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Percorrer por autor "Castro, David"

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  • An update on ciguatoxins and CTX-like toxicity in fish from different trophic levels of the Selvagens Islands (NE Atlantic, Madeira, Portugal)
    Publication . Reis Costa, Pedro; Estévez, Pablo; Soliño, Lucia; Castro, David; Rodrigues, Susana Margarida; Timoteo, Viriato; Leao-Martins, José Manuel; Santos, Carolina; Gouveia, Neide; Diogène, Jorge; Gago-Martínez, Ana
    The Selvagens Islands, which are a marine protected area located at the southernmost point of the Portuguese maritime zone, have been associated with fish harboring ciguatoxins (CTX) and linked to ciguatera fish poisonings. This study reports the results of a field sampling campaign carried out in September 2018 in these remote and rarely surveyed islands. Fifty-six fish specimens from different trophic levels were caught for CTX-like toxicity determination by cell-based assay (CBA) and toxin content analysis by liquid chromatography with tandem mass spectrometry (LC-MS/MS). Notably, high toxicity levels were found in fish with an intermediate position in the food web, such as zebra seabream (Diplodus cervinus) and barred hogfish (Bodianus scrofa), reaching levels up to 0.75 µg CTX1B equivalent kg−1 . The LC-MS/MS analysis confirmed that C-CTX1 was the main toxin, but discrepancies between CBA and LC-MS/MS in D. cervinus and top predator species, such as the yellowmouth barracuda (Sphyraena viridis) and amberjacks (Seriola spp.), suggest the presence of fish metabolic products, which need to be further elucidated. This study confirms that fish from coastal food webs of the Selvagens Islands represent a high risk of ciguatera, raising important issues for fisheries and environmental management of the Selvagens Islands.
    2021-08Artigo científico Acesso aberto Ver mais
  • Understanding risk factors of post-stroke mortality
    Publication . Castro, David; Antonio, Nuno; Marreiros, Ana; Nzwalo, Hipólito
    Stroke is one of the leading causes of death worldwide. Understanding the risk factors for poststroke mortality is crucial for improving patient outcomes. This study analyzes and predicts poststroke mortality using the modified Rankin Scale (mRS), a functional neurological evaluation scale. Several Machine Learning models were developed and assessed using a dataset of 332 stroke patients from Hospital de Faro, Portugal, from 2016 to 2018. The Random Forest model outperformed others, achieving an accuracy of 98.5% and a recall of 91.3. Twenty-four risk factors were identified, with stroke severity as the most critical. These findings provide healthcare professionals with valuable tools for early identification and intervention for high-risk stroke patients, enabling informed decision-making and customized treatment plans. This research advances healthcare predictive analytics, offering a precise mortality prediction model and a comprehensive analysis of risk factors, potentially improving clinical outcomes and reducing mortality rates. Future applications could extend to patient monitoring and management across various medical conditions.
    2025-03Artigo científico Acesso aberto Ver mais