Repositorio Universidad Europea del Atlántico
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Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning
No full textThe perception and recognition of objects around us empower environmental interaction. Harnessing the brain’s signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects’ responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models
Editorial: Host-bacteria interactions in fish pathogens
No full textIn order to promote the sustainable development of aquaculture, it is of great importance to better understand fish diseases caused by classic and emerging bacterial pathogens. Strains of classic fish pathogens such as Aeromonas, Vibrio, Photobacterium, Edwardsiella, Yersinia, Flavobacterium, or Piscirickettsia
Daily Intake of Two or More Servings of Vegetables Is Associated with a Lower Prevalence of Metabolic Syndrome in Older People
No full textObjectives: We sought to examine the correlation between the recommended consumption of at least two servings (400 g) of vegetables per day and the prevalence of metabolic syndrome (MetS) in an elderly population. Methods: This observational, cross-sectional, and descriptive study was conducted with 264 non-institutionalized people aged 65 to 79 years old. We adhered to the recommended guidelines for vegetable intake from the MEDAS-14 questionnaire, which has been validated for elderly populations at high cardiovascular risk. Diagnoses of MetS were made based on the criteria set forth by the International Diabetes Federation (IDF). Results: Among 264 individuals, who had a mean age of 71.9 (SD: 4.2) and comprised 39% men, the prevalence of MetS was 40.2%. A total of 17% of the participants adhered to the recommended vegetable consumption. Consuming the recommended amount of vegetables was correlated with a 19% reduction in the prevalence of MetS, to 24.4% from 43.4% among those with low vegetable consumption (p < 0.05). A main finding was that inadequate vegetable consumption was significantly associated with a higher prevalence of MetS (OR: 2.21; 95% CI: 1.06–4.63; p = 0.035), considering potential influences by nutritional (consumption of fruit and nuts) and socio-demographic (sex, age, and level of education) covariates. Conclusions: A beneficial inverse correlation was identified between the recommended vegetable intake and the prevalence of MetS. In contrast, inadequate vegetable consumption was revealed as an independent variable associated with the prevalence of MetS. Considering the very low adherence to the recommended vegetable intake we observed, encouraging increased vegetable consumption among older individuals, who have a high prevalence of MetS, is advisable
Advancement in medical report generation: current practices, challenges, and future directions
No full textThe correct analysis of medical images requires the medical knowledge and expertise of radiologists to understand, clarify, and explain complex patterns and diagnose diseases. After analyzing, radiologists write detailed and well-structured reports that contribute to the precise and timely diagnosis of patients. However, manually writing reports is often expensive and time-consuming, and it is difficult for radiologists to analyze medical images, particularly images with multiple views and perceptions. It is challenging to accurately diagnose diseases, and many methods are proposed to help radiologists, both traditional and deep learning-based. Automatic report generation is widely used to tackle this issue as it streamlines the process and lessens the burden of manual labeling of images. This paper introduces a systematic literature review with a focus on analyses and evaluating existing research on medical report generation. This SLR follows a proper protocol for the planning, reviewing, and reporting of the results. This review recognizes that the most commonly used deep learning models are encoder-decoder frameworks (45 articles), which provide an accuracy of around 92–95%. Transformers-based models (20 articles) are the second most established method and achieve an accuracy of around 91%. The remaining articles explored in this SLR are attention mechanisms (10), RNN-LSTM (10), Large language models (LLM-10), and graph-based methods (4) with promising results. However, these methods also face certain limitations such as overfitting, risk of bias, and high data dependency that impact their performance. The review not only highlights the strengths and challenges of these methods but also suggests ways to handle them in the future to increase the accuracy and timely generation of medical reports. The goal of this review is to direct radiologists toward methods that lessen their workload and provide precise medical diagnoses
Could Celiac Disease and Overweight/Obesity Coexist in School-Aged Children and Adolescents? A Systematic Review
No full textBackground: Celiac disease (CD) is a multifactorial, immune-mediated enteropathic disorder that may occur at any age with heterogeneous clinical presentation. In the last years, unusual manifestations have become very frequent, and currently, it is not so uncommon to diagnose CD in subjects with overweight or obesity, especially in adults; however, little is known in the pediatric population. This systematic review aims to evaluate the literature regarding the association between CD and overweight/obesity in school-age children.
Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. An electronic database search of articles published in the last 20 years in English was carried out in Web of Sciences, PubMed, and Medline. The quality of the included studies was assessed by using the STrengthening the Reporting of OBservational studies in Epidemiology statement.
Results: Of the 1396 articles identified, 9 articles, investigating overweight/obesity in children/adolescents affected by CD or screening CD in children/adolescents with overweight/obesity, met the inclusion criteria. Overall, the results showed that the prevalence of overweight or obesity in school-age children (6–17 years) affected by CD ranged between 3.5% and 20%, highlighting that the coexistence of CD with overweight/obesity in children is not uncommon as previously thought.
