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2025
Article
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
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 and Turab Mirza, Hamid and Ahmad, Adnan and Hussain, Ibrar and Raza, Ali and Garay, Helena and Alemany Iturriaga, Josep and Ashraf, Imran
mail
UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, helena.garay@uneatlantico.es, josep.alemany@uneatlantico.es, UNSPECIFIED
(2025)
On the correlation between Google Play Store application icons and downloads.
The Computer Journal, 68 (10).
pp. 1579-1593.
ISSN 0010-4620
Article
Subjects > Nutrition
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
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 and Armas Diaz, Yasmany and Cianciosi, Danila and Yang, Bei and Qi, Zexiu and Chen, Ge and Gracia Villar, Santos and Dzul López, Luis Alonso and Grosso, Giuseppe and Quiles, José L. and Xiao, Jianbo and Battino, Maurizio and Giampieri, Francesca
mail
manucassotta@gmail.com, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, 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
Article
Subjects > Nutrition
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
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 and Rosi, Alice and Giampieri, Francesca and Poveda-Loor, Carlos and Abdelkarim, Osama and Aly, Mohamed and Frias-Toral, Evelyn and Pons, Juancho and Vázquez-Araújo, Laura and Sumalla Cano, Sandra and Elío Pascual, Iñaki and Monasta, Lorenzo and Paladino, Nadia and Mata, Ana and Chacón, Adrián and Busó, Pablo and Grosso, Giuseppe
mail
UNSPECIFIED, UNSPECIFIED, francesca.giampieri@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, sandra.sumalla@uneatlantico.es, inaki.elio@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED
(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
Article
Subjects > Nutrition
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
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 and Armas Diaz, Yasmany and Qi, Zexiu and Yang, Bei and Chen, Ge and Cassotta, Manuela and Gracia Villar, Santos and Dzul López, Luis Alonso and Rivas Garcia, Lorenzo and Forbes Hernandez, Tamara Yuliet and Zhang, Di and Mazzoni, Luca and Mezzetti, Bruno and Battino, Maurizio and Giampieri, Francesca
mail
UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, manucassotta@gmail.com, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, 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
Article
Subjects > Nutrition
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
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 and Armas Diaz, Yasmany and Qi, Zexiu and Yang, Bei and Chen, Ge and Cassotta, Manuela and Gracia Villar, Santos and Dzul López, Luis Alonso and Rivas Garcia, Lorenzo and Forbes Hernandez, Tamara Yuliet and Zhang, Di and Mazzoni, Luca and Mezzetti, Bruno and Battino, Maurizio and Giampieri, Francesca
mail
UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, manucassotta@gmail.com, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, 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
2024
Article
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
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 and Shafi, Imran and Ahmad, Jamil and Osorio García, Carlos Manuel and Pascual Barrera, Alina Eugenia and Ashraf, Imran
mail
UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, carlos.osorio@uneatlantico.es, alina.pascual@unini.edu.mx, UNSPECIFIED
(2024)
Advancement in medical report generation: current practices, challenges, and future directions.
Medical & Biological Engineering & Computing.
ISSN 0140-0118
Article
Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
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 and Farooq, Muhammad Shoaib and Ishaq, Kashif and Alsubaie, Najah and Karamti, Hanen and Caro Montero, Elizabeth and Silva Alvarado, Eduardo René and Ashraf, Imran
mail
UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, elizabeth.caro@uneatlantico.es, eduardo.silva@funiber.org, UNSPECIFIED
(2024)
Prediction of leukemia peptides using convolutional neural network and protein compositions.
BMC Cancer, 24 (1).
