174

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Ir a: 2025 | 2024 | 2023 | 2022 | 2021 | 2018 | 2016 | 2015
Número de registros en este nivel: 174.

2025

Advancing Nutritional Science: Contemporary Perspectives on Diet’s Role in Metabolic Health and Disease Prevention.

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Client engagement solution for post implementation issues in software industry using blockchain.

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Detection of cotton crops diseases using customized deep learning model.

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Ensemble stacked model for enhanced identification of sentiments from IMDB reviews.

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Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review.

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Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices.

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Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.

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Novel transfer learning approach for hand drawn mathematical geometric shapes classification.

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Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults.

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Pupilometer efficacy in monitoring anxiety in undergraduate medical students during high-fidelity clinical simulation.

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Tensiomyography, functional movement screen and counter movement jump for the assessment of injury risk in sport: a systematic review of original studies of diagnostic tests.

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2024

Advanced Line-of-Sight (LOS) model for communicating devices in modern indoor environment.

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Blockchain based transparent and reliable framework for wheat crop supply chain.

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Carotenoids Intake and Cardiovascular Prevention: A Systematic Review.

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Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.

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Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.

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A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study.

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Correction: Prediction of leukemia peptides using convolutional neural network and protein compositions.

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Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential.

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Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble.

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Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.

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A Detectability Analysis of Retinitis Pigmetosa Using Novel SE-ResNet Based Deep Learning Model and Color Fundus Images.

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Detecting Cyberattacks to Federated Learning on Software-Defined Networks.

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Detecting Pragmatic Ambiguity in Requirement Specification Using Novel Concept Maximum Matching Approach Based on Graph Network.

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DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network.

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Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data.

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Efficient deep learning-based approach for malaria detection using red blood cell smears.

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Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model.

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Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.

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Evolving epidemiology, clinical features, and genotyping of dengue outbreaks in Bangladesh, 2000–2024: a systematic review.

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Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.

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Flavan-3-ols and Vascular Health: Clinical Evidence and Mechanisms of Action.

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Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model.

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Human‐based new approach methodologies to accelerate advances in nutrition research.

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Influence of E-learning training on the acquisition of competences in basketball coaches in Cantabria.

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Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved?

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Lifestyle Factors Associated with Children’s and Adolescents’ Adherence to the Mediterranean Diet Living in Mediterranean Countries: The DELICIOUS Project.

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Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence.

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Natural Language Processing-Based Software Testing: A Systematic Literature Review.

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Novel model to authenticate role-based medical users for blockchain-based IoMT devices.

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Organizational Culture Assessment Based on a Values-Based Coaching Program for Strategic Level Employees: The Case of GEDEME, Cuba.

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Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.

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Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.

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Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model.

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Prediction of leukemia peptides using convolutional neural network and protein compositions.

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PyDEMATEL: A Python-based tool implementing DEMATEL and fuzzy DEMATEL methods for improved decision making.

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Risk Factors for Eating Disorders in University Students: The RUNEAT Study.

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Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization.

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Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models.

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Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.

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StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides.

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Underrated aspects of a true Mediterranean diet: understanding traditional features for worldwide application of a “Planeterranean” diet.

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Virtual histopathology methods in medical imaging - a systematic review.

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A deep learning approach for Named Entity Recognition in Urdu language.

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An enhanced approach for predicting air pollution using quantum support vector machine.

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An improved deep convolutional neural network-based YouTube video classification using textual features.

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A real-time air-writing model to recognize Bengali characters.

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2023

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants.

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Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach.

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Anthocyanins: what do we know until now?

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Análisis crítico sobre el perfil de salida del bachillerato ecuatoriano. Una mirada desde el método de aprendizaje basado en proyectos.

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Análisis crítico sobre el perfil de salida del bachillerato ecuatoriano. Una mirada desde el método de aprendizaje basado en proyectos.

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The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment.

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An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features.

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An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation.

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Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach.

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Betalains: The main bioactive compounds of Opuntia spp and their possible health benefits in the Mediterranean diet.

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Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction.

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Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning.

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Can alpha‐linolenic acid be a modulator of “cytokine storm,” oxidative stress and immune response in SARS‐CoV‐2 infection?

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Can the phenolic compounds of Manuka honey chemosensitize colon cancer stem cells? A deep insight into the effect on chemoresistance and self-renewal.

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Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems.

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A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.

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Contextual Urdu Lemmatization Using Recurrent Neural Network Models.

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Current- and Voltage-Actuated Transmission Line Protection Scheme Using a Hybrid Combination of Signal Processing Techniques.

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Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection.

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Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance.

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Deep learning model for detection of brown spot rice leaf disease with smart agriculture.

