Jose L. Girela

Predicting seminal quality with artificial intelligence methods

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. Artificial intelligence techniques are now an emerging methodology as decision support systems in medicine. In this paper we compare three artificial intelligence techniques, decision trees, Multilayer Perceptron and Support Vector Machines, in order to evaluate their performance in the prediction of the seminal quality from the data of the environmental factors and lifestyle. To do that we collect data by a normalized questionnaire from young healthy volunteers and then, we use the results of a semen analysis to asses the accuracy in the prediction of the three classification methods mentioned above. The results show that Multilayer Perceptron and Support Vector Machines show the highest accuracy, with prediction accuracy values of 86% for some of the seminal parameters. In contrast decision trees provide a visual and illustrative approach that can compensate the slightly lower accuracy obtained. In conclusion artificial intelligence methods are a useful tool in order to predict the seminal profile of an individual from the environmental factors and life habits. From the studied methods, Multilayer Perceptron and Support Vector Machines are the most accurate in the prediction. Therefore these tools, together with the visual help that decision trees offer, are the suggested methods to be included in the evaluation of the infertile patient.

Semen Parameters Can Be Predicted from Environmental Factors and Lifestyle Using Artificial Intelligence Methods

ABSTRACT 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.

Characterization of the lectin binding pattern in human spermatozoa after swim-up selection

Capacitation is characterized by a hyperactivated pattern of sperm motility. The acquisition of highly motility is present in the early stages of capacitation. Sperm progressive motility is one of the most important parameters for determining the suitability of semen for processing. However, previous studies have shown that some sperm showing good motility have membrane damage. The aim of our study was to characterize the lectin staining pattern on the sperm plasma membrane of unselected and selected human sperm of normozoospermic donors. Sperm selection was performed by the swim-up technique. Fourteen samples from healthy consenting donors classified as normozoospermic according to the World Health Organization were used. We observed changes in the distribution of the carbohydrate residues after the swim-up selection. With Triticum vulgaris, the most abundant pattern was dotted labeling all over the head plasma membrane in the unselected sperm. However, this lectin was distributed homogenously over the acrosomal region after selection. With Arachis hypogaea, the most abundant pattern in fresh sperm was a highly stained acrosomal region. In the highly motility sperm population, the most frequent pattern was dotted fluorescence on the acrosomal region and a highly stained equatorial segment. Meanwhile, with the Aleuria aurantia and Canavalia ensiformis lectins, the most representative patterns were the same before and after the swim-up selection. Our data indicate that modifications which occur in carbohydrate residues during swim-up selection could be important for the regulation of progressive motility and prepare the sperm for capacitation.

Identifying central and peripheral nerve fibres with an artificial intelligence approach

Abstract 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.

Human sperm motility, capacitation and acrosome reaction are impaired by 2-arachidonoylglycerol endocannabinoid

The endocannabinoids are cannabinoids synthesized by mammalian tissues. These compounds are closely related to the regulation of the male reproductive system. However, little is known about the effects produced by 2-arachidonoylglycerol (2AG) on in vitro human sperm functions. This study was undertaken to determine the effects produced by 2AG on fresh human sperm and in the capacitation technique. Semen samples from healthy young men were exposed to different concentrations of 2AG before and during capacitation technique. In this work, we have demonstrated that 2AG induces the spontaneous acrosome reaction and reduces progressive motility in fresh human sperm. During the capacitation technique, sperm becomes more sensitive to low concentrations of 2AG, triggering the acrosome reaction and inhibiting protein phosphorylation. In summary, 2AG affects the in vitro functionality of human sperm by reducing motility, inhibiting capacitation and triggering the acrosome reaction.