Repositorio Universidad Europea del Atlántico
Not a member yet
2719 research outputs found
Sort by
Flavonoids for gastrointestinal tract local and associated systemic effects: A review of clinical trials and future perspectives
No full textBackground:
Flavonoids are naturally occurring dietary phytochemicals with significant antioxidant effects aside from several health benefits. People often consume them in combination with other food components. Compiling data establishes a link between bioactive flavonoids and prevention of several diseases in animal models, including cardiovascular diseases, diabetes, gut dysbiosis, and metabolic dysfunction-associated steatotic liver disease (MASLD). However, numerous clinical studies have demonstrated the ineffectiveness of flavonoids contradicting rodent models, thereby challenging the validity of using flavonoids as dietary supplements.
Aim of Review:
This review provides a clinical perspective to emphasize the effective roles of dietary flavonoids as well as to summarize their specific mechanisms in animals briefly
Virtual Patient (E+DIETing_LAB)
No full textSe trata de una plataforma que integra cinco bots diferentes disponibles en cinco idiomas. El bot enseña al estudiante de nutrición y dietética a realizar un proceso de exploración clínica de forma online/interactiva. Estos bots proporcionan los siguientes casos: Gastroenterología, Diabetes mellitus tipo 1, enfermedades cardiovasculares y diabetes, obesidad y enfermedades renales. Cada bot dispone de un cuestionario relacionado con el ámbito de la nutrición, y una encuesta final para conocer la experiencia del usuario. Desarrollada en el marco del proyecto E+DIETing_LA
Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization
No full textThe process of image formulation uses semantic analysis to extract influential vectors from image components. The proposed approach integrates DenseNet with ResNet-50, VGG-19, and GoogLeNet using an innovative bonding process that establishes algorithmic channeling between these models. The goal targets compact efficient image feature vectors that process data in parallel regardless of input color or grayscale consistency and work across different datasets and semantic categories. Image patching techniques with corner straddling and isolated responses help detect peaks and junctions while addressing anisotropic noise through curvature-based computations and auto-correlation calculations. An integrated channeled algorithm processes the refined features by uniting local-global features with primitive-parameterized features and regioned feature vectors. Using K-nearest neighbor indexing methods analyze and retrieve images from the harmonized signature collection effectively. Extensive experimentation is performed on the state-of-the-art datasets including Caltech-101, Cifar-10, Caltech-256, Cifar-100, Corel-10000, 17-Flowers, COIL-100, FTVL Tropical Fruits, Corel-1000, and Zubud. This contribution finally endorses its standing at the peak of deep and complex image sensing analysis. A state-of-the-art deep image sensing analysis method delivers optimal channeling accuracy together with robust dataset harmonization performance
Reasons for the Practice, Abandonment, and Non-Practice of Extracurricular Physical Activity and Sport Among Primary and Secondary School Students in Cantabria: What Can We Do About It?
No full text(1) Background: Physical education at school is not able to meet the need for physical activity and sport (PA and S) established by international organizations, making it necessary to implement its practice outside school hours. This study aimed to find out the reasons for practicing, abandoning, and never having practiced PA and S outside school hours among students of Primary Education (PE) and Secondary Education (SE) in Cantabria (Spain). (2) Overall, 1038 students participated (349 from PE and 689 from SE), consisting of 512 boys and 526 girls between 10 and 17 years old (M = 12.92; SD = 1.92). They completed an ad hoc questionnaire with 21 questions about reasons for practicing (12 items), abandoning (3 items), and never having practiced PA and S (6 items) between the months of May and June 2024. (3) Results: As for active students, boys argue that they do so because of the influence of friends (p = 0.024), search for excitement (p = 0.002), liking PA and S (p = 0.022), and entertainment (p = 0.001). In PE, compared to SE, the most important factors are excitement (p < 0.001), health (p = 0.005), and liking PA and S (p = 0.022). Students who abandon PA and S do so because of the competitive environment (p = 0.001), with boys predominating. SE students highlight reluctance and laziness (p < 0.001) and the loss of liking PA and S (p = 0.013). Students who have never practiced PA and S do so because they do not find any sport motivating (p = 0.047) and because of reluctance and laziness (p = 0.018), especially among girls. In SE, the differences appear due to reluctance and laziness (p = 0.009) and because friends do not practice PA and S (p = 0.049). (4) Conclusions: Boys prioritize emotional and competency aspects, while girls focus on social aspects and happiness. PE students tend to participate in sports for fun and to improve their skills, while SE students tend to show reluctance and laziness and a loss of interest in PA and S
Fundus image classification using feature concatenation for early diagnosis of retinal disease
No full textBackground
Deep learning models assist ophthalmologists in early detection of diseases from retinal images and timely treatment.
