Browsing by Author "Marques, Ana"
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- Oral language of school-aged children born pretermaturely: a population-based analysis from Madeira Island, PortugalPublication . Marques, Ana; Santos, Maria EmíliaPremature birth and low birth weight are very important factors in neurodevelopment. Current research in this population focuses on children born prematurely, with no underlying complications in the post-natal period, who are likely to develop specific disorders with their language development and consequently with their learning capabilities too. This study aims to analyse the oral language skills of prematurely born children in comparison to their school-aged peers. The children were assessed in the respective schools, 27 pretenn children (16 under 32 weeks and 11 with 32 or more weeks of gestation) and 49 term paired by gender, age, and school year. Tests including simple and complex structures for assessing semantics, morphosyntax, and phonology were used, as well as a test of verbal memory. Pretenn born children, regardless of their prematurity grade, showed significantly lower results than their peers, and more than a half of them, 52%, presented low scores in all language tests simultaneously, showing an important language deficit. In contrast, in the term born children group only 14% showed low scores simultaneously in all tests. Verbal memory ability proved to be lower than that of their term peers, regardless of the gestational age and birth weight of preterm children. As a result of this analysis we consider that the evaluation of the linguistic development of these children, even in cases of moderate to late prematurity, should be monitored in order to identify earlier the existence of deficits and prevent psychosocial and learning problems.
- Phytoremediation abilities of maize (Zea mays L.) inoculated with plant growth promoting rhizobacteria in Zinc and Cadmium contaminated soilsPublication . Moreira, Helena; Marques, Ana; Rangel, António O. S. S.; Castro, Paula M. L.
- Predição de lesões por pressão através de inteligência artificial em unidades de cuidados intensivos: protocolo de scoping reviewPublication . Alves, José; Azevedo, Rita; Marques, Ana; Alves, PauloIntrodução: As lesões por pressão são eventos adversos frequentes em unidades de cuidados intensivos, com impacto na qualidade de vida das pessoas e nos custos em saúde. As escalas tradicionais de avaliação de risco apresentam limitações no contexto do doente crítico. A inteligência artificial tem vindo a afirmar-se como uma abordagem promissora na identificação precoce do risco, com maior sensibilidade e capacidade de integração de dados clínicos complexos. Objetivo: Identificar e mapear a evidência científica sobre a utilização de inteligência artificial na predição de lesões por pressão em adultos em situação crítica internados em unidades de cuidados intensivos. Métodos: Será realizada uma scoping review segundo a metodologia do Joanna Briggs Institute e a checklist PRISMA-ScR. A pesquisa incluirá bases de dados e literatura cinzenta, sem restrições de idioma ou data. Serão incluídos estudos que abordem a utilização de inteligência artificial na predição de lesões por pressão em unidades de cuidados intensivos. Resultados: Os dados serão apresentados de forma descritiva e narrativa, com quadros e tabelas que evidenciam os tipos de inteligência artificial, variáveis preditoras, desempenho dos modelos e implicações clínicas. Conclusões: Esta revisão permitirá sistematizar o conhecimento disponível, identificar lacunas na literatura e apoiar a integração de soluções baseadas em inteligência artificial na prática de enfermagem em cuidados intensivos.
- Pressure injury prediction in intensive care units using artificial intelligence: a scoping reviewPublication . Alves, José; Azevedo, Rita; Marques, Ana; Encarnação, Rúben; Alves, PauloBackground/Objetives: Pressure injuries pose a significant challenge in healthcare, adversely impacting individuals’ quality of life and healthcare systems, particularly in intensive care units. The effective identification of at-risk individuals is crucial, but traditional scales have limitations, prompting the development of new tools. Artificial intelligence offers a promising approach to identifying and preventing pressure injuries in critical care settings. This review aimed to assess the extent of the literature regarding the use of artificial intelligence technologies in the prediction of pressure injuries in critically ill patients in intensive care units to identify gaps in current knowledge and direct future research. Methods: The review followed the Joanna Briggs Institute’s methodology for scoping reviews, and the study protocol was prospectively registered on the Open Science Framework platform. Results: This review included 14 studies, primarily highlighting the use of machine learning models trained on electronic health records data for predicting pressure injuries. Between 6 and 86 variables were used to train these models. Only two studies reported the clinical deployment of these models, reporting results such as reduced nursing workload, decreased prevalence of hospital-acquired pressure injuries, and decreased intensive care unit length of stay. Conclusions: Artificial intelligence technologies present themselves as a dynamic and innovative approach, with the ability to identify risk factors and predict pressure injuries effectively and promptly. This review synthesizes information about the use of these technologies and guides future directions and motivations.
- The role of microbial inoculants on plant growth promotion. Endophytes: from Discovery to ApplicationPublication . Pereira, Sofia; Franco, A. R.; Marques, Ana; Moreira, Helena; Sousa, N. R.; Ramos, Miguel; Castro, Paula M. L.
- Two genotypes of mycorrhizal Pinus pinaster respond differently to cadmium contaminationPublication . Sousa, N. R.; Ramos, Miguel A.; Marques, Ana; Castro, Paula M. L.