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Objetivo: Explorar a aplicação de inteligência artificial (IA) na predição da idade óssea a partir de imagens de raios-X. Método: Utilizou-se a Metodologia Interdisciplinar para o Desenvolvimento de Tecnologias em Saúde (MIDTS) para desenvolver uma ferramenta de predição. O treinamento foi realizado com redes neurais convolucionais (CNNs) usando um conjunto de dados de 14.036 imagens de raios-X. Resultados: A ferramenta alcançou um coeficiente de determinação (R²) de 0,94807 e um Erro Médio Absoluto (MAE) de 6,97, destacando sua precisão e potencial de aplicação clínica. Conclusão: O projeto demonstrou grande potencial para aprimorar a predição da idade óssea, com possibilidades de evolução conforme a base de dados aumenta e a IA se torna mais sofisticada.
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The COVID-19 pandemic has posed a significant public health challenge on a global scale. It is imperative that we continue to undertake research in order to identify early markers of disease progression, enhance patient care through prompt diagnosis, identification of high-risk patients, early prevention, and efficient allocation of medical resources. In this particular study, we obtained 100 5-min electrocardiograms (ECGs) from 50 COVID-19 volunteers in two different positions, namely upright and supine, who were categorized as either moderately or critically ill. We used classification algorithms to analyze heart rate variability (HRV) metrics derived from the ECGs of the volunteers with the goal of predicting the severity of illness. Our study choose a configuration pro SVC that achieved 76% of accuracy, and 0.84 on F1 Score in predicting the severity of Covid-19 based on HRV metrics.
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Background: Maternal infections are linked to neurodevelopmental impairments, highlighting the need to investigate SARS-CoV-2-induced immune activation. Objective: This study aimed to evaluate the impact of maternal infection on neurodevelopment and investigate whether cytokine and chemokine profiles predict delays at 24 months. Methods: Conducted in Brazil (January 2021–March 2022), this follow-up study included 18 SARS-CoV-2 positive pregnant women at 35–37 weeks’ gestation, 15 umbilical cord blood samples, and blood samples from 15 children at 6 months and 14 at 24 months. Developmental delay was defined using the Bayley Scales of Infant and Toddler Development, Third Edition, with scores below 90 in cognitive, communication, or motor domains. Results: At 6 months, 33.3% of infants exhibited cognitive delays, 20% communication delays, and 40% motor delays, increasing to 35.71%, 64.29%, and 57.14% at 24 months, respectively. Elevated interferon-gamma and tumor necrosis factor-alpha in cord blood correlated with cognitive delays, while interleukin (IL)-6, IL-8, IL-17, and IL-1β were associated with motor delays. Increased C-X-C motif chemokine ligand 10 and other cytokines were associated with communication delays. Conclusion: Maternal SARS-CoV-2 may impact infant neurodevelopment, as early cytokine elevations correlate with delays, highlighting the importance of early monitoring and interventions to reduce long-term effects. Impact: Prenatal SARS-COV-2 infection in pregnant women is linked to developmental delays in toddlers, with cytokine and chemokine changes associated with neurodevelopmental outcomes at 24 months. This study shows the long-term impact of maternal SARS-COV-2 infection on child development, highlighting inflammatory markers like IFN-γ, TNFα, IL-6, IL-8, IL-17, IL-1β, and CXCL10. Identifying specific cytokines correlating with cognitive, communication, and motor delays suggests potential biomarkers for early intervention. Conducted in Fortaleza, Brazil, the study emphasizes understanding local epidemiological impacts on child development, especially in regions with high infection rates. (Figure presented.)
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