ISSN 1471-2407
2023
Other
Subjects > Social Sciences
Europe University of Atlantic > Research > Software
Fundación Universitaria Internacional de Colombia > Research > Software
Ibero-american International University > Research > Software
Ibero-american International University > Research > Software
Universidad Internacional do Cuanza > Research > Software
University of La Romana > Research > Software
Cerrado
Inglés, Español
A partir de los datos introducidos y de diferentes escenarios, la herramienta del simulador digital genera distintos retos a los estudiantes-emprendedores para poner a prueba y evaluar la parte financiera de una propuesta de emprendimiento y también ofrece recomendaciones en función de la aportación real de diferentes agentes financieros como bancos, inversores privados, business angels o plataformas de financiación colaborativa.
metadata
UNSPECIFIED
mail
UNSPECIFIED
(2023)
Digital Simulator for Entrepreneurial Finance (FINANCEn_LAB).
Repositorio de la Universidad.
2019
Other
Subjects > Engineering
Europe University of Atlantic > Research > Software
Fundación Universitaria Internacional de Colombia > Research > Software
Ibero-american International University > Research > Software
Ibero-american International University > Research > Software
Universidad Internacional do Cuanza > Research > Software
University of La Romana > Research > Software
Cerrado
Español
El ahogamiento es una de las principales causas de muerte en el mundo, alrededor de 372.000 personas al año, siendo una cifra que se considera subestimada (OMS, 2014). En consecuencia, existe la necesidad de mejorar esta situación considerada de salud pública.
El objetivo del proyecto SOSeas es el desarrollo de una herramienta de evaluación para predecir el riesgo dinámico de los ahogamientos en las playas. En los espacios acuáticos recreativos se espera que una herramienta informática pueda mejorar la gestión de la seguridad por parte de los socorristas y también la información de riesgo de ahogamiento para los bañistas.
Este proyecto es una continuidad del trabajo realizado en PreventSOS. En aquel caso el foco era el desarrollo de un sistema experto para la identificación, análisis y gestión del riesgo en espacios acuáticos y el diseño de una aplicación web para el registro de incidentes y accidentes. SOSeas pretende mejorar el servicio anterior integrando el sistema de información que provee el Copernicus Marine Environment Monitoring Service (CMEMS) en todo el mundo. Se pretende conseguir suficientes datos para poder nutrir a un sistema basado en técnicas de aprendizaje-máquina. La herramienta SOSeas se desarrolla para dos tipos de usuarios : gestores de playas/socorristas y usuarios recreativos (nadadores, navegantes, surfistas...). Estos usuarios podrán acceder a las condiciones meteorológicas y oceanográficas así como a información a medida sobre las amenazas de estos entornos siempre cambiantes.
metadata
UNSPECIFIED
mail
UNSPECIFIED
(2019)
SOSeas: An assessment tool for predicting the dynamic risk of drowning on beaches.
Repositorio de la Universidad.
(Unpublished)
2016
Other
Subjects > Engineering
Europe University of Atlantic > Research > Software
Fundación Universitaria Internacional de Colombia > Research > Software
Ibero-american International University > Research > Software
Ibero-american International University > Research > Software
Universidad Internacional do Cuanza > Research > Software
University of La Romana > Research > Software
Cerrado
Español
Como resultado del proyecto “Nuevos mecanismos para conocer el riesgo de lesión en el deporte en diferentes tramos de la temporada deportiva” se ha generado una herramienta digital que permite llevar el control de las lesiones de cada deportista, así como sus constantes biomecánicas, hábitos de alimentación y estado de salud emocional de tal forma que, se cuenta con información que combina varios factores a un nivel de detalle importante y de modo personalizado para cada jugador. De este modo, se obtienen los inputs para generar el análisis estadístico que alerta sobre las probabilidades de sufrir determinada lesión.
Objetivo del Proyecto:
Desarrollar una herramienta que permita identificar el riesgo de lesión de un deportista, independientemente del nivel o categoría del mismo, y poder actuar en consecuencia de manera individualizada, según el período de la temporada en el que se encuentre.
Financiación:
Este proyecto ha sido cofinanciado por la Sociedad de Desarrollo Regional de Cantabria (SODERCAN) y el el Programa Operativo FEDER de Cantabria en el marco del programa denominado I+C= +C 2016 (Investigación + Conocimiento= +Cantabria) que tiene por objetivo el fortalecimiento del tejido industrial de la región.
