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Artículo

A 50 Hz magnetic field influences the viability of breast cancer cells 96 h after exposure.

The Absence of NLRP3-inflammasome Modulates Hepatic Fibrosis Progression, Lipid Metabolism, and Inflammation in KO NLRP3 Mice during Aging.

Accuracy of plasma Aβ40, Aβ42, and p-tau181 to detect CSF Alzheimer’s pathological changes in cognitively unimpaired subjects using the Lumipulse automated platform.

Este estudio fue dirigido a hallar factores motivacionales y ambientales (clima organizacional) que exacerban conductas de acoso hacia el maestro en dos municipios del área sur de Puerto Rico. Se indagó desde la perspectiva del maestro cómo el Departamento de Educación de Puerto Rico maneja las situaciones de acoso laboral y en los casos donde existe mobbing cómo ha afectado el rendimiento laboral del maestro en estos municipios. Se utilizó una muestra representativa de 202 maestros del sistema de educación pública de Puerto Rico. Se realizó un muestreo no probabilístico de tipo intencional. Se usó un cuestionario creado y utilizado por Del Álamo (2006) llamado: (A) Cuestionario del Álamo sobre Maltrato Laboral. (B) El segundo instrumento utilizado fue el Cuestionario de Clima Organizacional obtenido del Departamento de Salud de Lima, Perú (Ugarte et al., 2009). Los objetivos que enmarcaron esta investigación y que sirvieron como marco de referencia para el desarrollo de la tesis fueron logrados. Los resultados y las conclusiones nos muestran que: i) los maestros afectados por el acoso tenderán a percibir que su rendimiento laboral disminuye; ii) el clima organizacional negativo o tenso hace que sea mayor la predisposición al acoso y que el rendimiento laboral también disminuya; iii) no existe un protocolo formal en el Departamento de Educación para manejar situaciones de acoso; iv) los maestros reflejaron una proporción equivalente entre someterse o enfrentarse al acosador; v) la mayor cantidad de participantes identificaron al acosador como su jefe inmediato; vi) las consecuencias del acoso psicológico en el trabajo se manifiestan a nivel físico (trastornos cardiovasculares) y psicológico (estrés y depresión). Las recomendaciones del estudio son las siguientes: asesoramiento legal para los maestros y orientación a directores escolares. Se espera que se continúen nuevas investigaciones sobre el tema y que se apruebe la Ley en Puerto Rico.

Acoso psicológico laboral (Mobbing) y su impacto en el clima y desempeño laboral en maestros del sistema público de enseñanza del área sur de Puerto Rico.

Actividad física y calidad de vida de adultos mayores en Argentina: un estudio transversal (Physical activity and quality of life in Argentinian older adults: a cross-sectional study).

Acute muscle fatigue and CPR quality assisted by visual feedback devices: A randomized-crossover simulation trial.

Adalbert STIFTER, «Firme esperanza».

Los cuentos se ambientan en un trasfondo cristiano con el fin de provocar cierta catarsis a través de los contextos que plantean: por un lado, una pequeña niña enferma que se halla postrada en la cama y, por otro, una madre que llora la pérdida prematura de su joven hija.A través de estos cuentos, Stifter retrata diferentes actitudes propias de la naturaleza humana ante las dificultades terrenales. En «La Misericordia» se puede identificar la reacción infantil de una niña, agobiada por haber pecado y recelosa de Dios y de su virtud del perdón; asimismo, en «Muerte de una joven», se manifiesta el desgarro de una madre por la pérdida de un ser querido y la falta de esperanza en la vida eterna, consecuencia de la obnubilación tras haberse dejado dominar por el dolor.Igualmente, la actitud de Dios se evidencia en ambos cuentos por medio de sus actos: en el primero, se demuestra cómo Dios escucha y cuida de todos, además del hecho de que, para Él, la persona vale por encima de todo, ya que perdona a la niña sus pecados y la sana de su enfermedad. Del mismo modo, en el segundo cuento, Dios explica a través del ángel la necesidad de tener esperanza en la vida eterna, pues se lleva a cada alma en el momento en el que la encuentra más madura y la bendice con la vida eterna, libre de los castigos y sufrimientos propios del mundo terrenal. Por tanto, en el cuento se concibe la muerte como un acto de misericordia de Dios, pues «después de morir, recibe en su alma inmortal su retribución eterna». (Catecismo de la Iglesia Católica, art. 12: 1021). Por último, cabe destacar que, mediante el retrato de dichas actitudes, Stifter describe el mundo ideal en el que muestra cuáles serían las actitudes esperadas ante estas situaciones y lo contrasta con su vida personal llena de frustración ya que, como se señaló anteriormente él, a diferencia de los cuentos, no tuvo un final feliz.

Adalbert STIFTER, «La misericordia» y «Muerte de una joven».

Adaptación del Empathy Quotient (EQ) en una muestra española.

Adaptation and Validation of the 3 × 2 Achievement Goals Questionnaire in a Population of Athletes.

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

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis.

Adherence to the Mediterranean Diet Is Inversely Associated with the Prevalence of Metabolic Syndrome in Older People from the North of Spain.

Adherence to the Mediterranean-Style Eating Pattern and Macular Degeneration: A Systematic Review of Observational Studies.

Adherence to the healthy eating guide issued by the Sociedad Española de Nutrición Comunitaria (SENC) (2018) among non-institutionalized elderly in Santander, Spain.

Adjuvant treatment with an oleuropein-enriched olive leaf extract improves periodontal outcomes in older adults with periodontitis: Metabolomic insights from a randomized controlled trial.

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

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

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

Advancing fake news combating using machine learning: a hybrid model approach.

Aging, age-related diseases, oxidative stress and plant polyphenols: Is this a true relationship?

Excess alcohol consumption is known to be detrimental to human health. However, the role of light-to-moderate alcohol intake is under investigation for potential certain health benefits—mostly related to the cardiovascular system. Nevertheless, there is no univocal agreement on this matter, and research is still ongoing to clarify whether there might be other potential outcomes affected by alcohol intake. In this regard, there is evidence that excess alcohol intake may negatively influence the risk of osteoporotic fractures. However, there is no comprehensive evidence of literature assessing the role of alcohol consumption in bone mineral density (BMD) and the risk of osteoporotic fractures. Thus, the aim of this study was to quantitatively assess the dose–response relationship between alcohol intake and BMD and risk of osteoporotic fractures. The Embase and MEDLINE electronic databases were searched from their inception to December 2021 for articles providing a quantifiable measurement of alcohol consumption for at least three categories and (1) a measurement of BMD (and dispersion as continuous variables) in some area of the body or (2) risk of osteoporotic fracture provided as relative risk (RR) or hazard ratio (HR), with a 95% confidence interval (CI) as the measure of the association of each category with alcohol intake. A total of 11 studies including 46,916 individuals with BMD assessment and 8 studies including 240,871 individuals with risk of fracture analysis were included. Compared to non-drinkers, consumption of up to two standard drinks of alcohol per day was correlated with higher lumbar and femur neck BMD values, while up to one standard drink of alcohol was correlated with higher hip BMD compared to no alcohol consumption. Higher risk of hip fractures was found starting from three standard drinks of alcohol per day (RR = 1.33, 95% CI: 1.04; 1.69 for three alcoholic drinks/d, and RR = 1.59, 95% CI: 1.23; 2.05 for four alcoholic drinks/d) compared to no alcohol consumption, with no evidence of heterogeneity. Concerning the risk of any osteoporotic fractures, the risk steadily increased with higher intake of alcohol, although never reaching statistical significance. In conclusion, there is consistent evidence that increased alcohol consumption is associated with higher risk of osteoporotic hip fracture; however, the role of alcohol at lower doses is uncertain, as BMD was even higher in light drinkers compared to abstainers.

Alcohol Consumption, Bone Mineral Density, and Risk of Osteoporotic Fractures: A Dose–Response Meta-Analysis.

Amyloid β-but not Tau-induced neurotoxicity is suppressed by Manuka honey via HSP-16.2 and SKN-1/Nrf2 pathways in an in vivo model of Alzheimer's disease.

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

Analysis of congested schedule on competition external load in field hockey. [Análisis de la carga externa de competición en un periodo de congestión en hockey hierba].

Analysis of english for specific purposes materials: sports sciences and psychology ESP materials in the ELF classroom.

Background: To address the current pandemic, multiple studies have focused on the development of new mHealth applications to help curb the number of infections, these applications aim to accelerate the identification and self-isolation of people exposed to SARS-CoV- 2, the coronavirus known to cause COVID-19, by being in close contact with infected individuals. Objective: The main objectives of this paper are: 1)To analyze the current status of COVID-19 apps available the main virtual stores: Google Play Store and App Store, and 2)To propose a novel mobile application based on the limitations of the analyzed apps. Methods: The search for apps in this research was carried out in the main virtual stores: Google Play Store and App Store, until May 2021. After the analysis of the selected apps, a novel app is proposed whose main function will be the multiple transmission of information about the patient's symptoms from the application, without the need for phone calls or chat in real time. For its development, the flowchart shown in this session is followed. Results: The search yielded a total of 50 apps, of which 24 were relevant to this study. It is important to note that 23 of the apps analyzed are free. Of the total number of apps, 54% are available for Android and iOS operating systems. 50% of the apps have more than 5 thousand downloads. This means that Covid-19 related apps are in high demand among mobile device users today. The developed app is called COVINFO and its name comes from the union of the words COVID-19 and information, inserted in such a way that the user can get an idea of the app's functionality just by listening or reading the resulting name. The application has been created for mobile devices with Android operating system, being compatible with Android 4.4 and higher. Conclusions: Of the apps found, 37.5% only offer information about the virus and the necessary measures to avoid infection. During the analysis it was detected that 12.5% of the apps are focused on locating outbreaks and that none of them have been successful for the following reasons: not being interconnected to share data; and the request for access to the user's geolocation, generating distrust on the part of the user who, consequently, rejects them. This work addresses the development of an application for the transmission of the user's symptoms to his regular doctor, based on the fact that only 16.6% of the existing applications have this functionality. The COVINFO app offers a service that no other application on the market has: doctor-patient interaction without the need for calls or chat in real time for constant monitoring by the doctor of the patient's condition and evolution.

Analysis of mobile apps for information, prevention and monitoring of covid-19 and proposal of an innovative app in this field.

Analysis of urinary cathepsin C for diagnosing Papillon-Lefèvre syndrome.

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

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

Public concern regarding health systems has experienced a rapid surge during the last two years due to the COVID-19 outbreak. Accordingly, medical professionals and health-related institutions reach out to patients and seek feedback to analyze, monitor, and uplift medical services. Such views and perceptions are often shared on social media platforms like Facebook, Instagram, Twitter, etc. Twitter is the most popular and commonly used by the researcher as an online platform for instant access to real-time news, opinions, and discussion. Its trending hashtags (#) and viral content make it an ideal hub for monitoring public opinion on a variety of topics. The tweets are extracted using three hashtags #healthcare, #healthcare services, and #medical facilities. Also, location and tweet sentiment analysis are considered in this study. Several recent studies deployed Twitter datasets using ML and DL models, but the results show lower accuracy. In addition, the studies did not perform extensive comparative analysis and lack validation. This study addresses two research questions: first, what are the sentiments of people toward medical services worldwide? and second, how effective are the machine learning and deep learning approaches for the classification of sentiment on healthcare tweets? Experiments are performed using several well-known machine learning models including support vector machine, logistic regression, Gaussian naive Bayes, extra tree classifier, k nearest neighbor, random forest, decision tree, and AdaBoost. In addition, this study proposes a transfer learning-based LSTM-ETC model that effectively predicts the customer’s satisfaction level from the healthcare dataset. Results indicate that despite the best performance by the ETC model with an 0.88 accuracy score, the proposed model outperforms with a 0.95 accuracy score. Predominantly, the people are happy about the provided medical services as the ratio of the positive sentiments is substantially higher than the negative sentiments. The sentiments, either positive or negative, play a crucial role in making important decisions through customer feedback and enhancing quality.

Analyzing patients satisfaction level for medical services using twitter data.

Analyzing preventive precautions to limit spread of COVID-19.

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

Anthropometric and conditional profile in semiprofessional futsal players: differences between sexes. A case study. [Perfil antropométrico y condicional en jugadores semiprofesionales de futbol sala: diferencias entre sexos. Un estudio de caso].

Anti-inflammatory activities of Italian Chestnut and Eucalyptus honeys on murine RAW 264.7 macrophages.

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

Antimicrobial Resistance Determinants in Genomes and Plasmids from Acinetobacter baumannii Clinical Isolates.

Antimicrobial resistant enteric bacteria are widely distributed among environmental water sources in Dhaka, Bangladesh.

Antimicrobial-resistant Enterobacter cloacae complex strains isolated from fresh vegetables intended for raw consumption and their farm environments in the Northwest of Spain.

Análisis comparativo de dos propuestas de entrenamiento concurrente en la condición física en mujeres adultas con sobrepeso.

Análisis de las preferencias metodológicas del profesorado gallego de Educación Física en función del género y los años de experiencia docente.

Análisis y mejores prácticas proyectuales de una obra civil hidroeléctrica de Honduras.

Aplicación de contra-inteligencia empresarial: análisis sobre la protección contra el espionaje cibernético en las organizaciones.

Aplicación de corriente transcraneal directa como terapia no invasiva en trastornos de la conducta alimentaria: una propuesta de intervención.

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

Application of the Gaussian Model for Monitoring Scenarios and Estimation of SO2 Atmospheric Emissions in the Salamanca Area, Bajío, Mexico.

Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage.

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

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.

Behavioral economics and artificial intelligence (AI) have been two rapidly growing fields of research over the past few years. While behavioral economics aims to combine concepts from psychology, sociology, and neuroscience with classical economic thoughts to understand human decision-making processes in the complex economic environment, AI on the other hand, focuses on creating intelligent machines that can mimic human cognitive abilities such as learning, problem-solving, decision-making, and language understanding. The intersection of these two fields has led to thrilling research theories and practical applications. This study provides a bibliometric analysis of the literature on AI and behavioral economics to gain insight into research trends in this field. We conducted this bibliometric analysis using the Web of Science database on articles published between 2012 and 2022 that were related to AI and behavioral economics. VOSviewer and Bibliometrix R package were utilized to identify influential authors, journals, institutions, and countries in the field. Network analysis was also performed to identify the main research themes and their interrelationships. The analysis revealed that the number of publications on AI and behavioral economics has been increasing steadily over the past decade. We found that most studies focused on customer and consumer behavior, including topics such as decision-making under uncertainty, neuroeconomics, and behavioral game theory, combined mainly with machine learning and deep learning techniques. We also identified several emerging themes, including the use of AI in nudging and prospect theory in behavioral finance, as well as undeveloped themes such as AI-driven behavioral macroeconomics. The findings suggests that there is a need for more interdisciplinary collaboration between researchers in behavioral economics and AI. We also suggest that future research on AI and behavioral economics further consider the ethical implications of using AI and behavioral insights in decision-making. This study can serve as a valuable resource for researchers interested in AI and behavioral economics.

Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field.

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

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

Assessment Of Lower Limb Asymmetries In Soccer Players According To The Stage Of The Season.

Assessment of the external load of amateur soccer players during four consecutive training microcycles in relation to the external load during the official match.

Association between blood cortisol levels and numerical rating scale in prehospital pain assessment.

Association between diagnostic imaging and biochemical markers: a possible tool for monitoring metabolic disorders.

Association of planetary health diet indices with diet composition, nutritional quality and environmental impacts in Italian adults.

Associations between Sleep, Cortisol Regulation, and Diet: Possible Implications for the Risk of Alzheimer Disease.

Atletismo, rugby y fútbol: valoración de la motivación y autocompasión a lo largo de la temporada.

With the growing academic pressure and competitive educational environment, students often face mental stress, which can affect their academic performance and mental health. Its accurate and timely detection and prevention is important. Traditionally, mental stress has been reported by self-assessment, which is highly subjective and can be erroneous. With advances in neuroscience, electroencephalogram (EEG) signals have been used to study brain states more objectively. EEG-based features, including time-domain, frequency-domain, and various types of connectivity features, have been used to effectively classify stress signals. However, these individual features are only able to present one aspect of the brain under stress. Several studies have combined a distinct set of features extracted from EEG signals, including time and frequency domain features, with other peripheral signals. Stress is a complex mechanism which leads to alternation in brain dynamics, its connectivity patterns and information flow. This study proposed a feature-fusion model that can effectively combine spatial features, i.e. Microstates (MS), connectivity features like Transfer Entropy (TE) and Granger Causality (GC), which provided a new neuromarker for stress classification. These features are combined with attention fusion, which enhances the discriminant features and mitigates the individual limitations within each modality. We also extracted microstates for stress-based signals. It provided a new set of microstate topomaps to study brain networks when under stress, which was not explored previously. The proposed Attention-fusion based multi-feature set is classified using Support Vector Machine, Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) and gave a reliable accuracy of 95.47%, 98.91%, and 83.49%, respectively. To validate the proposed method, the classification results were compared with individual and binary fusion of MS, TE and GC features, which further confirmed the robustness of the framework. This proposed feature fusion provides a more robust stress classification neuromarker, which can effectively cover the brain dynamics for accurate reporting of the underlying mental state.

Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners.

Attitudes and Skills in Basic Life Support after Two Types of Training: Traditional vs. Gamification, of Compulsory Secondary Education Students: A Simulation Study.

Avelumab maintenance in advanced urothelial carcinoma: real-world data from Northern Spain (AVEBLADDER study).

Bee Products: An Emblematic Example of Underutilized Sources of Bioactive Compounds.

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

Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256 256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.

Benchmarking multiple instance learning architectures from patches to pathology for prostate cancer detection and grading using attention-based weak supervision.

Beneficios del ejercicio físico en mujeres diagnosticadas de cáncer de seno invasivo. Una revisión sistemática.

Bienestar psicológico, inteligencia emocional y resolución de conflictos en miembros de los Cuerpos y Fuerzas de Seguridad del Estado español: un estudio correlacional.

Big Data Analysis Framework for Water Quality Indicators with Assimilation of IoT and ML.

Tagetes erecta is an edible flower deeply rooted in traditional Mexican culture. It holds a central role in the most popular and iconic Mexican celebration, “the Day of the Dead”. Furthermore, it is currently receiving interest as a potential therapeutic agent, motivated mainly by its polyphenol content. The present study aims to evaluate the biological activity of an extract synthesized from the petals of the edible flower T. erecta. This extract showed significant antioxidant scores measured by the most common in vitro methodologies (FRAP, ABTS, and DPPH), with values of 1475.3 μM trolox/g extr, 1950.3 μM trolox/g extr, and 977.7 μM trolox/g extr, respectively. In addition, up to 36 individual polyphenols were identified by chromatography. Regarding the biomedical aspects of the petal extract, it exhibited antitumoral activity against ovarian carcinoma cells evaluated by the MTS assay, revealing a lower value of IC50 compared to other flower extracts. For example, the extract from T. erecta reported an IC50 value half as low as an extract from Rosa × hybrida and six times lower than another extract from Tulbaghia violacea. This antitumoral effect of T. erecta arises from the induction of the apoptotic process; thus, incubating ovarian carcinoma cells with the petal extract increased the rate of apoptotic cells measured by flow cytometry. Moreover, the extract also demonstrated efficacy as a therapeutic agent against tauopathy, a feature of Alzheimer’s disease (AD) in the Caenorhabditis elegans experimental model. Treating worms with the experimental extract prevented disfunction in several motility parameters such as wavelength and swimming speed. Furthermore, the T. erecta petal extract prevented the release of Reactive Oxygen Species (ROS), which are associated with the progression of AD. Thus, treatment with the extract resulted in an approximate 20% reduction in ROS production. These findings suggest that these petals could serve as a suitable source of polyphenols for biomedical applications.

Bioactive Properties of Tagetes erecta Edible Flowers: Polyphenol and Antioxidant Characterization and Therapeutic Activity against Ovarian Tumoral Cells and Caenorhabditis elegans Tauopathy.

Blockchain Enabled Transparent and Anti-Counterfeiting Supply of COVID-19 Vaccine Vials.

Blockchain Interoperability: Towards a Sustainable Payment System.

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

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

Botnet detection in internet of things using stacked ensemble learning model.

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

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

A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices.

Introduction: Jackfruit cultivation is highly affected by leaf diseases that reduce yield, fruit quality, and farmer income. Early diagnosis remains challenging due to the limitations of manual inspection and the lack of automated and scalable disease detection systems. Existing deep-learning approaches often suffer from limited generalization and high computational cost, restricting real-time field deployment. Methods: This study proposes CNNAttLSTM, a hybrid deep-learning architecture integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) units, and an attention mechanism for multi-class classification of algal leaf spot, black spot, and healthy jackfruit leaves. Each image is divided into ordered 56×56 spatial patches, treated as pseudo-temporal sequences to enable the LSTM to capture contextual dependencies across different leaf regions. Spatial features are extracted via Conv2D, MaxPooling, and GlobalAveragePooling layers; temporal modeling is performed by LSTM units; and an attention mechanism assigns adaptive weights to emphasize disease-relevant regions. Experiments were conducted on a publicly available Kaggle dataset comprising 38,019 images, using predefined training, validation, and testing splits. Results: The proposed CNNAttLSTM model achieved 99% classification accuracy, outperforming the baseline CNN (86%) and CNN–LSTM (98%) models. It required only 3.7 million parameters, trained in 45 minutes on an NVIDIA Tesla T4 GPU, and achieved an inference time of 22 milliseconds per image, demonstrating high computational efficiency. The patch-based pseudo-temporal approach improved spatial–temporal feature representation, enabling the model to distinguish subtle differences between visually similar disease classes. Discussion: Results show that combining spatial feature extraction with temporal modeling and attention significantly enhances robustness and classification performance in plant disease detection. The lightweight design enables real-time and edge-device deployment, addressing a major limitation of existing deep-learning techniques. The findings highlight the potential of CNNAttLSTM for scalable, efficient, and accurate agricultural disease monitoring and broader precision agriculture applications.

CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification.

Can Women Maintain Their Strength Performance Along the Menstrual Cycle?

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

Can the Functional Physical Fitness of Older People with Overweight or Obesity Be Improved through a Multicomponent Physical Exercise Program? A Chilean Population Study.

Caracterización de sistemas operacionales móviles celular: Android, Symbian, iphone y Windows phone.

Background: The cardiac parasympathetic reactivation is currently used in soccer with a daily or weekly monitoring. However, previous studies have not investigated how this cardiac parasympathetic reactivation is in elite soccer players along different types of traditional high-intensity training exercise and specific tests. In this context, the present study aim to analyse it and to determine the interests and limits of this type of physiological information. Objectives: The present study aims to examine how different traditional training exercise modes affect the cardiac parasympathetic reactivation function in elite soccer players. Materials and Methods: Twenty-two international soccer players participating in UEFA Champion’s League took part in this study (age: 24.3 ± 4.2 years; height: 178.1 ± 6.2 cm; body mass: 80.3 ± 5.7 kg). Players performed different training methods including: short-duration intermittent exercises (INT) in-line and with changes of direction (COD) (10 - 10 seconds, 15 - 15 seconds, 30 - 30 seconds, e.g. an alternance of 10 - 10 seconds is 10 seconds of running according to the maximal aerobic speed (MAS) and 10-sec of recovery), INT including agility and technical skills (8 - 24-seconds), small-sided-games (SSGs) with and without goalkeepers (2 vs. 2, 3 vs. 3, 4 vs. 4), and repeated sprint ability (RSA) efforts (10 × 20 m, 10 × 30 m, 15 × 20 m). Heart rate (HR) decline was recorded 3 minutes after each exercise. Results: HR declines were greater after the RSA compared to SSGs (P < 0.001) and INT (P < 0.01), especially at 1 min post-exercise. In addition, when the analysis focused on each type of exercise, greater HR declines were observed in on-field players at 1 minute when there was: inclusion of goalkeepers in SSGs (for 2 vs. 2 and 3 vs. 3, P < 0.01); increase of sprint distances or number of sprint repetitions in RSA (P < 0.01); increase of intensity (% of maximal aerobic speed), and the use of COD or inclusion of technical skills during INT, especially for the 30 - 30-seconds. Conclusions: This study revealed that cardiac parasympathetic reactivation function varied after INT, RSA and SSG, but also according to the rules manipulation. Therefore, this study provides interesting information for the training monitoring and players’ recovery profile, with the aim of facilitating a more efficient planning and manipulation of training recovery strategies according to their fitness markers.

Cardiac Parasympathetic Reactivation in Elite Soccer Players During Different Types of Traditional High-Intensity Training Exercise Modes and Specific Tests: Interests and Limits.

Carotenoids Intake and Cardiovascular Prevention: A Systematic Review.

Central and Peripheral Fatigue in Recreational Trail Runners: A Pilot Study.

Changes in Motor Competence after a Brief Physical Education Intervention Program in 4 and 5-Year-Old Preschool Children.

Changes in the Lifestyle of the Spanish University Population during Confinement for COVID-19.

Characterization and Comparison of Raw Brassica and Grass Field Sensorial and Nutritional Quality.

Characterization of Phenolic Compounds in Extra Virgin Olive Oil from Granada (Spain) and Evaluation of Its Neuroprotective Action.