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Diagnosing Training Needs in European Tourism SMEs: The TC-NAV Project for Managing and Overcoming Virulent Crises.

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Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm.

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DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents.

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Emotional Management in Journalism and Communication Studies.

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Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence.

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Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning.

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Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification.

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Forecasting of Post-Graduate Students’ Late Dropout Based on the Optimal Probability Threshold Adjustment Technique for Imbalanced Data.

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Formal modeling and analysis of security schemes of RPL protocol using colored Petri nets.

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Hybridizing Artificial Intelligence Algorithms for Forecasting of Sediment Load with Multi-Objective Optimization.

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Image Watermarking Using Least Significant Bit and Canny Edge Detection.

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Impact of Innovation-Oriented Human Resource on Small and Medium Enterprises’ Performance.

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Integration of Sustainable Criteria in the Development of a Proposal for an Online Postgraduate Program in the Projects Area.

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An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field.

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IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System.

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Mitigating 5G security challenges for next-gen industry using quantum computing.

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Model Driven Approach for Efficient Flood Disaster Management with Meta Model Support.

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Molecular epidemiology, transmission and clinical features of 2022‐mpox outbreak: A systematic review.

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A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.

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Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements.

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An Optimized Intelligent Computational Security Model for Interconnected Blockchain-IoT System & Cities.

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PRUS: Product Recommender System Based on User Specifications and Customers Reviews.

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Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study.

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Prehospital qSOFA, mSOFA, and NEWS2 performance for sepsis prediction: A prospective, multi-center, cohort study.

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Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis.

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Prevalence and impact of long COVID-19 among patients with diabetes and cardiovascular diseases in Bangladesh.

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Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data.

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Real Word Spelling Error Detection and Correction for Urdu Language.

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Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms.

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SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations.

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The Scope of Technostress and Care of The Self on Journalists During the Pandemic.

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Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective.

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Software Cost and Effort Estimation: Current Approaches and Future Trends.

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Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety.

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A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions.

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Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight.

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Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19.

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Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band.

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Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis.

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Use of Fuzzy Approach Methodology and Consensus in Creating a Hierarchy of Satisfaction for Measurement Criteria: Application to Online Training Actions Directed at Classification by Key Competency Profiles in Sales Supervision (SPV) within the Automotive.

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Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches.

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An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks.

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A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation.

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2022

Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives.

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Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix.

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An Analytical Framework for Innovation Determinants and Their Impact on Business Performance.

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An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring.

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Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.

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An Artificial Neural Network Model for Water Quality and Water Consumption Prediction.

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Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model.

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Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics.

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Design and Development of Smart Weight Measurement, Lateral Turning and Transfer Bedding for Unconscious Patients in Pandemics.

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Development Agencies and Local Governments—Coexistence within the Same Territory.

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Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city.

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Efecto de un entrenamiento propioceptivo para prevenir el riesgo de caída en adultos mayores.

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An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.

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Emotion Detection Using Facial Expression Involving Occlusions and Tilt.

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Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center.

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Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean.

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FairHealth: Long-Term Proportional Fairness-Driven 5G Edge Healthcare in Internet of Medical Things.

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Funcionalidad motriz, estado nutricional e índices antropométricos de riesgo cardiometabólico en adolescentes chilenos de 12 a 15 años.

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Imperative Role of Integrating Digitalization in the Firms Finance: A Technological Perspective.

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Implementation of photovoltaic energy, sustainability, economic and social development in a Higher Education Institution in Brazil.

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Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities.

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Inequalities and Asymmetries in the Development of Angola’s Provinces: The Impact of Colonialism and Civil War.

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IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition.

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Modelo geoespacial para priorizar los factores de riesgo ambiental de las comunidades sin alcantarillado sanitario en la cuenca del Río Grande de Loíza en Puerto Rico.

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Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring.

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NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange.

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A Novel Approach to Integrating Uncertainty into a Push Re-Label Network Flow Algorithm for Pit Optimization.

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A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization.

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An Optimized Open Pit Mine Application for Limestone Quarry Production Scheduling to Maximize Net Present Value.

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População infantil e adolescente nas ruas e estudantes estrangeiros: impactos interculturais no desenvolvimento e no acesso às escolas.

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Prediction β-Thalassemia carriers using complete blood count features.

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Prickly pear fruits from "Opuntia ficus-indica" varieties as a source of potential bioactive compounds in the Mediterranean diet.

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Relación entre funcionalidad motriz y factores antropométricos de riesgo cardio metabólico en bomberos de la región de Valparaíso, Chile.

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A Smart Cloud and IoVT-Based Kernel Adaptive Filtering Framework for Parking Prediction.

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Smart Fault Monitoring and Normalizing of a Power Distribution System Using IoT.

