Síndrome de Burnout en trabajadores de salud del Hospital de Arequipa (Perú) durante la pandemia / Burnout syndrome in health care workers of the Hospital Arequipa (Peru) during the pandemic

RESUMEN: Introducción: a finales del año 2019, la comunidad global era sorprendida con la aparición de un brote de coronavirus en China. Se plantea que la exposición crónica a factores de riesgo psicosocial durante varios meses y de manera constante, podrían desencadenar el síndrome de burnout en el personal de salud que atiende pacientes con COVID-19. Objetivo: determinar la frecuencia y severidad del síndrome de burnout en personal de salud que labora en el Hospital II Goyeneche del Ministerio de Salud en Arequipa en el contexto durante la pandemia. Material y Métodos: estudio descriptivo transeccional, en el que se registraron las características sociodemográficas de 147 trabajadores de salud del Hospital II Goyeneche un hospital del Ministerio de Salud y se aplicó el Inventario de Burnout de Maslach. Resultados: el 70,7% del personal de salud del Hospital II Goyeneche de Arequipa presenta síndrome de burnout, y de este porcentaje, la mayoría tiene preocupación por atender pacientes con COVID-19, no se siente capacitado para ello, le preocupa no contar con Equipos de Protección Personal y desconoce los protocolos de seguridad. Conclusión: existe una asociación significativa entre la presencia de síndrome de burnout y la atención de pacientes con COVID-19.

ABSTRACT Introduction: At the end of 2019, the global community was surprised by the new outbreak of coronavirus in China. We argued that the chronic exposure to psychosocial risk factors during four months, could precipitate the burnout syndrome among the healthcare workers who attend patients with COVID-19. Objective: To determine the frequency and severity of burnout syndrome in healthcare personnel who working Goyeneche Hospital from Ministry of Health Hospital from Arequipa City along the COVID-19 pandemic. Material and Methods: Descriptive transectional study, in which there were registered the sociodemographic characteristics of 147 healthcare workers in Goyeneche Hospital and there was applied the Burnout Maslach Inventory. Results: The 70.7% of the Goyeneche Hospital health care personnel presents burnout syndrome, and major part of the percentage have concerns about the attention of patients with COVID-19, also they don't feel trained enough for this, they also are concern because don´t have the Personal protective equipment and they don't know the safety attention protocols. Conclusion: There is a significant association among the burnout syndrome punctuation and the attention of patients with COVID-19.

Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis.

Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.

Combining pedigree and genomic information to improve prediction quality: an example in sorghum.

KEY MESSAGE: The use of a kinship matrix integrating pedigree- and marker-based relationships optimized the performance of genomic prediction in sorghum, especially for traits of lower heritability. Selection based on genome-wide markers has become an active breeding strategy in crops. Genomic prediction models can make use of pedigree information to account for the residual polygenic effects not captured by markers. Our aim was to evaluate the impact of using pedigree and genomic information on prediction quality of breeding values for different traits in sorghum. We explored BLUP models that use weighted combinations of pedigree and genomic relationship matrices. The optimal weighting factor was empirically determined in order to maximize predictive ability after evaluating a range of candidate weights. The phenotypic data consisted of testcross evaluations of sorghum parental lines across multiple environments. All lines were genotyped, and full pedigree information was available. The performance of the best predictive combined matrix was compared to that of models fitting the component matrices independently. Model performance was assessed using cross-validation technique. Fitting a combined pedigree-genomic matrix with the optimal weight always yielded the largest increases in predictive ability and the largest reductions in prediction bias relative to the simple G-BLUP. However, the weight that optimized prediction varied across traits. The benefits of including pedigree information in the genomic model were more relevant for traits with lower heritability, such as grain yield and stay-green. Our results suggest that the combination of pedigree and genomic relatedness can be used to optimize predictions of complex traits in crops when the additive variation is not fully explained by markers.

Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model.

KEY MESSAGE: A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials. Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multi-step modelling process where global and local trends are fitted after trying several candidate spatial models. This paper reports the application of a novel spatial method that accounts for all types of continuous field variation in a single modelling step by fitting a smooth surface. The method uses two-dimensional P-splines with anisotropic smoothing formulated in the mixed model framework, referred to as SpATS model. We applied this methodology to a series of large and partially replicated sorghum breeding trials. The new model was assessed in comparison with the more elaborate standard spatial models that use autoregressive correlation of residuals. The improvements in precision and the predictions of genotypic values produced by the SpATS model were equivalent to those obtained using the best fitting standard spatial models for each trial. One advantage of the approach with SpATS is that all patterns of spatial trend and genetic effects were modelled simultaneously by fitting a single model. Furthermore, we used a flexible model to adequately adjust for field trends. This strategy reduces potential parameter identification problems and simplifies the model selection process. Therefore, the new method should be considered as an efficient and easy-to-use alternative for routine analyses of plant breeding trials.

Características morfológicas de dientes permanentes en dos comunidades colombianas / Morphologic characteristics of permanent teeth in two Colombian communities

Objetico: Comparar las características morfológicas, de número y de dimensión, de la dentición permanente de sujetos negros y mestizos de dos comunidades colombianas. Método: La muestra para el grupo mestizo consistió en 70 individuos habitantes del corregimiento Potrerito, Valle del Cauca. El muestreo fue de tipo no probabilístico. Las características de inclusión fueron: dentición permanente con un representante de cada diente como mínimo. Las características de exclusión fueron: presencia de facetas de desgaste, dientes con obturacioens o restauraciones extensas y generalizadas y pérdida bilateral de dientes por caries, enfermedad periodontal o trauma. Se usaron modelos de yeso con un compás de dos puntas de alta precisión y un dentímetro. Hallazgos: Las variaciones más importantes fueron la mayor dimensión en general de los dientes de la población negra respecto de la mestiza, la presencia de cíngulo tipo II en incisivos de la comnidad negra y del tipo I en la mestiza, y la presencia de los tipos Bi y tricuspídeos en segundos premolares inferiores de la comunidad negra y sólo el bicuspídeo en la mestiza