Aim
Owing to robust and accurate results from deep learning models, we aim to use convolutional neural network (CNN) to provide a non-invasive method for early detection of eye diseases.
Methodology
We used a hybridized CNN with deep learning (DL) based on two separate CNN blocks, to identify multiple Optic Disc Cupping, Diabetic Retinopathy, Media Haze, and Healthy images. We used the RFMiD dataset, which contains various categories of fundus images representing different eye diseases. Data augmenting, resizing, coping, and one-hot encoding are used among other preprocessing techniques to improve the performance of the proposed model. Color fundus images have been analyzed by CNNs to extract relevant features. Two CCN models that extract deep features are trained in parallel. To obtain more noticeable features, the gathered features are further fused utilizing the Canonical Correlation Analysis fusion approach. To assess the effectiveness, we employed eight classification algorithms: Gradient boosting, support vector machines, voting ensemble, medium KNN, Naive Bayes, COARSE- KNN, random forest, and fine KNN.
Results
With the greatest accuracy of 93.39%, the ensemble learning performed better than the other algorithms.
Conclusion
The accuracy rates suggest that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. It contributes to the field of eye disease detection through the analysis of color fundus images by providing a reliable and efficient diagnostic system
Modernizing gut-brain axis research in nutritional Science: The role of human-centered New Approach Methodologies
No full textBackground
The gut-brain axis is a complex communication network that connects the gastrointestinal system with the central nervous system, significantly influencing various health outcomes, such as mental health, cognitive function, metabolic regulation, and immune responses. While traditional research models, particularly animal studies, have provided valuable insights, they often overlook the intricate and human-specific interactions within this axis. Consequently, translating findings from these models into clinical applications has been challenging. However, recent advancements in human-based Novel Approach Methodologies (NAMs), like organoids, organs-on-chip, and omic sciences, present innovative tools for investigating the gut-brain axis with improved accuracy and relevance to human physiology. These methodologies facilitate a deeper understanding of the molecular and cellular mechanisms by which nutritional interventions affect not only mental health but also a wider range of gut-brain-related health outcomes. Scope and approach: Scope and approach: This paper explores how NAMs are revolutionizing gut-brain axis research by providing more accurate models that replicate human physiology, thereby replacing less effective traditional approaches.
Key findings and conclusion
By using these advanced methods, researchers can produce detailed data that better mirror human responses to dietary components, resulting in more effective and personalized strategies for managing and enhancing gut-brain health. Future research should concentrate on utilizing NAMs to deepen our understanding of the gut-brain axis in nutritional science, which will ultimately lead to more targeted and effective health interventions for various conditions
Detection of cotton crops diseases using customized deep learning model
Full text linkThe agricultural industry is experiencing revolutionary changes through the latest advances in artificial intelligence and deep learning-based technologies. These powerful tools are being used for a variety of tasks including crop yield estimation, crop maturity assessment, and disease detection. The cotton crop is an essential source of revenue for many countries highlighting the need to protect it from deadly diseases that can drastically reduce yields. Early and accurate disease detection is quite crucial for preventing economic losses in the agricultural sector. Thanks to deep learning algorithms, researchers have developed innovative disease detection approaches that can help safeguard the cotton crop and promote economic growth. This study presents dissimilar state-of-the-art deep learning models for disease recognition including VGG16, DenseNet, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet models. For this purpose, real cotton disease data is collected from fields and preprocessed using different well-known techniques before using as input to deep learning models. Experimental analysis reveals that the ResNet152 model outperforms all other deep learning models, making it a practical and efficient approach for cotton disease recognition. By harnessing the power of deep learning and artificial intelligence, we can help protect the cotton crop and ensure a prosperous future for the agricultural sector
Shoulder ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization, and arthroscopic trillat for the treatment of shoulder instability: a systematic review of original studies on surgical techniques
Full text linkBackground
Anterior shoulder instability is a common condition, especially among young and active individuals, often associated with both osseous and soft tissue injuries. Recent innovations have introduced various surgical options for managing critical and subcritical instability. Therefore, the primary objective of this systematic review was to collect, synthesize, and integrate international research published across multiple scientific databases on shoulder ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization (DAS), and arthroscopic Trillat techniques used in the treatment of shoulder instability.
Method
A structured search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the PICOS model, up to January 30, 2025, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus, and Scopus databases. The risk of bias was evaluated, and the PEDro scale was used to assess methodological quality.
Results
The initial search yielded a total of 964 articles. After applying the inclusion and exclusion criteria, the final sample consisted of 25 articles. These studies demonstrated a high standard of methodological quality. The review summarized the effects of ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization, and arthroscopic Trillat techniques in treating shoulder instability, detailing the sample population, immobilization period, frequency of instability episodes—including recurrent dislocations and subluxations—surgical methods, study designs, assessed variables, main findings, and reported outcomes.
