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Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Cerrado Inglés Icons are the first visual element users encounter when searching for applications in online store. Icons with eye-catching features can make an app stand out in user searches, playing a crucial role in attracting user attention and influencing selection. This increases the likelihood of downloads, which can expand the user base, improve revenue, and enhance engagement, contributing to the application’s overall success. However, the majority of research focused on evaluating appeal of apps through application icons is empirical in nature and may lack comprehensive data analytical approaches. While empirical research holds its significance, it may still be limited by the size of the dataset analyzed and could also be subjective. This proposed research presents a novel data-analytical methodology to analyze a large dataset of application icons from Google Play to determine their influence on downloads. It clusters the icons using three different techniques: -means clustering with two distinct feature vectors and agglomerative clustering, extracting various visual features from the clusters that are strongly correlated with application installs. Subsequently, validation of results has revealed that factors of varied colors, the dominance of white or black colors, text, and exposure in the icons can be linked to downloads. metadata Bilal, Ahmad; Turab Mirza, Hamid; Ahmad, Adnan; Hussain, Ibrar; Raza, Ali; Garay, Helena; Alemany Iturriaga, Josep y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, helena.garay@uneatlantico.es, josep.alemany@uneatlantico.es, SIN ESPECIFICAR (2025) On the correlation between Google Play Store application icons and downloads. The Computer Journal, 68 (10). pp. 1579-1593. ISSN 0010-4620

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Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Cerrado Inglés Understanding how dietary compounds affect human health is challenged by their molecular complexity and cell-type–specific effects. Conventional multi-cell type (bulk) analyses obscure cellular heterogeneity, while animal and standard in vitro models often fail to replicate human physiology. Single-cell omics technologies—such as single-cell RNA sequencing, as well as single-cell–resolved proteomic and metabolomic approaches—enable high-resolution investigation of nutrient–cell interactions and reveal mechanisms at a single-cell resolution. When combined with advanced human-derived in vitro systems like organoids and organ-on-chip platforms, they support mechanistic studies in physiologically relevant contexts. This review outlines emerging applications of single-cell omics in nutrition research, emphasizing their potential to uncover cell-specific dietary responses, identify nutrient-sensitive pathways, and capture interindividual variability. It also discusses key challenges—including technical limitations, model selection, and institutional biases—and identifies strategic directions to facilitate broader adoption in the field. Collectively, single-cell omics offer a transformative framework to advance human-centric nutrition research. metadata Cassotta, Manuela; Armas Diaz, Yasmany; Cianciosi, Danila; Yang, Bei; Qi, Zexiu; Chen, Ge; Gracia Villar, Santos; Dzul López, Luis Alonso; Grosso, Giuseppe; Quiles, José L.; Xiao, Jianbo; Battino, Maurizio y Giampieri, Francesca mail manucassotta@gmail.com, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, maurizio.battino@uneatlantico.es, francesca.giampieri@uneatlantico.es (2025) Single-cell omics for nutrition research: an emerging opportunity for human-centric investigations. Critical Reviews in Food Science and Nutrition. pp. 1-15. ISSN 1040-8398

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Cerrado Inglés Strawberries are commonly consumed berries in the Mediterranean area. The fruits present a high concentration of micronutrients and bioactive compounds that confer a plethora of biological activities, including antioxidant and anti-inflammatory properties. This review discusses and updates the recent results of in vivo studies, in animals and humans, focusing on the impact that strawberry consumption has on many common human diseases, such as obesity, cancer, cardiovascular diseases and metabolic disorders; particular attention has been given to the biological effects and molecular mechanisms involved in the beneficial effects exerted by this berry. Evidence suggests these fruits can contribute to preventing or slowing down the progression of many diseases, even though further research is necessary to confirm their long-term effectiveness, to improve patients’ quality of life or prognosis. metadata Cianciosi, Danila; Armas Diaz, Yasmany; Qi, Zexiu; Yang, Bei; Chen, Ge; Cassotta, Manuela; Gracia Villar, Santos; Dzul López, Luis Alonso; Rivas Garcia, Lorenzo; Forbes Hernandez, Tamara Yuliet; Zhang, Di; Mazzoni, Luca; Mezzetti, Bruno; Battino, Maurizio y Giampieri, Francesca mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, manucassotta@gmail.com, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, maurizio.battino@uneatlantico.es, francesca.giampieri@uneatlantico.es (2025) Strawberry as a health promoter: an evidence-based review. Where are we 10 years later? Food & Function, 16 (14). pp. 5705-5732. ISSN 2042-6496