Chemopreventive and Therapeutic Effects of Edible Berries: A Focus on Colon Cancer Prevention and Treatment.

Background: Physical activity in children and adolescents represents one of the most important lifestyle factors to determine current and future health. Aim: The aim of the study is to assess the lifestyle and dietary factors linked to physical activity in younger populations across five countries in the Mediterranean region. Design: A total of 2,011 parents of children and adolescents (age range 6–17 years) participating to a preliminary survey of the DELICIOUS project were investigated to determine children's adequate physical activity level (identified using the short form of the international physical activity questionnaire) as well as diet quality parameters [measured as Youth-Healthy Eating Index (Y-HEI)] and eating and lifestyle factors (i.e., meal habits, sleep duration, screen time, etc.). Logistic regression analyses were performed to assess the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between variables of interest. Results: Younger children of younger parents currently working had higher rates and probability to have adequate physical activity. Multivariate analysis showed that children and adolescents who had breakfast (OR = 1.88, 95% CI: 1.38, 2.56) and often ate with their family (OR = 1.80, 95% CI: 0.90, 3.61) were more likely to have an adequate level of physical activity. Children and adolescents who reported a sleep duration (8–10 h) closest to the recommended one were significantly more likely to achieve adequate levels of physical activity (OR = 1.88, 95% CI: 1.38, 2.56). Conversely, those with more than 4 h of daily screen time were less likely to engage in adequate physical activity (OR = 0.77, 95% CI: 0.54, 1.10). Furthermore, children and adolescents in the highest tertile of YEHI scores showed a 60% greater likelihood of engaging in adequate physical activity (OR = 1.60, 95% CI: 1.27, 2.01). Conclusion: These results emphasize the importance of promoting healthy diet and lifestyle habits, including structured and high quality shared meals, sufficient sleep, and screen time moderation, as key strategies to support active behaviors in younger populations. Future interventions should focus on reinforcing these behaviors through parental guidance and community-based initiatives to foster lifelong healthy habits.

Children's and adolescents' lifestyle factors associated with physical activity in five Mediterranean countries: the DELICIOUS project.

Chronotype and Cancer: Emerging Relation Between Chrononutrition and Oncology from Human Studies.

Cidadania digital e EJA: análise qualitativa da ausência de educação midiática em contextos periféricos.

Clasificación y pronóstico del nivel de satisfacción de egresados de programas de salud en el contexto de una metodología de aprendizaje automático: un análisis de caso orientado a posgrados online de una institución educativa iberoamericana.

In the rapidly advanced and evolving information technology industry, adequate client engagement plays a critical role as it is very important to understand the client’s concerns, and requirements, have the records, authorizations, and go-ahead of previously agreed requirements, and provide the feasible solution accordingly. Previously multiple solutions have been proposed to enhance the efficiency of client engagement, but they lack traceability, trust, transparency, and conflict in agreements of previous contracts. Due to the lack of these shortcomings, the client requirement is getting delayed which is causing client escalations, integrity issues, project failure, and penalties. In this study, we proposed the UniferCollab framework to overcome the issues of collaboration between various teams, transparency, the record of client authorizations, and the go-ahead on previous developments by implementing blockchain technology. We store the data on the permissible network in the proposed approach. It allows us to compile all the requirements and information shared by clients on permissible blockchain to secure a large amount of data which enhances the traceability of all the requirements. All the authorizations from the client generate push notifications for any changes in their current system executed through smart contracts. It removes the ambiguity between various development teams if the client has only shared the requirement with one team. The data is stored in the decentralized network from where information is gathered which resolves the traceability, transparency, and trust issues. Lastly, evaluations involved a total of 800 hypertext transfer protocol (HTTP) requests tested using Postman with blockchain block sizes ranging from 0.568 KB to 550 KB and an average size increase of 280 KB was observed as new blocks were added. The longest chain in the network was observed during 800 repetitions of blockchain operations. Latency analysis revealed that delays in processing HTTP requests were influenced by decentralized node processing, local machine response times, and internet bandwidth through various experiments. Results show that the proposed framework resolves all client engagement issues in implementation between all stakeholders which enhances trust, and transparency improves client experience and helps us manage disputes effectively.

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

Clinical epidemiology of dengue and COVID-19 co-infection among the residents in Dhaka, Bangladesh, 2021-2023: A cross-sectional study.

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

Coaxial Synthesis of PEI-Based Nanocarriers of Encapsulated RNA-Therapeutics to Specifically Target Muscle Cells.

Coenzyme Q and Its Role in the Dietary Therapy against Aging.

Comparación de la calidad en la ventilación de socorristas nóveles y veteranos. Un estudio piloto de simulación.

Comparación de los tipos de Foam Roller evaluando su efecto agudo en el músculo recto femoral mediante tensiomiografía.

Comparative Study of the Information about the COVID-19 Pandemic and COVID-19 Vaccines on the Covers of United Kingdom, France, Spain and United States’ Main Newspapers.

A Comparison between Three Different Techniques Considering Quality Skills, Fatigue and Hand Pain during a Prolonged Infant Resuscitation: A Cross-Over Study with Lifeguards.

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

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

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

A Comprehensive Review on Techno-Economic Analysis and Optimal Sizing of Hybrid Renewable Energy Sources with Energy Storage Systems.

Concordance of a new IMU in different small-sided games and real game tasks in indoor sports.

Conocimientos, actitudes y prácticas de familias de adolescentes con discapacidad cognitiva en sexualidad y afectividad.

Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics.

Constraint of Lignin–Carbohydrate Complex Orchestrated on Polyphenol in Oil–Water Interface Targeting Ulcerative Colitis Therapy.

Contextual Urdu Lemmatization Using Recurrent Neural Network Models.

Contribución del Ejército Nacional de Colombia en el control de delitos ambientales y la restauración de ecosistemas.

Correction to: Systematic Review of Telemedicine and eHealth Systems Applied to Vascular Surgery.

Correction to: The effect of anthocyanins and anthocyanin‑rich foods on cognitive function: a meta‑analysis of randomized controlled trials.

Correction: Enhancing fault detection in new energy vehicles via novel ensemble approach.

A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud.

Several randomised controlled trials (RCT) have demonstrated the superiority of transdiagnostic group cognitive-behavioural therapy (TD-CBT) to treatment as usual (TAU) for emotional disorders in primary care. To date, however, no RCTs have been conducted to compare TD-CBT to another active intervention in this setting. Our aim is to conduct a single-blind RCT to compare group TD-CBT plus TAU to progressive muscle relaxation (PMR) plus TAU in adults (age 18 to 65 years) with a suspected emotional disorder. We expect that TD-CBT + TAU will be more cost-effective than TAU + PMR, and that these gains will be maintained at the 12-month follow-up. Seven therapy sessions (1.5 hours each) will be offered over a 24-week period. The study will be carried out at four primary care centres in Cantabria, Spain. The study will take a societal perspective. Psychological assessments will be made at three time points: baseline, post-treatment, and at 12-months. The following variables will be evaluated: clinical symptoms (anxiety, depression, and/or somatic); functioning; quality of life (QoL); cognitive-emotional factors (rumination, worry, attentional and interpretative biases, emotion regulation and meta-cognitive beliefs); and satisfaction with treatment. Data on health service use, medications, and sick days will be obtained from electronic medical records. Primary outcome measures will include: incremental cost-effectiveness ratios (ICER) and incremental cost-utility ratios (ICURs). Secondary outcome measures will include: clinical symptoms, QoL, functioning, and treatment satisfaction. Bootstrap sampling will be used to assess uncertainty of the results. Secondary moderation and mediation analyses will be conducted. Two questionnaires will be administered at sessions 1, 4, and 7 to assess therapeutic alliance and group satisfaction. If this trial is successful, widespread application of this cost-effective treatment could greatly improve access to psychological treatment for emotional disorders in the context of increasing demand for mental healthcare in primary care.

Cost-effectiveness of transdiagnostic group cognitive behavioural therapy versus group relaxation therapy for emotional disorders in primary care (PsicAP-Costs2): Protocol for a multicentre randomised controlled trial.

Creciendo en la adversidad: la resiliencia del estudiantado con discapacidad en la Universidad Nacional, Costa Rica.

Ever since coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, was declared a pandemic on March 11, 2020, by the WHO, a concerted effort has been made to find compounds capable of acting on the virus and preventing its replication. In this context, researchers have refocused part of their attention on certain natural compounds that have shown promising effects on the virus. Considering the importance of this topic in the current context, this study aimed to present a critical review and analysis of the main reports of plant-derived compounds as possible inhibitors of the two SARS-CoV-2 proteases: main protease (Mpro) and Papain-like protease (PLpro). From the search in the PubMed database, a total of 165 published articles were found that met the search patterns. A total of 590 unique molecules were identified from a total of 122 articles as potential protease inhibitors. At the same time, 114 molecules reported as natural products and with annotation of theoretical support and antiviral effects were extracted from the COVID-19 Help database. After combining the molecules extracted from articles and those obtained from the database, we identified 648 unique molecules predicted as potential inhibitors of Mpro and/or PLpro. According to our results, several of the predicted compounds with higher theoretical confidence are present in many plants used in traditional medicine and even food, such as flavonoids, carboxylic acids, phenolic acids, triterpenes, terpenes phytosterols, and triterpenoids. These are potential inhibitors of Mpro and PLpro. Although the predictions of several molecules against SARS-CoV-2 are promising, little experimental information was found regarding certain families of compounds. Only 45 out of the 648 unique molecules have experimental data validating them as inhibitors of Mpro or PLpro, with the most frequent scaffold present in these 45 compounds being the flavone. The novelty of this work lies in the analysis of the structural diversity of the chemical space among the molecules predicted as inhibitors of SARS-CoV-2 Mpro and PLpro proteases and the comparison to those molecules experimentally validated. This work emphasizes the need for experimental validation of certain families of compounds, preferentially combining classical enzymatic assays with interaction-based methods. Furthermore, we recommend checking the presence of Pan-Assay Interference Compounds (PAINS) and the presence of molecules previously reported as inhibitors of Mpro or PLpro to optimize resources and time in the discovery of new SARS-CoV-2 antivirals from plant-derived molecules.

Critical Review of Plant-Derived Compounds as Possible Inhibitors of SARS-CoV-2 Proteases: A Comparison with Experimentally Validated Molecules.

Crononutrición: efecto de la hora de la ingesta en el metabolismo de los nutrientes.

Context: Medical educators agree that empathy is essential for physicians' professionalism. The Health Professional Version of the Jefferson Scale of Empathy (JSE-HP) was developed in response to a need for a psychometrically sound instrument to measure empathy in the context of patient care. Although extensive support for its validity and reliability is available, the authors recognize the necessity to examine psychometrics of the JSE-HP in different socio-cultural contexts to assure the psychometric soundness of this instrument. The first aim of this study was to confirm its psychometric properties in the cross-cultural context of Spain and Latin American countries. The second aim was to measure the influence of social and cultural factors on the development of medical empathy in health practitioners. Methods: The original English version of the JSE-HP was translated into International Spanish using back-translation procedures. The Spanish version of the JSE-HP was administered to 896 physicians from Spain and 13 Latin American countries. Data were subjected to exploratory factor analysis using principal component analysis (PCA) with oblique rotation (promax) to allow for correlation among the resulting factors, followed by a second analysis, using confirmatory factor analysis (CFA). Two theoretical models, one based on the English JSE-HP and another on the first Spanish student version of the JSE (JSE-S), were tested. Demographic variables were compared using group comparisons. Results: A total of 715 (80%) surveys were returned fully completed. Cronbach's alpha coefficient of the JSE for the entire sample was 0.84. The psychometric properties of the Spanish JSE-HP matched those of the original English JSE-HP. However, the Spanish JSE-S model proved more appropriate than the original English model for the sample in this study. Group comparisons among physicians classified by gender, medical specialties, cultural and cross-cultural backgrounds yielded statistically significant differences (p < 0.001). Conclusions: The findings support the underlying factor structure of the Jefferson Scale of Empathy (JSE). The results reveal the importance of culture in the development of medical empathy. The cross-cultural differences described could open gates for further lines of medical education research.

Cross-Validation of the Spanish HP-Version of the Jefferson Scale of Empathy Confirmed with Some Cross-Cultural Differences.

Cross-country analysis of sustainable innovation and female entrepreneurship and their influence on the presence of women in managerial positions.

Cuantificación de especificidad en un microciclo estructurado en fútbol profesional.

Cultural factors related to childhood and adolescent obesity in Mexico: A systematic review of qualitative studies.

Curcumina en niños con trastorno del espectro autista: inflamación sistémica, regulación sensorial y trastornos de la interacción intestino-cerebro. Ensayo clínico aleatorizado, doble ciego y controlado con placebo (CURATEA).

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

Cytotoxicity and Antimicrobial Resistance of Aeromonas Strains Isolated from Fresh Produce and Irrigation Water.

Código Nutricia: nutrición y conflicto de interés en la academia.

Daily Intake of Two or More Servings of Vegetables Is Associated with a Lower Prevalence of Metabolic Syndrome in Older People.

Data on body weight and liver functionality in aged rats fed an enriched strawberry diet.

Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning.

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

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

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

Deep image features sensing with multilevel fusion for complex convolution neural networks & cross domain benchmarks.

Osteoarthritis (OA) affects nearly 240 million people worldwide. It is a common degenerative illness that typically affects the knee joint OA causes pain, and functional disability, especially in older adults is a common disease. One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis involves observing X-ray images of the knee area and classifying it into different five grades. This requires the physician's expertise, suitable experience, and a lot of time, and even after that, the diagnosis can be prone to errors. Therefore, researchers in the machine learning (ML) and deep learning (DL) domains have employed the capabilities of deep neural network (DNN) models to identify and classify medical images in an automated, faster, and more accurate manner. Combining multiple imaging modalities or utilizing three-dimensional reconstructions can enhance the accuracy and completeness of 2D Images in diagnostic information. Hence to overcome the drawbacks of 2D imaging, the reconstruction of 3D models using 2D images is the main theme of our research. In this paper, we propose a deep learning-based model for the detection and classification of the early diagnosis of arthritis. It is a four-step procedure starting with data collection followed by data conversion. In this step, our proposed model deforms the target's convex hull to produce a 3D model. Herein, a series of 2D photos is utilized, along with surface rendering methods, to create a 3D model. In the third step, the feature extraction is performed followed by mesh refinement. The chamfer loss is optimized based on the rotational shape of the leg bones, and subsequently, the weight of the loss function can be allocated to the target's geometric properties. We have used a modified Gray Level Co-occurrence Matrix (GLCM) for feature extraction. In the fourth step, the image classification is performed and the suggested optimization strategy raises the model's accuracy. A comparison of results with current 3D reconstruction techniques proves that the suggested method can consistently produce a waterproof model with a greater reconstruction accuracy. The deep-seated intricacies and distinct patterns across arthritic phases are estimated through the extraction of complicated statistical variables combined with power spectral density. The high-dimensional data is divided into separate, easily observable groups using the Lion Optimization Algorithm and proposed distance metric. The F1 Score and Jaccard Metric showed an average of 0.85 and 0.23, indicating effective differentiation across clusters.

Deep learning-assisted 3D model for the detection and classification of knee arthritis.

Deep transfer learning-based bird species classification using mel spectrogram images.

Demandas cinemáticas de competición internacional en el hockey sobre hierba femenino.

Depresión en hombres y su relación con la ideología masculina tradicional y la alexitimia.

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications, including predicting early psychological disorders and stress in individuals or the general public. A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text. This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces (APIs). A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus. Furthermore, an algorithm is developed to annotate the data into three depression classes: ‘Mild,’ ‘Moderate,’ and ‘Severe,’ based on International Classification of Diseases-10 (ICD-10) depression diagnostic criteria. Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus. Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model, which significantly increases the depression classification performance to an 84% F1 score and 90% accuracy compared to baselines. Finally, a FastText-based weighted soft voting ensemble (WSVE) is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances. The proposed WSVE outperformed all baselines as well as FastText alone, with an F1 of 89%, 5% higher than FastText alone, and an accuracy of 93%, 3% higher than FastText alone. The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances.

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

Desafios e perspectivas de educação: una visão dos professores durante a pandemia.

Desarrollando Competencias Docentes en AICLE: Experiencias y Desafíos en la Università degli Studi di Palermo.

Desarrollo de tecnologías para la reutilización sostenible del lactosuero.

Desarrollo de un bioplástico comestible y compostable a partir de residuos de la industria alimentaria.

To address the current pandemic, multiple studies have focused on the development of new mHealth apps to help in curbing the number of infections, these applications aim to accelerate the identification and self-isolation of people exposed to SARS-CoV-2, the coronavirus known to cause COVID-19, by being in close contact with infected individuals. The main objectives of this paper are: (1) Analyze the current status of COVID-19 apps available on the main virtual stores: Google Play Store and App Store for Spain, and (2) Propose a novel mobile application that allows interaction and doctor-patient follow-up without the need for real-time consultations (face-to-face or telephone). In this research, a search for eHealth and telemedicine apps related to Covid-19 was performed in the main online stores: Google Play Store and App Store, until May 2021. Keywords were entered into the search engines of the online stores and relevant apps were selected for study using a PRISMA methodology. For the design and implementation of the proposed app named COVINFO, the main weaknesses of the apps studied were taken into account in order to propose a novel and useful app for healthcare systems. The search yielded a total of 50 apps, of which 24 were relevant to this study, of which 23 are free and 54% are available for Android and iOS operating systems (OS). The proposed app has been developed for mobile devices with Android OS being compatible with Android 4.4 and higher. This app enables doctor-patient interaction and constant monitoring of the patient's progress without the need for calls, chats or face-to-face consultation in real time. This work addresses design and development of an application for the transmission of the user's symptoms to his regular doctor, based on the fact that only 16.6% of existing applications have this functionality. The COVINFO app offers a novel service: asynchronous doctor-patient communication, as well as constant monitoring of the patient’s condition and evolution. This app makes it possible to better manage the time of healthcare personnel and avoid overcrowding in hospitals, with the aim of preventing the collapse of healthcare systems and the spread of the coronavirus.

Descriptive Analysis of Mobile Apps for Management of COVID-19 in Spain and Development of an Innovate App in that field.

Patient care and convenience remain the concern of medical professionals and caregivers alike. An unconscious patient confined to a bed may develop fluid accumulation and pressure sores due to inactivity and deficiency of oxygen flow. Moreover, weight monitoring is crucial for an effective treatment plan, which is difficult to measure for bedridden patients. This paper presents the design and development of a smart and cost-effective independent system for lateral rotation, movement, weight measurement, and transporting immobile patients. Optimal dimensions and practical design specifications are determined by a survey across various hospitals. Subsequently, the proposed hoist-based weighing and turning mechanism is CAD-modeled and simulated. Later, the structural analysis is carried out to select suitable metallurgy for various sub-assemblies to ensure design reliability. After fabrication, optimization, integration, and testing procedures, the base frame is designed to mount a hydraulic motor for the actuator, a DC power source for self-sustenance, and lockable wheels for portability. The installation of a weighing scale and a hydraulic actuator is ensured to lift the patient for weight measuring up to 600 pounds or lateral turning of 80 degrees both ways. The developed system offers simple operating characteristics, allows for keeping patient weight records, and assists nurses in changing patients’ lateral positions both ways, comfortably massage patients’ backs, and transport them from one bed to another. Additionally, being lightweight offers reduced contact with the patient to increase the healthcare staff’s safety in pandemics; it is also height adjustable and portable, allowing for use with multiple-sized beds and easy transportation across the medical facility. The feedback from paramedics is encouraging regarding reducing labor-intensive nursing tasks, alleviating the discomfort of long-term bed-ridden patients, and allowing medical practitioners to suggest better treatment plans

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

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

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

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

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

Accurate diagnosis of brain tumors is critical in understanding the prognosis in terms of the type, growth rate, location, removal strategy, and overall well-being of the patients. Among different modalities used for the detection and classification of brain tumors, a computed tomography (CT) scan is often performed as an early-stage procedure for minor symptoms like headaches. Automated procedures based on artificial intelligence (AI) and machine learning (ML) methods are used to detect and classify brain tumors in Computed Tomography (CT) scan images. However, the key challenges in achieving the desired outcome are associated with the model’s complexity and generalization. To address these issues, we propose a hybrid model that extracts features from CT images using classical machine learning. Additionally, although MRI is a common modality for brain tumor diagnosis, its high cost and longer acquisition time make CT scans a more practical choice for early-stage screening and widespread clinical use. The proposed framework has different stages, including image acquisition, pre-processing, feature extraction, feature selection, and classification. The hybrid architecture combines features from ResNet50, AlexNet, LBP, HOG, and median intensity, classified using a multilayer perceptron. The selection of the relevant features in our proposed hybrid model was extracted using the SelectKBest algorithm. Thus, it optimizes the proposed model performance. In addition, the proposed model incorporates data augmentation to handle the imbalanced datasets. We employed a scoring function to extract the features. The Classification is ensured using a multilayer perceptron neural network (MLP). Unlike most existing hybrid approaches, which primarily target MRI-based brain tumor classification, our method is specifically designed for CT scan images, addressing their unique noise patterns and lower soft-tissue contrast. To the best of our knowledge, this is the first work to integrate LBP, HOG, median intensity, and deep features from both ResNet50 and AlexNet in a structured fusion pipeline for CT brain tumor classification. The proposed hybrid model is tested on data from numerous sources and achieved an accuracy of 94.82%, precision of 94.52%, specificity of 98.35%, and sensitivity of 94.76% compared to state-of-the-art models. While MRI-based models often report higher accuracies, the proposed model achieves 94.82% on CT scans, within 3–4% of leading MRI-based approaches, demonstrating strong generalization despite the modality difference. The proposed hybrid model, combining hand-crafted and deep learning features, effectively improves brain tumor detection and classification accuracy in CT scans. It has the potential for clinical application, aiding in early and accurate diagnosis. Unlike MRI, which is often time-intensive and costly, CT scans are more accessible and faster to acquire, making them suitable for early-stage screening and emergency diagnostics. This reinforces the practical and clinical value of the proposed model in real-world healthcare settings.

Detection and classification of brain tumor using a hybrid learning model in CT scan images.

Detection of Upper Limb Asymmetries in Athletes According to the Stage of the Season—A Longitudinal Study.

Detection of cotton crops diseases using customized deep learning model.

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

Development of a Short Questionnaire for the Screening for Vitamin D Deficiency in Italian Adults: The EVIDENCe-Q Project.

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

Diabetes Mellitus and Periodontitis Share Intracellular Disorders as the Main Meeting Point.

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

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

Diagnostic Accuracy of Plasma p-tau217 for Detecting Pathological Cerebrospinal Fluid Changes in Cognitively Unimpaired Subjects Using the Lumipulse Platform.

A Diet Rich in Saturated Fat and Cholesterol Aggravates the Effect of Bacterial Lipopolysaccharide on Alveolar Bone Loss in a Rabbit Model of Periodontal Disease.

Diet, Eating Habits, and Lifestyle Factors Associated with Adequate Sleep Duration in Children and Adolescents Living in 5 Mediterranean Countries: The DELICIOUS Project.

Introduction Parental diet is a key determinant of offspring health and immune function, in part through epigenetic regulation. Metabolic and epigenetic networks integrate nutrient sensing with chromatin dynamics to maintain cellular and organismal homeostasis. However, the mechanism by which specific dietary bioactive compounds reshape metabolic-epigenetic networks to drive transgenerational adaptive responses remains poorly understood. Objectives Here, we investigate whether and how epigallocatechin-3-gallate (EGCG), a well-characterized dietary bioactive compound, modulates heritable host defense through metabolic-epigenetic crosstalk. Methods To address both physiological relevance and mechanistic insight, we employed mouse and Drosophila melanogaster models. Parental animals were administered EGCG, and offspring were subsequently assessed for immune function upon infection with Escherichia coli, Pseudomonas aeruginosa, or Staphylococcus aureus. By integrating transcriptomics, metabolite analysis, and isotopic tracing, we analyzed metabolism-related pathways and constructed a dynamic network linking metabolic changes to epigenetic modifications in Drosophila. Results In mice, EGCG administration led to a decrease in Escherichia coli burden across multiple tissues in paternal and male offspring in a sex-specific manner, accompanied by metabolic and pro-inflammatory factor changes. In Drosophila melanogaster, early-life EGCG exposure increased survival upon Pseudomonas aeruginosa or Staphylococcus aureus infection and persisted for two subsequent generations. Mechanistically, EGCG reduced intestinal amino acids, thereby moderately inducing activation of activating transcription factor 4 (ATF4), which in turn enhanced maternal glycolysis and immune adaptation. Tyrosine supplementation abolished the enhanced host defense and metabolic changes. Furthermore, ATF4-induced activation of glycolysis promoted ovarian lactate production, serving as a substrate for increased global H3K27 acetylation in the offspring. Conclusion Together, these findings suggest that dietary bioactive compounds modulate metabolic and gene regulatory processes, with functional evidence supporting a role for amino acid metabolism and lactate in linking metabolic remodeling to enhanced resistance to infection in the offspring. This work provides mechanistic insight into how diet can shape heritable immune function through metabolic-epigenetic interplay.

Dietary EGCG reshapes metabolic-epigenetic interplay to induce transgenerational host defense.

Dietary Phytoestrogen Intake and Cognitive Status in Southern Italian Older Adults.

