Repositorio Universidad Europea del Atlántico
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Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic
Full text linkIn March 2020, the WHO declared the start of the COVID-19 pandemic. Peru was among the most affected countries, and one of the first ones to impose a strict lockdown. As a consequence, Peruvian universities were forced to change their classes from an in-person to a remote methodology during the academic year 2020–21. During this period, a cross-sectional online survey-based study was performed in medical and nursing faculties from four Peruvian universities (two of them public). The study sample, composed by 1707 undergraduate students (441 males) attending classes from home, answered three scales for measuring specific elements of professionalism (clinical empathy, teamwork, and lifelong learning abilities) in medicine and nursing, and four scales for measuring loneliness, anxiety, depression, and subjective wellbeing. In addition, 15 demographic, epidemiological, and academic variables (including dropout intention) were also collected. All this information is presented in a dataset that is available to other researchers and medical and nursing educators. Information concerning the data records, technical validation, and usage notes are also reported
E+DIETing_LAB Digital Lab for Education in Dietetics Combining Experiential Learning and Community Service
No full textThe E-Dieting Lab project addresses the critical need for improved practical education in dietetics, responding to the growing obesity crisis and its associated health and economic impacts. This innovative approach combines digital tools, virtual patients, and a service-learning model to enhance the training of dietetics students. The project aims to bridge the gap between theoretical knowledge and real-world practice by creating simulated professional experiences based on the Nutrition Care Process (NCP). By incorporating realistic patient scenarios and virtual interactions, students develop crucial interpersonal skills and patient follow-up abilities. The initiative also promotes community engagement, allowing students to apply their knowledge in meaningful, socially beneficial ways. Preliminary results and impressions from participants will be analysed to assess the effectiveness of this novel educational approach in improving dietetics training and preparing future professionals for the challenges of modern healthcare
Natural Products in Alzheimer’s Disease: A Systematic Review of Clinical Trials and Underlying Molecular Mechanisms
Full text linkThis systematic review included 31 clinical trial articles examining the effects of natural compounds on Alzheimer’s disease (AD) and mild cognitive impairment (MCI), involving 3582 participants aged 50–90. Treatment durations ranged from 8 weeks to 2 years, with an average of 12.5 months. Notably, 11 studies focused on herbal extracts highlighting their prominence in current research. These extracts showed potential cognitive and neuroprotective benefits, although results varied across compounds and study designs. Other natural compounds—including flavonoids, polyphenols, omega-3 fatty acids, Aloe vera, Spirulina, and citrus phytochemicals—may provide cognitive and neuroprotective benefits, with ginseng and Ginkgo biloba combinations also showing promise. Curcumin and Melissa officinalis had limited effects, resveratrol showed mixed outcomes with some side effects, and matcha green tea may improve cognition and sleep quality. Despite generally favorable results, the studies varied considerably in design and quality; nonetheless, herbal extracts represent a prominent category of natural interventions in AD and MCI, underscoring the need for further large-scale, high-quality clinical trials to confirm their therapeutic potential
Polyphenols-mediated immune regulation: Metabolite-driven epigenetic regulatory mechanisms
No full textBackground
Dietary polyphenols are recognized modulators of immune function. Although metabolic and epigenetic effects have been examined separately, relationships and implications for immune regulation remain unclear. Addressing the gap is critical to understanding how diet shapes immune homeostasis and disease risk.
Previous studies examined metabolic and epigenetic effects separately, leaving their interconnections—and implications for immune regulation—unclear.
Purpose
This review aims to elucidate, from a novel perspective, the polyphenols–metabolic–epigenetic modification–immunity regulatory axis that underlies polyphenols-mediated immunoregulation. By emphasizing this framework, we highlight mechanistic insights into the interplay among diet, metabolism, and immune homeostasis, providing potential strategies for preventing and treating chronic inflammatory and metabolic diseases.
Methods
A comprehensive search of peer-reviewed publications was performed from core collections of electronic databases such as PubMed, Web of Science, Google Scholar, and Science Direct.