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Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Cerrado Inglés Leukemia is a type of blood cell cancer that is in the bone marrow’s blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are further classified into lymphocytic and myeloid leukemias. This work evaluates a unique deep convolutional neural network (CNN) classifier that improves identification precision by carefully examining concatenated peptide patterns. The study uses leukemia protein expression for experiments supporting two different techniques including independence and applied cross-validation. In addition to CNN, multilayer perceptron (MLP), gated recurrent unit (GRU), and recurrent neural network (RNN) are applied. The experimental results show that the CNN model surpasses competitors with its outstanding predictability in independent and cross-validation testing applied on different features extracted from protein expressions such as amino acid composition (AAC) with a group of AAC (GAAC), tripeptide composition (TPC) with a group of TPC (GTPC), and dipeptide composition (DPC) for calculating its accuracies with their receiver operating characteristic (ROC) curve. In independence testing, a feature expression of AAC and a group of GAAC are applied using MLP and CNN modules, and ROC curves are achieved with overall 100% accuracy for the detection of protein patterns. In cross-validation testing, a feature expression on a group of AAC and GAAC patterns achieved 98.33% accuracy which is the highest for the CNN module. Furthermore, ROC curves show a 0.965% extraordinary result for the GRU module. The findings show that the CNN model is excellent at figuring out leukemia illnesses from protein expressions with higher accuracy. metadata Khawaja, Seher Ansar; Farooq, Muhammad Shoaib; Ishaq, Kashif; Alsubaie, Najah; Karamti, Hanen; Caro Montero, Elizabeth; Silva Alvarado, Eduardo René y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, elizabeth.caro@uneatlantico.es, eduardo.silva@funiber.org, SIN ESPECIFICAR (2024) Prediction of leukemia peptides using convolutional neural network and protein compositions. BMC Cancer, 24 (1). ISSN 1471-2407

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Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Cerrado Inglés The correct analysis of medical images requires the medical knowledge and expertise of radiologists to understand, clarify, and explain complex patterns and diagnose diseases. After analyzing, radiologists write detailed and well-structured reports that contribute to the precise and timely diagnosis of patients. However, manually writing reports is often expensive and time-consuming, and it is difficult for radiologists to analyze medical images, particularly images with multiple views and perceptions. It is challenging to accurately diagnose diseases, and many methods are proposed to help radiologists, both traditional and deep learning-based. Automatic report generation is widely used to tackle this issue as it streamlines the process and lessens the burden of manual labeling of images. This paper introduces a systematic literature review with a focus on analyses and evaluating existing research on medical report generation. This SLR follows a proper protocol for the planning, reviewing, and reporting of the results. This review recognizes that the most commonly used deep learning models are encoder-decoder frameworks (45 articles), which provide an accuracy of around 92–95%. Transformers-based models (20 articles) are the second most established method and achieve an accuracy of around 91%. The remaining articles explored in this SLR are attention mechanisms (10), RNN-LSTM (10), Large language models (LLM-10), and graph-based methods (4) with promising results. However, these methods also face certain limitations such as overfitting, risk of bias, and high data dependency that impact their performance. The review not only highlights the strengths and challenges of these methods but also suggests ways to handle them in the future to increase the accuracy and timely generation of medical reports. The goal of this review is to direct radiologists toward methods that lessen their workload and provide precise medical diagnoses. metadata Rehman, Marwareed; Shafi, Imran; Ahmad, Jamil; Osorio García, Carlos Manuel; Pascual Barrera, Alina Eugenia y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carlos.osorio@uneatlantico.es, alina.pascual@unini.edu.mx, SIN ESPECIFICAR (2024) Advancement in medical report generation: current practices, challenges, and future directions. Medical & Biological Engineering & Computing. ISSN 0140-0118