Dietary antioxidants and lifespan: Relevance of environmental conditions, diet, and genotype of experimental models.

Diferencia del perfil de los estados de ánimo en jóvenes escolares que practican deporte extraescolar federado vs no federados (Difference in the profile of moods in young schoolchildren who practice federated extracurricular sports vs. schoolchildren).

Differences on Motor Competence in 4-Year-Old Boys and Girls Regarding the Quarter of Birth: Is There a Relative Age Effect?

Differences reported in the lifespan and aging of male Wistar rats maintained on diets containing fat with different fatty acid profiles (virgin olive, sunflower or fish oils) are not reflected by histopathological lesions found at death in central nervous and endocrine systems.

Different race pacing strategies among runners covering the 2017 Berlin Marathon under 3 hours and 30 minutes.

Digital competencies: perceptions of primary school teachers pursuing master’s degrees from eight African countries.

Diseño y validación de un instrumento de investigación para proponer metodología de gestión de proyectos.

Distinct Gastrointestinal and Reproductive Microbial Patterns in Female Holobiont of Infertility.

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

Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.

Do Young People Really Know How to Collaborate for Common Success? Study on Undergraduate Students’ Perception of Collaborative Work in a Spanish University.

Does Arch Stiffness Influence Running Spatiotemporal Parameters? An Analysis of the Relationship between Influencing Factors on Running Performance.

Double-Stranded RNA Targeting Dicer-Like Genes Compromises the Pathogenicity of Plasmopara viticola on Grapevine.

Drogodependencia y desregulación emocional: una revisión sistemática.

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

Mango is one of the most beloved fruits and plays an indispensable role in the agricultural economies of many tropical countries like Pakistan, India, and other Southeast Asian countries. Similar to other fruits, mango cultivation is also threatened by various diseases, including Anthracnose and Red Rust. Although farmers try to mitigate such situations on time, early and accurate detection of mango diseases remains challenging due to multiple factors, such as limited understanding of disease diversity, similarity in symptoms, and frequent misclassification. To avoid such instances, this study proposes a multimodal deep learning framework that leverages both leaf and fruit images to improve classification performance and generalization. Individual CNN-based pre-trained models, including ResNet-50, MobileNetV2, EfficientNet-B0, and ConvNeXt, were trained separately on curated datasets of mango leaf and fruit diseases. A novel Modality Attention Fusion (MAF) mechanism was introduced to dynamically weight and combine predictions from both modalities based on their discriminative strength, as some diseases are more prominent on leaves than on fruits, and vice versa. To address overfitting and improve generalization, a class-aware augmentation pipeline was integrated, which performs augmentation according to the specific characteristics of each class. The proposed attention-based fusion strategy significantly outperformed individual models and static fusion approaches, achieving a test accuracy of 99.08%, an F1 score of 99.03%, and a perfect ROC-AUC of 99.96% using EfficientNet-B0 as the base. To evaluate the model’s real-world applicability, an interactive web application was developed using the Django framework and evaluated through out-of-distribution (OOD) testing on diverse mango samples collected from public sources. These findings underline the importance of combining visual cues from multiple organs of plants and adapting model attention to contextual features for real-world agricultural diagnostics.

Dual-modality fusion for mango disease classification using dynamic attention based ensemble of leaf & fruit images.

E-Learning Environment Based Intelligent Profiling System for Enhancing User Adaptation.

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network.

Eating Behavior during First-Year College Students, including Eating Disorders—RUVIC-RUNEAT-TCA Project. Protocol of an Observational Multicentric Study.

Eating Habits Associated with Nutrition-Related Knowledge among University Students Enrolled in Academic Programs Related to Nutrition and Culinary Arts in Puerto Rico.

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications.

Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2.

Edible insects: A novel nutritious, functional, and safe food alternative.

Editorial: Host-bacteria interactions in fish pathogens.

Educação em contexto: experiências e práticas.

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

Efecto dual de los aminoácidos de cadena ramificada y su relación con la resistencia a la insulina.

Efectos de un entrenamiento con cargas excéntricas sobre el rendimiento en jugadores de fútbol sala.

Efectos del baile en pacientes con Párkinson: revisión sistemática.

Efectos del ciclo menstrual en el estado físico y psicológico de una mujer activa.

Efectos del ejercicio físico en la dismenorrea primaria. Revisión sistemática.

Effect of Brazil Nuts on Selenium Status, Blood Lipids, and Biomarkers of Oxidative Stress and Inflammation: A Systematic Review and Meta-Analysis of Randomized Clinical Trials.

Effect of Chronic Resistance Training on Circulating Irisin: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

The Effect of Dietary Polyphenols on Vascular Health and Hypertension: Current Evidence and Mechanisms of Action.

Effect of Sulfonic Groups Concentration on IEC Properties in New Fluorinated Copolyamides.

Effect of Supplementation with Probiotics in Patients with Schizophrenia: Systematic Review and Meta-Analysis of Randomized Controlled Clinical Trials.

Effect of a 6-Week Physical Education Intervention on Motor Competence in Pre-School Children with Developmental Coordination Disorder.

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

Effectiveness of a Kegel protocol on urinary incontinence in female weightlifters.

Effectiveness of a Mindfulness-Based Professional Development Program for Primary School Teachers in the Czech Republic: A Quasi-Experimental Study.

Effects of Core Strength Training Using Stable and Unstable Surfaces on Physical Fitness and Functional Performance in Professional Female Futsal Players.

Effects of Different Irrigation Rates on Remontant Strawberry Cultivars Grown in Soil.

Introduction Cancer in older adults is often associated with functional limitations, geriatric syndromes, poor self-rated health, vulnerability, and frailty, and these conditions might worsen treatment-related side effects. Recent guidelines for patients with cancer during and after treatment have documented the beneficial effects of exercise to counteract certain side effects; however, little is known about the role of exercise during cancer treatment in older adults. Materials and Methods This is a multicentre randomised controlled trial in which 200 participants will be allocated to a control group or an intervention group (the sample size has been calculated to detect a clinical difference of 1 point in Short Physical Performance Battery (SPPB) score, assuming an α error of 0.05, a β error of 0.20, and a 10 % loss rate). Patients aged ≥70 years, diagnosed with any type of solid cancer and candidates for systemic treatment are eligible. Subjects in the intervention group are invited to participate in a 12-week supervised multicomponent exercise programme in addition to receiving usual care. Study assessments are conducted at baseline and three months. The primary outcome measure is physical function as assessed by the SPPB. Secondary outcome measures include comprehensive geriatric assessment scores (including social situation, basic and instrumental activities of daily living, cognitive function, depression, nutritional status, polypharmacy, geriatric syndromes, pain, and emotional distress), anthropometric characteristics, frailty status, physical fitness, physical activity, cognitive function, quality of life, fatigue, and nutritional status. Study assessments also include analysis of inflammatory, endocrine, and nutritional mediators in serum and plasma as potential frailty biomarkers at mRNA and protein levels and multiparametric flow cytometric analysis to measure immunosenescence markers on T and NK cells. Discussion This study seeks to extend our knowledge on exercise interventions during systemic anticancer treatment in patients over 70 years of age. Results from this research will guide the management of older adults during systemic treatment in hospitals seeking to enhance the standard of care.

Effects of a 12-week multicomponent exercise programme on physical function in older adults with cancer: Study protocol for the ONKO-FRAIL randomised controlled trial.

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

Effects of different strength and velocity training programs on physical performance in youth futsal players.

Effects of ergo-nutritional strategies on recovery in combat sports disciplines.

Effects of extremely low-frequency magnetic fields on human MDA-MB-231 breast cancer cells: proteomic characterization.

Objectives To assess the effects of lifestyle interventions on gestational diabetes, determine whether the effects vary by maternal body mass index, age, parity, ethnicity, education level, or intervention, and rank interventions by effectiveness. Design Individual participant data (IPD) and network meta-analysis. Data sources Major electronic databases (January 1990 to April 2025). Methods This meta-analysis included randomised trials on the effects of lifestyle interventions (physical activity based, diet based, or mixed) in pregnancy on gestational diabetes. Main outcomes were gestational diabetes defined by any criteria and by UK NICE (National Institute for Health and Care Excellence) criteria; other outcomes included IADPSG (International Association of Diabetes in Pregnancy Study Group) and modified IADPSG defined gestational diabetes. A two stage IPD meta-analysis estimated summary odds ratios and 95% confidence intervals and interactions (subgroup effects), along with absolute risk reduction estimates. Aggregate data from non-IPD trials were added to the meta-analysis when possible. Intervention effects were ranked using network meta-analysis. Results 104 randomised trials (35 993 women) were included, with IPD for 68% of participants (24 391 women; 54 studies). Lifestyle interventions reduced gestational diabetes defined by any criteria by 10% in IPD trials (odds ratio 0.90, 95% confidence interval (CI) 0.80 to 1.02; absolute risk reduction 1.3%, 95% CI −0.3% to 2.6%), and by 20% when combining IPD and non-IPD trials (odds ratio 0.80, 95% CI 0.73 to 0.88; absolute risk reduction 2.6%, 95% CI 1.6% to 3.6%), and no reduction was observed using NICE criteria (odds ratio 0.98, 95% CI 0.84to 1.13). Lifestyle interventions reduced gestational diabetes defined using IADPSG criteria by 14% in IPD trials (odds ratio 0.86, 95% CI 0.75 to 0.97; absolute risk reduction 2.7%, 95% CI 0.6% to 5.0%) and by 18% when combining IPD and non-IPD trials (odds ratio 0.82, 95% CI 0.72 to 0.93; absolute risk reduction 3.5%, 95% CI 1.3% to 5.7%). Effects did not vary by maternal characteristics, except for education. Although women of all educational levels benefited from the intervention, the benefit was less in those with low education (low v middle interaction: odds ratio 0.68, 95% CI 0.51 to 0.90; low v high interaction: odds ratio 0.71, 95% CI 0.54 to 0.93). Benefits did not vary by intervention characteristics, except for greater effectiveness with group format (odds ratio 0.81, 95% CI 0.68 to 0.97; absolute risk reduction 2.5%, 95% CI 0.4% to 4.3%) and newly trained facilitators (odds ratio 0.82, 95% CI 0.69 to 0.96; absolute risk reduction 2.4%, 95% CI 0.5% to 4.2%). Physical activity based interventions ranked highest (mean rank 1.1, 95% CI 1 to 2) in preventing gestational diabetes. Conclusions Lifestyle interventions in pregnancy are likely to prevent gestational diabetes, with effects varying according to diagnostic criteria. Implementation strategies should address inequalities by maternal education, and consider group formats, provider training, and physical activity based interventions to prevent gestational diabetes.

Effects of lifestyle interventions in pregnancy on gestational diabetes: individual participant data and network meta-analysis.

Background Physical inactivity and suboptimal diet in pregnancy are important modifiable risk factors for gestational diabetes, a major contributor to pregnancy complications. Objectives We aimed to assess the effects of physical activity and/or diet-based lifestyle interventions during pregnancy on gestational diabetes and if these vary by maternal (body mass index, age, parity, ethnicity, education) and intervention characteristics using individual participant data meta-analysis of randomised trials, and a cost-effectiveness analysis. Data sources International Weight Management in Pregnancy Collaborative Network database was updated by searching major databases from February 2017 to March 2022. Review methods The main outcomes were gestational diabetes by any criteria and by the National Institute for Health and Care Excellence. Other outcomes were gestational diabetes as per International Association of Diabetes in Pregnancy Study Group and maternal and perinatal outcomes. We performed a two-stage random-effects individual participant data meta-analysis to obtain summary estimates (odds ratio) with 95% confidence intervals. Study quality of included trials was assessed, and heterogeneity summarised using τ2. Where possible, we added the aggregate data from non-individual participant data trials to the meta-analysis. We ranked interventions by effectiveness using network meta-analysis and undertook model-based economic evaluation to assess cost-effectiveness. The cost-effectiveness analysis took an NHS cost perspective compared an overall lifestyle intervention versus usual care with a time horizon covering the beginning of pregnancy until the discharge of the mother and infant from the hospital following delivery. Results Ninety-two trials (32,284 women) were included; 54 (23,698 women) provided individual participant data. Lifestyle interventions reduced the odds of gestational diabetes (any criteria) by 10% in individual participant data trials (odds ratio 0.90, 95% confidence interval 0.80 to 1.02, 54 studies, 23,361 women), and the findings reached statistical significance when non-individual participant data were included (odds ratio 0.81, 95% confidence interval 0.73 to 0.89, 92 studies, 31,947 women). Physical activity significantly reduced the odds of gestational diabetes by 36% (odds ratio 0.64; 95% confidence interval 0.48 to 0.84), and diet by 19% (odds ratio 0.81; 0.69 to 0.96), but not mixed interventions. Women with middle (odds ratio 0.68, 95% confidence interval 0.51 to 0.90) and high educational level (odds ratio 0.71, 95% confidence interval 0.54 to 0.93) benefited more than those with low educational status, and no differences by maternal body mass index, age, parity or ethnicity. There was no significant reduction in gestational diabetes defined by National Institute for Health and Care Excellence criteria (odds ratio 0.98, 95% confidence interval 0.84 to 1.13) in individual participant data trials. For gestational diabetes defined using International Association of Diabetes in Pregnancy Study Group criteria, interventions reduced gestational diabetes by 14% (odds ratio 0.86, 95% confidence interval 0.75 to 0.97, τ2 = 0.00, 16 studies, 6174 women) in individual participant data trials and by 17% (odds ratio 0.83, 95% confidence interval 0.72 to 0.95, τ2 = 0.01, 25 studies, 7883 women) when non-individual participant data trials were added. Overall, physical activity reduced caesarean section (odds ratio 0.83; 0.72 to 0.96), small-for-gestational age (odds ratio 0.72; 0.56 to 0.92) and large-for-gestational age babies (odds ratio 0.81; 0.71 to 0.94); diet-based interventions reduced any preterm birth (odds ratio 0.37; 0.20 to 0.68) compared to controls. No differences were observed for other outcomes. Lifestyle interventions were on average more expensive and more effective at averted gestational diabetes and major outcome averted compared to usual care. Limitations We could not identify the specific intervention components and delivery methods associated with improved outcomes, due to variations in reporting. Conclusion Lifestyle interventions in pregnancy prevent gestational diabetes, and the effects vary according to the definition of gestational diabetes. Physical activity-based interventions may be the most effective.

Effects of physical activity and diet in pregnancy to prevent gestational diabetes: an individual participant data (IPD) meta-analysis on the differential effects of interventions with economic evaluation.

Effects of the Menstrual Cycle on Jumping, Sprinting and Force-Velocity Profiling in Resistance-Trained Women: A Preliminary Study.

Effects on Frailty and Cognitive Decline in Individuals Over 65 Years After Participating in a Multicomponent Exercise Program: A Systematic Review.

Background and objectives: As microbes are developing resistance to antibiotics, natural, botanical drugs or traditional herbal medicine are presently being studied with an eye of great curiosity and hope. Hence, complementary and alternative treatments for uncomplicated pelvic inflammatory disease (uPID) are explored for their efficacy. Therefore, this study determined the therapeutic efficacy and safety of Sesamum indicum Linn seeds with Rosa damascena Mill Oil in uPID with standard control. Additionally, we analyzed the data with machine learning. Materials and methods: We included 60 participants in a double-blind, double-dummy, randomized standard-controlled study. Participants in the Sesame and Rose oil group (SR group) (n = 30) received 14 days course of black sesame powder (5 gm) mixed with rose oil (10 mL) per vaginum at bedtime once daily plus placebo capsules orally. The standard group (SC), received doxycycline 100 mg twice and metronidazole 400 mg thrice orally plus placebo per vaginum for the same duration. The primary outcome was a clinical cure at post-intervention for visual analogue scale (VAS) for lower abdominal pain (LAP), and McCormack pain scale (McPS) for abdominal-pelvic tenderness. The secondary outcome included white blood cells (WBC) cells in the vaginal wet mount test, safety profile, and health-related quality of life assessed by SF-12. In addition, we used AdaBoost (AB), Naïve Bayes (NB), and Decision Tree (DT) classifiers in this study to analyze the experimental data. Results: The clinical cure for LAP and McPS in the SR vs SC group was 82.85% vs 81.48% and 83.85% vs 81.60% on Day 15 respectively. On Day 15, pus cells less than 10 in the SR vs SC group were 86.6% vs 76.6% respectively. No adverse effects were reported in both groups. The improvement in total SF-12 score on Day 30 for the SR vs SC group was 82.79% vs 80.04% respectively. In addition, our Naive Bayes classifier based on the leave-one-out model achieved the maximum accuracy (68.30%) for the classification of both groups of uPID. Conclusion: We concluded that the SR group is cost-effective, safer, and efficacious for curing uPID. Proposed alternative treatment (test drug) could be a substitute of standard drug used for Female genital tract infections.

Efficacy and classification of Sesamum indicum linn seeds with Rosa damascena mill oil in uncomplicated pelvic inflammatory disease using machine learning.

Objective: This study aims to determine the efficacy of the Acacia arabica (Lam.) Willd. and Cinnamomum camphora (L.) J. Presl. vaginal suppository in addressing heavy menstrual bleeding (HMB) and their impact on participants' health-related quality of life (HRQoL) analyzed using machine learning algorithms. Method: A total of 62 participants were enrolled in a double-dummy, single-center study. They were randomly assigned to either the suppository group (SG), receiving a formulation prepared with Acacia arabica gum (Gond Babul) and camphor from Cinnamomum camphora (Kafoor) through two vaginal suppositories (each weighing 3,500 mg) for 7 days at bedtime along with oral placebo capsules, or the tranexamic group (TG), receiving oral tranexamic acid (500 mg) twice a day for 5 days and two placebo vaginal suppositories during menstruation at bedtime for three consecutive menstrual cycles. The primary outcome was the pictorial blood loss assessment chart (PBLAC) for HMB, and secondary outcomes included hemoglobin level and SF-36 HRQoL questionnaire scores. Additionally, machine learning algorithms such as k-nearest neighbor (KNN), AdaBoost (AB), naive Bayes (NB), and random forest (RF) classifiers were employed for analysis. Results: In the SG and TG, the mean PBLAC score decreased from 635.322 ± 504.23 to 67.70 ± 22.37 and 512.93 ± 283.57 to 97.96 ± 39.25, respectively, at post-intervention (TF3), demonstrating a statistically significant difference (p < 0.001). A higher percentage of participants in the SG achieved normal menstrual blood loss compared to the TG (93.5% vs 74.2%). The SG showed a considerable improvement in total SF-36 scores (73.56%) compared to the TG (65.65%), with a statistically significant difference (p < 0.001). Additionally, no serious adverse events were reported in either group. Notably, machine learning algorithms, particularly AB and KNN, demonstrated the highest accuracy within cross-validation models for both primary and secondary outcomes. Conclusion: The A. arabica and C. camphora vaginal suppository is effective, cost-effective, and safe in controlling HMB. This botanical vaginal suppository provides a novel and innovative alternative to traditional interventions, demonstrating promise as an effective management approach for HMB.

Efficacy of a vaginal suppository formulation prepared with Acacia arabica (Lam.) Willd. gum and Cinnamomum camphora (L.) J. Presl. in heavy menstrual bleeding analyzed using a machine learning technique.

Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization.

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

Efficient prediction of anticancer peptides through deep learning.

Eficacia de un programa de intervención basado en la terapia dialéctico-conductual en pacientes con trastorno límite de la personalidad.

Egobierno: sistema de información para el seguimiento de indicadores y su incidencia en la producción judicial - caso Perú.

El encoder Ivolution es un dispositivo válido y fiable para medir la velocidad media de la barra durante el press de banca en máquina Smith.

El espacio multinacional de países de lenguas española y portuguesa: La iberofonía.

El portafolio como herramienta de desarrollo del aprendizaje reflexivo en los entornos virtuales.

El trasvase de la funcionalidad de los referentes sexuales en la animación para adultos.

El tratamiento de la desigualdad económica en cibermedios internacionales: análisis de contenido desde la perspectiva del nuevo sistema híbrido.

El uso de la videograbación y la videollamada para la enseñanza de español como lengua extranjera.

El uso de organizadores textuales para comprensión lectora en lengua meta, una experiencia durante la pandemia por la Covid-19.

Eletricidade estática: o processo da geração à distribuição de energia elétrica, enfatizando o contexto histórico e social.

Elucidating the Therapeutic Potential of Bis(Maltolato)OxoVanadium(IV): The Protective Role of Copper in Cellular Metabolism.

Emotion Detection Using Facial Expression Involving Occlusions and Tilt.

Emotional Management in Journalism and Communication Studies.

Empathy and Occupational Health and Well-Being in Ecuadorian Physicians Working with COVID-19 Patients: A Mixed-Method Study.

Empatía médica en médicos que realizan el programa de formación médica especializada. Estudio comparativo intercultural en España.

Empatía, habilidades de colaboración interprofesional y aprendizaje médico permanente en residentes españoles y latinoamericanos que inician los programas de formación médica especializada en España. Resultados preliminares.

The Empirical Study of the Impact of Firm-and Country-level Factors on Debt Financing Decisions of ICT Firms.

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence.

End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning.

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

Enhanced FPGA-based smart power grid simulation using Heun and Piecewise analytic method.

An Enhanced Feed-Forward Back Propagation Levenberg–Marquardt Algorithm for Suspended Sediment Yield Modeling.

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.

Introduction: Weather classification plays a crucial role in applications such as environmental monitoring, disaster management, and smart city infrastructure. Accurate and efficient classification of weather conditions from images remains a challenging task due to variations in illumination, texture, and atmospheric conditions.Methods: This study proposes an efficient deep learning framework for multi-class weather classification by integrating the Xception architecture with Squeeze-and-Excitation (SE) blocks and a spatial attention mechanism. Transfer learning with pre-trained ImageNet weights was employed, and a comparative analysis was conducted using EfficientNet-B3, ResNet152V2, and Xception architectures. The proposed enhanced Xception model incorporates channel-wise recalibration and spatial feature refinement to improve representational capability. The model was trained and evaluated on the Multi-Class Weather Dataset (MWD), which consists of 1,125 images categorized into four classes: sunshine, cloudy, rain, and sunrise. To ensure robustness and generalization, 5-fold cross-validation, statistical significance testing, calibration analysis, and robustness evaluation under image perturbations were performed.Results: The proposed model achieved a classification accuracy of 99.06% on the test set. Additionally, it attained a macro precision of 98.3%, macro recall of 97.7%, and macro F1-score of 98.0%. The model demonstrated strong generalization capability and robustness under varying perturbation conditions, with only moderate computational overhead.Discussion: The integration of SE blocks and spatial attention significantly enhances feature representation by emphasizing informative channels and spatial regions. Compared to baseline architectures, the proposed framework shows superior performance in terms of accuracy and robustness. These results indicate that the model is well-suited for real-world weather classification applications, particularly in intelligent environmental monitoring systems.

Enhanced weather classification using xception with SENet and attention mechanisms.

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

Humans can carry various diseases, some of which are poorly understood and lack comprehensive solutions. Such a disease can exists in human eye that can affect one or both eyes is diabetic retinopathy (DR) which can impair function, vision, and eventually result in permanent blindness. It is one of those complex complexities. Therefore, early detection of DR can significantly reduce the risk of vision impairment by appropriate treatment and necessary precautions. The primary aim of this study is to leverage cutting-edge models trained on diverse image datasets and propose a CNN model that demonstrates comparable performance. Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. It has been shown that the proposed approach of multi-label classification with softmax functions and categorical cross-entropy works more effectively, yielding perfect accuracy, precision, and recall values. In particular, Xception achieved an impressive 82% accuracy among all the transfer learning models, setting a new benchmark for the dataset used. However, our proposed CNN model shows superior performance, achieving an accuracy of 95.27% on this dataset, surpassing the state-of-the-art Xception model. Moreover, for single-label (binary classifications), our proposed model achieved perfect accuracy as well. Through exploration of these advances, our objective is to provide a comprehensive overview of the leading methods for the early detection of DR. The aim is to discuss the challenges associated with these methods and highlight potential enhancements. In essence, this paper provides a high-level perspective on the integration of deep learning techniques and machine learning models, coupled with explainable artificial intelligence (XAI) and gradient-weighted class activation mapping (Grad-CAM). We prese...

Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence.

Enhancing Urban Traffic Management Through Real-Time Anomaly Detection and Load Balancing.

Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals.

Enhancing e-commerce logistics efficiency and sustainability via quantum computing and artificial intelligence-based quantum hybrid models.

Enhancing fault detection in new energy vehicles via novel ensemble approach.

Enhancing human gut health: Global innovations in dysbiosis management.