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Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models.

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Sustentabilidade em sistemas de segurança do trabalho na construção civil.

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Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape.

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Threatening URDU Language Detection from Tweets Using Machine Learning.

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Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques.

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Towards Security Mechanism in D2D Wireless Communication: A 5G Network Approach.

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Underwater Wireless Sensor Networks: Enabling Technologies for Node Deployment and Data Collection Challenges.

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Wastewater Treatment with Technical Intervention Inclination towards Smart Cities.

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White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.

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A ferramenta SWOT na gestão escolar.

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A internacionalização em casa na pós-graduação na América Latina e Caribe na modalidade a distância.

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A violência doméstica no município da Cela, província do Cuanza-Sul –Angola: um fenómeno que tem preocupado o governo e a sociedade.

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2021

Instrumentalization of a Model for the Evaluation of the Level of Satisfaction of Graduates under an E-Learning Methodology: A Case Analysis Oriented to Postgraduate Studies in the Environmental Field.

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La comunidad docente y las competencias digitales: la formación a lo largo de la vida.

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Perseguindo o inédito viável: a pedagogia freiriana, a necesssidade da linguagem inclusiva e a denúncia à neolíngua do generismo queer.

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2018

Estudio comparativo entre metodología de aula invertida y metodología tradicional en clases de español, inglés y matemáticas.

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2016

Role of gut microbiota and nutrients in amyloid formation and pathogenesis of Alzheimer disease.

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2015

The Use of Neuroimaging to Assess Associations Among Diet, Nutrients, Metabolic Syndrome, and Alzheimer’s Disease.

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<a class="ep_document_link" href="/17788/1/s40537-025-01167-w.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis

The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.

Producción Científica

Hafiz Muhammad Raza Ur Rehman mail , Mahpara Saleem mail , Muhammad Zeeshan Jhandir mail , Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Helena Garay mail helena.garay@uneatlantico.es, Imran Ashraf mail ,

Raza Ur Rehman

<a class="ep_document_link" href="/17794/1/s41598-025-95836-8.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review

Accurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear relationships within large datasets. This study presents a systematic literature review (SLR) of deep learning applications for solar PV forecasting, addressing a gap in the existing literature, which often focuses on traditional ML or broader renewable energy applications. This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. We also found that ambient temperature, pressure, and humidity are the most common input features. Our systematic evaluation provides critical insights into state-of-the-art DL-based solar forecasting and identifies key areas for upcoming research. Future research should prioritize the development of more robust and interpretable models, as well as explore the integration of multi-source data to further enhance forecasting accuracy. Such advancements are crucial for the effective integration of solar energy into future power grids.

Producción Científica

Oussama Khouili mail , Mohamed Hanine mail , Mohamed Louzazni mail , Miguel Ángel López Flores mail miguelangel.lopez@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es, Imran Ashraf mail ,

Khouili

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Measurement of chest muscle mass in COVID-19 patients on mechanical ventilation using tomography

Background: Sarcopenia, characterized by a reduction in skeletal muscle mass and function, is a prevalent complication in the Intensive Care Unit (ICU) and is related to increased mortality. This study aims to determine whether muscle and fat mass measurements at the T12 and L1 vertebrae using chest tomography can predict mortality among critically ill COVID-19 patients requiring invasive mechanical ventilation (MV). Methods: Fifty-one critically ill COVID-19 patients on MV underwent chest tomography within 72 h of ICU admission. Muscle mass was measured using the Core Slicer program. Results: After adjustment for potential confounding factors related to background and clinical parameters, a 1-unit increase in muscle mass, subcutaneous, and intra-abdominal fat mass at the L1 level was associated with approximately 1–2% lower odds of negative outcomes and in-hospital mortality. No significant association was found between muscle mass at the T12 level and patient outcomes. Furthermore, no significant results were observed when considering a 1-standard deviation increase as the exposure variable. Conclusion: Measuring muscle mass using chest tomography at the T12 level does not effectively predict outcomes for ICU patients. However, muscle and fat mass at the L1 level may be associated with a lower risk of negative outcomes. Additional studies should explore other potential markers or methods to improve prognostic accuracy in this critically ill population.