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Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Cerrado Inglés Food security is a universal need worldwide. This study explored the relationship between food security and adherence to the Mediterranean diet in the context of the DELICIOUS project. A survey involving 2,011 parents of children and adolescents aged 6–17 years was conducted. Adherence to the Mediterranean diet was assessed through the KIDMED score. Information regarding the ease of accessing Mediterranean foods, economic allowance, employment and residence was collected. Logistic regressions analyses were performed to test the associations. Individuals living in rural areas and reporting difficulty in obtaining all studied foods were less likely to follow the Mediterranean diet. Higher adherence was associated with a household monthly income higher than €4000. No associations with family status and no differences across countries were found. The progressive shift away from the Mediterranean diet may depend not only on cultural preferences for unhealthier, industrial alternatives but also on family budgets and food accessibility. metadata Scazzina, Francesca; Rosi, Alice; Giampieri, Francesca; Poveda-Loor, Carlos; Abdelkarim, Osama; Aly, Mohamed; Frias-Toral, Evelyn; Pons, Juancho; Vázquez-Araújo, Laura; Sumalla Cano, Sandra; Elío Pascual, Iñaki; Monasta, Lorenzo; Paladino, Nadia; Mata, Ana; Chacón, Adrián; Busó, Pablo y Grosso, Giuseppe mail SIN ESPECIFICAR, SIN ESPECIFICAR, francesca.giampieri@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, sandra.sumalla@uneatlantico.es, inaki.elio@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2025) Socio-economic status, food security and adherence to the Mediterranean diet in five Mediterranean countries: the DELICIOUS project. International Journal of Food Sciences and Nutrition, 76 (8). pp. 869-877. ISSN 0963-7486

Este listado fue generado el Fri Feb 27 23:44:33 2026 UTC.

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

Background: Dietary quality is widely acknowledged as a key factor in maintaining good health. Recommendations that promote plant-based eating patterns are largely grounded in evidence showing that dietary choices can modulate the immune function. In line with such a hypothesis, diet may be considered as a potential driver of persistent low-grade inflammation. Quality of life (QoL), on the other hand, serves as a broad indicator that encompasses both physical and psychological wellbeing.Aim: The purpose of this cross-sectional study was to examine the relationship between the inflammatory potential of the diet and QoL in a population sample of Italian adults.Design: A total of 1,936 participants completed a 110-item food frequency questionnaire to assess eating habits. The inflammatory potential of their diet was calculated using the dietary inflammatory score (DIS). Quality of life was measured with the Manchester Short Appraisal (MANSA).Results: Higher DIS values, reflecting a more pro-inflammatory diet, were linked to reduced likelihood of reporting high QoL (OR = 0.56; 95% CI: 0.40–0.78). Several specific domains of QoL, including general life satisfaction, social relationships, personal safety, satisfaction with cohabitation, physical health, and mental health, also showed significant associations with DIS.Conclusion: The findings suggest an association between the inflammatory potential of the diet and QoL.

Producción Científica

Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Justyna Godos mail , Giuseppe Caruso mail , Marco Antonio Olvera-Moreira mail , Fabrizio Furnari mail , Andrea Di Mauro mail , Irma Dominguez Azpíroz mail irma.dominguez@unini.edu.mx, Raynier Zambrano-Villacres mail , Evelyn Frias-Toral mail , Fabio Galvano mail , Giuseppe Grosso mail ,

Giampieri

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

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.