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

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

Background/Objectives: In the present study, ergosterol, a novel natural and animal-free alternative sterol, was investigated, and its effects on liposomal properties were assessed. Importantly, ergosterol’s fungal origin offers a sustainable substitute for cholesterol, aligning with current trends in natural and vegan-friendly formulations. Methods: This study explored the effect of ergosterol content (10 mol% vs. 20 mol%) on the encapsulation efficiency (EE), physical properties, morphology, antioxidant activity, lipid peroxidation, and storage stability of Serpylli herba extract-loaded liposomes. Results: Liposomes with 20 mol% ergosterol exhibited significantly higher EE (~81.0%) than those with 10 mol% (~75.6%), along with improved resistance to UV- and freeze-drying-induced reduction in EE. Extract loading resulted in a reduced particle size, indicating favorable bilayer interactions, whereas lyophilization increased size and polydispersity, reflecting structural destabilization. However, 20 mol% ergosterol improved vesicle uniformity and surface charge stability, suggesting enhanced bilayer rigidity. Zeta potential and mobility trends supported improved colloidal stability in ergosterol-enriched systems under all tested conditions. Over 28 days at 4 °C, non-treated extract-loaded liposomes with a higher ergosterol content demonstrated enhanced vesicle integrity. During storage, UV-treated and lyophilized liposomes with 20 mol% ergosterol maintained more consistent size and charge profiles, indicating better membrane reorganization and stability. Nanoparticle tracking analysis demonstrated that ergosterol content modulates vesicle concentration in a dose-dependent manner, highlighting the role of membrane composition in liposome formation and potential dose uniformity. Transmission electron microscopy analysis of extract-loaded liposomes demonstrated well-defined vesicles with intact structural features. A study in a Franz diffusion cell revealed that ergosterol-enriched liposomes significantly delayed polyphenol release compared to free extract, confirming their potential for controlled delivery. Antioxidant activity was preserved in all liposomal systems, with higher ergosterol content supporting improved ABTS radical scavenging potential after stress treatments. FRAP assay results remained stable across formulations, with no major differences between sterol levels. TBARS analysis demonstrated that Serpylli herba extract significantly reduced UV-induced lipid peroxidation in ergosterol-enriched liposomes, underscoring its protective antioxidant role. Conclusions: Higher ergosterol content enhanced liposomal performance in terms of encapsulation, structural resilience, and antioxidant retention, particularly under UV and lyophilization stress. Ergosterol-containing liposomes exhibited improved stability, favorable particle size distribution, and high encapsulation efficiency, while maintaining the antioxidant functionality of the incorporated Serpylli herba polyphenol-rich extract. These findings highlight the potential of ergosterol-based liposomes as robust carriers for bioactive compounds in pharmaceutical and nutraceutical applications that align with current trends in green and vegan-friendly formulations.

Ergosterol-Enriched Liposomes with Post-Processing Modifications for Serpylli Herba Polyphenol Delivery: Physicochemical, Stability and Antioxidant Assessment.

Estrés percibido en adultos mayores mediante el uso de robots sociales durante Covid 19.

Estudio Correlacional: Evitación experiencial, insomnio y rumiación en adolescentes.

Evaluación de los efectos del ejercicio físico en pacientes con cáncer de mama: una revisión sistemática.

Evaluación de los efectos del entrenamiento de fuerza con bandas elásticas en jugadores jóvenes de balonmano. Revisión sistemática.

Evaluación e intervención ante un caso de lateralidad cruzada. Caso único.

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

Evaluation of Single-Cropping under Reduced Water Supply in Strawberry Cultivation.

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.

Biofilms are associated with infections that are resistant to conventional therapies, contributing to the antimicrobial resistance crisis. The need for alternative approaches against biofilms is well-known. Although natural products like stingless bee honeys (tribe: Meliponini) constitute an alternative treatment, much is still unknown. Our main goal was to evaluate the antibiofilm activity of stingless bee honey samples against multidrug-resistant (MDR) pathogens through biomass assays, fluorescence (cell count and viability), and scanning electron (structural composition) microscopy. We analyzed thirty-five honey samples at 15% (v/v) produced by ten different stingless bee species (Cephalotrigona sp., Melipona sp., M. cramptoni, M. fuscopilosa, M. grandis, M. indecisa, M. mimetica, M. nigrifacies, Scaptotrigona problanca, and Tetragonisca angustula) from five provinces of Ecuador (Tungurahua, Pastaza, El Oro, Los Ríos, and Loja) against 24-h biofilms of Staphylococcus aureus, Klebsiella pneumoniae, Candida albicans, and Candida tropicalis. The present honey set belonged to our previous study, where the samples were collected in 2018–2019 and their physicochemical parameters, chemical composition, mineral elements, and minimal inhibitory concentration (MIC) were screened. However, the polyphenolic profile and their antibiofilm activity on susceptible and multidrug-resistant pathogens were still unknown. According to polyphenolic profile of the honey samples, significant differences were observed according to their geographical origin in terms of the qualitative profiles. The five best honey samples (OR24.1, LR34, LO40, LO48, and LO53) belonging to S. problanca, Melipona sp., and M. indecisa were selected for further analysis due to their high biomass reduction values, identification of the stingless bee specimens, and previously reported physicochemical parameters. This subset of honey samples showed a range of 63–80% biofilm inhibition through biomass assays. Fluorescence microscopy (FM) analysis evidenced statistical log reduction in the cell count of honey-treated samples in all pathogens (P <0.05), except for S. aureus ATCC 25923. Concerning cell viability, C. tropicalis, K. pneumoniae ATCC 33495, and K. pneumoniae KPC significantly decreased (P <0.01) by 21.67, 25.69, and 45.62%, respectively. Finally, scanning electron microscopy (SEM) analysis demonstrated structural biofilm disruption through cell morphological parameters (such as area, size, and form). In relation to their polyphenolic profile, medioresinol was only found in the honey of Loja, while scopoletin, kaempferol, and quercetin were only identified in honey of Los Rios, and dihydrocaffeic and dihydroxyphenylacetic acids were only detected in honey of El Oro. All the five honey samples showed dihydrocoumaroylhexose, luteolin, and kaempferol rutinoside. To the authors’ best knowledge, this is the first study to analyze stingless bees honey-treated biofilms of susceptible and/or MDR strains of S. aureus, K. pneumoniae, and Candida species.

Evaluation of the polyphenolic profile of native Ecuadorian stingless bee honeys (Tribe: Meliponini) and their antibiofilm activity on susceptible and multidrug-resistant pathogens: An exploratory analysis.

Evitación experiencial y ansiedad en deportistas de alto rendimiento.

An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices.

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

Exploring body composition and somatotype profiles among youth professional soccer players.

Exploring factors influencing the severity of pregnancy anemia in India: a study using proportional odds model.

SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, francesca.giampieri@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, maurizio.battino@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR

Exploring nutritional supplement use for countering respiratory tract infections through an X (formerly Twitter)-based survey.

Exploring the Antioxidant, Neuroprotective, and Anti-Inflammatory Potential of Olive Leaf Extracts from Spain, Portugal, Greece, and Italy.

Exploring the Chemistry of Ocimum Species under Specific Extractions and Chromatographic Methods: A Systematic Review.

Exploring the Potential of Microservices in Internet of Things: A Systematic Review of Security and Prospects.

Exploring the nexus: Hausman test application in tourism, globalization, and environmental sustainability- evidence of top 10 visited countries.

External Load Evaluation in Elite Futsal: Influence of Match Results and Game Location with IMU Technology.

External Load Variability in Elite Futsal: Positional Demands and Microcycle Structuring for Player Well-Being and Performance.

FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas.

Facial and Emotion Recognition Deficits in Myasthenia Gravis.

Factor protector de las estrategias de afrontamiento y la cohesión de grupo sobre el bienestar psicológico ante situaciones de ansiedad competitiva en futbolistas.

Factores psicológicos en la conducción: análisis de la relación entre estilos atribucionales y conductas de riesgo.

Factores que influyen en el perfil motivacional laboral de los millennials.

Factors Associated With Employment and Quality of Working Life in Patients With Metastatic Breast Cancer.

Factors linked to informal caregiver burden in dementia across Latin America and the Caribbean: A systematic review and meta‐analysis.

Context: Family offers an important source of social support where individuals acquire social abilities that are necessary to create positive human relationships. This influence has been discussed by different sociological and psychological theories along the life span of individuals. In medicine, empathy, teamwork, and lifelong learning have been described as specific elements of professionalism that have special importance in the interaction with patients and in physicians’ well-being at the workplace. This study was performed with the aim of demonstrating the following hypothesis: In the absence of specific training in empathy and teamwork and lifelong learning abilities, their development in medical students is associated with the students’ perception of loneliness from their family environment. Methods: A cross-sectional study was performed in the only two medical schools of Cusco (Peru), one private and the other public. Jefferson Scales of Empathy, Teamwork, and Lifelong Learning were used as the main measures. Mother–son and father–son relationships and family loneliness were measured to characterize the family environment. In addition, information related to sex, medical school, academic achievements, and place of origin were collected to control possible biases. Comparative, correlation, and multiple regression analyses were performed among the variables studied. Results: In a sample of 818 medical students, differences by school appeared in empathy, teamwork, lifelong learning, and family loneliness. In addition, family loneliness showed an inverse correlation with empathy, teamwork, and learning measures. While having a positive relationship with the mother was associated with a greater development of empathy and learning abilities in the entire sample, a similar effect was observed in father–son relationships, but only in the private medical school group. Finally, in the public medical group, a multiple regression model explained 43% of the variability of empathy based on a lineal relationship with teamwork (p < 0.001), lifelong learning (p < 0.001), and family loneliness (p < 0.001). Conclusion: These findings confirm how family loneliness is detrimental to the development of medical professionalism. Also, they support the important role that the family, and especially parents, plays in the development of empathy, teamwork, and abilities in medical students. Finally, these findings highlighted important differences among students enrolled in public and private medical schools.

Family Loneliness: Its Effects in the Development of Empathy, Teamwork and Lifelong Learning Abilities in Medical Students.

Fascial Nomenclature: Update 2022.

Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve.

Fear of COVID-19: the mediation role between the COVID-19 diagnosis and KAP in Spanish university students.

Feasibility of a Brief Online Mindfulness and Compassion-Based Intervention to Promote Mental Health Among University Students During the COVID-19 Pandemic.

Feature Paper Special Issue for Editorial Board Members (EBMs) of Diseases.

In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research.

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

The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation. However, FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios. The paper systematically reviewed the available literature using the PRISMA guiding principle. The study aims to provide a detailed overview of the increasing use of FL in IoT networks, including the architecture and challenges. A systematic review approach is used to collect, categorize and analyze FL-IoT-based articles. A search was performed in the IEEE, Elsevier, Arxiv, ACM, and WOS databases and 92 articles were finally examined. Inclusion measures were published in English and with the keywords “FL” and “IoT”. The methodology begins with an overview of recent advances in FL and the IoT, followed by a discussion of how these two technologies can be integrated. To be more specific, we examine and evaluate the capabilities of FL by talking about communication protocols, frameworks and architecture. We then present a comprehensive analysis of the use of FL in a number of key IoT applications, including smart healthcare, smart transportation, smart cities, smart industry, smart finance, and smart agriculture. The key findings from this analysis of FL IoT services and applications are also presented. Finally, we performed a comparative analysis with FL IID (independent and identical data) and non-ID, traditional centralized deep learning (DL) approaches. We concluded that FL has better performance, especially in terms of privacy protection and resource utilization. FL is excellent for preserving privacy because model training takes place on individual devices or edge nodes, eliminating the need for centralized data aggregation, which poses significant privacy risks. To facilitate development in this rapidly evolving field, the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.

Federated Learning on Internet of Things: Extensive and Systematic Review.

Fingerprints as Predictors of Schizophrenia: A Deep Learning Study.

Fish Intake in Relation to Fatal and Non-Fatal Cardiovascular Risk: A Systematic Review and Meta-Analysis of Cohort Studies.

Epidemiological studies consistently link higher fish intake with slower rates of cognitive decline and lower dementia incidence. The aim of the present study was to systematically review existing observational studies investigating the association between fish consumption and cognitive function in older adults. A total of 25 studies (8 cross-sectional and 17 prospective including mainly healthy older adults, age range of participants ranging from 18 to 30 years at baseline in prospective studies to 65 to 91 years, representing the upper limit of the age spectrum) were reviewed. Cognitive functions currently investigated in most published studies included various domains, such as global cognition, memory (episodic, working), executive function (planning, inhibition, flexibility), attention and processing speed. Existing studies greatly vary in terms of design (cross-sectional and prospective), geographical area, number of participants involved, and tools used to assess the outcomes of interest. The main findings across studies are not univocal, with some studies reporting stronger evidence of association between fish consumption and various cognitive domains, while others addressed rather null findings. The most consistently responsive domains were processing speed, executive functioning, semantic memory, and global cognitive ability among individuals consuming fish at least weekly, which are highly relevant to both neurodegenerative and vascular forms of cognitive impairment. Positive associations were also observed for verbal memory and general memory, though these were less uniform and often attenuated after multivariable adjustment. In contrast, associations with reaction time, verbal-numerical reasoning, and broad composite scores were inconsistent, and several fully adjusted models showed null results. In conclusion, the evidence suggests that regular fish intake (typically ≥1–2 servings per week) is linked to preserved cognitive performance, although some inconsistent findings require further investigations.

Fish consumption and cognitive function in aging: a systematic review of observational studies.

Fish consumption, cognitive impairment and dementia: an updated dose-response meta-analysis of observational studies.

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

Flavonoids for gastrointestinal tract local and associated systemic effects: A review of clinical trials and future perspectives.

Food knowledge level among Tanzanian women of childbearing age: developing a score for the food knowledge questionnaire.

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

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

From by-products to new application opportunities: the enhancement of the leaves deriving from the fruit plants for new potential healthy products.

Fundus image classification using feature concatenation for early diagnosis of retinal disease.

A Fuzzy-Genetic-Based Integration of Renewable Energy Sources and E-Vehicles.

Gender Differences on Motor Competence in 5-Year-Old Preschool Children Regarding Relative Age.

Genetic Resistance Determinants in Clinical Acinetobacter pittii Genomes.

Genotoxic and antigenotoxic medicinal plant extracts and their main phytochemicals: “A review”.

Geometric and radiometric recording of prehistoric graphic expression: the case of Peña Tu (Asturias, Spain).

Gestational Exercise and Maternal and Child Health: Effects until Delivery and at Post-Natal Follow-up.

Gestão da diversidade no campo educacional: o papel da gestão escolar nesse processo.

Gestão escolar democrática e participativa: um olhar para as habilidades, competências, perspectivas e desafios dos directores escolares do município da Caála.

Grado de autocompasión en deportistas de alto rendimiento lesionados.

Growth and Yield of Strawberry Cultivars under Low Nitrogen Supply in Italy.

Guía metodológica para la implementación de televisión digital en Bolivia.

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

Harvesting Scenedesmus obliquus via Flocculation of Moringa oleifera Seed Extract from Urban Wastewater: Proposal for the Integrated Use of Oil and Flocculant.

The Health Benefits of Tamarindus indica: A Focus on the Relationship Between Phytochemical Composition and Physiological Effects.

Health Benefits of Vegetarian Diets: An Insight into the Main Topics.

Heart Rate Variability in Anorexia Nervosa: A Systematic Review and Meta‐Analysis on the Moderating Role of Measurement Duration and Clinical Stage.

Heart Rate, Technical Performance, and Session-RPE in Elite Youth Soccer Small-Sided Games Played With Wildcard Players.

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

Higher Education Teachers’ Training in Attention to SEN Students: Testing a Mediation Model.

História da Educação Física no ensino infantil.

Hollow Fiber Membranes of PCL and PCL/Graphene as Scaffolds with Potential to Develop In Vitro Blood—Brain Barrier Models.

Human activity recognition (HAR) is essential in many applications, such as smart homes, assisted living, healthcare monitoring, rehabilitation, physiotherapy, and geriatric care. Conventional methods of HAR use wearable sensors, e.g., acceleration sensors and gyroscopes. However, they are limited by issues such as sensitivity to position, user inconvenience, and potential health risks with long-term use. Optical camera systems that are vision-based provide an alternative that is not intrusive; however, they are susceptible to variations in lighting, intrusions, and privacy issues. The paper uses an optical method of recognizing human domestic activities based on pose estimation and deep learning ensemble models. The skeletal keypoint features proposed in the current methodology are extracted from video data using PoseNet to generate a privacy-preserving representation that captures key motion dynamics without being sensitive to changes in appearance. A total of 30 subjects (15 male and 15 female) were sampled across 2734 activity samples, including nine daily domestic activities. There were six deep learning architectures, namely, the Transformer (Transformer), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D CNN), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture. The results on the hold-out test set show that the CNN–LSTM architecture achieves an accuracy of 98.78% within our experimental setting. Leave-One-Subject-Out cross-validation further confirms robust generalization across unseen individuals, with CNN–LSTM achieving a mean accuracy of 97.21% ± 1.84% across 30 subjects. The results demonstrate that vision-based pose estimation with deep learning is a useful, precise, and non-intrusive approach to HAR in smart healthcare and home automation systems.

Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models.

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

A high penetration of renewable energy (RE) in utility grids creates the problems of power system flexibility, high transmission losses, and voltage variations. These problems can be solved using a hybrid combination of transmission network restructuring and optimal placement of distributed energy generator (DEG) units. Hence, this work investigated a technologically and economically feasible solution for improving the flexibility of power networks and reducing losses in a practical transmission utility network by implementing a restructuring of the network and optimal deployment of the distributed energy generators (DEGs). Two solutions for this network restructuring were proposed. Furthermore, a grid-oriented genetic algorithm (GOGA) was designed by combining the conventional genetic algorithm (GA) and mathematical solutions to identify optimal DEG placement. A power system restructuring and GOGA flexibility index (PSRGFI) was formulated for the assessment of network flexibility. A cost–benefit assessment was also performed to estimate the payback period for the investment required for restructuring of the network and DEG placement. The least-square approximation technique was applied for load projection for the year 2031 considering the base year 2021. It was established that minimization of transmission losses, reduction in voltage deviations, and improvement of network flexibility were achieved through hybrid application of network restructuring and DEG placement using GOGA. A network loss saving of 61.19 MW was achieved via optimal restructuring and GOGA. For the projected year 2031, the PSRGFI increased from 30.94 to 132.78 after the placement of DEGs using GOGA and optimal restructuring, indicating that network flexibility increased significantly. The payback period for the investment was very small, equal to 0.985 years. The performance of the designed method was superior to the GA-based method, simulated annealing technique, and bee colony algorithm (BCA) used for placement of DEG units in the test network. The study was completed using MATLAB software, considering data from a practical transmission network owned by Rajasthan Rajya Vidyut Prasaran Nigam Ltd. (RVPN), India.

Hybrid Combination of Network Restructuring and Optimal Placement of Distributed Generators to Reduce Transmission Loss and Improve Flexibility.

A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images.

A Hybrid Model for Improving Software Cost Estimation in Global Software Development.

Hybrid Model with Wavelet Decomposition and EfficientNet for Accurate Skin Cancer Classification.

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

An Image Retrieval Framework Design Analysis Using Saliency Structure and Color Difference Histogram.

Image Watermarking Using Least Significant Bit and Canny Edge Detection.

Background/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.

Image-Based Dietary Energy and Macronutrients Estimation with ChatGPT-5: Cross-Source Evaluation Across Escalating Context Scenarios.

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

Impact of Learning in the COVID-19 Era on Academic Outcomes of Undergraduate Psychology Students.

Impacto de la actividad física orientada en el desarrollo psicomotor durante la primera infancia.

Impacto del consumo de aminoácidos de cadena ramificada (BCAA) en la Diabetes Mellitus Tipo 2.

Imperative Role of Automation and Wireless Technologies in Aquaponics Farming.

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

Implicaciones de la autoestima y el autoconcepto en el bienestar psicológico de los adolescentes españoles.

Importancia de la seguridad de la información en las empresas de tecnología de información corporativa.

An Improved Binomial Distribution-Based Trust Management Algorithm for Remote Patient Monitoring in WBANs.

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

The In Vitro Effects of Romina Strawberry Extract on 3D Uterine Leiomyosarcoma Cells.

In Vitro and In Vivo Insights into a Broccoli Byproduct as a Healthy Ingredient for the Management of Alzheimer’s Disease and Aging through Redox Biology.

In Vivo Anti-Alzheimer and Antioxidant Properties of Avocado (Persea americana Mill.) Honey from Southern Spain.

In silico prediction, molecular docking and simulation of natural flavonoid apigenin and xanthoangelol E against human metapneumovirus.

An In-Depth Study on the Inhibition of Quorum Sensing by Bacillus velezensis D-18: Its Significant Impact on Vibrio Biofilm Formation in Aquaculture.

Incidencia del entrenamiento de fuerza en la población infantojuvenil: revisión sistemática.

Incidencia lesional en el fútbol.

Incorporating soil information with machine learning for crop recommendation to improve agricultural output.

Angola, as with many countries on the African continent, has great inequalities or asymmetries between its provinces. At the economic, financial, and technological level, there is a great disparity between them, where it is observed that the province of Luanda is the largest financial business center to the detriment of others, such as Moxico, Zaire, and Cabinda. In the latter, despite the advantages of high oil production, from a regional point of view, they remain almost stagnant in time, in a social dysfunction where the population lives on extractivism and artisanal fishing. This article analyzes the most important events in contemporary regional history, the Portuguese occupation that was the Portuguese colonial rule over Angola (1890–1930) and the civil war that was a struggle between Angolans for control of the country (1975–2002), in the consolidation of the asymmetries between provinces. For this work, a theoretical-reflective study was conducted based on the reading of books, articles, and previous investigations on the phenomenon studied. Considering the interpretation and analysis of the theoretical content obtained through the bibliographic research conducted, this theoretical construction approaches the qualitative approach. We conclude that the deep inequalities between regions and within them, between the provinces studied, originated historically in the form of exploitation of the regions and from the consequences of the war. The asymmetries, observed through the variables studied show that the provinces historically explored and considered object regions present a lower growth compared to those that were considered subject regions in which the applied geopolitical strategy, as they are centers of primary production flows, was different. We also observe that, due to the conflicts of the civil war in the less developed regions, the inequalities have deepened, contributing seriously to a higher level of poverty and a lower development of the provinces where these conflicts took place.

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

Inequality in times of pandemics: How online media are starting to treat the economic consequences of the coronavirus crisis.

Inflamm-ageing or inflammasom-ageing as independent events.

Inflammatory potential of the diet and self-rated quality of life in Italian adults.

Influence of Altitudes and Development Stages on the Chemical Composition, Antioxidant, and Antimicrobial Capacity of the Wild Andean Blueberry (Vaccinium floribundum Kunth).

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

Influence of Physiological Variables and Comorbidities on Plasma Aβ40, Aβ42, and p-tau181 Levels in Cognitively Unimpaired Individuals.

Influence of altitude on the physicochemical composition and antioxidant capacity of strawberry: a preliminary systematic review and meta-analysis.

The phenolic composition and content of olive fruit are some of the attributes that determine oil quality. This composition depends on the olive variety, the cultivation system, and the fruit's ripeness. This study considered two olive varieties (Manzanilla and Morisca), under two water regimes (irrigated and rainfed), harvested at three stages of maturation (S1, S2, and S3), over three consecutive campaigns (2011, 2012, and 2013). The accumulation of phenols in the fruit was found to depend only on the stage of ripeness, while the flavonoid and phenylpropanoid contents depended also on the variety and the water regime. Superoxide dismutase (SOD) activity was linked to O2- production, which in turn depended on water regime, variety, and stage of maturation (this last being a process involving ROS). The peroxidase (POX) activity seemed only to depend on ripeness, while polyphenol oxidase (PPO) activity varied from year to year as well as presenting a strong ripeness dependence that was in clear coherence with the levels of phenolic compounds that the olives accumulate. All these relationships between the variables and the factors conform a dataset with the structure of a multidimensional array that is difficult to interpret using conventional techniques of statistical analysis. This work takes a novel approach (MultiDimensional Scaling associated with a Partial Triadic Analysis, MDS-PTA) to the analysis of this type of data structure which allows its correct interpretation. The analysis showed that the state of maturation of the olives is the most clearly discriminating factor, far more so than the cultivar, water regime, or year. Thus, the phenols and the total antioxidant activity (FRAP) showed strong clustering, being closely related in all three years studied. The oxidant and antioxidant activities showed a certain tendency to cluster, although in these cases the year also had an influence as a factor, indicating that these parameters depend more on external factors and less on ripeness

Influence of cultivar, irrigation, ripening stage, and annual variability on the oxidant/antioxidant systems of olives as determined by MDS-PTA.

Influence of maternal physical exercise on fetal and maternal heart rate responses.

Influence of the Encapsulating Agent on the Bioaccessibility of Phenolic Compounds from Microencapsulated Propolis Extract during "In Vitro" Gastrointestinal Digestion.

Influence of the extraction method on the recovery of bioactive phenolic compounds from food industry by-products.

Influence of the properties of different graphene-based nanomaterials dispersed in polycaprolactone membranes on astrocytic differentiation.

Influencia de la composición corporal sobre el rendimiento en salto vertical dependiendo de la categoría de la formación y la demarcación en futbolistas.

Influencia de la densidad de jugadores sobre la frecuencia cardíaca y respuestas técnicas en jóvenes jugadores de fútbol. [Influence of the density of players on their heart rate and its technical implications on young football players].

Influencia de las competencias parentales en la manifestación de problemas de conducta, en niños de 8 a 11 años, que residen en la provincia de San José, Costa Rica.

Influencia del tamaño del campo y horario del partido en la respuesta física de equipos de la Segunda División Española de Fútbol (Effect of pitch size and time of the match in the physical performance of teams the Spanish Second Division).

Infrared thermography to assess fatigue, injury risk factors and recovery in soccer: a systematic review of original studies.