Results
Polyphenols regulate immunity by reprogramming metabolic pathways and modulating epigenetic mechanisms. Metabolite-driven crosstalk between metabolism and epigenetics offers insights into immune phenotype stability, particularly across generations. These mechanisms are relevant to clinical phenotypes highlighted in the manuscript, including obesity, features of metabolic syndrome, and autoimmune conditions. Challenges remain in translation, including bioavailability, dose-response variability, and limited evidence on transgenerational effects. Future studies should explore how polyphenols-mediated metabolic shifts affect epigenetic regulators during early development and immune inheritance. Integrating polyphenols into immunometabolism and epigenetic regulation offers novel strategies for disease prevention and precision nutrition.
Conclusion
This review provides a new perspective on polyphenols-mediated immune regulation, offering a theoretical basis for understanding how small molecules influence immunity through metabolism and epigenetics. This framework, rarely highlighted in current studies, may also guide future research in epigenetics
Intervenciones basadas en ejercicio físico y variabilidad de la frecuencia cardíaca para la mejora de la salud en mujeres con anorexia nerviosa
No full textLa anorexia nerviosa (AN) es una enfermedad mental caracterizada por la restricción de la ingesta calórica que afecta principalmente a mujeres adolescentes, con tasas de prevalencia a lo largo de la vida de hasta el 4%. Es importante destacar que esta enfermedad representa la tasa de mortalidad más elevada de cualquier trastorno mental, alcanzando el 5%.
Por todo ello, el proyecto que se propone, trata de aportar valor ante la AN en el contexto de la actividad física y el deporte.
Ante este problema, el ejercicio físico ha demostrado su efectividad para revertir las consecuencias negativas de la enfermedad, aunque todavía se desconocen los efectos de aplicar tipos de ejercicio concretos y orientados a la ganancia de fuerza máxima en esta patología. Además, la monitorización de la variabilidad de la frecuencia cardíaca se presenta como una estrategia prometedora, aún con necesidades de investigación, para comprobar la mejora de las variables relativas a la función del corazón y sus beneficios derivados, además del control de la intensidad de ejercicio en esta población a través de este biomarcador. Por tanto, el presente proyecto pivota sobre tres grandes pilares: la anorexia nerviosa, la variabilidad de la frecuencia cardíaca y el ejercicio físico de fuerza para la mejora de la sintomatología y en consiguiente, la calidad de vida, de este grupo poblacional
Enhanced FPGA-based smart power grid simulation using Heun and Piecewise analytic method
Full text linkThe increasing complexity of modern power systems requires engineers to design, build, and test equipment with a high degree of accuracy. The demand for precise equipment design, testing, and evaluation has reached extraordinary levels within modern power systems. To meet this challenge, engineers rely heavily on real-time simulators, which are essential tools for assessing power network dynamics. This study introduces a novel approach, an adaptable and cost-effective simulator, poised to revolutionize traditional hardware-in-the-loop (HIL) systems. Leveraging field-programmable gate arrays (FPGAs) and a comprehensive implementation of Heun and Piecewise analytic methods (PAM), provided simulator offers unparalleled capabilities for embedded real-time simulation of smart grids, ensuring swift and accurate measurements. Augmented by Python-based process simulation and integrated with industry-standard tools like Modelica and MATLAB, the proposed system promises versatility and efficiency. Through comprehensive testing, including rigorous evaluations of excitation system responses to diverse scenarios such as voltage set-point variations, automatic voltage regulator step responses, and fault conditions, we demonstrate the simulator’s robustness and precision. Experimental findings underscore its potential as an effective alternative to conventional HIL systems, marking a significant advancement in smart grid simulation technology
Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals
No full textEpileptic seizures are neurological events characterized by sudden and excessive electrical discharges in the brain, leading to disruptions in brain function. Epileptic seizures can lead to life-threatening situations such as status epilepticus, which is characterized by prolonged or recurrent seizures and may lead to respiratory distress, aspiration pneumonia, and cardiac arrhythmias. Therefore, there is a need for an automated approach that can efficiently diagnose epileptic seizures at an early stage. The primary objective of this study is to develop a highly accurate approach for the early diagnosis of epileptic seizures. We use electroencephalography (EEG) signal data based on different brain activities to conduct experiments for epileptic seizure detection. For this purpose, a novel transfer learning technique called random forest-gated recurrent unit (RFGR) is proposed. The EEG brain activity signal data is fed into the RFGR model to generate a new feature set. The newly generated features are based on the class prediction probabilities extracted by the RFGR and are utilized to train models. Extensive experiments are carried out to investigate the performance of the proposed approach. Results demonstrate that the RFGR, when used with the random forest model, outperforms state-of-the-art techniques, achieving a high accuracy of 99.00 %. Additionally, explainable artificial intelligence analysis is utilized to provide transparent and understandable explanations of the decision-making processes of the proposed approach
Parentalidad Positiva y Prevención de la Población infanto-juvenil con relación a su Salud Mental: una Revisión Actualizada
No full textLa parentalidad positiva se presenta como un nuevo reto para la sociedad actual en la que profesionales de la salud mental y profesionales del ámbito psicoeducativo proporcionan a las familias programas parentales, para fortalecer el funcionamiento familiar y empoderar a los progenitores con relación a la crianza de sus hijos. El presente trabajo busca describir un conjunto de publicaciones científicas para tratar de buscar correlaciones significativas entre la parentalidad positiva y la prevención del fututo de la población infanto-juvenil con relación a su salud mental, a partir del fortalecimiento del funcionamiento familiar y la reducción del impacto de las experiencias adversas durante la infancia y la adolescencia. Se realizó una revisión sistemática exploratoria del tema con un cribado de los parámetros “Positive parenting AND Mental disorders AND Prevention” a través de artículos de investigación publicados en revistas arbitradas y con revisión en cuatro bases de datos —Redalyc, la Biblioteca Virtual de Salud (BVS), PubMed y SciencieDirect—, de las que se examinaron 229, 31, 20 y 48 artículos, respectivamente. Los artículos fueron seleccionados basándose en criterios predefinidos y haciendo uso de limitadores. Finalmente, se seleccionaron un total de 61 artículos que fueron analizados y categorizados en los apartados correspondientes planteados
Plataforma digital para la creación de material docente basado en casos prácticos multiformato
No full textLa actividad científico-técnica que se propone se relaciona con el desarrollo de soluciones digitales para la docencia en el marco de instituciones de formación inicial o continua.
El objetivo del proyecto es desarrollar el prototipo de una plataforma para que los docentes puedan crear casos prácticos de estudio en diferentes formatos digitales de forma dirigida
Botnet detection in internet of things using stacked ensemble learning model
Full text linkBotnets are used for malicious activities such as cyber-attacks, spamming, and data theft and have become a significant threat to cyber security. Despite existing approaches for cyber attack detection, botnets prove to be a particularly difficult problem that calls for more advanced detection methods. In this research, a stacking classifier is proposed based on K-nearest neighbor, support vector machine, decision tree, random forest, and multilayer perceptron, called KSDRM, for botnet detection. Logistic regression acts as the meta-learner to combine the predictions from the base classifiers into the final prediction with the aim of increasing the overall accuracy and predictive performance of the ensemble. The UNSW-NB15 dataset is used to train machine learning models and evaluate their effectiveness in detecting cyber-attacks on IoT networks. The categorical features are transformed into numerical values using label encoding. Machine learning techniques are adopted to recognize botnet attacks to enhance cyber security measures. The KSDRM model successfully captures the complex patterns and traits of botnet attacks and obtains 99.99% training accuracy. The KSDRM model also performs well during testing by achieving an accuracy of 97.94%. Based on 3, 5, 7, and 10 folds, the k-fold cross-validation results show that the proposed method’s average accuracy is 99.89%, 99.88%, 99.89%, and 99.87%, respectively. Further, the demonstration of experiments and results shows the KSDRM model is an effective method to identify botnet-based cyber attacks. The findings of this study have the potential to improve cyber security controls and strengthen networks against changing threats