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A
An Analytical Framework for Innovation Determinants and Their Impact on Business Performance.
Blockchain based transparent and reliable framework for wheat crop supply chain.
Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems.
Detecting Pragmatic Ambiguity in Requirement Specification Using Novel Concept Maximum Matching Approach Based on Graph Network.
Ensemble stacked model for enhanced identification of sentiments from IMDB reviews.
A Fuzzy-Genetic-Based Integration of Renewable Energy Sources and E-Vehicles.
Hybrid Model with Wavelet Decomposition and EfficientNet for Accurate Skin Cancer Classification.
Impact of Innovation-Oriented Human Resource on Small and Medium Enterprises’ Performance.
Influence of E-learning training on the acquisition of competences in basketball coaches in Cantabria.
IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System.
Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring.
Novel model to authenticate role-based medical users for blockchain-based IoMT devices.
Novel transfer learning approach for hand drawn mathematical geometric shapes classification.
Novel transfer learning based bone fracture detection using radiographic images.
Novel transfer learning based bone fracture detection using radiographic images.
Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model.
Prickly pear fruits from "Opuntia ficus-indica" varieties as a source of potential bioactive compounds in the Mediterranean diet.
Real Word Spelling Error Detection and Correction for Urdu Language.
Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization.
Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.
Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis.
A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation.
B
Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach.
FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas.
Formal modeling and analysis of security schemes of RPL protocol using colored Petri nets.
Imperative Role of Integrating Digitalization in the Firms Finance: A Technological Perspective.
Natural Language Processing-Based Software Testing: A Systematic Literature Review.
SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations.
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.
C
Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model.
Human‐based new approach methodologies to accelerate advances in nutrition research.
An Image Retrieval Framework Design Analysis Using Saliency Structure and Color Difference Histogram.
Inequalities and Asymmetries in the Development of Angola’s Provinces: The Impact of Colonialism and Civil War.
Organizational Culture Assessment Based on a Values-Based Coaching Program for Strategic Level Employees: The Case of GEDEME, Cuba.
Possible role of nutrition in the prevention of Inflammatory Bowel Disease-related colorectal cancer: a focus on human studies.
PyDEMATEL: A Python-based tool implementing DEMATEL and fuzzy DEMATEL methods for improved decision making.
A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective.
Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques.
D
Enhancing Urban Traffic Management Through Real-Time Anomaly Detection and Load Balancing.
Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome.
A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis.
Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.
Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.
Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study.
E
A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study.
Emotional Management in Journalism and Communication Studies.
Exploring the Potential of Microservices in Internet of Things: A Systematic Review of Security and Prospects.
Fundus image classification using feature concatenation for early diagnosis of retinal disease.
Risk Factors for Eating Disorders in University Students: The RUNEAT Study.
The Scope of Technostress and Care of The Self on Journalists During the Pandemic.
F
Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives.
Client engagement solution for post implementation issues in software industry using blockchain.
Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics.
Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection.
Detection of cotton crops diseases using customized deep learning model.
DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents.
Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices.
Image Watermarking Using Least Significant Bit and Canny Edge Detection.
IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition.
Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms.
Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight.
An enhanced approach for predicting air pollution using quantum support vector machine.
G
Adherence to the Mediterranean-Style Eating Pattern and Macular Degeneration: A Systematic Review of Observational Studies.
Approach to a Project Framework in the Environment of Sustainability and Corporate Social Responsibility (CSR): Case Study of a Training Proposal to a Group of Students in a Higher Education Institution.
Chronotype and Cancer: Emerging Relation Between Chrononutrition and Oncology from Human Studies.
Development Agencies and Local Governments—Coexistence within the Same Territory.
Diet, Eating Habits, and Lifestyle Factors Associated with Adequate Sleep Duration in Children and Adolescents Living in 5 Mediterranean Countries: The DELICIOUS Project.
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.
Exercise Addiction and Perfectionism, Joint in the Same Path? A Systematic Review.
Flavan-3-ols and Vascular Health: Clinical Evidence and Mechanisms of Action.
Imperative Role of Automation and Wireless Technologies in Aquaponics Farming.
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.
Integration of Sustainable Criteria in the Development of a Proposal for an Online Postgraduate Program in the Projects Area.
Mediterranean Diet and Quality of Life in Adults: A Systematic Review.
Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence.
Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults.
“Oh, My God! My Season Is Over!” COVID-19 and Regulation of the Psychological Response in Spanish High-Performance Athletes.
Resveratrol and vascular health: evidence from clinical studies and mechanisms of actions related to its metabolites produced by gut microbiota.
Towards Security Mechanism in D2D Wireless Communication: A 5G Network Approach.
A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment.
WPAN and IoT Enabled Automation to Authenticate Ignition of Vehicle in Perspective of Smart Cities.
H
Analysis of mobile apps for information, prevention and monitoring of covid-19 and proposal of an innovative app in this field.
Contextual Urdu Lemmatization Using Recurrent Neural Network Models.
Descriptive Analysis of Mobile Apps for Management of COVID-19 in Spain and Development of an Innovate App in that field.
Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review.
PRUS: Product Recommender System Based on User Specifications and Customers Reviews.
Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models.
I
Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction.
Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.
Investigation of structural frustration in symmetric diblock copolymers confined in polar discs through cell dynamic simulation.
Virtual histopathology methods in medical imaging - a systematic review.
A deep learning approach to optimize remaining useful life prediction for Li-ion batteries.
J
Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential.
Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification.
A Novel Large-Scale Stochastic Pushback Design Merged with a Minimum Cut Algorithm for Open Pit Mine Production Scheduling.
K
Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants.
Correction: Prediction of leukemia peptides using convolutional neural network and protein compositions.
Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data.
Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization.
Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review.
An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field.
Model Driven Approach for Efficient Flood Disaster Management with Meta Model Support.
Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation.
Prediction of leukemia peptides using convolutional neural network and protein compositions.
A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions.
A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis.
Underwater Thermal Energy Harvesting: Frameworks, Challenges, Applications, and Future Investigation.
A deep learning approach for Named Entity Recognition in Urdu language.
L
Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.
Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.
Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved?
Measurement of chest muscle mass in COVID-19 patients on mechanical ventilation using tomography.
M
Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix.
Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.
Efficient deep learning-based approach for malaria detection using red blood cell smears.
MLS Pedagogy, Culture and Innovation.
Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network.
Prehospital qSOFA, mSOFA, and NEWS2 performance for sepsis prediction: A prospective, multi-center, cohort study.
Pupilometer efficacy in monitoring anxiety in undergraduate medical students during high-fidelity clinical simulation.
Threatening URDU Language Detection from Tweets Using Machine Learning.
P
Aplicación del método continuo variable en la planificación de las clases de bailoterapia para el mejoramiento de la resistencia de las participantes de la parroquia "grl. Pedro J. Montero" del cantón Yaguachi, Ecuador.
Effects of caloric restriction on immunosurveillance, microbiota and cancer cell phenotype: Possible implications for cancer treatment.
Metaheuristic-based optimal energy assessment of hybrid multi-effect evaporator with synergy of solar and wind energy sources.
Smart Fault Monitoring and Normalizing of a Power Distribution System Using IoT.
Wastewater Treatment with Technical Intervention Inclination towards Smart Cities.
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Emotion Detection Using Facial Expression Involving Occlusions and Tilt.
R
Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis.
Advancement in medical report generation: current practices, challenges, and future directions.
Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach.
An Artificial Neural Network Model for Water Quality and Water Consumption Prediction.
Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble.
Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis.
Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model.
Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.
Forecasting of Post-Graduate Students’ Late Dropout Based on the Optimal Probability Threshold Adjustment Technique for Imbalanced Data.
From by-products to new application opportunities: the enhancement of the leaves deriving from the fruit plants for new potential healthy products.
Lifestyle Factors Associated with Children’s and Adolescents’ Adherence to the Mediterranean Diet Living in Mediterranean Countries: The DELICIOUS Project.
Molecular Mechanisms of the Protective Effects of Olive Leaf Polyphenols against Alzheimer’s Disease.
Prediction β-Thalassemia carriers using complete blood count features.
Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data.
Software Cost and Effort Estimation: Current Approaches and Future Trends.
Ventilator pressure prediction employing voting regressor with time series data of patient breaths.
White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.
An improved deep convolutional neural network-based YouTube video classification using textual features.
S
The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment.
An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features.
An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation.
Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning.
A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices.
Carotenoids Intake and Cardiovascular Prevention: A Systematic Review.
A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.
Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance.
Design and Development of Smart Weight Measurement, Lateral Turning and Transfer Bedding for Unconscious Patients in Pandemics.
Digital competencies: perceptions of primary school teachers pursuing master’s degrees from eight African countries.
An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.
Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence.
Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning.
Evolving epidemiology, clinical features, and genotyping of dengue outbreaks in Bangladesh, 2000–2024: a systematic review.
Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.
Molecular epidemiology, transmission and clinical features of 2022‐mpox outbreak: A systematic review.
Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN.
Portfólio digital docente para o desenvolvimento do aprendizado reflexivo.
Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis.
Prevalence and impact of long COVID-19 among patients with diabetes and cardiovascular diseases in Bangladesh.
A Review of Image Processing Techniques for Deepfakes.
Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety.
A Systematic Review and Meta-Analysis of Premenstrual Syndrome with Special Emphasis on Herbal Medicine and Nutritional Supplements.
Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape.
Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19.
Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.
Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches.
T
Exploring factors influencing the severity of pregnancy anemia in India: a study using proportional odds model.
Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.
U
Analyzing patients satisfaction level for medical services using twitter data.
V
Application of the Gaussian Model for Monitoring Scenarios and Estimation of SO2 Atmospheric Emissions in the Salamanca Area, Bajío, Mexico.
Detection of Upper Limb Asymmetries in Athletes According to the Stage of the Season—A Longitudinal Study.
IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0.
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.
Y
An Enhanced Feed-Forward Back Propagation Levenberg–Marquardt Algorithm for Suspended Sediment Yield Modeling.
Z
Exploring body composition and somatotype profiles among youth professional soccer players.
Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective.
<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.
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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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.
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.
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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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.
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 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" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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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.
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