Spontaneous Abortions and Shift Work in a Cohort of Nurses in Norway

Background: An association between spontaneous abortions and shift work has been suggested, but present research results are conflicting. The aim of the study is to evaluate the relationship between spontaneous abortions among nurses, shift schedules, and nights worked. Methods: This is a longitudinal study where we identified 914 females from a cohort of nurses in Norway who had worked the same type of shift schedule 2008-2010; either permanent day shift, three-shift rotation or permanent night shift. Information on age, work and life-style factors, as well as spontaneous abortions during lifetime and the past three years (2008-2010) was obtained by annual questionnaires. Results: A higher prevalence of experienced spontaneous abortions before study start (2008) was found among nurses working permanent night shift compared to other nurses. In a linear regression analysis, a risk of 1.3 was found for experienced spontaneous abortions before study start among permanent night shift nurses, with day shift as reference, when adjusting for age, smoking, caffeine and job strain, but the finding was not statistical significant (95 per cent confidence interval 0.8-2.1). Permanent night shift workers had a risk of 1.5 experiencing spontaneous abortions in 2008-2010 compared to day shift nurses, although not statistical significant (95 per cent confidence interval 0.7-3.5). The number of night shifts the past three years was not associated with experiencing spontaneous abortions 2008-2010, but associated with a reduced risk of experiencing spontaneous abortions during lifetime. The results must be interpreted in the light of a possible selection bias; both selections into the occupation of nursing and into the different shift types of the more healthy persons may have occurred in this population. Conclusion: No significant increased risk of spontaneous abortion among permanent night shift nurses compared to day-time nurses was found in this study, and no association was found between spontaneous abortions and the number of worked night shifts.

Variations in coastal fish species composition captured by traps in Romsdalsfjord, Western Norway

Fish traps are widely used in Norwegian fjords, especially those designed for monitoring salmonid populations in the marine environment, although many other marine fish species are also captured. The composition and spatio-temporal variations of fish species captured by fish traps were monitored in five different coastal locations throughout the Romsdalsfjord region, Western Norway, from May to August during the three consecutive years (2011–2013). Twenty-three fish species were captured by traps in coastal waters, both resident and migratory fishes. The most common fish and with greater catchability were saithe (Pollachis virens) and sea trout (Salmo trutta), followed by cod (Gadus morhua), pollack (P. pollachius), herring (Clupea harengus) and mackerels (Trachurus trachurus and Scomber scombrus). However, the captured assemblage presented great spatial and seasonal variations, in terms of mean daily catch, probably associated with hydrographical conditions and migrational patterns. Information obtained in this study will help us to better understand the compositions and dynamic of coastal fish populations inhabiting Norwegian coastal waters. In addition, traps are highly recommended as a management tool for fish research (e.g. fish-tagging experiments, mark and recapture) and conservation purposes (coastal use and fisheries studies).

Descubrimiento de fármacos basado en cribado virtual refinado con enfoques neuronales paralelos

En el campo de la medicina clínica es crucial poder determinar la seguridad y la eficacia de los fármacos actuales y además acelerar el descubrimiento de nuevos compuestos activos. Para ello se llevan a cabo ensayos de laboratorio, que son métodos muy costosos y que requieren mucho tiempo. Sin embargo, la bioinformática puede facilitar enormemente la investigación clínica para los fines mencionados, ya que proporciona la predicción de la toxicidad de los fármacos y su actividad en enfermedades nuevas, así como la evolución de los compuestos activos descubiertos en ensayos clínicos. Esto se puede lograr gracias a la disponibilidad de herramientas de bioinformática y métodos de cribado virtual por ordenador (CV) que permitan probar todas las hipótesis necesarias antes de realizar los ensayos clínicos, tales como el docking estructural, mediante el programa BINDSURF. Sin embargo, la precisión de la mayoría de los métodos de CV se ve muy restringida a causa de las limitaciones presentes en las funciones de afinidad o scoring que describen las interacciones biomoleculares, e incluso hoy en día estas incertidumbres no se conocen completamente. En este trabajo abordamos este problema, proponiendo un nuevo enfoque en el que las redes neuronales se entrenan con información relativa a bases de datos de compuestos conocidos (proteínas diana y fármacos), y se aprovecha después el método para incrementar la precisión de las predicciones de afinidad del método de CV BINDSURF.