Inhibition of the NLRP3 inflammasome improves lifespan in animal murine model of Hutchinson–Gilford Progeria.

Inhibition of the NLRP3 inflammasome prevents ovarian aging.

Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were updated, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations.

Innovative Application of Chatbots in Clinical Nutrition Education: The E+DIEting_Lab Experience in University Students.

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.

Instruments to Assess Physical Activity in Primary Education Students with Autism Spectrum Disorder: A Systematic Review.

Integrated Building Modelling Using Geomatics and GPR Techniques for Cultural Heritage Preservation: A Case Study of the Charles V Pavilion in Seville (Spain).

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

Introduction: Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information retention, and student well-being. Thus, the integration of AI in emotional assessment within educational environments offers several advantages that can transform how we understand and address the socio-emotional development of students. However, there remains a lack of comprehensive approach that systematizes advancements, challenges, and opportunities in this field. Aim: This systematic literature review aims to explore how artificial intelligence (AI) is used to evaluate emotions within educational settings. We provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in the domain of AI-driven emotional assessment within educational settings. Method: The review involved a search across the following academic databases: Pubmed, Web of Science, PsycINFO and Scopus. Forty-one articles were selected that meet the established inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment within educational environments. Results: The findings reveal a variety of AI-driven approaches that were developed to capture and analyze students’ emotional states during learning activities. The findings are summarized in four fundamental topics: (1) emotion recognition in education, (2) technology integration and learning outcomes, (3) special education and assistive technology, (4) affective computing. Among the key AI techniques employed are machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review identified emerging factors that, while important, require further investigation to understand their relationships and implications fully. These elements could significantly enhance the use of AI in assessing emotions within educational settings. Specifically, we are referring to: (1) federated learning, (2) convolutional neural network (CNN), (3) recurrent neural network (RNN), (4) facial expression databases, and (5) ethics in the development of intelligent systems. Conclusion: This systematic literature review showcases the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts.

Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review.

Integration of Remote-Sensing Techniques for the Preventive Conservation of Paleolithic Cave Art in the Karst of the Altamira Cave.

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

Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome.

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

Intelligent Load-Balancing Framework for Fog-Enabled Communication in Healthcare.

Inter-Professional Collaboration and Occupational Well-Being of Physicians Who Work in Adverse Working Conditions.

Intercambio Virtual y Enseñanza del Español como Lengua Extranjera: un Proyecto Didáctico entre Italia y España.

International Competition Kinematic Demands in Male Field Hockey.

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The International Natural Product Sciences Taskforce (INPST) and the power of Twitter networking exemplified through #INPST hashtag analysis.

Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review.

Interpreting Foreign Smiles: Language Context and Type of Scale in the Assessment of Perceived Happiness and Sadness.

Intervenciones de salud mental para trabajadores sanitarios durante la primera ola de la pandemia de COVID-19 en España.

Interventions to Prevent Obesity in Mexican Children and Adolescents: Systematic Review.

Interventions to Treat Obesity in Mexican Children and Adolescents: Systematic Review and Meta-Analysis.

Investigation of structural frustration in symmetric diblock copolymers confined in polar discs through cell dynamic simulation.

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

IoT Fog-Enabled Multi-Node Centralized Ecosystem for Real Time Screening and Monitoring of Health Information.

IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0.

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

Is It Possible to Reduce the Relative Age Effect through an Intervention on Motor Competence in Preschool Children?

Is Marathon Training Harder than the Ironman Training? An ECO-method Comparison.

Is Quarter of Birth a Risk Factor for Developmental Coordinator Disorder in Preschool Children?

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

Jugadores comodines durante diferentes juegos de posición.

Knowledge and learning management: cognitive and emotional processes in learning. Proposed solutions for difficulty in learning.

“¡La Sirenita es como yo!”: diversidad intercultural, inclusión y autoestima infantil en TikTok.

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

La gestión de proyectos de traducción: una tarea pendiente en los planes de estudio del Grado en Traducción e Interpretación en España.

La importancia de la aplicación y uso de las redes sociales en la divulgación científica dirigida a jóvenes universitarios.

La influencia del ciclo menstrual en el entrenamiento de fuerza: revisión bibliográfica.

La percepción de la religión cristiana en la sociedad austríaca del s. XIX a través de dos cuentos de Stifter.

La pose como acto social: tecnología y representación en el retrato foto-gráfico femenino, de la solemnidad al "selfie".

La relevancia de la desigualdad en los cibermedios españoles en un año de pandemia.

La traducción de canciones en películas: análisis contrastivo de géneros cinematográficos.

La traducción de los cuestionarios de salud para pacientes.

Las estrategias de la terapia cognitivo conductual (TCC) para pacientes de cirugía bariátrica: revisión sistemática.

Las relaciones de las influencias en los procesos de producción informativa y sus efectos en la calidad periodística. Una visión desde Latinoamérica.

"Late Motiv": la transformación del "late night" antes, durante y después del confinamiento provocado por el Covid-19 en 2020.

The Internet of Things (IoT) is a sophisticated network of objects embedded with electronic systems that enable devices to collect and exchange data. IoT is a recent trending leading technology and changing the way we live. However, it has several challenges especially efficiency, architecture, complexity, and network topology. The traditional technologies are not enough to provide support. It is evident from the literature that complex networks are used to study the topology and the structure of a network and are applied to modern technologies. Thus, the capability of powerful computational tools and the existence of theoretical frameworks enable complex networks to derive new approaches in analyzing IoT-based technologies in terms of improving efficiency, architecture, complexity, and topology. In this direction, limited research has been carried out. The integration aspect remains a key challenge. Therefore, in order to fill this gap. Herein, we design a comprehensive literature review. In this research effort, we explore a newly leading emerging technology named the Social Internet of Things (SIoT). It is developed to overcome the challenges in IoT. We discuss the importance and the key applications of SIoT. We first presented a conceptual view along with a recent technological roadmap. The big data play an important role in the modern world. We discuss big data and the 5 Vs along with suitable applications and examples. Then, we highlighted the key concepts in complex networks, scale-free, random networks, and small-world networks. We explored and presented various graph models and metrics aligned with social networks and the most recent trends. The novelty of this research is to propose a synergy of complex networks to the IoT, SIoT, and big data together. We discuss the advantages of integration in detail. We present a detailed discussion on complex networks emerging technologies and cyber-physical systems (CPS). Briefly, our literature review covers the most recent advancements and developments in 10 years. In addition, our critical analysis is based on up-to-date surveys and case studies. Finally, we outline the impact of recent emerging technologies on challenges applications, and solutions for the future. This paper provides a good reference for researchers and readers in the IoT domain.

Latest advancements and prospects in the next-generation of Internet of Things technologies.

Learning English in the Digital Age: The Application of ChatGPT and MagicSchool at Istinye University.

Aims Nuclear envelope integrity is essential for compartmentalisation of nucleus and cytoplasm. Importantly, mutations in genes encoding nuclear envelope and associated proteins are the second-highest cause of familial dilated cardiomyopathy. One such nuclear envelope protein that causes cardiomyopathy in humans and affects mouse heart development is Lem2. However, its role in heart remains poorly understood. Methods and results We generated mice in which Lem2 was specifically ablated either in embryonic cardiomyocytes (Lem2 cKO) or adult cardiomyocytes (Lem2 iCKO) and carried out detailed physiological, tissue and cellular analyses. High resolution episcopic microscopy was used for 3D reconstructions and detailed morphological analyses. RNA-sequencing and immunofluorescence identified altered pathways and cellular phenotypes, and cardiomyocytes were isolated to interrogate nuclear integrity in more detail. In addition, echocardiography provided physiological assessment of Lem2 iCKO adult mice. We found that Lem2 was essential for cardiac development, and hearts from Lem2 cKO mice were morphologically and transcriptionally underdeveloped. Lem2 cKO hearts displayed high levels of DNA damage, nuclear rupture, and apoptosis. Crucially, we found that these defects were driven by muscle contraction as they were ameliorated by inhibiting myosin contraction and L-type calcium channels. Conversely, reducing Lem2 levels to ∼45% in adult cardiomyocytes did not lead to overt cardiac dysfunction up to 18 months of age. Conclusions Our data suggest that Lem2 is critical for integrity at the nascent nuclear envelope in fetal hearts, and protects the nucleus from the mechanical forces of muscle contraction. In contrast, the adult heart is not detectably affected by partial Lem2 depletion, perhaps owing to a more established nuclear envelope and increased adaptation to mechanical stress. Taken together, these data provide insights into mechanisms underlying cardiomyopathy in patients with mutations in Lem2 and cardio-laminopathies in general.

Lem2 is essential for cardiac development by maintaining nuclear integrity.

Lem2 is essential for cardiac development by maintaining nuclear integrity.

Lenguaje inclusivo y representación del colectivo queer en solicitudes de adopción: análisis contrastivo inglés-español.

A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model.

Lesión de ligamento cruzado anterior (LCA) en futbolistas cántabros. Análisis descriptivo de los factores de riesgo.

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

Lightweight Hybrid Deep Learning Architecture and Model for Security in IIOT.

A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks.

When we talk about the Internet of Things, we are referring to the connecting of things to a1 physical network that is embedded with software, sensors, and other devices that allow information2 to be exchanged between devices. It is possible that the interconnection of devices will present3 issues in terms of security, trustworthiness, reliability, and confidentiality, among other things.4 The proposed approach is effective at detecting intrusions into the Internet of Things network.5 Initially, the privacy-preserving technology was deployed utilising a Blockchain-based methodology6 to ensure that personal information was protected. Patients’ health records (PHR) security is the7 most crucial component of encryption over the Internet because of the value and importance of these8 records, particularly in the context of the Internet of Medical Things (IoMT). The search terms access9 mechanism is one of the most common approaches used to access personal health records from a10 database, but it is vulnerable to a number of security flaws. However, while blockchain-enabled11 healthcare systems provide increased security, they may also introduce weaknesses into the current12 state of the art. Blockchain-enabled frameworks have been proposed in the literature as a means13 of resolving those challenges. These solutions, on the other hand, are primarily concerned with14 data storage, with Blockchain serving as a database. To enable secure search and keyword-based15 access to a distributed database, this study proposes the use of blockchain technology as a distributed16 database, together with a homomorphic encryption mechanism. Aside from that, the suggested17 system includes a secure key revocation mechanism that can be used to automatically update various18 policies.As a result, our proposed approach provides greater security, efficiency, and transparency19 while also being more cost-effective. We have compared the findings of our proposed models with20 those of the benchmark models, if appropriate. Our comparison research demonstrates that our21 suggested framework provides a more secure and searchable mechanism for the healthcare system22 than the current state of the art.

A Lightweight Trust-less Authentication Framework for Massive IoT Systems [preprint].

Links between Nutrition, Infectious Diseases, and Microbiota: Emerging Technologies and Opportunities for Human-Focused Research.

Lipid Accumulation in HepG2 Cells Is Attenuated by Strawberry Extract through AMPK Activation.

Loneliness, Aloneness, and Adherence to the Mediterranean Diet in Southern Italian Individuals.

Los valores asociados a juguetes en los contenidos de canales YouTube: Estudio de caso.

Mechanistic insights into the changes of enzyme activity in food processing under microwave irradiation.

Medical Professionalism and Its Association with Dropout Intention in Peruvian Medical Students during the COVID-19 Pandemic.

Mediterranean Diet and Quality of Life in Adults: A Systematic Review.

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

Mejora de las propiedades organolépticas de un producto sometido a las exigencias de un marco regulatorio de Indicación Geográfica Protegida: El sobao pasiego.

Mental health interventions for healthcare workers during the first wave of COVID-19 pandemic in Spain.

Metaheuristic-based optimal energy assessment of hybrid multi-effect evaporator with synergy of solar and wind energy sources.

Metformin and caloric restriction induce an AMPK-dependent restoration of mitochondrial dysfunction in fibroblasts from Fibromyalgia patients.

Background: The increasing complexity of basketball and the need for optimal decision-making in order to maximize competitive performance highlight the necessity of specialized training for basketball coaches. This systematic review aims to compile, synthesize, and integrate international research published in specialized journals on the training of basketball coaches and students, examining their characteristics and needs. Specifically, it analyzes the content, technical-tactical actions, and methodologies used in practice and education programs to determine which essential parameters for their technical and tactical development. Methods: A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA®) guidelines and the PICOS® model until 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 assessed and the PEDro scale was used to analyze methodological quality. Results: A total of 14,090 articles were obtained in the initial search. After inclusion and exclusion criteria, the final sample was 23 articles. These studies maintained a high standard of quality. This revealed data on the technical-tactical actions addressed in different categories; the profiles, characteristics, and influence of coaches on player development; and the approaches, teaching methods, and evaluation methodologies used in acquiring knowledge and competencies for the professional development of basketball coaches. Conclusions: Adequate theoretical and practical training for basketball coaches is essential for player development. Therefore, training programs for basketball coaches must integrate technical-tactical, physical, and psychological knowledge with the acquisition of skills and competencies that are refined through practice. This training should be continuous, more specialized, and comprehensive, focusing on understanding and constructing knowledge that supports the professional growth of basketballers. Additionally, training should incorporate digital tools and informal learning opportunities, with blended learning emerging as the most effective methodology for this purpose.

Methodology and content for the design of basketball coach education programs: a systematic review.

Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira.

Metodología de transformación digital para incrementar la competitividad de las pymes de logística ligera en el Perú.

Mindfulness y Coaching: promoviendo el desarrollo de la presencia y la conciencia plena.

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

Mitigating Low-Frequency Oscillations and Enhancing the Dynamic Stability of Power System Using Optimal Coordination of Power System Stabilizer and Unified Power Flow Controller.

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

Modelo de comunicación efectiva para la difusión de los programas y proyectos de inversión pública del Departamento de Loreto, Perú.

Modelo de comunicación efectiva para la difusión de los programas y proyectos de inversión pública del departamento de Loreto (Perú).

Modelo holístico para la innovación tecnológica en la pequeña empresa en Panamá.

Modernizing gut-brain axis research in nutritional Science: The role of human-centered New Approach Methodologies.

A Modified Marx Generator Circuit with Enhanced Tradeoff between Voltage and Pulse Width for Electroporation Applications.

The Molecular Basis of Different Approaches for the Study of Cancer Stem Cells and the Advantages and Disadvantages of a Three-Dimensional Culture.

Molecular Interactions between Dietary Lipids and Bone Tissue during Aging.

Molecular Mechanisms of the Protective Effects of Olive Leaf Polyphenols against Alzheimer’s Disease.

The Molecular Weaponry Produced by the Bacterium Hafnia alvei in Foods.

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

Polyphenols are naturally occurring compounds that can be found in plant-based foods, including fruits, vegetables, nuts, seeds, herbs, spices, and beverages, the use of which has been linked to enhanced brain health and cognitive function. These natural molecules are broadly classified into two main groups: flavonoids and non-flavonoid polyphenols, the latter including phenolic acids, stilbenes, and tannins. Flavonoids are primarily known for their potent antioxidant properties, which help neutralize harmful reactive oxygen species (ROS) in the brain, thereby reducing oxidative stress, a key contributor to neurodegenerative diseases. In addition to their antioxidant effects, flavonoids have been shown to modulate inflammation, enhance neuronal survival, and support neurogenesis, all of which are critical for maintaining cognitive function. Phenolic acids possess strong antioxidant properties and are believed to protect brain cells from oxidative damage. Neuroprotective effects of these molecules can also depend on their ability to modulate signaling pathways associated with inflammation and neuronal apoptosis. Among polyphenols, hydroxycinnamic acids such as caffeic acid have been shown to enhance blood-brain barrier permeability, which may increase the delivery of other protective compounds to the brain. Another compound of interest is represented by resveratrol, a stilbene extensively studied for its potential neuroprotective properties related to its ability to activate the sirtuin pathway, a molecular signaling pathway involved in cellular stress response and aging. Lignans, on the other hand, have shown promise in reducing neuroinflammation and oxidative stress, which could help slow the progression of neurodegenerative diseases and cognitive decline. Polyphenols belonging to different subclasses, such as flavonoids, phenolic acids, stilbenes, and lignans, exert neuroprotective effects by regulating microglial activation, suppressing pro-inflammatory cytokines, and mitigating oxidative stress. These compounds act through multiple signaling pathways, including NF-κB, MAPK, and Nrf2, and they may also influence genetic regulation of inflammation and immune responses at brain level. Despite their potential for brain health and cognitive function, polyphenols are often characterized by low bioavailability, something that deserves attention when considering their therapeutic potential. Future translational studies are needed to better understand the right dosage, the overall diet, the correct target population, as well as ideal formulations allowing to overcome bioavailability limitations.

Molecular mechanisms underlying the neuroprotective effects of polyphenols: implications for cognitive function.

Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation.

Monitoring Workloads of a Professional Female Futsal Team over a Season: A Case Study.

Moral perception, educational environment, and development of medical professionalism in medical students during the clinical rotations in Peru.

More than Socio- and Geo-demographics: How Complementary Education and Business Experience Shape Students' Financial Behaviour in Europe.

Most running demand passages of match play in youth soccer congestion period.

Motives for the Use or Not of Protective Equipment for the Recreational Practice of Skiing and Snowboarding in Spanish Winter Stations.

O presente artigo reflete sobre a realidade das mulheres em tempos de pandemia. A problemática que envolve o presente trabalho parte da pergunta: como tem sido a experiência das mulheres na pandemia do coronavírus (Covid-19), devido ao acúmulo dos cuidados como tarefa feminina e o grito uterino que vem dessa situação? Para responder a essa questão, buscamos referências da teologia feminista, que parte do princípio da experiência das mulheres para a análise da realidade e a reflexão teológica e que coloca a vida mesma em sua amplitude como critério hermenêutico. A metodologia utilizada é bibliográfica, a partir de artigos de revistas, entrevistas e livros. Além do mais, somos três mulheres, profissionais, afetadas também pelo home office que se mistura com o trabalho da casa e a necessidade de uma nova organização. O processo de ensino aprendizagem da pandemia tem sido cruel e tem afetado, especialmente, a vida das mulheres. A casa, que deveria ser um lugar seguro, apresenta-se para muitas como um lugar de perigo constante. Muitos trabalhos de cuidado remunerados ou não são realizados pelas mulheres. Historicamente o cuidado tem sido delegado às mulheres, sendo, por um lado, exaltado como parte do ser/fazer feminino (mãe e dona da casa) e, por outro lado, é um trabalho não remunerado ou mal remunerado (enfermeiras, assistentes sociais). Apresenta-se o artigo em três partes: a experiência das mulheres, a necessidade de reinventar a economia do cuidado e o grito uterino que ecoa com justa indignação. Evidencia-se que a pandemia visibilizou questões preexistentes: o aumento do cuidado sob os ombros das mulheres seja em casa ou nas diferentes profissões em que as mulheres estão na linha de frente, a violência contra as mulheres. A pandemia acentua a desigualdade social, racial e de gênero da sociedade brasileira, sendo que as mais atingidas são mulheres pobres, negras, pardas, idosas e com deficiência. O grito que nasce do feminismo clama por uma reinvenção do mundo que habitamos

Mulheres em tempos de pandemia: a cotidianidade, a economia do cuidado e o grito uterino!

Multidrug resistance pattern and molecular epidemiology of pathogens among children with diarrhea in Bangladesh, 2019–2021.

A Multimodal Research Approach to Assessing the Karst Structural Conditions of the Ceiling of a Cave with Palaeolithic Cave Art Paintings: Polychrome Hall at Altamira Cave (Spain).

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

A Multisensory Analysis of the Moisture Course of the Cave of Altamira (Spain): Implications for Its Conservation.

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

NLRP3 Inflammasome Inhibition by MCC950 in Aged Mice Improves Health via Enhanced Autophagy and PPARα Activity.

NLRP3 inflammasome inhibition rescues Hutchinson-Gilford Progeria cellular phenotype and extend longevity of an animal model.

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

Natural Products in Alzheimer’s Disease: A Systematic Review of Clinical Trials and Underlying Molecular Mechanisms.

Natural infection of Lutzomyia longipalpis (Lutz & Neiva, 1912) by Leishmania infantum in a municipality with a high incidence of visceral leishmaniasis in the Brazilian Midwest.

Necessidade de políticas públicas para combater a violência de género no Brasil.

Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN.

A Network of Macrophages Supports Mitochondrial Homeostasis in the Heart.

Neuroinflammation and neuroprogression produced by oxidative stress in euthymic bipolar patients with different onset disease times.

Nivel de conocimiento sobre el tiburón toro (Carcharhinus leucas) como base para su conservación y gestión medioambiental en la Reserva de la Biósfera Caribe Mexicano.

Non-homogeneous dispersion of graphene in polyacrylonitrile substrates induces a migrastatic response and epithelial-like differentiation in MCF7 breast cancer cells.

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

A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis.

COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.

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

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

A Novel Large-Scale Stochastic Pushback Design Merged with a Minimum Cut Algorithm for Open Pit Mine Production Scheduling.

A Novel Smart Belt for Anxiety Detection, Classification, and Reduction Using IIoMT on Students’ Cardiac Signal and MSY.

A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction.

Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.

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

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

Objectives: Point-of-care testing available in prehospital settings requires the establishment of new medical decision points. The aim of the present work was to determine the cut-off of the lactate threshold that activates alert triggers for all-cause 2-day mortality. Design: Multicentre, prospective, ambulance-based, observational study. Setting: Patients treated via emergency medical services (EMSs) and delivered to the emergency department between 2019 and 2023 were selected in Spain. Participants: Adults with any acute disease. Primary and secondary outcome measures: Epidemiological data, vital signs and prehospital point-of-care glucose and lactate levels were obtained. The outcome was all-cause 2-day in-hospital mortality. The cut-offs were obtained via three different methods: (i) indirect (which considers survivors and non-survivors), direct (which considers only survivors) assessment and lactate quartile. Additionally, the quartile approach was used to determine the differences in lactate distribution between survivors and non-survivors. Three different back-to-back studies with the same methodology were used. Results: A total of 11 713 patients fulfilled the inclusion criteria. The mortality rate was 4.6% (542 patients). The difference in the median prehospital lactate concentration (mmol/L) between survivors and non-survivors was statistically significant (p<0.001): 2.29 (95% CI 1.43 to 3.38) and 7.14 (95% CI 5.11 to 9.71), respectively. Globally, the cut-off for all the studies combined was estimated by the direct method to be 3.71 mmol/L (95% CI 2.92 to 3.91), which was similar to the indirect value of 3.07 (95% CI 2.95 to 5.49) and the third quartile of 4.00. The mortality rate in patients who were less than 3.71 mmol/L was 0.004%, and that above that cut-off was 18%. Conclusions: This study established a real-world lactate cut-off for 2-day in-hospital mortality of 3.71 mmol/L (95% CI 2.92 to 3.91) on the basis of data from the EMS. Considering this cut-off point could improve patient management via EMS services, allowing quick identification of patients at high risk of clinical worsening.

Novel prehospital lactate cut-off estimation for mortality: a multicentre observational study.

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

Novel transfer learning based bone fracture detection using radiographic images.

Nuclear Reorganization in Hippocampal Granule Cell Neurons from a Mouse Model of Down Syndrome: Changes in Chromatin Configuration, Nucleoli and Cajal Bodies.

Nuevos tratamientos dietético-nutricionales en diabetes mellitus tipo 2: una revisión narrativa.

Spinal muscular atrophy (SMA) is a devastating autosomal recessive neuromuscular disease characterized by degeneration of spinal cord alpha motor neurons (αMNs). SMA is caused by the homozygous deletion or mutation of the survival motor neuron 1 (SMN1) gene, resulting in reduced expression of SMN protein, which leads to αMN degeneration and muscle atrophy. The majority of transcripts of a second gene (SMN2) generate an alternative spliced isoform that lacks exon 7 and produces a truncated nonfunctional form of SMN. A major function of SMN is the biogenesis of spliceosomal snRNPs, which are essential components of the pre-mRNA splicing machinery, the spliceosome. In recent years, new potential therapies have been developed to increase SMN levels, including treatment with antisense oligonucleotides (ASOs). The ASO-nusinersen (Spinraza) promotes the inclusion of exon 7 in SMN2 transcripts and notably enhances the production of full-length SMN in mouse models of SMA. In this work, we used the intracerebroventricular injection of nusinersen in the SMN∆7 mouse model of SMA to evaluate the effects of this ASO on the behavior of Cajal bodies (CBs), nuclear structures involved in spliceosomal snRNP biogenesis, and the cellular distribution of polyadenylated mRNAs in αMNs. The administration of nusinersen at postnatal day (P) 1 normalized SMN expression in the spinal cord but not in skeletal muscle, rescued the growth curve and improved motor behavior at P12 (late symptomatic stage). Importantly, this ASO recovered the number of canonical CBs in MNs, significantly reduced the abnormal accumulation of polyadenylated RNAs in nuclear granules, and normalized the expression of the pre-mRNAs encoding chondrolectin and choline acetyltransferase, two key factors for αMN homeostasis. We propose that the splicing modulatory function of nusinersen in SMA αMN is mediated by the rescue of CB biogenesis, resulting in enhanced polyadenylated pre-mRNA transcription and splicing and nuclear export of mature mRNAs for translation. Our results support that the selective restoration of SMN expression in the spinal cord has a beneficial impact not only on αMNs but also on skeletal myofibers. However, the rescue of SMN expression in muscle appears to be necessary for the complete recovery of motor function.

