Introduction: Advances in molecular diagnosis have made it possible to detect previously unknown viral agents as causative agents of lower respiratory tract infections (LRTI). The frequency and relevance of viral coinfections is still debatable. Objective: Compare clinical presentation and severity between single virus infection and viral coinfection in children admitted for LRTI. Methods: A 3-year period observational study (2012---2015) included children younger than two years admitted for LRTI. Viral identification was performed using PCR technique for 16 viruses. Clinical data and use of health resources was gathered during hospital stay using a standard collection form and we compared single virus infection and viral coinfections. Results: The study included 524 samples (451 patients); 448 (85.5%) had at least one virus identified. Viral coinfections were found in 159 (35.5%). RSV and HRV were the most commonly identified virus; bronchiolitis and pneumonia the most frequent diagnosis. Patients with viral coinfections were older, attended day-care centers, had previous recurrent wheezing more frequently and were more symptomatic at admission. These patients did not have more duración de la estancia hospitalaria, de la necesidad de oxígeno, de UCI o soporte ventilatorio. Discusión: Nuestro estudio mostró una proporción significativa de coinfecciones virales en los niños pequeños ingresados con IVRI y confirma dados previos que muestran que la prescripción es más frecuente en las coinfecciones virales, sin asociación con peor resultado clínico.

Objetivos: Verificar se há alteração significativa na manutenção do equilíbrio estático em indivíduos portadores de deficiência visual adquirida (DVA) e se há correlação entre o tempo de perda visual e a oscilação corporal. Métodos: Foram avaliados onze indivíduos portadores de DVA e onze indivíduos com visão normal. As avaliações dos deslocamentos ântero-posterior (A/P) e latero-lateral (L/L) do centro de gravidade corporal na postura bípede estática foram realizadas utilizando uma plataforma de força AMTI modelo OR6. Resultados: Verificou-se que os deficientes visuais apresentam um deslocamento máximo L/L significativamente maior que os indivíduos com visão normal (t=2,397; p=0,026). Porém, no deslocamento A/P não houve diferença significativa entre os grupos (t=0,144; p=0,887). Não se obteve correlação entre o tempo de perda visual e o deslocamento L/L (p=0,971). Contudo, encontrou-se correlação positiva entre o tempo de perda visual e o deslocamento A/P (p=0,041). Conclusão: Há alteração significativa na manutenção do equilíbrio estático corporal em indivíduos portadores de DVA apenas no deslocamento L/ L e existe uma correlação positiva entre o deslocamento A/P e o tempo de perda visual.

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The past decade has seen an abundance of work seeking to detect, characterize, and measure online hate speech. A related, but less studied problem, is the specification of identity groups targeted by that hate speech. Predictive accuracy on this task can supplement additional analyses beyond hate speech detection, motivating its study. Using the Measuring Hate Speech corpus, which provided annotations for targeted identity groups on roughly 50,000 social media comments, we create neural network models to perform multi-label binary prediction of identity groups targeted by a social media comment. Specifically, we study 8 broad identity groups and 12 identity sub-groups within race and gender identity. We find that these networks exhibited good predictive performance, achieving ROC AUCs of greater than 0.9 and PR AUCs of greater than 0.7 on several identity groups. At the same time, we find performance suffered on identity groups less represented in the dataset. We validate model performance on the HateCheck and Gab Hate Corpora, finding that predictive performance generalizes in most settings. We additionally examine the performance of the model on comments targeting multiple identity groups. Lastly, we discuss issues with a standardized conceptualization of a "target" in hate speech corpora, and its relation to intersectionality. Our results demonstrate the feasibility of simultaneously detecting a broad range of targeted groups in social media comments, and offer suggestions for future work on modeling and dataset annotation for this task.