Producción Científica

Iñaki Elío Pascual mail inaki.elio@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Imanol Eguren García mail imanol.eguren@uneatlantico.es, Álvaro Lasarte García mail , Arturo Ortega-Mansilla mail arturo.ortega@uneatlantico.es, Thomas Prola mail thomas.prola@uneatlantico.es, Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es,

Elío Pascual

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

Identifying the emotional state of individuals has useful applications, particularly to reduce the risk of suicide. Users’ thoughts on social media platforms can be used to find cues on the emotional state of individuals. Clinical approaches to suicide ideation detection primarily rely on evaluation by psychologists, medical experts, etc., which is time-consuming and requires medical expertise. Machine learning approaches have shown potential in automating suicide detection. In this regard, this study presents a soft voting ensemble model (SVEM) by leveraging random forest, logistic regression, and stochastic gradient descent classifiers using soft voting. In addition, for the robust training of SVEM, a hybrid feature engineering approach is proposed that combines term frequency-inverse document frequency and the bag of words. For experimental evaluation, “Suicide Watch” and “Depression” subreddits on the Reddit platform are used. Results indicate that the proposed SVEM model achieves an accuracy of 94%, better than existing approaches. The model also shows robust performance concerning precision, recall, and F1, each with a 0.93 score. ERT and deep learning models are also used, and performance comparison with these models indicates better performance of the SVEM model. Gated recurrent unit, long short-term memory, and recurrent neural network have an accuracy of 92% while the convolutional neural network obtains an accuracy of 91%. SVEM’s computational complexity is also low compared to deep learning models. Further, this study highlights the importance of explainability in healthcare applications such as suicidal ideation detection, where the use of LIME provides valuable insights into the contribution of different features. In addition, k-fold cross-validation further validates the performance of the proposed approach.

Producción Científica

Erol KINA mail , Jin-Ghoo Choi mail , Abid Ishaq mail , Rahman Shafique mail , Mónica Gracia Villar mail monica.gracia@uneatlantico.es, Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Isabel de la Torre Diez mail , Imran Ashraf mail ,

KINA

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

Human metapneumovirus (hMPV) is one of the potential pandemic pathogens, and it is a concern for elderly subjects and immunocompromised patients. There is no vaccine or specific antiviral available for hMPV. We conducted an in-silico study to predict initial antiviral candidates against human metapneumovirus. Our methodology included protein modeling, stability assessment, molecular docking, molecular simulation, analysis of non-covalent interactions, bioavailability, carcinogenicity, and pharmacokinetic profiling. We pinpointed four plant-derived bio-compounds as antiviral candidates. Among the compounds, apigenin showed the highest binding affinity, with values of − 8.0 kcal/mol for the hMPV-F protein and − 7.6 kcal/mol for the hMPV-N protein. Molecular dynamic simulations and further analyses confirmed that the protein-ligand docked complexes exhibited acceptable stability compared to two standard antiviral drugs. Additionally, these four compounds yielded satisfactory outcomes in bioavailability, drug-likeness, and ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) and STopTox analyses. This study highlights the potential of apigenin and xanthoangelol E as an initial antiviral candidate, underscoring the necessity for wet-lab evaluation, preclinical and clinical trials against human metapneumovirus infection.

Producción Científica

Hasan Huzayfa Rahaman mail , Afsana Khan mail , Nadim Sharif mail , Wasifuddin Ahmed mail , Nazmul Sharif mail , Rista Majumder mail , Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Isabel De la Torre Díez mail , Shuvra Kanti Dey mail ,

Rahaman

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

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.

Producción Científica

Gaurav Tuteja mail , Fuad Ali Mohammed Al-Yarimi mail , Amna Ikram mail , Rupesh Gupta mail , Ateeq Ur Rehman mail , Jeewan Singh mail , Irene Delgado Noya mail irene.delgado@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es,

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