Nusinersen ameliorates motor function and prevents motoneuron Cajal body disassembly and abnormal poly(A) RNA distribution in a SMA mouse model.

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

Nutrition and Rheumatoid Arthritis in the ‘Omics’ Era.

Nutritional Modulation of Hepcidin in the Treatment of Various Anemic States.

Nutritional Value and Preventive Role of Nigella sativa L. and Its Main Component Thymoquinone in Cancer: An Evidenced-Based Review of Preclinical and Clinical Studies.

O constitucionalismo liberal norte-americano no controle de constitucionalidade das leis: uma análise dos debates judiciais a respeito da liberdade contratual e propriedade privada na Era Lochner (1897-1937).

Obesity and its association with mental health among Mexican children and adolescents: systematic review.

Obesity and oral health in Mexican children and adolescents: systematic review and meta-analysis.

Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors.

“Oh, My God! My Season Is Over!” COVID-19 and Regulation of the Psychological Response in Spanish High-Performance Athletes.

Oleuropein Attenuates Oxidative Stress in Human Trophoblast Cells.

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

An Olive-Derived Extract 20% Rich in Hydroxytyrosol Prevents β-Amyloid Aggregation and Oxidative Stress, Two Features of Alzheimer Disease, via SKN-1/NRF2 and HSP-16.2 in Caenorhabditis elegans.

Optimal Modeling and Feasibility Analysis of Grid-Interfaced Solar PV/Wind/Pumped Hydro Energy Storage Based Hybrid System.

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.

Optimal Sizing and Power System Control of Hybrid Solar PV-Biogas Generator with Energy Storage System Power Plant.

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

Orbits Theory. A Complete Proof of the Collatz Conjecture.

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

Organ‐on‐Chip: The Future of Nutrition Research in a One Health World.

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

Parentalidad Positiva y Prevención de la Población infanto-juvenil con relación a su Salud Mental: una Revisión Actualizada.

Parents’ nutrition knowledge, perceived barriers and enablers, and healthy-eating attitudes associated with children’s adherence to the Mediterranean diet: the DELICIOUS project.

Patterns of cognitive-emotional change after cognitive-behavioural treatment in emotional disorders: A 12-month longitudinal cluster analysis.

Perceptions of the Body and Body Dissatisfaction in Primary Education Children According to Gender and Age. A Cross-Sectional Study.

Perceptions of the technical staff of professional teams regarding injury prevention in Spanish national futsal leagues: a cross-sectional study.

In the world of remote sensing, hyperspectral imaging has emerged as a powerful tool that captures incredibly detailed information about our environment. These images contain hundreds of spectral bands that reveal what the human eye cannot see, making them invaluable for applications ranging from precision agriculture to environmental monitoring. However, extracting insights from complex data requires sophisticated analytical approaches. Our research dives into the performance comparison of two popular machine learning approaches: the support vector machine (SVM) and the more recent deep learning-based stacked autoencoder (SAE). We wanted to understand which approach works better under different real-world conditions that researchers and practitioners face. Through extensive experiments across five diverse public hyperspectral datasets, we discovered that the choice between these models is not straightforward, it depends significantly on your specific circumstances. When labeled data are scarce, which is a common challenge in remote sensing, SVM proves more reliable and efficient. Conversely, when abundant training data are available, SAE demonstrates impressive capabilities in learning complex patterns. One interesting finding was how active learning as a technique that intelligently selects the most informative samples for labeling, improved SAE’s performance on medium-sized datasets, potentially offering a practical solution to the data scarcity problem. The proposed approaches showed vulnerability to noise, highlighting the importance of preprocessing steps in real-world applications. Although SVM generally requires less computational resources, SAE’s potential to handle large and complex datasets makes it an attractive option when the appropriate computing infrastructure is available. The model training also achieved high accuracy, compared to other models published in the literature. The results achieved provide a practical path for researchers and practitione...

Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis.

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

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

Background To successfully implement exercise programs for patients with metastatic breast cancer (MBC), services and patient education should consider patients’ knowledge, preferences, values, and goals. Hence, gaining insight into their perspectives on exercise and exercise programming is important. Method In this cross-sectional survey, we recruited patients with MBC from the Netherlands, Germany, Poland, Spain, and Sweden. We collected data on patients’ knowledge and skills about exercise and outcome expectations. We identified barriers to and facilitators of participation in exercise programs, and patients’ preferences for program content and modes of exercise delivery. Results A total of 420 patients participated in the survey. Respondents were, on average, 56.5 years old (SD 10.8) and 70% had bone metastases. Sixty-eight percent reported sufficient skills to engage in aerobic exercise, but only 35% did so for resistance exercise. Respondents expected exercise to have multiple physical benefits, but a few patients expected exercise to worsen their pain (5%). Not having access to an exercise program for cancer patients (27%), feeling too tired (23%), and/or weak (23%) were the most often reported barriers. Facilitators for exercising regularly were previous positive physical (72%) and emotional (68%) experiences with exercising, and receiving personalized advice from a physiotherapist or sport/fitness instructor (62%). Patients were most interested in walking and preferred exercising at a public gym, although there were differences by country. Fifty-seven percent did not know whether their insurance company reimburses exercise programs and only 9% would be willing to pay more than €50 per month to participate. Conclusion A large percentage of patients with MBC lack the skills to engage in regular exercise as recommended by exercise guidelines for people with cancer. Patients may benefit from personalized advice and appropriate training facilities to overcome barriers. When implementing exercise interventions, attention should be given to reimbursement and the relatively low willingness-to-pay.

Perspectives of patients with metastatic breast cancer on physical exercise programs: results from a survey in five European countries.

Phage Therapy as a Focused Management Strategy in Aquaculture.

Phages and Enzybiotics in Food Biopreservation.

Phenolic Compounds in Honey and Their Associated Health Benefits: A Review.

Physical Performance During Soccer-7 Competition and Small-Sided Games in U12 Players.

Phytochemical Composition and Cytotoxic Effects on Liver Hepatocellular Carcinoma Cells of Different Berries Following a Simulated In Vitro Gastrointestinal Digestion.

A Plant-Based Food Guide Adapted for Low-Fat Diets: The VegPlate Low-Fat (VP_LF).

Plant-Based Milk Alternatives in Child Nutrition.

Plant-Based and Plant-Rich Diet Patterns during Gestation: Beneficial Effects and Possible Shortcomings.

Plataforma virtual de educación Moodle para mejorar el proceso de enseñanza aprendizaje virtual en el modelo educativo por competencias. Estudio de caso: educación secundaria en Perú.

Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network.

Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model.

Polymyxin Resistance and Heteroresistance Are Common in Clinical Isolates of Achromobacter Species and Correlate with Modifications of the Lipid A Moiety of Lipopolysaccharide.

Polyphenol-Rich Strawberry Extract Protects Human Dermal Fibroblasts against Hydrogen Peroxide Oxidative Damage and Improves Mitochondrial Functionality.

Polyphenol-rich strawberry extract (PRSE) shows in vitro and in vivo biological activity against invasive breast cancer cells.

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

Positional demands for various-sided games with goalkeepers according to the most demanding passages of match play in football.

The post-pandemic stage covid-19 has revealed overloads, ambiguities, and conflicts of teachers in the performance of new roles in hybrid classrooms that demanded an urgent adaptation, this highlighted the need for priority attention to the mental health of teachers, however, there are still insufficient studies that transcend the diagnosis and are committed to establish proposals for improvement. OBJECTIVE: This study aims to establish a proposal for the promotion of positive mental health (PMH). METHODS: The study was deployed from a qualitative approach; using an ethnomethodological design that allowed studying how teachers create meanings and sense in their work context, an appreciative interview was conducted with an affirmative theme that allowed teachers to expose their experiences that were systematized and processed with ATLAS. ti software. The application of the interview was conducted online through a Google form, during the months of February and March 2023. Three hundred university professors who experienced the pandemic in universities in Brazil, Chile, Colombia, Ecuador, Mexico, and Peru participated, based on a convenience sampling. RESULTS: The results of the deductive phase confirmed Lluch's PMH theoretical framework; however, new nuances or variations have been identified, which must be considered in the complex and dynamic nature of each PMH factor. From there, the results of the inductive phase allowed revealing emerging concepts, that is, new categories that would have the function of improving the PMH factors, which is why they have been denominated: dynamizing nuclei. PMH dynamizing nuclei are adjustment to work environment, soft skills, work-family balance, self-motivation, self-efficacy, subjective well-being, proactive strategies, engagement, resilience. CONCLUSIONS: Finally, with the results of both phases, the creation of an integrated model was generated, which was evaluated by six experts in a round of feedback, who highlighted the relevance of the findings and offered recommendations that were considered in the study. The new integrated model has revealed an interesting association, since it not only legitimizes the PMH's dynamizing cores, but also informs on which specific factor of the PMH these cores have the greatest impact, which has a high guiding value for intervention and improvement based on focused strategies.

Positive mental health of Latin American university professors: A scientific framework for intervention and improvement.

Potential Role of the Mitochondria for the Dermatological Treatment of Papillon-Lefèvre.

This paper introduces a power quality (PQ) detection and categorization algorithm actuated by multiple signal processing techniques and rule-based decision tree (RBDT). This is aimed to recognize PQ events of simple nature and higher order multiplicity with less computational time using hybridization of the signal processing techniques. A voltage waveform with a PQ event (PQE) is processed using the Stockwell transform (ST) to compute the Stockwell PQ detection index (SPDI). The voltage waveform is also processed using the Hilbert transform (HT) to compute the Hilbert PQ detection index (HPDI). A voltage waveform is also decomposed using the Discrete Wavelet transform (DWT) to compute the classification feature index (CFI) [CFI1 to CFI4]. A combined PQ detection index (CPDI) is computed by multiplication of the SPDI, the HPDI and CFI1 to CFI4. Incidence of a PQE on a voltage signal is located with the help of a location PQ disturbance index (LPDI) which is computed by differentiating the CPDI with respect to time. CFI5, CFI6 and CFI7 are computed from the SPDI, the HPDI and the CPDI, respectively. Categorization of PQ events is performed using CFI1 to CFI7 by the rule-based decision tree (RBDT) with the help of simple decision rules. We conclude that the proposed algorithm is effective to identify the PQE with an accuracy of 98.58% in a noise-free environment and 97.62% in the presence of 20 dB SNR (signal-to-noise ratio) noise. Ten simple nature PQEs and eight combined PQ events (CPQEs) with multiplicity of two, three and four are effectively detected and categorized using the algorithm. The algorithm is also tested to detect a sag PQ event due to a line-to-ground (LG) fault incident on a practical distribution utility network. The performance of the investigated method is compared with a DWT-based technique in terms of accuracy of classification with and without noise, maximum computational time of PQ detection and multiplicity of PQE which can be effectively detected. A simulation is performed using the MATLAB software. MATLAB codes are used for modelling the PQE disturbances and the proposed algorithm using mathematical formulations.

Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree.

Predicción de la accidentabilidad en función de los comportamientos arriesgados y agresivos al volante: diferencias según la edad y el género.

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

Prediction β-Thalassemia carriers using complete blood count features.

The Predictive and Moderating role of Psychological Flexibility in the Development of Job Burnout.

Preferencias de textos literarios y científicos según el grado universitario.

Prehospital Emergency Medicine at the Beach: What Is the Effect of Fins and Rescue Tubes in Lifesaving and Cardiopulmonary Resuscitation After Rescue?

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

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

Introduction: Rotavirus infection is a major cause of mortality among children under 5 years in Bangladesh. There is lack of integrated studies on rotavirus prevalence and genetic diversity during 1973 to 2023 in Bangladesh. Methods: This meta-analysis was conducted to determine the prevalence, genotypic diversity and seasonal distribution of rotavirus during pre-vaccination period in Bangladesh. This study included published articles on rotavirus A, rotavirus B and rotavirus C. We used Medline, Scopus and Google Scholar for published articles. Selected literatures were published between 1973 to 2023. Results: This study detected 12431 research articles published on rotavirus. Based on the inclusion criteria, 29 of 75 (30.2%) studies were selected. Molecular epidemiological data was taken from 29 articles, prevalence data from 29 articles, and clinical symptoms from 19 articles. The pooled prevalence of rotavirus was 30.1% (95% CI: 22%-45%, p = 0.005). Rotavirus G1 (27.1%, 2228 of 8219) was the most prevalent followed by G2 (21.09%, 1733 of 8219), G4 (11.58%, 952 of 8219), G9 (9.37%, 770 of 8219), G12 (8.48%, 697 of 8219), and G3 (2.79%, 229 of 8219), respectively. Genotype P[8] (40.6%, 2548 of 6274) was the most prevalent followed by P[4] (12.4%, 777 of 6274) and P[6] (6.4%, 400 of 6274), respectively. Rotavirus G1P[8] (19%) was the most frequent followed by G2P [4] (9.4%), G12P[8] (7.2%), and G9P[8], respectively. Rotavirus infection had higher odds of occurrence during December and February (aOR: 2.86, 95% CI: 2.43-3.6, p = 0.001). Discussion: This is the first meta-analysis including all the studies on prevalence, molecular epidemiology, and genetic diversity of rotavirus from 1973 to 2023, pre-vaccination period in Bangladesh. This study will provide overall scenario of rotavirus genetic diversity and seasonality during pre-vaccination period and aids in policy making for rotavirus vaccination program in Bangladesh. This work will add valuable knowledge for vaccination against rotavirus and compare the data after starting vaccination in Bangladesh.

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.

Prevalence of undiagnosed diabetes and prediabetes related to periodontitis and its risk factors in elderly individuals.

Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic.

Propiedades psicométricas de una versión breve del Driving Anger Expression Inventory en conductores españoles [Psychometric Properties of a Short Version of the Driving Anger Expression Inventory (DAX) in Spanish Drivers].

Propuesta preventiva sobre el esguince de tobillo en jugadoras de 2ªRFEF Futsal.

A Prospective Observational Study of Frailty in Geriatric Revitalization Aimed at Community-Dwelling Elderly.

Protections in the Recreational Practice of Ski and Snowboard—An Age and Gender Discussion? A Case Study in Spain.

Protective Effect of Strawberry Extract against Inflammatory Stress Induced in Human Dermal Fibroblasts.

A Proxy Approach to Family Involvement and Neurocognitive Function in First Episode of Non-Affective Psychosis: Sex-Related Differences.

Psicomotricidade relacional e o ensino aprendizagem na educação infantil.

Psychological Adjustment in Adult Adoptees: A Meta-Analysis.

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

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

Pyroptosis: A Novel Therapeutic Target for Bioactive Compounds in Human Disease Treatment? A Narrative Review.

Quantification of a Professional Football Team's External Load Using a Microcycle Structure.

RNA Interference Strategies for Future Management of Plant Pathogenic Fungi: Prospects and Challenges.

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

Readaptación de un instrumento para la evaluación de entornos virtuales de aprendizaje en el proyecto europeo de educación inclusiva denominado LOVEDISTANCE.

Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices.

Real Word Spelling Error Detection and Correction for Urdu Language.

Background: Current standard of care for la-mUC pts who show no progression after platinum-based chemotherapy is maintenance with avelumab based on survival improvement (JAVELIN 100; Powles et al. NEJM 2020). However, the available RWD evidence on the use of avelumab in the E.U. is limited and there are concerns about the low uptake of this strategy based on figures from American series (Mamtani R et al; JAMA Netw Open 2023). We present here data on the use of avelumab in a large cohort of pts from different centres in Spain within the academic group GO NORTE. Methods: AVEBLADDER is a retrospective observational analysis in which clinical information was retrieved from pts treated across 14 centres in 13 provinces in Northern Spain. The study population included adult pts diagnosed with la/mUC (January 1, 2021-June 30, 2023) followed from date of diagnosis until death, loss to follow-up or end of study. Median overall survival (OS) was determined using the Kaplan-Meier method. Results: 419 pts were included. Median age was 71 [range 42-88]; 80% were males; 81% had primary bladder tumours and 94% predominant urothelial histology. Seventy three percent of pts had visceral/bony metastases and 59% were unfit for cisplatin. Most common 1st line treatment (tx) [88%] was platinum-based chemotherapy [median number of cycles 4]. Out of 369 pts who received platinum-based chemotherapy, non-progression (CR, PR or SD) was reported in 230 pts [62%], of whom only 85 pts [37%] received maintenance avelumab. Fifty-eight pts treated with avelumab were evaluable for response: 7 (10 %) achieved a CR, 12 (14%) PR, 22 (26%) SD and 17 (20%) PD. The most common reason for non-receiving avelumab in our series was lack of access/reimbursement according to country/region policy. Overall, 168 pts [40%] started 2nd line tx and atezolizumab was the most used agent, only 41 pts [24%] received third line tx. With a median follow-up of 11 months, 194 pts [46%] are still alive and median overall survival (OS) is estimated to be 28 months (95% CI 23-not reached) with maintenance avelumab vs 11 months (95% CI 9-13) for those who did not receive this drug. Conclusions: Despite the demonstrated improvement in OS for maintenance avelumab, its uptake in our series was low with roughly 40% of the pts. New policies and better access to the drug will most likely improve these figures and hopefully also the proportion of patients who progress to receive a third line where novel therapies are currently being implemented. AVEBLADDER is a study sponsored by GO NORTE a non-for-profit GU cooperative group.

Real-world data (RWD) with avelumab in patients (pts) with locally advanced or metastatic urothelial cancer (la-mUC): The AVEBLADDER study.

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?

Reconocimiento de expresión facial emocional en el trastorno de déficit de atención e hiperactividad en la infancia.

Recovering high value-added anthocyanins from blueberry pomace with ultrasound-assisted extraction.

Recovery of value-added anthocyanins from mulberry by a cation exchange chromatography.

The Regular Consumption of Nuts Is Associated with a Lower Prevalence of Abdominal Obesity and Metabolic Syndrome in Older People from the North of Spain.

Relación entre estilos de apego y regulación emocional.

Relación entre la dismetría de los miembros inferiores y la distribución de fuerzas en el pedaleo en ciclistas no profesionales.

The Relationship Between Intensity Indicators in Small-Sided Soccer Games.

The Relative Age Effect in Professional Futsal Players.

Relative Age Effect on Motor Competence in Children Aged 4–5 Years.

Reliability and Usefulness of the 30-15 Intermittent Fitness Test in Male and Female Professional Futsal Players.

Remote Sensing and Environmental Monitoring Analysis of Pigment Migrations in Cave of Altamira’s Prehistoric Paintings.

Repercusiones del estigma en la calidad de vida de los adultos con VIH/SIDA: Una revisión sistemática.

Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms.

Cardiovascular diseases are among the leading causes of mortality worldwide, with dietary factors being the main risk contributors. Diets rich in bioactive compounds, such as (poly)phenols, have been shown to potentially exert positive effects on vascular health. Among them, resveratrol has gained particular attention due to its potential antioxidant and anti-inflammatory action. Nevertheless, the results in humans are conflicting possibly due to interindividual different responses. The gut microbiota, a complex microbial community that inhabits the gastrointestinal tract, has been called out as potentially responsible for modulating the biological activities of phenolic metabolites in humans. The present review aims to summarize the main findings from clinical trials on the effects of resveratrol interventions on endothelial and vascular outcomes and review potential mechanisms interesting the role of gut microbiota on the metabolism of this molecule and its cardioprotective metabolites. The findings from randomized controlled trials show contrasting results on the effects of resveratrol supplementation and vascular biomarkers without dose-dependent effect. In particular, studies in which resveratrol was integrated using food sources, i.e., red wine, reported significant effects although the resveratrol content was, on average, much lower compared to tablet supplementation, while other studies with often extreme resveratrol supplementation resulted in null findings. The results from experimental studies suggest that resveratrol exerts cardioprotective effects through the modulation of various antioxidant, anti-inflammatory, and anti-hypertensive pathways, and microbiota composition. Recent studies on resveratrol-derived metabolites, such as piceatannol, have demonstrated its effects on biomarkers of vascular health. Moreover, resveratrol itself has been shown to improve the gut microbiota composition toward an anti-inflammatory profile. Considering the contrasting findings from clinical studies, future research exploring the bidirectional link between resveratrol metabolism and gut microbiota as well as the mediating effect of gut microbiota in resveratrol effect on cardiovascular health is warranted.

Resveratrol and vascular health: evidence from clinical studies and mechanisms of actions related to its metabolites produced by gut microbiota.

Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in deepfake images and video detection, such as deepfake creation, various detection algorithms on self-made datasets, and existing benchmark datasets.

A Review of Image Processing Techniques for Deepfakes.

The Revisions of the First Autobiography of AT Still, the Founder of Osteopathy, as a Step towards Integration in the American Healthcare System: A Comparative and Historiographic Review.

Revisión sistemática sobre la mejora de la velocidad en jugadores de fútbol sub-19.

Revisión sistemática: Estrategias para la mejora de la sintomatología en tendinopatía aquílea en atletas.

Revolutionary Strategies Analysis and Proposed System for Future Infrastructure in Internet of Things.

Riesgo cardiometabólico y variación en el contenido graso/adiposo según el índice de masa corporal en niños de seis a nueve años.

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

Spice by-products, often discarded as waste, represent an untapped resource for sustainable packaging solutions due to their unique, multifunctional, and bioactive profiles. Unlike typical plant residues, these materials retain diverse phytochemicals—including phenolics, polysaccharides, and other compounds, such as essential oils and vitamins—that exhibit controlled release antimicrobial and antioxidant effects with environmental responsiveness to pH, humidity, and temperature changes. Their distinctive advantage is in preserving volatile bioactives, demonstrating enzyme-inhibiting properties, and maintaining thermal stability during processing. This review encompasses a comprehensive characterization of phytochemicals, an assessment of the re-utilization pathway from waste to active materials, and an investigation of processing methods for transforming by-products into films, coatings, and nanoemulsions through green extraction and packaging film development technologies. It also involves the evaluation of their mechanical strength, barrier performance, controlled release mechanism behavior, and effectiveness of food preservation. Key findings demonstrate that ginger and onion residues significantly enhance antioxidant and antimicrobial properties due to high phenolic acid and sulfur-containing compound concentrations, while cinnamon and garlic waste effectively improve mechanical strength and barrier attributes owing to their dense fiber matrix and bioactive aldehyde content. However, re-using these residues faces challenges, including the long-term storage stability of certain bioactive compounds, mechanical durability during scale-up, natural variability that affects standardization, and cost competitiveness with conventional packaging. Innovative solutions, including encapsulation, nano-reinforcement strategies, intelligent polymeric systems, and agro-biorefinery approaches, show promise for overcoming these barriers. By utilizing these spice by-products, the packaging industry can advance toward a circular bio-economy, depending less on traditional plastics and promoting environmental sustainability in light of growing global population and urbanization trends.

The Road to Re-Use of Spice By-Products: Exploring Their Bioactive Compounds and Significance in Active Packaging.

The Role of Dietary Polyphenols in Pregnancy and Pregnancy-Related Disorders.

Role of Empathy and Lifelong Learning Abilities in Physicians and Nurses Who Work in Direct Contact with Patients in Adverse Working Conditions.

The Role of Oxidative Stress in Periodontitis.

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

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

Routing Protocol for MANET Based on QoS-Aware Service Composition with Dynamic Secured Broker Selection.

The SAM-m6A axis as an unexplored therapeutic hub for plant-derived regulation of disease metabolism.

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

SEED-ML: A multi-parametric clinical dataset on male infertility for predictive modeling and AI research.

Satellite Glial Cells of the Dorsal Root Ganglion: A New “Guest/Physiopathological Target” in ALS.

Scalable Comprehensive Automatic Inspection, Cleaning, and Evaluation Mechanism for Large‐Diameter Pipes.

Schoolchildren’s Thinking on the Subject and Teachers of Physical Education According to Gender and Educational Grade.

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

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

A Secure and Efficient Signature Scheme for IoT in Healthcare.

With increased Internet of Things (IoT) devices, complexity and protection are more challenging. Lightweight cryptographic algorithms are secure and suitable for limited-resource environments; however, their hash functions provide encrypted data but not integrity. Strong security features are available, but setup is difficult and expensive. Network security mechanisms increase power consumption and latency. As IoT networks grow, managing cryptographic keys and securely authenticating large numbers of devices become complex tasks. Efficient key management strategies are required to ensure the scalability required. Existing state-of-the-art solutions lack standardization, scalability, complex and costly. Thus, this research proposes a secure solution for IoT resource-constrained devices, combining strong data integrity and lightweight encryption, and is thus named a hybrid. This hybrid approach integrates SHA-512 and the present cipher in our proposed approach and thus ensuring higher security than state-of-the-art models. This intelligent combination not only enhances the algorithm’s resistance against cryptographic attacks but also improves its processing speed. The proposed approach is used to reduce the processing time for encryption in the IoT platform and to preserve the trade-off between security and efficiency. In terms of memory use, execution time, and precision, the proposed approach is compared with recent state-of-the-art research. The experimental results indicate that our approach is efficient using the avalanche, authentication success rate, collision events, and execution time. The efficiency is 53% to 65%, and the avalanche effect indicates sensitivity to input variations, suggesting moderate-to-considerable reactivity to small data changes. The experimental tests conducted across 10,000 and 80,000 runs reveal no collisions and found that the proposed approach is resilient in managing unique IDs. Moreover, our approach performs consistently, with an average execution time of 0.088246 s, ranging from 0.075954 to 0.094583 s. Finally, our approach provides a practical and scalable solution for securing IoT devices in resource-constrained environments, addressing practical problems for IoT devices.

