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Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

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Distinguishing axons from central or peripheral nervous systems (CNS or PNS, respectively) is often a complicated task. The main objective of this work was to facilitate and support the process of automatically distinguishing the different types of nerve fibres by analysing their morphological characteristics. Our approach was based on a multi-level hierarchical classifier architecture that can handle the complexity of directly identifying nerve-fibre groups belonging to either the CNS or the PNS. The approach adopted comprises supervised methods (multilayer perceptron and decision trees), which are responsible for distinguishing the origin of the axons (CNS or PNS), whereas the unsupervised method (K-Means clustering) performs nerve fibre clustering based on similar characteristics for both the CNS and PNS. Our experiments produced results with an accuracy higher than 88%. Our findings suggest that the development and implementation of a multi-level system improves automation capabilities and increases accuracy in the classification of nerves. Furthermore, our architecture allows for generalisation and flexibility, which can subsequently be extended to other biological control systems.

Cell biology is an academic discipline that organises and coordinates the learning of the structure, function and molecular composition of cells in some undergraduate biomedical programs. Besides course content and teaching methodologies, the laboratory environment is considered a key element in the teaching of and learning of cell biology. The aim of this study was to determine students'opinions about the quality of the teaching and learning environment in cell biology laboratory practice. For this study, we used a short form of the Science Laboratory Environment Inventory (SLEI), which we adapted and translated into Spanish. The questionnaire, administered to students enrolled in four undergraduate programs, consisted of 24 questions divided into four scales: integration of content, clarity of laboratory rules, cohesion between students and teachers, and quality of laboratory infrastructures and materials. The results suggested that (1) students positively assessed the learning environment provided for cell biology practice, (2) the short Spanish form of the SLEI was a valid, reliable instrument for evaluating student satisfaction, laboratory activities, the degree of cooperation between students and teachers, and theoretical and practical organisation of content and (3) the questionnaire detected differential perceptions of the learning environment based on gender and the program studied.

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Estudio de los daños estructurales tras la inmovilización espermática previa a la ICSI en sujetos teratozoospérmicos Resumen Objetivo: Describir y comparar las alteraciones ultraestructurales que puede provocar la inmovilización espermática, previa a la ICSI, así como los daños en el DNA y estado del acrosoma en espermatozoides de sujetos teratozoospérmicos y normozoospérmicos, Material y métodos: Se utilizaron 15 muestras seminales procedentes de donantes normozoospérmicos y 20 muestras de pacientes teratozoospérmicos del Instituto Bernabeu de Alicante. Para el estudio el Microscopia Electrónica de Transmisión se utilizaron ovocitos humanos como receptáculo de los espermatozoides. La fragmentación del ADN se valoró mediante la técnica TUNEL y el estado del acrosoma utilizando la lectina Pisum sativum conjugada con FITC. El análisis estadístico entre los diferentes grupos se realizó mediante un test ANOVA. Resultados: Los resultados mostraron que, tras la inmovilización, tanto los espermatozoides procedentes de sujetos normozoospérmicos y teratozoospérmicos sufrieron las mismas alteraciones ultraestructurales a nivel de la membrana plasmática y acrosomal. En cambio, no se observaron daños a nivel del núcleo. Tras valorar la fragmentación de ADN mediante TUNEL en sujetos normozoospérmicos y teratozoospérmicos, observamos que en los espermatozoides inmovilizados el porcentaje de espermatozoides con ADN fragmentado era similar en ambos grupos. En cambio, el porcentaje de espermatozoides reaccionados fue significativamente más elevado en los espermatozoides inmovilizados procedentes de sujetos teratozoospérmicos que en el grupo control. *Manuscrito (anónimo) Conclusión: Los resultados de este estudio sugieren que los daños provocados por la inmovilización, previa a la ICSI, en espermatozoides procedentes de sujetos normozoospérmicos y teratozoospérmicos son independientes de la morfología espermática.