Inicio:
15/12/2016
Fin:
14/12/2018
Código Externo:
ID16-IN-022
metadata
UNSPECIFIED
mail
UNSPECIFIED
(2016)
Nuevos mecanismos para conocer el riesgo de lesión en el deporte en diferentes tramos de la temporada deportiva. R&P (Recovery and Performance).
Repositorio de la Universidad.
(Unpublished)
Other
Subjects > Engineering
Europe University of Atlantic > Research > Software
Fundación Universitaria Internacional de Colombia > Research > Software
Ibero-american International University > Research > Software
Ibero-american International University > Research > Software
Universidad Internacional do Cuanza > Research > Software
University of La Romana > Research > Software
Cerrado
Español
El proyecto se centra en el desarrollo de tecnologías para la identificación de riesgos en espacios acuáticos naturales. A partir del conocimiento que se pretende generar, la entidad espera comercializar servicios de soporte para la gestión de riesgos, la acción preventiva y comunicación de emergencias.
La propuesta se orienta a crear un sistema experto en la gestión de riesgos en espacios acuáticos naturales (playas), basado por un lado en una aplicación para la evaluación de riesgos, y por otro, en un sistema de registro y análisis de sucesos y accidentes.
Esta herramienta debe permitir a los responsables de la gestión de la seguridad en zonas de baño una gestión adecuada y eficaz de los recursos preventivos para minimizar la probabilidad y severidad de riesgos que puedan afectar a la integridad física o a la salud de las personas, y en consecuencia, el aumento de la seguridad acuática en las costas.
Objetivo del Proyecto:
Desarrollar tecnologías para la identificación de riesgos en espacios acuáticos naturales con el objeto de prevenir ahogamientos y otros incidentes en zonas de playa.
Financiación:
Este proyecto ha sido cofinanciado por la Sociedad de Desarrollo Regional de Cantabria (SODERCAN) y el el Programa Operativo FEDER de Cantabria en el marco del programa denominado I+C= +C 2016 (Investigación + Conocimiento= +Cantabria) que tiene por objetivo el fortalecimiento del tejido industrial de la región.
Inicio:
09/12/2016
Fin:
08/12/2018
Código Externo:
ID16-IN-038
metadata
UNSPECIFIED
mail
UNSPECIFIED
(2016)
PREVENT-SOS: Desarrollo de tecnologías para la identificación de riesgos en espacios acuáticos naturales.
Repositorio de la Universidad.
(Unpublished)
Other
Subjects > Engineering
Subjects > Teaching
Europe University of Atlantic > Research > Software
Fundación Universitaria Internacional de Colombia > Research > Software
Ibero-american International University > Research > Software
Ibero-american International University > Research > Software
Universidad Internacional do Cuanza > Research > Software
University of La Romana > Research > Software
Cerrado
Español
A pesar del gran incremento de la práctica deportiva en la sociedad occidental en los últimos años, aún hay, según fuentes de la UE, aproximadamente un 50% de la población europea que no hace ejercicio regularmente, lo que está generando un grave problema de salud, especialmente preocupante en la población infantil y juvenil. Del 50% de la población que hace deporte de forma regular, un porcentaje muy alto lo hace solo, en casa o en lugares abiertos públicos sin ninguna supervisión o control por parte de personal especializado, lo que conlleva un cierto riesgo de sufrir lesiones y/o patologías de diferente pronósticos. Ante esta situación compleja de tener la necesidad de promover la actividad física pero intentando aminorar el riesgo de la propia práctica, se propone el desarrollo de una aplicación móvil “freemium” que fomente el ejercicio y que integre una serie de tecnologías innovadoras para incorporar inteligencia artificial que aplicará sobre unos elementos de alerta que puedan generar avisos y geolocalizar al practicante de una forma rápida y eficaz. Entendemos que el desarrollo de este tipo de negocios de carácter tecnológico y de alto grado de responsabilidad social hacia la ciudadanía incrementará el tejido empresarial de Cantabria y generará nuevos puestos de trabajo estables y de alto nivel de formación. Las sinergias que se proponen con instituciones universitarias y de investigación fomentarán los ecosistemas profesionales relacionados con las nuevas tecnologías de la información, la salud y la seguridad. El objetivo de este sistema complejo que se propone es promover la actividad física segura de forma global.