Securing internet of things devices using a hybrid approach.

Self-Perceptions on Digital Competences for M-Learning and Education Sustainability: A Study with Teachers from Different Countries.

Self-bias and the emotionality of foreign languages.

Self-perception of efficacy and attitudes towards physical activity and sport in schoolchildren.

Sequential Changes of NLRP3 Inflammasome Activation in Sepsis and its Relationship With Death.

Background 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.

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.

Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification.

Assessment of side effects associated with COVID-19 vaccination is required to monitor safety issues and acceptance of vaccines in the long term. We found a significant knowledge gap in the safety profile of COVID-19 vaccines in Bangladesh. We enrolled 1805 vaccine recipients from May 5, 2021, to April 4, 2023. Kruskal-Wallis test and χ2 test were performed. Multivariable logistic regression was also performed. First, second and third doses were administered among 1805, 1341, and 923 participants, respectively. Oxford–AstraZeneca (2946 doses) was the highest administered followed by Sinopharm BIBP (551 doses), Sinovac (214 doses), Pfizer-BioNTech (198 doses), and Moderna (160 doses), respectively. Pain at the injection site (80-90%, 3200–3600), swelling (85%, 3458), redness (78%, 3168), and heaviness in hand (65%, 2645) were the most common local effects, and fever (85%, 3458), headache (82%, 3336), myalgia (70%, 2848), chills (67%, 2726), muscle pain (60%, 2441) were the most prevalent systemic side effects reported within 48 h of vaccination. Thrombosis was only reported among the Oxford–AstraZeneca recipients (3.5-5.7%). Both local and systemic effects were significantly associated with the Oxford–AstraZeneca (p-value < 0.05), Pfizer–BioNTech (p-value < 0.05), and Moderna (p-value < 0.05) vaccination. Chronic urticaria and psoriasis were reported by 55-60% of the recipients after six months or later. The highest percentage of local and systemic effects after 2nd and 3rd dose were found among recipients of Moderna followed by Pfizer-BioNTech and Oxford–AstraZeneca. Homogenous doses of Oxford–AstraZeneca and heterogenous doses of Moderna and Pfizer-BioNTech were significantly associated with elevated adverse effects. Females, aged above 60 years with preexisting health conditions had higher risks. Vaccination with Pfizer-BioNTech (OR 4.34, 95% CI 3.95–4.58) had the highest odds of severe and long-term effects followed by Moderna (OR 4.15, 95% CI 3.92–4.69) and Oxford–AstraZeneca (OR 3.89, 95% CI 3.45–4.06), respectively. This study will provide an integrated insight into the safety profile of COVID-19 vaccines.

Side effects associated with homogenous and heterogenous doses of Oxford–AstraZeneca vaccine among adults in Bangladesh: an observational study.

Similarities between the Effects of Prenatal Chlorpyrifos and Valproic Acid on Ultrasonic Vocalization in Infant Wistar Rats.

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

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

Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses.

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

Social cognition, personality dimensions and clinical symptoms as variable predictors in people with polydrug abuse in treatment.

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

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

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

Strawberry (Fragaria × ananassa cv. Romina) methanolic extract attenuates Alzheimer’s beta amyloid production and oxidative stress by SKN-1/NRF and DAF-16/FOXO mediated mechanisms in C. elegans.

Strawberry (cv. Romina) Methanolic Extract and Anthocyanin-Enriched Fraction Improve Lipid Profile and Antioxidant Status in HepG2 Cells.

Strawberry-Based Cosmetic Formulations Protect Human Dermal Fibroblasts against UVA-Induced Damage.

Strawberry-Tree Honey Induces Growth Inhibition of Human Colon Cancer Cells and Increases ROS Generation: A Comparison with Manuka Honey.

Strength Training Habits in Amateur Endurance Runners in Spain: Influence of Athletic Level.

Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI.

Stressful life events and hopelessness in adults: the mediating role of mentalization and emotional dysregulation.

Study of Motor Competence in 4–5-Year-Old Preschool Children: Are There Differences among Public and Private Schools?

Background: Suicidal ideation and non-suicidal selfinjury (NSSI) are highly prevalent during early adolescence and represent serious public health concerns. Emotional dysregulation has been identified as a transdiagnostic factor underlying various risk behaviors, particularly in youth with externalizing difficulties such as attentiondeficit/hyperactivity disorder (ADHD). While prior research has examined these factors independently, few studies have explored their joint and differential contribution to suicide-related outcomes in early adolescence. This study aimed to examine the association of ADHD-related symptoms, positive and negative affect, and emotional dysregulation with suicidal ideation and NSSI, as well as to explore whether emotional dysregulation moderates the relationship between ADHD-related symptoms and these risk behaviors. Methods: A total of 1079 Spanish adolescents (Mage = 12.6 years, SD = 0.6) enrolled in the first year of compulsory secondary education participated in a cross-sectional study. Standardized self-report measures were used to assess ADHD-related symptoms (Strengths and Difficulties Questionnaire [SDQ]), affect (Positive and Negative Affect Schedule for Children and Adolescents [PANAS-N]), and difficulties in emotion regulation (Difficulties in Emotion Regulation Scale [DERS-18]), alongside indicators of suicidal ideation and NSSI. Data analysis included group comparisons and hierarchical logistic regression models. Results: Suicidal ideation was reported by 10.1% of participants, and NSSI by 16.4%. Adolescents with either behavior exhibited significantly more hyperactivityinattention symptoms, greater negative affect, lower positive affect, and substantially elevated emotional dysregulation. In regression analyses, ADHD-related symptoms remained significant predictors after accounting for affect, although emotional dysregulation emerged as the strongest predictor, reducing the effects of other variables. Specific dimensions such as lack of emotional clarity and nonacceptance of emotional responses were associated with suicidal ideation, while impulse control difficulties and lack of adaptive strategies predicted NSSI. No moderation effects were found. Conclusions: ADHD-related symptoms and emotional dysregulation both contribute independently to adolescent suicidal ideation and NSSI, with emotional dysregulation showing the most robust predictive value. Schoolbased prevention efforts should incorporate emotion regulation skill-building to reduce suicide risk and NSSI in early adolescence.

Suicidal Ideation and Non-suicidal Self-injury in Early Adolescence: The Role of ADHD Symptoms, Affect, and Emotion Regulation.

Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model.

Supervised, structured and individualized exercise in metastatic breast cancer: a randomized controlled trial.

Suplementación con ácidos grasos poliinsaturados omega 3 frente a una dieta mediterránea como tratamiento para la enfermedad del hígado graso no alcohólico.

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

Sustentabilidade em sistemas de segurança do trabalho na construção civil.

Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective.

A Systematic Literature Review on Integrated Deep Learning and Multi-Agent Vision-Language Frameworks for Pathology Image Analysis and Report Generation.

A Systematic Review and Meta-Analysis of Premenstrual Syndrome with Special Emphasis on Herbal Medicine and Nutritional Supplements.

Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques.

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

Objective: The objective of this paper is to review and analyze the current state of telemedicine and ehealth in the field of vascular surgery. Methods: This paper collects the relevant information obtained after reviewing the articles related to telemedicine in vascular surgery, published from 2012 to 2022 contained in scientific databases. In addition, the results obtained are statistically studied based on various factors, such as the year of publication or the search engine. In this way, we obtain a complete vision of the current state of telemedicine in the field of vascular surgery. Results: After performing this search and applying selection criteria, 29 articles were obtained for subsequent study and discussion, of which 20 were published in the second half of the decade, representing 70% of the results. In the analysis carried out according to the search criteria used, it can be seen that using the word telemedicine we obtained 69% of the articles while with the criteria mHealth and eHealth we only obtained 22% and 9% of the results, respectively. It can be seen that the filter with the most potential content articles was “vascular surgery AND telemedicine”. In the analysis performed according to the search engine, it was observed that the Google Scholar database contains 93% of the articles found in the massive search and the relevant articles contained therein represent 52% of the total. Conclusion: An upward trend has been observed in recent years, with a clear increase in the number of publications and much lower figures in the first years. One aspect to highlight is that 47.8% of the articles analyzed focus only on postoperative treatment, which may be due to the help provided by telemedicine in detecting surgical site infections by sending images and videos, this being one of the most common postoperative complications. The analyzed works show the importance of telemedicine in vascular surgery and identify possible future lines of research. In the analysis carried out on the origin of the selected relevant papers, an important interest of the US in this topic is demonstrated since more than 50% of the research contains authors from this country, it is also observed that there is no research from Spain, so this research would be an initial step to determine the weaknesses of telemedicine in this field of medicine and a good opportunity to open a research gap in this branch.

Systematic Review of Telemedicine and eHealth Systems Applied to Vascular Surgery.

A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis.

A Taxonomy and Analysis on Internet of Vehicles: Architectures, Protocols, and Challenges.

Teaching cornerball: a didactic proposal based on the sport education model.

Technocreativity, Social Networks and Entrepreneurship: Diagnostics of Skills in University Students.

The probability of medical staff to get affected from COVID19 is much higher due to their working environment which is more exposed to infectious diseases. So, as a preventive measure the body temperature monitoring of medical staff at regular intervals is highly recommended. Infrared temperature sensing guns have proved its effectiveness and therefore such devices are used to monitor the body temperature. These devices are either used on hands or forehead. As a result, there are many issues in monitoring the temperature of frontline healthcare professionals. Firstly, these healthcare professionals keep wearing PPE (Personal Protective Equipment) kits during working hours and as a result it would be very difficult to monitor their body temperature. Secondly, these healthcare professionals also wear face shields and in such cases monitoring temperature by exposing forehead needs removal of face shield. Doing so after regular intervals is surely uncomfortable for healthcare professionals. To avoid such issues, this paper is disclosing a technologically advanced face shield equipped with sensors capable of monitoring body temperature instantly without the hassle of removing the face shield. This face shield is integrated with a built-in infrared temperature sensor. A total of 10 such face shields were printed and assembled within the university lab and then handed over to a group of ten members including faculty and students of nursing and health science department. This sequence was repeated four times and as a result 40 healthcare workers participated in the study. Thereafter, feedback analysis was conducted on questionnaire data and found a significant overall mean score of 4.59 out of 5 which indicates that the product is effective and worthy in every facet. Stress analysis is also performed in the simulated environment and found that the device can easily withstand the typically applied forces. The limitations of this product are difficulty in cleaning the product and comparatively high cost due to the deployment of electronic equipment

Technologically Advanced Reusable 3D Face Shield for Health Workers Confronting COVID19.

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

Tecnologías de conservación de frutos rojos basadas en residuos de Eucalyptus Globulus.

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

Telemedicine and e-Health research solutions in literature for combatting COVID-19: a systematic review.

Background: Scientific research should be carried out to prevent sports injuries. For this purpose, new assessment technologies must be used to analyze and identify the risk factors for injury. The main objective of this systematic review was to compile, synthesize and integrate international research published in different scientific databases on Countermovement Jump (CMJ), Functional Movement Screen (FMS) and Tensiomyography (TMG) tests and technologies for the assessment of injury risk in sport. This way, this review determines the current state of the knowledge about this topic and allows a better understanding of the existing problems, making easier the development of future lines of research. Methodology: A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines and the PICOS model until November 30, 2024, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus and Scopus databases. The risk of bias was assessed and the PEDro scale was used to analyze methodological quality. Results: A total of 510 articles were obtained in the initial search. After inclusion and exclusion criteria, the final sample was 40 articles. These studies maintained a high standard of quality. This revealed the effects of the CMJ, FMS and TMG methods for sports injury assessment, indicating the sample population, sport modality, assessment methods, type of research design, study variables, main findings and intervention effects. Conclusions: The CMJ vertical jump allows us to evaluate the power capacity of the lower extremities, both unilaterally and bilaterally, detect neuromuscular asymmetries and evaluate fatigue. Likewise, FMS could be used to assess an athlete's basic movement patterns, mobility and postural stability. Finally, TMG is a non-invasive method to assess the contractile properties of superficial muscles, monitor the effects of training, detect muscle asymmetries, symmetries, provide information on muscle tone and evaluate fatigue. Therefore, they should be considered as assessment tests and technologies to individualize training programs and identify injury risk factors.

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.

Threatening URDU Language Detection from Tweets Using Machine Learning.

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques.

Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework.

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

This study addresses the features of the internal structure of the geological layers adjacent to the Polychrome Hall ceiling of the Cave of Altamira (Spain) and their link to the distribution of moisture and geological discontinuities mainly as fractures, joints, bedding planes and detachments, using 3D Ground Penetrating Radar (GPR) mapping. In this research, 3D GPR data were collected with 300 MHz, 800 MHz and 1.6 GHz center frequency antennas. The data recorded with these three frequency antennas were combined to further our understanding of the layout of geological discontinuities and how they link to the moisture or water inputs that infiltrate and reach the ceiling surface where the rock art of the Polychrome Hall is located. The same 1 × 1 m2 area was adopted for 3D data acquisition with the three antennas, obtaining 3D isosurface (isoattribute-surface) images of internal distribution of moisture and structural features of the Polychrome Hall ceiling. The results derived from this study reveal significant insights into the overlying karst strata of Polychrome Hall, particularly the interface between the Polychrome Layer and the underlying Dolomitic Layer. The results show moisture patterns associated with geological features such as fractures, joints, detachments of strata and microcatchments, elucidating the mechanisms driving capillary rise and water infiltration coming from higher altitudes. The study primarily identifies areas of increased moisture content, correlating with earlier observations and enhancing our understanding of water infiltration patterns. This underscores the utility of 3D GPR as an essential tool for informing and putting conservation measures into practice. By delineating subsurface structures and moisture dynamics, this research contributes to a deeper analysis of the deterioration processes directly associated with the infiltration water both in this ceiling and in the rest of the Cave of Altamira, providing information to determine its future geological and hydrogeological evolution.

Tracking Moisture Dynamics in a Karst Rock Formation Combining Multi-Frequency 3D GPR Data: A Strategy for Protecting the Polychrome Hall Paintings in Altamira Cave.

Training Habits of Eumenorrheic Active Women during the Different Phases of Their Menstrual Cycle: A Descriptive Study.

Training intensity distribution and performance of a recreational male endurance runner. A case report.

A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment.

Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing.

Transmission Network Loss Reduction and Voltage Profile Improvement Using Network Restructuring and Optimal DG Placement.

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

Turismo Cultural y Policy Network: caracterización del modelo de negocio en la comuna de Camarones, Región de Arica y Parinacota, Chile.

Twitting Against the Enemy: Populist Radical Right Parties Discourse Against the (Political) “Other”.

Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence.

Ultra-processed food (UPF) consumption has been the focus of major attention due to their potential effects on human health. The aim of this study was to investigate the intake of UPFs in a sample of southern Italian individuals and assess its relationship with nutrient profile and dietary quality parameters. A cross-sectional study was conducted on 1936 individuals older than 18 years randomly selected from the general population. A total of 110 food times have been categorized based on the level of processing using the NOVA classification. The average daily energy intake of the sample was 2091.1 kcal, 38.7% of which were from the NOVA group of unprocessed/minimally processed foods, 5.7% from processed culinary ingredients, 38.3% from processed foods, and 17.9% from the UPFs group. UPFs were more consumed among young, unmarried individuals, with high cultural level, smokers, and often eating out of home. The mean energy share of UPFs varied from 6.3% of total daily energy intake for individuals in the lowest quintile of UPF consumption to 34.2% for those in the upper quintile. Within the UPF group, the highest energy contribution was provided by fast foods and sweets. Compared to the lowest quintile of UPF consumption, individuals in the highest quintile consumed, on average, additional 300 kcals per day and less fiber. Some plant-derived vitamins, such as vitamin A and vitamin C showed an inverse trend toward increasing shares of UPF consumption, while sodium intake increased. A significant higher intake of UPFs in individuals meeting the European and Italian dietary recommendations for carbohydrates, vitamin B12, vitamin D, and vitamin E was found, while UPFs were less consumed among those meeting the recommendations for total fats, fiber, sodium, potassium, and vitamin C. Finally, individuals displaying a “healthier” dietary profile, such as higher adherence to either the Mediterranean diet, the Dietary Approaches to Stop Hypertension, the Alternate Diet Quality Index, and the Diet Quality Index-International, consumed less UPFs and more unprocessed/minimally processed foods, with minor variation in the other NOVA food categories. In conclusion, consumption of UPF in southern Italy is in line with those reported in some other Mediterranean countries, although it negatively impacted the nutrient profile. It is important to monitor the consumption of UPFs before their availability and popularity put the grounds on younger generations’ dietary habits

Ultra-Processed Food Consumption and Relation with Diet Quality and Mediterranean Diet in Southern Italy.

Ultra-Small Iron Nanoparticles Target Mitochondria Inducing Autophagy, Acting on Mitochondrial DNA and Reducing Respiration.

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.

Un enfoque psicosocial aplicado a la planificación y desarrollo territorial el caso de la costa de Cantabria (España).

Over the last decades, the Mediterranean diet gained enormous scientific, social, and commercial attention due to proven positive effects on health and undeniable taste that facilitated a widespread popularity. Researchers have investigated the role of Mediterranean-type dietary patterns on human health all around the world, reporting consistent findings concerning its benefits. However, what does truly define the Mediterranean diet? The myriad of dietary scores synthesizes the nutritional content of a Mediterranean-type diet, but a variety of aspects are generally unexplored when studying the adherence to this dietary pattern. Among dietary factors, the main characteristics of the Mediterranean diet, such as consumption of fruit and vegetables, olive oil, and cereals should be accompanied by other underrated features, such as the following: (i) specific reference to whole-grain consumption; (ii) considering the consumption of legumes, nuts, seeds, herbs and spices often untested when exploring the adherence to the Mediterranean diet; (iii) consumption of eggs and dairy products as common foods consumed in the Mediterranean region (irrespectively of the modern demonization of dietary fat intake). Another main feature of the Mediterranean diet includes (red) wine consumption, but more general patterns of alcohol intake are generally unmeasured, lacking specificity concerning the drinking occasion and intensity (i.e., alcohol drinking during meals). Among other underrated aspects, cooking methods are rather simple and yet extremely varied. Several underrated aspects are related to the quality of food consumed when the Mediterranean diet was first investigated: foods are locally produced, minimally processed, and preserved with more natural methods (i.e., fermentation), strongly connected with the territory with limited and controlled impact on the environment. Dietary habits are also associated with lifestyle behaviors, such as sleeping patterns, and social and cultural values, favoring commensality and frugality. In conclusion, it is rather reductive to consider the Mediterranean diet as just a pattern of food groups to be consumed decontextualized from the social and geographical background of Mediterranean culture. While the methodologies to study the Mediterranean diet have demonstrated to be useful up to date, a more holistic approach should be considered in future studies by considering the aforementioned underrated features and values to be potentially applied globally through the concept of a “Planeterranean” diet.

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

Understanding the reasons to avoid seeking mental health professionals: Validation of the MITOS-MENTAL questionnaire in Peru population.

Underwater Thermal Energy Harvesting: Frameworks, Challenges, Applications, and Future Investigation.

Unhealthy Ultra-Processed Food Consumption in Children and Adolescents Living in the Mediterranean Area: The DELICIOUS Project.

Unhealthy Ultra-Processed Food, Diet Quality and Adherence to the Mediterranean Diet in Children and Adolescents: The DELICIOUS Project.

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

Unveiling the truth: A systematic review of fact-checking and fake news research in social sciences.

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.

Utilization of Stockwell Transform, Support Vector Machine and D-STATCOM for the Identification, Classification and Mitigation of Power Quality Problems.

VRK1 (Y213H) homozygous mutant impairs Cajal bodies in a hereditary case of distal motor neuropathy.

Validation and Reliability of the Spanish Internet Addiction Test-7 (IAT-7) for Adolescents.

Validation of a Food Knowledge Questionnaire on Tanzanian Women of Childbearing Age.

Validation of the 15-Item and 5-Item Versions of the Perceived Physical Literacy Instrument for Spanish Adolescents Aged 11–18: A Study Using the Original 18-Item Version.

Validation of the CaMir-R attachment questionnaire in an adult spanish sample.

Validity of a new tracking device for futsal match.

Validity, Reliability and Reproducibility of OctoBalance Test as Tool to Measure the Upper Limb Compared to Modified-Upper Quarter Y-Balance Test.

Valorization of Purple Prickly Pear Peel By‐Products: Antiproliferative and Pro‐Apoptotic Effects on Human Colorectal Cancer Cells HCT116.

Variables related to Physical Exercise in Cancer Patients and Survivors.

The VegPlate for Sports: A Plant-Based Food Guide for Athletes.

Ventilator pressure prediction employing voting regressor with time series data of patient breaths.

Violencia de género en período de pandemia de coronavirus en los países del G-20: Campañas publicitarias en redes sociales.

Virtual histopathology methods in medical imaging - a systematic review.

Virtues and shortcomings of guidance and tutoring in higher education: a longitudinal study of the TIMONEL Project.

Visualization of Customized Convolutional Neural Network for Natural Language Recognition.

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

WPAN and IoT Enabled Automation to Authenticate Ignition of Vehicle in Perspective of Smart Cities.

Wastewater Treatment with Technical Intervention Inclination towards Smart Cities.

What Kind of Islamophobia? Representation of Muslims and Islam in Italian and Spanish Media.

What works in financial education? Experimental evidence on program impact.

When Populists Govern the Country: Strategies of Legitimization of Anti-Immigration Policies in Salvini’s Italy.

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

Whole Genome Analysis of Pediococcus acidilactici XJ-24 and Its Role in Preventing Listeria monocytogenes ATCC® 19115TM Infection in C57BL/6 Mice.

Why Percussive Massage Therapy Does Not Improve Recovery after a Water Rescue? A Preliminary Study with Lifeguards.

A Wider Impedance Bandwidth Dual Filter Symmetrical MIMO Antenna for High-Speed Wideband Wireless Applications.

Wireless Body Area Routing Protocols Impact Analysis on Entity Mobility Models with Static Sink Node.

Worry, rumination and negative metacognitive beliefs as moderators of outcomes of Transdiagnostic group cognitive-behavioural therapy in emotional disorders.

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease driven by persistent inflammation and oxidative stress. Ilex paraguariensis (yerba mate) contains bioactive compounds—particularly chlorogenic acids, quercetin, and rutin—with documented antioxidant and anti-inflammatory properties. Objectives: To systematically review the mechanistic and clinical evidence on Ilex paraguariensis and its main constituents in RA-relevant inflammatory, oxidative, and bone metabolic pathways. Methods: Following PRISMA 2020, PubMed/MEDLINE, LILACS, and SciELO were searched up to September 2025. Eligible studies included yerba mate preparations (last 10 years) or isolated compounds (last 5 years) assessing RA-relevant clinical, inflammatory, oxidative, or bone metabolic outcomes. Non-original studies were excluded. Owing to heterogeneity, findings were narratively synthesized, and risk of bias was evaluated using RoB 2, ROBINS-I, OHAT, and SYRCLE. Results: Twenty-three studies met inclusion criteria: 11 human (clinical or observational), 7 human-based in vitro, and 5 animal studies. Interventions with yerba mate infusions or standardized extracts suggest reductions in inflammatory markers (e.g., C-reactive protein, interleukin-6) and indicate improvements in glutathione-related oxidative balance. Evidence from isolated compounds, particularly quercetin and rutin, suggests comparable anti-inflammatory and antioxidant effects. Preclinical studies appear to indicate modulation of inflammatory and redox pathways relevant to RA. Conclusions: Yerba mate and its constituents show preliminary indications of anti-inflammatory and antioxidant effects with potential relevance to RA pathophysiology. However, in the absence of clinical trials in RA patients, conclusions remain tentative, constrained by small sample sizes, methodological heterogeneity, species differences, and internal validity concerns. Future research should include rigorously designed randomized trials and mechanistic studies using advanced human-relevant platforms, such as organoids and organ-on-chip systems.

Yerba Mate (Ilex paraguariensis) and Rheumatoid Arthritis: A Systematic Review of Mechanistic and Clinical Evidence.

Background/Objectives: The diet quality of younger individuals is decreasing globally, with alarming trends also in the Mediterranean region. The aim of this study was to assess diet quality and adequacy in relation to country-specific dietary recommendations for children and adolescents living in the Mediterranean area. Methods: A cross-sectional survey was conducted of 2011 parents of the target population participating in the DELICIOUS EU-PRIMA project. Dietary data and cross-references with food-based recommendations and the application of the youth healthy eating index (YHEI) was assessed through 24 h recalls and food frequency questionnaires. Results: Adherence to recommendations on plant-based foods was low (less than ∼20%), including fruit and vegetables adequacy in all countries, legume adequacy in all countries except for Italy, and cereal adequacy in all countries except for Portugal. For animal products and dietary fats, the adequacy in relation to the national food-based dietary recommendations was slightly better (∼40% on average) in most countries, although the Eastern countries reported worse rates. Higher scores on the YHEI predicted adequacy in relation to vegetables (except Egypt), fruit (except Lebanon), cereals (except Spain), and legumes (except Spain) in most countries. Younger children (p < 0.005) reporting having 8–10 h adequate sleep duration (p < 0.001), <2 h/day screen time (p < 0.001), and a medium/high physical activity level (p < 0.001) displayed a better diet quality. Moreover, older respondents (p < 0.001) with a medium/high educational level (p = 0.001) and living with a partner (p = 0.003) reported that their children had a better diet quality. Conclusions: Plant-based food groups, including fruit, vegetables, legumes, and even (whole-grain) cereals are underrepresented in the diets of Mediterranean children and adolescents. Moreover, the adequate consumption of other important dietary components, such as milk and dairy products, is rather disregarded, leading to substantially suboptimal diets and poor adequacy in relation to dietary guidelines.