Conclusion: Although CD has been historically correlated with being underweight due to malabsorption, it should be evaluated also in children with overweight and obesity, especially those who have a familiar predisposition to other autoimmune diseases and/or manifest unusual symptoms of CD
Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments
No full textThe evolution of the COVID-19 pandemic has been associated with variations in clinical presentation and severity. Similarly, prediction scores may suffer changes in their diagnostic accuracy. The aim of this study was to test the 30-day mortality predictive validity of the 4C and SEIMC scores during the sixth wave of the pandemic and to compare them with those of validation studies. This was a longitudinal retrospective observational study. COVID-19 patients who were admitted to the Emergency Department of a Spanish hospital from December 15, 2021, to January 31, 2022, were selected. A side-by-side comparison with the pivotal validation studies was subsequently performed. The main measures were 30-day mortality and the 4C and SEIMC scores. A total of 27,614 patients were considered in the study, including 22,361 from the 4C, 4,627 from the SEIMC and 626 from our hospital. The 30-day mortality rate was significantly lower than that reported in the validation studies. The AUCs were 0.931 (95% CI: 0.90–0.95) for 4C and 0.903 (95% CI: 086–0.93) for SEIMC, which were significantly greater than those obtained in the first wave. Despite the changes that have occurred during the coronavirus disease 2019 (COVID-19) pandemic, with a reduction in lethality, scorecard systems are currently still useful tools for detecting patients with poor disease risk, with better prognostic capacity
Investigation of structural frustration in symmetric diblock copolymers confined in polar discs through cell dynamic simulation
No full textNanotechnology has opened new avenues for advanced research in various fields of soft materials. Materials scientists, chemists, physicists, and computational mathematicians have begun to take a keen interest in soft materials due to their potential applications in nanopatterning, membrane separation, drug delivery, nanolithography, advanced storage media, and nanorobotics. The unique properties of soft materials, particularly self-assembly, have made them useful in fields ranging from nanotechnology to biomedicine. The discovery of new morphologies in the diblock copolymer system in curved geometries is a challenging problem for mathematicians and theoretical scientists. Structural frustration under the effects of confinement in the system helps predict new structures. This mathematical study evaluates the effects of confinement and curvature on symmetric diblock copolymer melt using a cell dynamic simulation model. New patterns in lamella morphologies are predicted. The Laplacian involved in the cell dynamic simulation model is approximated by generating a 17-point stencil discretized to a polar grid by the finite difference method. Codes are programmed in FORTRAN to run the simulation, and IBM open DX is used to visualize the results. Comparison of computational results with existing studies validates this study and identifies defects and new patterns
Carotenoids Intake and Cardiovascular Prevention: A Systematic Review
No full textBackground: Cardiovascular diseases (CVDs) encompass a variety of conditions that affect the heart and blood vessels. Carotenoids, a group of fat-soluble organic pigments synthesized by plants, fungi, algae, and some bacteria, may have a beneficial effect in reducing cardiovascular disease (CVD) risk. This study aims to examine and synthesize current research on the relationship between carotenoids and CVDs. Methods: A systematic review was conducted using MEDLINE and the Cochrane Library to identify relevant studies on the efficacy of carotenoid supplementation for CVD prevention. Interventional analytical studies (randomized and non-randomized clinical trials) published in English from January 2011 to February 2024 were included. Results: A total of 38 studies were included in the qualitative analysis. Of these, 17 epidemiological studies assessed the relationship between carotenoids and CVDs, 9 examined the effect of carotenoid supplementation, and 12 evaluated dietary interventions. Conclusions: Elevated serum carotenoid levels are associated with reduced CVD risk factors and inflammatory markers. Increasing the consumption of carotenoid-rich foods appears to be more effective than supplementation, though the specific effects of individual carotenoids on CVD risk remain uncertain
Identificación del riesgo de lesión a través de termografía y la aplicación de inteligencia artificial para la prevención de patologías musculares
No full textEl punto de partida del proyecto es el conocimiento que se dispone sobre la predicción de lesiones, ya validado en la literatura científica. El propósito final tiene por finalidad correlacionar diferentes tecnologías para obtener una valoración objetiva a través de la inteligencia artificial como predictor preciso.
Como aspectos novedosos en relación al estado del arte proponemos un nuevo enfoque rico en criterios de análisis para identificar el riesgo de lesión, aplicando técnicas innovadoras (como la termografía infrarroja), no invasivas y que se realizan con rapidez. Pretendemos, además, iniciar un proceso de diseño de soluciones de software aplicadas a la salud y el deporte donde se aprovechen técnicas de fusión de datos multivariable mediante el uso de inteligencia artificial. En el caso que nos atañe en este proyecto, el sistema nos debería permitir obtener perfil/patrones de deportistas y finalmente establecer el riesgo de lesión individual
Formação de professor na materialização do Plano Educacional Individualizado (PEI), para pessoas com Transtorno do Espectro Autista (TEA) na Educação Infantil: Uma análise do protocolo cientificamente validado considerando os marcos do desenvolvimento infantil de Jean Piaget na EMEI Maria Suely Medrado Araújo
No full textA pesquisa aborda o fortalecimento da Política Nacional da
Educação Especial na Perspectiva da Educação Inclusiva
(PENEEPEI), na Educação Infantil considerando o parecer nº
50/2023 que trata das Orientações Específicas para o Público da Educação Especial: Atendimento de Estudantes com Transtorno do Espectro Autista (TEA). Investiga a implementação do Plano Educacional Individualizado (PEI), utilizando um protocolo cientificamente validado, considerando os marcos do desenvolvimento infantil de Jean Piaget para formação de professores