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Browsing by Author "Lopes, Miguel"

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  • Gut microbiota profile of COVID-19 patients: prognosis and risk stratification (microCOVID-19 study)
    Publication . Nobre, José Guilherme; Delgadinho, Mariana; Silva, Carina; Mendes, Joana; Mateus, Vanessa; Ribeiro, Edna; Costa, Diogo Alpuim; Lopes, Miguel; Pedroso, Ana Isabel; Trigueiros, Frederico; Rodrigues, Maria Inês; Sousa, Cristina Lino de; Brito, Miguel
    Background: Gut microbiota is intrinsically associated with the immune system and can promote or suppress infectious diseases, especially viral infections. This study aims to characterize and compare the microbiota profile of infected patients with SARS-CoV-2 (milder or more severe symptoms), non-infected people, and recovered patients. This is a national, transversal, observational, multicenter, and case-control study that analyzed the microbiota of COVID-19 patients with mild or severe symptoms at home, at the hospital, or in the intensive care unit, patients already recovered, and healthy volunteers cohabiting with COVID-19 patients. DNA was isolated from stool samples and sequenced in a NGS platform. A demographic questionnaire was also applied. Statistical analysis was performed in SPSS. Results: Firmicutes/Bacteroidetes ratios were found to be significantly lower in infected patients (1.61 and 2.57) compared to healthy volunteers (3.23) and recovered patients (3.89). Furthermore, the microbiota composition differed significantly between healthy volunteers, mild and severe COVID-19 patients, and recovered patients. Furthermore, Escherichia coli, Actinomyces naeslundii, and Dorea longicatena were shown to be more frequent in severe cases. The most common COVID-19 symptoms were linked to certain microbiome groups. Conclusion: We can conclude that microbiota composition is significantly affected by SARS-CoV-2 infection and may be used to predict COVID-19 clinical evolution. Therefore, it will be possible to better allocate healthcare resources and better tackle future pandemics.
    2022-11Journal article Open access Show more
  • Short-term Feature Space and Music Genre Classification
    Publication . Marques, Gonçalo; Langlois, Thibault; Gouyon, Fabien; Lopes, Miguel; Sordo, Mohamed
    In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to class boundaries, and compare different processing techniques to partition this space. These partitions are evaluated in terms of accuracy on two genre classification tasks, with several types of classifiers. Experiments show that a randomized and unsupervised partition of the space, used in conjunction with a Markov Model classifier lead to accuracies comparable to the state of the art. We also show that unsupervised partitions of the space tend to create less hubs.
    2011Journal article Open access Show more