo se propone una educación a través del uso de estrategias, técnicas y medios adecuados para la enseñanza y el aprendizaje utilizando las TIC y gamificación, fomentando en el estudiante herramientas de observación, análisis y síntesis. En el estudio se observó la importancia mantener lineamientos claros para el trabajo de gamificación por medio de tic. Entre los resultados más relevantes se pudo observar, que al momento de utilizar la gamificación en el aula hubo un crecimiento de un 33,5% de aceptación en comparación a la educación tradicional. Se utilizó la prueba estadística alfa de Cronbach teniendo como fiabilidad un 0.7 siendo aceptable las correlaciones entre las variables. En la actualidad el docente se enfrenta al reto de innovarse de forma permanente para renovar las metodologías utiliza-das en sus clases y adoptar herramientas tecnológicas que incrementen las capacidades cognitivas de los estudiantes.

This review assessed the use of electronic medical record (EMR) systems in outcomes research. We systematically searched PubMed to identify articles published from January 2000 to January 2007 involving EMR use for outpatient-based outcomes research in the United States. EMR-based outcomes research studies ( n = 126) have increased sixfold since 2000. Although chronic conditions were most common, EMRs were also used to study less common diseases, highlighting the EMRs' flexibility to examine large cohorts as well as identify patients with rare diseases. Traditional multi-variate modeling techniques were the most commonly used technique to address confounding and potential selection bias. Data validation was a component in a quarter of studies, and many evaluated the EMR's ability to achieve similar results previously achieved using other data sources. Investigators using EMR data should aim for consistent terminology, focus on adequately describing their methods, and consider appropriate statistical methods to control for confounding and treatment-selection bias.

Understanding relationships between diseases, such as comorbidities, has important socio-economic implications, ranging from clinical study design to health care planning. Most studies characterize disease comorbidity using shared genetic origins, ignoring pathway-based commonalities between diseases. In this study, we define the disease pathways using an interactome-based extension of known disease-genes and introduce several measures of functional overlap. The analysis reveals 206 significant links among 94 diseases, giving rise to a highly clustered disease association network. We observe that around 95% of the links in the disease network, though not identified by genetic overlap, are discovered by functional overlap. This disease network portraits rheumatoid arthritis, asthma, atherosclerosis, pulmonary diseases and Crohn's disease as hubs and thus pointing to common inflammatory processes underlying disease pathophysiology. We identify several described associations such as the inverse comorbidity relationship between Alzheimer's disease and neoplasms. Furthermore, we investigate the disruptions in protein interactions by mapping mutations onto the domains involved in the interaction, suggesting hypotheses on the causal link between diseases. Finally, we provide several proof-of-principle examples in which we model the effect of the mutation and the change of the association strength, which could explain the observed comorbidity between diseases caused by the same genetic alterations. ; Authors acknowledge support of Spanish Ministry of Economy MINECO grant BIO2014-57518-R, Instituto Carlos III (ISCIII) FEDER grants CP10/00524 and PI13/00082, and EU H2020 Programme 2014-2020 under grant agreement no. 634143 (MedBioinformatics). CRP is supported by Spanish Ministry of Economy and Competitiveness FPI fellowship (BES2013063354). EG is supported by EU cofounded AGAUR Beatriu de Pinos fellowship from Government of Catalunya. The Research Programme on Biomedical Informatics (GRIB) is member of the Spanish National Bioinformatics Institute (INB) and PRB2-ISCIII, supported by grant PT13/0001/0023 of the PE I+D+I 2013-2016 funded by ISCII-FEDER.

Air pollution has been associated with adverse health effects across the life-course. Although underlying mechanisms are unclear, several studies suggested pollutant-induced changes in transcriptomic profiles. In this meta-analysis of transcriptome-wide association studies of 656 children and adolescents from three European cohorts participating in the MeDALL Consortium, we found two differentially expressed transcript clusters (FDR p < 0.05) associated with exposure to particulate matter < 2.5 µm in diameter (PM2.5) at birth, one of them mapping to the MIR1296 gene. Further, by integrating gene expression with DNA methylation using Functional Epigenetic Modules algorithms, we identified 9 and 6 modules in relation to PM2.5 exposure at birth and at current address, respectively (including NR1I2, MAPK6, TAF8 and SCARA3). In conclusion, PM2.5 exposure at birth was linked to differential gene expression in children and adolescents. Importantly, we identified several significant interactome hotspots of gene modules of relevance for complex diseases in relation to PM2.5 exposure. ; European Community's 75 Seventh Framework Program under grant agreement numbers: 211250 (ESCAPE), and 76 261357 (MeDALL) ; European Research Council (grant agreement number 757919, TRIBAL) ; Swedish Research Council ; The Swedish Heart-Lung Foundation, Region Stockholm ; Strategic Research Programme (SFO) in Epidemiology, Karolinska Institutet ; Formas ; Swedish Environment Protection Agency ; Swedish Asthma and Allergy Research Foundation ; Cancer and Allergy Foundation ; Swedish Research Council for Health, Working life and Welfare (FORTE 2017-01146) ; Federal Ministry for Education, Science, Research and Technology ; Helmholtz Zentrum Munich ; Research Institute at Marien-Hospital Wesel, LMU Munich, TU Munich ; Leibniz Research-Institute for Environmental Medicine at the University of Düsseldorf ; Federal Ministry for Environment (IUF Düsseldorf, FKZ 20462296) ; Mead Johnson ; Nestlé ; Instituto de Salud Carlos III (Red INMA G03/176; CB06/02/0041; PI04/1436; PI06/0867; PI08/1151; and PI18/01142 ; Generalitat de Catalunya-CIRIT 1999SGR 00241 ; Department of Health of the Basque Government (2005111093) ; Provincial Government of Gipuzkoa (DFG06/002) ; Municipalities of the study area (Zumarraga, Urretxu , Legazpi, 101 Azkoitia y Azpeitia y Beasain) ; Accepted