Producción Científica

Natalia Daniela Llobera mail , Evelyn Frias-Toral mail , Mariel Aquino mail , María Jimena Reberendo mail , Laura Cardona Díaz mail , Adriana García mail , Martha Montalván mail , Álvaro Velarde Sotres mail alvaro.velarde@uneatlantico.es, Sebastián Chapela mail ,

Llobera

<a class="ep_document_link" href="/17569/1/Food%20Frontiers%20-%202025%20-%20Romero%E2%80%90Marquez%20-%20Olive%20Leaf%20Extracts%20With%20High%20%20Medium%20%20or%20Low%20Bioactive%20Compounds%20Content.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Olive Leaf Extracts With High, Medium, or Low Bioactive Compounds Content Differentially Modulate Alzheimer's Disease via Redox Biology

Alzheimer's disease (AD) involves β-amyloid plaques and tau hyperphosphorylation, driven by oxidative stress and neuroinflammation. Cyclooxygenase-2 (COX-2) and acetylcholinesterase (AChE) activities exacerbate AD pathology. Olive leaf (OL) extracts, rich in bioactive compounds, offer potential therapeutic benefits. This study aimed to assess the anti-inflammatory, anti-cholinergic, and antioxidant effects of three OL extracts (low, mid, and high bioactive content) in vitro and their protective effects against AD-related proteinopathies in Caenorhabditis elegans models. OL extracts were characterized for phenolic composition, AChE and COX-2 inhibition, as well as antioxidant capacity. Their effects on intracellular and mitochondrial reactive oxygen species (ROS) were tested in C. elegans models expressing human Aβ and tau proteins. Gene expression analyses examined transcription factors (DAF-16, skinhead [SKN]-1) and their targets (superoxide dismutase [SOD]-2, SOD-3, GST-4, and heat shock protein [HSP]-16.2). High-OL extract demonstrated superior AChE and COX-2 inhibition and antioxidant capacity. Low- and high-OL extracts reduced Aβ aggregation, ROS levels, and proteotoxicity via SKN-1/NRF-2 and DAF-16/FOXO pathways, whereas mid-OL showed moderate effects through proteostasis modulation. In tau models, low- and high-OL extracts mitigated mitochondrial ROS levels via SOD-2 but had limited effects on intracellular ROS levels. High-OL extract also increased GST-4 levels, whereas low and mid extracts enhanced GST-4 levels. OL extracts protect against AD-related proteinopathies by modulating oxidative stress, inflammation, and proteostasis. High-OL extract showed the most promise for nutraceutical development due to its robust phenolic profile and activation of key antioxidant pathways. Further research is needed to confirm long-term efficacy.

Producción Científica

Jose M. Romero‐Marquez mail , María D. Navarro‐Hortal mail , Alfonso Varela‐López mail , Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Juan G. Puentes mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Cristina Sánchez‐González mail , Jianbo Xiao mail , Roberto García‐Ruiz mail , Sebastián Sánchez mail , Tamara Y. Forbes‐Hernández mail , José L. Quiles mail jose.quiles@uneatlantico.es,

Romero‐Marquez

<a class="ep_document_link" href="/17570/1/eFood%20-%202025%20-%20Navarro%E2%80%90Hortal%20-%20Effects%20of%20a%20Garlic%20Hydrophilic%20Extract%20Rich%20in%20Sulfur%20Compounds%20on%20Redox%20Biology%20and.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Effects of a Garlic Hydrophilic Extract Rich in Sulfur Compounds on Redox Biology and Alzheimer's Disease Markers in Caenorhabditis Elegans

Garlic is a horticultural product highly valued for its culinary and medicinal attributes. The aim of this study was to evaluate the composition of a garlic hydrophilic extract as well as the influence on redox biology, Alzheimer's Disease (AD) markers and aging, using Caenorhabditis elegans as experimental model. The extract was rich in sulfur compounds, highlighting the presence of other compounds like phenolics, and the antioxidant property was corroborated. Regarding AD markers, the acetylcholinesterase inhibitory capacity was demonstrated in vitro. Although the extract did not modify the amyloid β-induced paralysis degree, it was able to improve, in a dose-dependent manner, some locomotive parameters affected by the hyperphosphorylated tau protein in C. elegans. It could be related to the effect found on GFP-transgenic stains, mainly regarding to the increase in the gene expression of HSP-16.2. Moreover, an initial investigation into the aging process revealed that the extract successfully inhibited the accumulation of intracellular and mitochondrial reactive oxygen species in aged worms. These results provide valuable insights into the multifaceted impact of garlic extract, particularly in the context of aging and neurodegenerative processes. This study lays a foundation for further research avenues exploring the intricate molecular mechanisms underlying garlic effects and its translation into potential therapeutic interventions for age-related neurodegenerative conditions.

Producción Científica

María D. Navarro‐Hortal mail , Jose M. Romero‐Marquez mail , Johura Ansary mail , Cristina Montalbán‐Hernández mail , Alfonso Varela‐López mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Jianbo Xiao mail , Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Cristina Sánchez‐González mail , Tamara Y. Forbes‐Hernández mail , José L. Quiles mail jose.quiles@uneatlantico.es,

Navarro‐Hortal