Browsing by Issue Date, starting with "2020-01-28"
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- Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learningPublication . Asif, Muhammad; Martiniano, Hugo F.M.C.; Marques, Ana Rita; Santos, João Xavier; Vilela, Joana; Rasga, Celia; Oliveira, Guiomar; Couto, Francisco M.; Vicente, Astrid M.The complex genetic architecture of Autism Spectrum Disorder (ASD) and its heterogeneous phenotype makes molecular diagnosis and patient prognosis challenging tasks. To establish more precise genotype-phenotype correlations in ASD, we developed a novel machine-learning integrative approach, which seeks to delineate associations between patients' clinical profiles and disrupted biological processes, inferred from their copy number variants (CNVs) that span brain genes. Clustering analysis of the relevant clinical measures from 2446 ASD cases in the Autism Genome Project identified two distinct phenotypic subgroups. Patients in these clusters differed significantly in ADOS-defined severity, adaptive behavior profiles, intellectual ability, and verbal status, the latter contributing the most for cluster stability and cohesion. Functional enrichment analysis of brain genes disrupted by CNVs in these ASD cases identified 15 statistically significant biological processes, including cell adhesion, neural development, cognition, and polyubiquitination, in line with previous ASD findings. A Naive Bayes classifier, generated to predict the ASD phenotypic clusters from disrupted biological processes, achieved predictions with a high precision (0.82) but low recall (0.39), for a subset of patients with higher biological Information Content scores. This study shows that milder and more severe clinical presentations can have distinct underlying biological mechanisms. It further highlights how machine-learning approaches can reduce clinical heterogeneity by using multidimensional clinical measures, and establishes genotype-phenotype correlations in ASD. However, predictions are strongly dependent on patient's information content. Findings are therefore a first step toward the translation of genetic information into clinically useful applications, and emphasize the need for larger datasets with very complete clinical and biological information.
- Candida auris, an Agent of Hospital-Associated Outbreaks: Which Challenging Issues Do We Need to Have in Mind?Publication . Sabino, Raquel; Veríssimo, Cristina; Pereira, Álvaro Ayres; Antunes, FranciscoThe emergence of Candida auris is considered as one of the most serious problems associated with nosocomial transmission and with infection control practices in hospital environment. This multidrug resistant species is rapidly spreading worldwide, with several described outbreaks. Until now, this species has been isolated from different hospital surfaces, where it can survive for long periods. There are multiple unanswered questions regarding C. auris, such as prevalence in population, environmental contamination, effectiveness of infection prevention and control, and impact on patient mortality. In order to understand how it spreads and discover possible reservoirs, it is essential to know the ecology, natural environment, and distribution of this species. It is also important to explore possible reasons to this recent emergence, namely the environmental presence of azoles or the possible effect of climate change on this sudden emergence. This review aims to discuss some of the most challenging issues that we need to have in mind in the management of C. auris and to raise the awareness to its presence in specific indoor environments as hospital settings.