Youth Healthy Eating Index (YHEI) and Diet Adequacy in Relation to Country-Specific National Dietary Recommendations in Children and Adolescents in Five Mediterranean Countries from the DELICIOUS Project.

A análise PDCA como ferramenta de suporte a instituição escolar.

An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants.

Elite performance and sporting success are often the result of optimal integration and synergy of all components of sports preparedness (i.e., health, technical and tactical skills, bioenergetic and neuromuscular abilities and capacities, anthropometric characteristics, cognition, emotions, creativity, or personality), which evolve because of systematic long-term sports preparation. However, the relative importance of these characteristics varies between individual and team sports. While some individual sports require a high standard of bioenergetic and neuromuscular abilities and capacities, team sports performance is closely related to technical and tactical skills, which may compensate for weakness within the fitness level (1). Nonetheless, successful team sport performances seem to be much more dependent on the interaction among a wide range of factors than on the maximum development of one or two factors in isolation. In team sports, elite performance emerges from the interaction among the individual parts (2) to overcome the opponent during competition. Sports may be categorized according to the degree of predictability of the environment that they are played in (3). Team sports occur in highly unpredictable environments due to the interactions with both teammates and opponents, with performance dealing with this unpredictability. Thus, it is important to have a clear understanding of the integrative systems and the principles that rule their interactions with the environment, keeping in mind the main aim of the process: developing the diversity/unpredictability potential of athletes/teams (4) to afford the emergence of rich patterns of behavior from players to adapt quickly and effectively in dynamically changing and unpredictable environments (5). Performance in team sports is affected by several factors that affect the organization of training and competitions. These include, for example, COVID-19 cases (6), PCR tests (7), air flights and their effects prior to competition (8), injuries (9), or match-congested schedules (10). The interaction among these factors may also influence player availability. The concept of player availability is a common one in elite team sports. Available players can be considered the ones who are injury-free and ready to compete whether the head coach chooses to put them on the lineup. Thus, an available state would be when a player is fit and recovered enough to compete. On the other hand, player unavailability would be considered a state which includes injury, sanction or suspension, or other reasons that would keep a player out of match. However, this topic needs to be explored more in elite team sport environments. Considering previous enriching work, it remains important to further progress and provide academic knowledge in order to support coaches/managers, strength and conditioning coaches, sport scientists, and medical team members (e.g., doctors, physicians, and physiotherapists) in their working environments. While widely-advocated scientific groundwork is considered throughout this manuscript, the main aim of this opinion article is to provide a review of factors related to player availability and its influence on performance in elite team sports (Figure 1). Finally, some practical suggestions and recommendations are provided to deal with constant alterations in player's availability and performance fluctuations.

A commentary of factors related to player availability and its influence on performance in elite team sports.

Background Purple corn (Zea mays L.) is a pigmented cereal crop distinguished by its high content of anthocyanins (ANCS) and other phenolic phytochemicals, which contribute to its characteristic color and biological functionality. In recent years, increasing consumer demand for natural and health-promoting food ingredients has stimulated growing scientific interest in purple corn due to its unique nutritional profile and functional properties. A comprehensive understanding of its phytochemical composition and associated bioactivities is therefore essential to support its high-value applications in functional foods, nutraceuticals, and pharmaceutical products. Scope and approach This review critically summarizes recent advances in the phytochemical composition and bioactive properties of purple corn, with particular emphasis on gut microbiota modulation and its effects on metabolic health, including reduced risk of developing type 2 diabetes and cardiovascular disease. In addition, emerging applications of purple corn–derived bioactive compounds in functional foods, delivery systems, and animal nutrition are highlighted, alongside current challenges and research gaps. Key findings and conclusions Purple corn is abundant in diverse bioactive compounds, with composition significantly influenced by cultivar, environmental conditions, maturity stage at harvest, and processing methods. Accumulating evidence from both in vitro and in vivo indicates its efficacy as a potent antioxidant, anti-inflammatory, and anticancer agent, with additional antihypertensive, anti-obesogenic, neuroprotective and immunomodulatory properties. Innovative strategies, such as nanoencapsulation, show promise in improving stability and bioavailability of bioactive compounds in purple corn, which is notably rich in anthocyanins (typically ranging from 55.8 to 1970 mg/100 g dry weight). However, toxicological evidence and clinical trials are key limiting factors for developing successful applications for purple corn as a nutritional dietary supplement or nutraceutical. Future studies should emphasize in vivo validation, safety assessment, and advanced delivery systems to support the development of purple corn as a functional food ingredient and potential therapeutic agent.

A comprehensive review on purple corn kernels: phytochemical composition, bioactivity, bioaccessibility, health benefits, and industry applications.

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

A deep learning approach to optimize remaining useful life prediction for Li-ion batteries.

The effect of anthocyanins and anthocyanin-rich foods on cognitive function: a meta-analysis of randomized controlled trials.

An empirical analysis of factors determining changes in physical exercise during the COVID-19 pandemic.

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

A ferramenta SWOT na gestão escolar.

A flexible and lightweight signcryption scheme for underwater wireless sensor networks.

A gliclazide complex based on palladium towards Alzheimer's disease: promising protective activity against Aβ-induced toxicity in C. elegans.

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

An improved hybrid image steganography method using AES algorithm.

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

The most demanding passages of play in football competition: a comparison between halves.

A novel and efficient digital image steganography technique using least significant bit substitution.

A novel approach for disease and pests detection in potato production system based on deep learning.

A novel dominant mutation inCRYABgene leading to a severe phenotype with childhood onset.

A novel hybrid deep learning approach for super-resolution and objects detection in remote sensing.

An oleuropein rich-olive (Olea europaea L.) leaf extract reduces β-amyloid and tau proteotoxicity through regulation of oxidative- and heat shock-stress responses in Caenorhabditis elegans.

In the contemporary digital era, the Internet of Things (IoT) and its applications have proliferated extensively, particularly within smart city environments, resulting in increased network traffic and raising the significance of efficient service discovery (SD) mechanisms. The social Internet of Things (SIoT) is an emerging paradigm that enables IoT devices to autonomously establish social relationships based on rules defined by their owners, thereby enhancing services through social relations. Things can interact with others; thus, the huge volume of traffic is increased. Each node or device could select an appropriate peer for the discovery of services, which is thus helpful for human beings. Although numerous service discovery and query processing models have been proposed in the recent literature. However, the existing state-of-the-art approaches often lack a comprehensive analysis of the parameters. Most traditional state-of-the-art models primarily focus on relationships or device similarity. Also neglecting the vital factors, for instance, query processing, efficiency, spatial-temporal dynamics, and service provisioning, etc. Thus, to solve this issue, this research proposes an exhaustive analysis of the main parameters needed to implement service discovery mechanisms for Social IoT and studies their relative importance based on a dataset of real objects. Based on the advanced parameters’ selection, an efficient service discovery algorithm is proposed. The proposed model emphasizes efficiency by optimizing the service discovery through reduced social graph traversal (i.e., fewer hops), consideration of the service types, and integration of caching mechanisms. We have conducted a comprehensive analysis of key parameters essential for implementing an effective service discovery mechanism in SIoT, prioritizing based on their importance. Experimental validation demonstrates the superiority of the proposed over state-of-the-art models, confirming its efficacy, scalability.

A parameter centric service discovery framework for social digital twins in smart City.

A pilot study of younger sibling adaptation: Contributions of individual variables, daily stress, interparental conflict and older sibling’s variables.

The preventive and inhibitory effects of red raspberries on cancer.

Objective To determine the psychometric properties of the Person-Centered Therapeutic Relationship in Physiotherapy Scale (PCTR-PT) in order to find the most appropriate fit for the tool. Methods Patients who had received treatment at the physiotherapy service of nine hospitals in Spain were invited to complete the 31 items of the PCTR-PT scale. To select the most appropriate items of the PCTR-PT, an exploratory factorial analysis (EFA) was performed using the maximum likelihood and oblique rotation (promin) methods. Factor validity, goodness-of-fit and psychometric properties were analyzed by confirmatory factor analysis (CFA). Convergent (CFA) and discriminant validity were calculated. Internal consistency was verified using the Cronbach's alpha coefficient. The intraclass correlation coefficient (ICC) was used to examine temporal stability. Results 366 patients over 18 years old who had received, at least, 15 physiotherapy treatment sessions completed the questionnaire. The results of the exploratory factor analysis revealed a tool with 15 items in four factors [Relational Bond (N items = 4); Individualized Partnership (N items = 4); Professional Empowerment (N items = 3) and Therapeutic Communication (N items = 4)], explaining 78.4% of the variance of the total variables of this tool. The confirmatory factor analysis further confirmed the four-structure model. Reliability of the tool was approved by Cronbach's alpha in all four dimensions, as all were above .70, ranging from .84 (Individualized Partnership) to .91 (Professional Empowerment). = 0.94. Test-retest was performed with two-week intervals, indicating an appropriate stability for the scale (ICC = 0.900). Conclusion The Person-Centered Therapeutic Relationship in Physiotherapy Scale (PCTR-PT) is a useful, valid and applicable instrument to evaluate the person-centered therapeutic relationship during physiotherapy interventions. It would be interesting to investigate the predictive capacity (sensitivity and specificity) of the PCTR-PT scale.

The psychometric properties of the person-centered therapeutic relationship in physiotherapy scale.

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

Introduction: The study aims to explore whether NIRS derived data can be used to identify the second ventilatory threshold (VT2) during a maximal incremental treadmill test in non-professional runners and to determine if there is a correlation between SmO2 and other valid and reliable exercise performance assessment measures or parameters for maximal incremental test, such as lactate concentration (LT), RPE, HR, and running power (W). Methods: 24 participants were recruited for the study (5 women and 19 men). The devices used consisted of the following: i) a muscle oxygen saturation analyzer placed on the vastus lateralis of the right leg, ii) the Stryd power meter for running, iii) the Polar H7 heart rate band; and iv) the lactate analyzer. In addition, a subjective perceived exertion scale (RPE 1-10) was used. All of the previously mentioned devices were used in a maximal incremental treadmill test, which began at a speed of 8 km/h with a 1% slope and a speed increase of 1.2 km/h every 3 min. This was followed by a 30-s break to collect the lactate data between each 3-min stage. Spearman correlation was carried out and the level of significance was set at p < 0.05. Results: The VT2 was observed at 87,41 ± 6,47% of the maximal aerobic speed (MAS) of each participant. No relationship between lactate data and SmO2 values (p = 0.076; r = −0.156) at the VT2 were found. No significant correlations were found between the SmO2 variables and the other variables (p > 0.05), but a high level of significance and strong correlations were found between all the following variables: power data (W), heart rate (HR), lactate concentration (LT) and RPE (p < 0.05; r > 0.5). Discussion: SmO2 data alone were not enough to determine the VT2, and there were no significant correlations between SmO2 and the other studied variables during the maximal incremental treadmill test. Only 8 subjects had a breakpoint at the VT2 determined by lactate data. Conclusion: The NIRS tool, Humon Hex, does not seem to be useful in determining VT2 and it does not correlate with the other variables in a maximal incremental treadmill test.

The relationship of muscle oxygen saturation analyzer with other monitoring and quantification tools in a maximal incremental treadmill test.

The role of Internet of Things (IoT) technology in modern cultivation for the implementation of greenhouses.

The role of dietary polyphenols in the control of chronic noncommunicable diseases.

A scalable and secure federated learning authentication scheme for IoT.

A systematic review of deep learning methods for community detection in social networks.

The use of authentic materials in an English for Specific Purposes university class.

Sección/Capítulo de Libro

Análisis de preferencias tradumáticas de los traductores actualmente en formación: el procesador de textos online vs. offline.

Discourse creation: translation technique or spanish film pattern. A case study.

El diagnóstico del comportamiento emprendedor en la población universitaria femenina y la solución práctica mediante la Incubadora Universitaria.

Emprendimiento basado en el liderazgo: diagnóstico de las habilidades de liderazgo entre los estudiantes universitarios.

Introduciendo la posedición en el aula de traducción especializada.

La dicotomía ética entre la deontología frente a la teleología.

Liga de Debate como herramienta emergente para el aprendizaje cooperativo: análisis empírico de la mejora de competencias en enseñanza superior.

Los adverbios preposicionales alemanes: una propuesta didáctica traductológica a través de la literatura.

Motivación laboral como eje de la gestión de recursos humanos en una empresa. Estudio empírico sobre la valoración de los motores de la motivación por los empleados y alumnos universitarios.

Teletrabajo en la era híper digital y tras la pandemia para la mujer, ¿suerte o desgracia?

Un análisis empírico de los factores que determinan la brecha sexista en emprendimiento.

Ver para creer: repensando el poder de la religión a través del no-posicionamiento de Hume.

Violencia de género en Iberoamérica durante la crisis de la Covid-19: campañas en RR. SS. impulsadas por organismos gubernamentales internacionales.

Ponencia/Presentación en Jornada, Congreso

Experiencia con una herramienta digital para la educación en finanzas de estudiantes de la Universidad de Valladolid dentro del marco del Proyecto Erasmus+ “FINANCEn-LAB”.

Libro

Hablando de valores en las organizaciones: 101 preguntas y respuestas.

Mujeres con talento de Cantabria.

Tesis

Beneficios del consumo de insectos como fuente de alimento en la salud humana.

Impacto del consumo de aminoácidos de cadena ramificada (BCAA) en la Diabetes Mellitus Tipo 2.

Intervención dietética en síndrome de ovario poliquístico.

Suplementación con ácidos grasos poliinsaturados omega 3 frente a una dieta mediterránea como tratamiento para la enfermedad del hígado graso no alcohólico.

Revista

Environmental Sciences and Practices.

MLS Communication Journal.

MLS Educational Research.

MLS Health and Nutrition Research.

MLS Inclusion and Society Journal.

MLS Law and International Politics.

MLS Pedagogy, Culture and Innovation.

MLS Psychology Research.

MLS Sport Research.

Project Design and Management.

Otro

Análisis de la vida útil en tortillas.

Composición Nutricional.

Correlatos cerebrales y medidas neurofisiológicas relacionadas con la inteligencia emocional en estudiantes universitarios.

Estudio de la influencia del ejercicio físico durante el embarazo en la prevención de las consecuencias de la ganancia excesiva de peso - EFEMBARAZO.

La línea de actividad de I+D que se propone se orienta al estudio y desarrollo de estrategias de comunicación digital en el marco de la Inteligencia Artificial Para llevar a cabo el objetivo general, se han definido los siguientes objetivos específicos: OE1. Analizar el estado del arte en el uso de la inteligencia artificial en creación de contenidos para medios digitales. OE2. Estudiar los mecanismos de la GenAI (Inteligencia Artificial generativa) para analizar contenidos en webs corporativas. OE3. Implementar un prototipo web con contenidos para GenAI. OE4. Diseñar y proponer un plan de mejora para la creación de contenidos digitales adaptados para entornos de GenAI. La presente iniciativa se orienta a generar un conocimiento que permita mejorar la operatividad en la comunicación a través de medios digitales en un entorno de uso generalizado de Inteligencia Artificial. La iniciativa se enmarca en la adaptación de las organizaciones para crear medios de comunicación digital teniendo presente las herramientas de Inteligencia Artificial, lo que es un ámbito claramente en alza. Estratégicamente, se pretende avanzar en este campo de conocimiento para que se pueda transferir tanto hacia el mercado español como latinoamericano por nuestra relación con estos mercados. En este ámbito, la creación de medios digitales tan populares como los portales webs corporativos debe adaptarse para lograr el mejor impacto posible en el marco de la que podríamos denominar sociedad de la Inteligencia Artificial. Como es sabido, las herramientas de IA permiten a los usuarios acceder a los medios digitales disponibles de un modo diferente a las casuísticas anteriores. Ello implica que cabe modificar las estrategias de comunicación y creación de contenidos digitales con objeto a generar resultados para la IA que sean ciertamente representativos, evitar la generación de informaciones falsas o incompletas, así como también facilitar que estas herramientas “nos encuentren” con mayor facilidad. Esto a fines de marketing y publicidad. Población beneficiaria: creadores de contenidos, comunicadores, periodistas, publicistas, SEOs Segmento de mercado: empresas, especialmente pymes. Resultados esperados: - Conocimientos actualizados en el uso de la inteligencia artificial en creación de contenidos para medios digitales. - Mecanismos de la GenAI (Inteligencia Artificial generativa) para analizar contenidos en webs corporativas. - Estrategias para la creación de contenidos digitales adaptados para entornos de GenAI. - Mecanismos para el control de calidad del contenido digital.

Estudio y desarrollo de estrategias de comunicación digital en el marco de la Inteligencia Artificial.

Fostering Responsible Management: Enhancing ESG and SDG Competences in Higher Education to Meet the Future Skill Set Demand by the European Labour Market (FORM-ESG).

Generación de modelos de aprendizaje automático para la creación de grafos de conocimiento a partir de datos no estructurados (AI.NEEDS.DATA).

Identificación del riesgo de lesión a través de termografía y la aplicación de inteligencia artificial para la prevención de patologías musculares.

El presente proyecto se centra en crear un producto formativo basado en un simulador digital y un conjunto de herramientas que permitan extender la formación en soporte vital básico (SVB) a toda la población, y en especial a niños/as y adolescentes a través de sus docentes. Las enfermedades cardiovasculares son la principal causa de muerte en el mundo, siendo la muerte súbita cardiaca la causa más frecuente de muerte extrahospitalaria (25% de toda la mortalidad mundial). Los testigos son de los factores más importantes que influyen para salvar la vida de alguien en paro cardiaco, mientras que la formación en SVB no está extendida entre la población. Así, por ejemplo, entre la población escolar solo se recibe en un 16% de los colegios. Sabemos que se debe extender la formación en SVB a la población en general, y seguir las recomendaciones del European Resuscitation Council, el cual indica que las prácticas de SVB deben ser incluidas en los planes de estudio para capacitar a todos los docentes e impulsar la formación en los centros escolares a pesar de los escasos recursos materiales disponibles. Por ello, dado que los profesores son formadores eficaces para educar a los escolares también en SVB, nos proponemos desarrollar herramientas que mejoren la formación de estos docentes (futuros y en activo). Nuestra propuesta propone crear dichas herramientas en un enfoque centrado en simulación virtual que sabemos puede ser adecuado para facilitar la extensión de esta formación a mayor población sin las limitaciones actuales de soluciones dependientes de instructores sanitarios y equipamientos costosos. Y así también consideramos los desafíos pendientes para lograr una solución pedagógicamente adecuada, de resultados objetivamente evaluables y utilizable para resolver la problemática de la formación continua y superar la curva de olvido. De acuerdo al estado de arte, el proyecto debe aportar una serie de avances tecnológicos, como son: diseñar un sistema preciso para la valoración de los conocimientos y habilidades en soporte vital básico, crear un producto formativo liderado por pedagogo/as y en colaboración con profesionales sanitarios y tecnólogos, utilizar y evaluar ayudas cognitivas en formación de SVB, y medir el tiempo de retención de los conocimientos y habilidades en SVB. El éxito de esta iniciativa radica en el trabajo conjunto de un equipo multidisciplinar que integra especialistas en pedagogía, tecnologías educativas, salud (enfermería, emergencias), diseño y comunicación. Siendo componente esencial de este proyecto la formación docente, que será el puente entre el producto tecnológico y su implementación en el aula, se debe lograr familiarizar a los docentes con el uso de las herramientas digitales y también capacitarlos en la planificación y elaboración de actividades educativas sólidas, capaces de generar motivación y fomentar un aprendizaje práctico.

Integración de ciencias aplicadas para la formación en soporte vital básico.

Intervenciones basadas en ejercicio físico y variabilidad de la frecuencia cardíaca para la mejora de la salud en mujeres con anorexia nerviosa.

Navigating SMEs in the tourism sector through crisis (T-CRISIS-NAV).

Plataforma digital para la creación de material docente basado en casos prácticos multiformato.

Resilience, Innovation, and University Student Empowerment through reflective Climate Education and Action (RISE UP).

Simulation based training and digital technologies combined with service-learning approach for experiential and reflective learning in nutrition and dietetic education (MAHINE).

La línea de actividad de I+D que se propone se orienta a tecnologías educativas para la creación de simuladores virtuales de aprendizaje basados en asistentes conversacionales. La iniciativa se encuadra totalmente en la digitalización de servicios y en concreto en la digitalización de servicios educativos con aplicación inicial en el ámbito de la sanidad. Las tecnologías educativas engloban un espectro amplio de conocimientos y aplicaciones que se centran en integrar de forma efectiva las herramientas tecnológicas en el ámbito educativo. Este campo no sólo se orienta a la implantación de soluciones mediante la digitalización sino que también promueve un cambio paradigmático en la conceptualización de la enseñanza. Este cambio se manifiesta en varias dimensiones clave del proceso educativo, resaltando la importancia de la personalización del aprendizaje, el acceso universal al conocimiento y el desarrollo de habilidades esenciales para el siglo XXI. Estos elementos cruciales evidencian la relevancia de integrar tecnologías digitales en la educación. La transición hacia la era digital está reformulado profundamente el panorama educativo, especialmente en la creación y distribución de recursos didácticos cada vez más ricos e interactivos. En este contexto, los sistemas virtuales simulados permiten mejorar la accesibilidad y disponibilidad de los medios educativos, y así por ejemplo en el campo de la salud, permiten reducir el riesgo de errores frente a los pacientes, en las fases de aprendizaje. Estos sistemas han avanzado enormemente, evolucionando desde simples sistemas que reproducen condiciones particulares y previamente definidas hasta soluciones, que incorporando la inteligencia artificial, se acercan más si cabe a poder reproducir situaciones adaptativas y realistas. Así, la oportunidad tecnológica que se pretende aprovechar en el ámbito educativo se refiere a las nuevas herramientas disponibles, en especial de los modelos de aprendizaje automático y LLMs (Large Language Models) y las técnicas de procesamiento de lenguaje natural (NLP o natural language processing). Para llevar a cabo el objetivo general, se han definido los siguientes objetivos específicos: OE1. Analizar herramientas disponibles de inteligencia artificial aplicables a un entorno de asistentes conversacionales. OE2. Definir las funcionalidades de un sistema experimental piloto. OE3. Establecer la arquitectura tecnológica y de servicios para optimizar la eficiencia de la plataforma y facilitar su interoperabilidad. OE4. Desarrollar un simulador digital prototipo. OE5. Validar el funcionamiento del prototipo implementado.

Tecnologías para la creación de simuladores virtuales de aprendizaje basados en asistentes conversacionales.

La anchoa del Cantábrico es uno de los alimentos españoles más emblemáticos y lleva asociada una tradición pesquera, social y cultural de relevancia, partiendo de su “cuna” en Santoña (Cantabria). La elaboración de la semiconserva de anchoa es un procedimiento que logra la conservación de la anchoa durante un tiempo limitado (vida útil) mediante la utilización de sal como conservante. El proyecto pretende realizar una investigación con el fin último de extender su vida útil, en línea con lo indicado en la convocatoria: prolongar “la vida útil de los materiales y/o productos”. Para extender la vida útil y mantener la calidad sensorial de las anchoas, una de las líneas de investigación que parece más prometedora es la incorporación de antioxidantes en su elaboración. La incorporación de extractos naturales y/o suero de queso como antioxidantes aporta un valor añadido fundamental, tanto desde el punto de vista tecnológico como de sostenibilidad. Para alcanzar la meta propuesta, en primer lugar, se plantea realizar un estudio experimental de la vida útil de semiconservas de anchoa y posteriormente explorar vías para su extensión. La investigación a realizar conlleva alcanzar los siguientes objetivos específicos: 1. Analizar la relación entre la composición lipídica estacional de las anchoas y su susceptibilidad oxidativa. 2. Evaluar la vida útil de anchoas de otoño/invierno y de primavera/verano a través de oxitest para establecer diferencias de vida útil en función del contenido graso. 3. Estudiar el efecto de antioxidantes naturales para inhibir la oxidación, ajustando concentraciones según el perfil lipídico de la anchoa. 4. Analizar el efecto sobre las características organolépticas de la anchoa en función de las concentraciones antioxidantes más efectivas. 5. Diseñar un proceso de mejora de la vida útil de las anchoas a través de la adición de antioxidantes naturales considerando la influencia organoléptica.

VidAanchoa: Estudio de vida útil de la anchoa en semiconserva y desarrollo de tecnologías para extenderla en la industria conservera.

Virtual Patient (E+DIETing_LAB).

Virtual nutritional clinic (E+DIETing_LAB).

Este listado fue generado el Tue Jul 14 02:01:42 2026 UTC.