Conclusions
Arthroscopic ligamentoplasty is advantageous in preserving the patient’s native anatomy, maintaining joint integrity, and allowing for alternative interventions in case of failure. The arthroscopic Trillat technique offers a minimally invasive solution for anterior instability without significant bone loss. The DAS technique utilizes the biceps tendon to provide dynamic stabilization, aiming to generate a sling effect over the subscapularis muscle. The Latarjet procedure remains the gold standard for managing anterior glenoid bone loss greater than 20%. Each surgical option for anterior shoulder instability carries specific implications, and treatment decisions should be tailored based on bone loss severity, capsuloligamentous quality, and the patient’s functional needs
Image-Based Dietary Energy and Macronutrients Estimation with ChatGPT-5: Cross-Source Evaluation Across Escalating Context Scenarios
Full text linkBackground/Objectives: Estimating energy and macronutrients from food images is clinically relevant yet challenging, and rigorous evaluation requires transparent accuracy metrics with uncertainty and clear acknowledgement of reference data limitations across heterogeneous sources. This study assessed ChatGPT-5, a general-purpose vision-language model, across four scenarios differing in the amount and type of contextual information provided, using a composite dataset to quantify accuracy for calories and macronutrients. Methods: A total of 195 dishes were evaluated, sourced from Allrecipes.com, the SNAPMe dataset, and Home-prepared, weighed meals. Each dish was evaluated under Case 1 (image only), Case 2 (image plus standardized non-visual descriptors), Case 3 (image plus ingredient lists with amounts), and Case 4 (replicates Case 3 but excluding the image). The primary endpoint was kcal Mean Absolute Error (MAE); secondary endpoints included Median Absolute Error (MedAE) and Root Mean Square Error (RMSE) for kcal and macronutrients (protein, carbohydrates, and lipids), all reported with 95% Confidence Intervals (CIs) via dish-level bootstrap resampling and accompanied by absolute differences (Δ) between scenarios. Inference settings were standardized to support reproducibility and variance estimation. Source stratified analyses and quartile summaries were conducted to examine heterogeneity by curation level and nutrient ranges, with additional robustness checks for error complexity relationships. Results and Discussion: Accuracy improved from Case 1 to Case 2 and further in Case 3 for energy and all macronutrients when summarized by MAE, MedAE, and RMSE with 95% CIs, with absolute reductions (Δ) indicating material gains as contextual information increased. In contrast to Case 3, estimation accuracy declined in Case 4, underscoring the contribution of visual cues. Gains were largest in the Home-prepared dietitian-weighed subset and smaller yet consistent for Allrecipes.com and SNAPMe, reflecting differences in reference curation and measurement fidelity across sources. Scenario-level trends were concordant across sources, and stratified and quartile analyses showed coherent patterns of decreasing absolute errors with the provision of structured non-visual information and detailed ingredient data. Conclusions: ChatGPT-5 can deliver practically useful calorie and macronutrient estimates from food images, particularly when augmented with standardized nonvisual descriptors and detailed ingredients, as evidenced by reductions in MAE, MedAE, and RMSE with 95% CIs across scenarios. The decline in accuracy observed when the image was omitted, despite providing detailed ingredient information, indicates that visual cues contribute meaningfully to estimation performance and that improvements are not solely attributable to arithmetic from ingredient lists. Finally, to promote generalizability, it is recommended that future studies include repeated evaluations across diverse datasets, ensure public availability of prompts and outputs, and incorporate systematic comparisons with non-artificial-intelligence baselines
Factores psicológicos en la conducción: análisis de la relación entre estilos atribucionales y conductas de riesgo
No full textLos accidentes de tráfico comprenden una de las principales causas de mortalidad y daños económicos a nivel mundial. La conducción es una conducta compleja influenciada por factores cognitivos y conductuales que desempeñan un papel significativo en la ocurrencia de accidentes e infracciones, a menudo debidos a conductas de riesgo. La presente investigación tiene como objetivo analizar la relación entre las dimensiones de atribución causal (locus de control, controlabilidad y estabilidad) y las conductas aberrantes (violaciones, violaciones agresivas, errores y lapsus) en la conducción. Para ello, una muestra de 42 conductores (13 hombres y 28 mujeres) completó una serie de medidas autoinformadas. Los resultados revelaron una asociación positiva entre el locus de control interno y la estabilidad con una mayor prevalencia de conductas agresivas. Asimismo, se encontró una asociación negativa entre la percepción de controlabilidad y las conductas de riesgo, sugiriendo que una mayor percepción de control disminuye la probabilidad de presentar conductas de riesgo. Estos hallazgos subrayan el papel de los estilos atribucionales en la predicción de comportamientos de riesgo en la conducción, lo cual tiene importantes implicaciones para la promoción de la seguridad vial y el diseño de intervenciones preventivas