Predictores de la agresión en videojuegos de consola

This study was designed to investigate the aggression levels of college students found in the Northeastern part of the United States following exposure to video games. The 59 participants played their assigned game, Mortal Kombat on Nintendo Wii or Halo 2 on the Xbox, for 45 minutes with a partner. The researchers employed twelve t-tests (alpha adjusted to .004) and three multiple linear regressions to explore the difference of aggression levels in gender, violent video game, and predictors of aggression. Results showed no aggression differences in all twelve t-tests for the three aggression variables (physical, verbal, and general) pre and post-tests for gender or violent video game played. Additionally, there was no support found suggesting the violent video games, gender, and time spent playing video games caused aggression as previously touted by past researchers. In fact, the only significance found for predicting aggression were the pre-aggression scores in all three areas of measured aggression suggesting a need for proper control of variables and that aggression may be preexisting within the individual rather than caused by violent video game play.Este estudio fue diseñado para investigar los niveles de agresión en estudiantes universitarios de la región noreste de los Estados Unidos después de la exposición a videojuegos. 59 participantes jugaron a un videojuego asignado, Mortal Kombat en Nintendo Wii o Halo 2 en Xbox, durante 45 minutos con un compañero. Se emplearon doce pruebas t (alfa ajustado a 0.004) y tres regresiones lineales múltiples para explorar la diferencia de niveles de agresión en género, videojuegos violentos y predictores de la agresión. Los resultados no mostraron diferencias en agresión a lo largo de las doce pruebas t para las tres variables de agresión (física, verbal y general) pre y post-tests para género o videojuego violento jugado. Además, no se halló soporte sugiriendo que los videojuegos violentos, el género y el tiempo dedicado a jugar videojuegos causen agresión. De hecho, el único resultado significativo encontrado para predecir la agresión fueron las puntuaciones pre-agresión en las tres áreas medidas, sugiriendo la necesidad de un control adecuado de las variables y que la agresión puede ser pre-existente en el individuo y no causada por videojuegos violentos

Hospital-wide natural language processing summarising the health data of 1 million patients

Electronic health records (EHRs) represent a major repository of real world clinical trajectories, interventions and outcomes. While modern enterprise EHR's try to capture data in structured standardised formats, a significant bulk of the available information captured in the EHR is still recorded only in unstructured text format and can only be transformed into structured codes by manual processes. Recently, Natural Language Processing (NLP) algorithms have reached a level of performance suitable for large scale and accurate information extraction from clinical text. Here we describe the application of open-source named-entity-recognition and linkage (NER+L) methods (CogStack, MedCAT) to the entire text content of a large UK hospital trust (King's College Hospital, London). The resulting dataset contains 157M SNOMED concepts generated from 9.5M documents for 1.07M patients over a period of 9 years. We present a summary of prevalence and disease onset as well as a patient embedding that captures major comorbidity patterns at scale. NLP has the potential to transform the health data lifecycle, through large-scale automation of a traditionally manual task

Identification of an immunodominant CD4+ T cell epitope in the VP6 protein of rotavirus following intranasal immunization of BALB/c mice

AbstractThe only lymphocytes required for protection against fecal rotavirus shedding after intranasal immunization of BALB/c (H-2d) mice with a chimeric rotavirus VP6 protein (MBP∷VP6) and the mucosal adjuvant LT(R192G) are CD4+ T cells. The purpose of this study was to identify CD4+ T cell epitopes within VP6 that might be responsible for this protection. To make this determination, spleen cells obtained from BALB/c mice following intranasal immunization with MBP∷VP6/LT(R192G) were stimulated in vitro with either MBP∷VP6 or overlapping VP6 peptides containing ≤30 amino acids (AA). The numbers of memory (CD44high) CD4+ T cells stimulated to produce TH1 and TH17 cytokines (IFNγ and IL-17), as well as the quantities of these cytokines released into the cell supernatants, were then measured relative to those produced in mock-stimulated cells from the same animals. One epitope expected to be found was the VP6 14-mer AA289–302, previously identified as a CD4+ T cell epitope in H-2d mice. This was not observed but instead the only VP6 epitope identified was AA242–259, the dominant CD4+ T cell epitope previously reported after oral, live rotavirus immunization

Low dose influenza virus challenge in the ferret leads to increased virus shedding and greater sensitivity to oseltamivir

Ferrets are widely used to study human influenza virus infection. Their airway physiology and cell receptor distribution makes them ideal for the analysis of pathogenesis and virus transmission, and for testing the efficacy of anti-influenza interventions and vaccines. The 2009 pandemic influenza virus (H1N1pdm09) induces mild to moderate respiratory disease in infected ferrets, following inoculation with 106 plaque-forming units (pfu) of virus. We have demonstrated that reducing the challenge dose to 102 pfu delays the onset of clinical signs by 1 day, and results in a modest reduction in clinical signs, and a less rapid nasal cavity innate immune response. There was also a delay in virus production in the upper respiratory tract, this was up to 9-fold greater and virus shedding was prolonged. Progression of infection to the lower respiratory tract was not noticeably delayed by the reduction in virus challenge. A dose of 104 pfu gave an infection that was intermediate between those of the 106 pfu and 102 pfu doses. To address the hypothesis that using a more authentic low challenge dose would facilitate a more sensitive model for antiviral efficacy, we used the well-known neuraminidase inhibitor, oseltamivir. Oseltamivir-treated and untreated ferrets were challenged with high (106 pfu) and low (102 pfu) doses of influenza H1N1pdm09 virus. The low dose treated ferrets showed significant delays in innate immune response and virus shedding, delayed onset of pathological changes in the nasal cavity, and reduced pathological changes and viral RNA load in the lung, relative to untreated ferrets. Importantly, these observations were not seen in treated animals when the high dose challenge was used. In summary, low dose challenge gives a disease that more closely parallels the disease parameters of human influenza infection, and provides an improved pre-clinical model for the assessment of influenza therapeutics, and potentially, influenza vaccines

A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data

The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission from time-series vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked platform comprises two components: a) an unsupervised LSTM Autoencoder that learns an optimal representation of the time-series, using it to differentiate the less frequent patterns which conclude with an adverse event from the majority patterns that do not, and b) a gradient boosting model, which relies on the constructed representation to refine prediction, incorporating static features of demographics, admission details and clinical summaries. The model is used to assess a patient's risk of adversity over time and provides visual justifications of its prediction based on the patient's static features and dynamic signals. Results of three case studies for predicting mortality and ICU admission show that the model outperforms all existing outcome prediction models, achieving PR-AUC of 0.891 (95$$ CI: 0.878 - 0.969) in predicting mortality in ICU and general ward settings and 0.908 (95$$ CI: 0.870-0.935) in predicting ICU admission.Comment: 14 page