21 years of Wiskott-Aldrich syndrome in Spain: incidence, mortality, and gender bias.

BACKGROUND: Wiskott-Aldrich syndrome (WAS) is a rare X-linked disorder considered to predominantly affect males. OBJECTIVE: This study aims to investigate the incidence and intrahospital death associated with WAS in Spain as well as the gender bias. METHODS: A population-based retrospective epidemiological study of 97 WAS patients that were diagnosed in Spanish hospitals between 1997 and 2017 was conducted by using data from the National Surveillance System for Hospital Data. RESULTS: Our results revealed that the mean annual incidence of WAS in Spain was 1.1 per 10,000,000 inhabitants (IC95% 0,45-2,33). The relative risk was higher in male than female (2.42). WAS diagnosis occurs at later ages in women (median age of 47 years) compared to men (median age of 5.5 years). Only male were admitted to the hospital at least in 10 different occasions and all deaths were detected in men. The intra-hospital death rate was of 9.28% in WAS, being most of the deaths associated with brain hemorrhage or infection. CONCLUSIONS: WAS, a rare disease, is diagnoses at later ages in women and the mortality was found in males mostly associated with brain hemorrhage and infection.

Integrated Signaling Pathways Mediate Bordetella Immunomodulation, Persistence, and Transmission.

The mammalian immune system includes a sophisticated array of antimicrobial mechanisms. However, successful pathogens have developed subversive strategies to detect, modulate, and/or evade immune control and clearance. Independent disciplines study host immunology and bacterial pathogenesis, but interkingdom signaling between bacteria and host during natural infection remains poorly understood. An efficient natural host infection system has revealed complex communication between Bordetella spp. and mice, identified novel regulatory mechanisms, and demonstrated that bordetellae can respond to microenvironment and inflammatory status cues. Understanding these bacterial signaling pathways and their complex network that allows precisely timed expression of numerous immunomodulatory factors will serve as a paradigm for other organisms lacking such a powerful experimental infection system. VIDEO ABSTRACT.

Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification.

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.

Analysis of the functional relevance of a putative regulatory SNP of PDCD1, PD1.3, associated with systemic lupus erythematosus.

This study aimed to test the functional effects of the PD1.3 single nucleotide polymorphism (SNP) (rs11568821), which were proposed based on its association to systemic lupus erythematosus (SLE) susceptibility and in electrophoretic mobility shift assays (EMSA) results. We analysed transcriptional effects of the PD1.3 locus by enhancer reporter assays. Results were against the hypothesis that the PD1.3 locus acts as enhancer in transcriptional regulation of PDCD1. In addition, they excluded a differential effect of the PD1.3 alleles. EMSA results confirmed that oligonucleotides with the PD1.3 G allele bind RUNX1 but not those with the A allele. However, binding to PD1.3 G oligonucleotides was much lower than binding to positive control oligonucleotides. Criss-cross experiments showed that this was due to flanking nucleotides in the PD1.3 sequence that negatively affect RUNX1 binding. These results cast doubts on the functional relevance of the PD1.3 SNP and, together with the lack of association in several studies, put into question its role as an SLE susceptibility factor. Investigation of other PDCD1 polymorphisms is needed to uncover the possible effect of this gene on SLE susceptibility.

Chemically driven variable selection by focused multimodal genetic algorithms in mid-IR spectra.

Four genetic-algorithm-based approaches to variable selection in spectral data sets are presented. They range from a pure black-box approach to a chemically driven one. The latter uses a fitness function that takes into account not only typical parameters like the number of errors when classifying a training set but also the chemical interpretability of the selected variables. In order to cope with the fact that multiple solutions may be acceptable, a multimodal genetic algorithm (GA) is employed and the most satisfactory solution selected. The multimodal GA uses two populations (denominated "hybrid two populations" GA or HTP-GA): a classical population, from which potential solutions emerge, and a new population, which maintains diversity in the search space (as required by multimodal problems). Results show that the HTP-GA approach improves the chemical understanding of the selected solution (compared to other GA approaches) and that the classification capabilities of the approach are still good. All of the GA strategies for variable selection were compared with a classical parametric technique, Procrustes rotation, which does not consider interpretability.