metadata
UNSPECIFIED
mail
UNSPECIFIED
(2016)
SMART ACTIVE LIFE: Desarrollo de tecnologías inteligentes para la promoción de la vida activa y segura.
Repositorio de la Universidad.
(Unpublished)
<a href="/28319/1/s41598-026-45575-1_reference.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/28319/1.hassmallThumbnailVersion/s41598-026-45575-1_reference.pdf" border="0"/></a>
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open
A novel approach for disease and pests detection in potato production system based on deep learning
Vulnerability of potato crops to diseases and pest infestation can affect its quality and lead to significant yield losses. Timely detection of such diseases can help take effective decisions. For this purpose, a deep learning-based object detection framework is designed in this study to identify and classify major potato diseases and pests under real-world field conditions. A total of 2,688 field images were collected from two research farms in Punjab, Pakistan, across multiple growth stages in various seasonal conditions. Excluding 285 symptoms-free images from the earliest collection led to 2,403 images which were annotated into four biotic-stress classes: blight disease (n = 630), leaf spot disease (n = 370), leafroll virus (viral symptom complex; n = 888), and Colorado potato beetle (larvae/adults; n = 515), indicating class imbalance. Several state-of-the-art models were used including YOLOv8 variants (n/s/m), YOLOv7, YOLOv5, and Faster R-CNN, and the results are discussed in relation to recent potato disease classification studies involving cropped leaf images. Stratified splitting (70% training, 20% validation, 10% testing) was applied to preserve class distribution across all subsets. YOLOv8-medium achieve the best performance with mean average precision (mAP)@0.5 of 98% on the held-out test images. Results for stable 5-fold cross-validation show a mean mAP@0.5 of 97.8%, which offers a balance between accuracy and inference time. Model robustness was evaluated using 5-fold cross-validation and repeated training with different random seeds, showing a low variance of ±0.4% mAP. Results demonstrate promising outcomes under the real-world field conditions, while, broader cross-region and cross-season validation is intended for the future.
Ahmed Abbas mail , Saif Ur Rehman mail , Khalid Mahmood mail , Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Aseel Smerat mail , Imran Ashraf mail ,
Abbas
<a class="ep_document_link" href="/27825/1/s41598-026-39196-x_reference.pdf"><img class="ep_doc_icon" alt="[img]" src="/27825/1.hassmallThumbnailVersion/s41598-026-39196-x_reference.pdf" border="0"/></a>
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open
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.
Naveed Anwer Butt mail , Dilawaiz Sarwat mail , Irene Delgado Noya mail irene.delgado@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Nagwan Abdel Samee mail , Imran Ashraf mail ,
Butt
<a class="ep_document_link" href="/27915/1/csbj.0023.pdf"><img class="ep_doc_icon" alt="[img]" src="/27915/1.hassmallThumbnailVersion/csbj.0023.pdf" border="0"/></a>
en
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This systematic literature review (SLR) investigates the integration of deep learning (DL), vision-language models(VLMs), and multi-agent systems in the analysis of pathology images and automated report generation. The rapidadvancement of whole-slide imaging (WSI) technologies has posed new challenges in pathology, especially due to thescale and complexity of the data. DL techniques in general and convolutional neural networks (CNNs) and transform-ers in particular have significantly enhanced image analysis tasks including segmentation, classification, and detection.However, these models often lack generalizability to generate coherent, clinically relevant text, thus necessitating theintegration of VLMs and large language models (LLMs). This review examines the effectiveness of VLMs and LLMsin bridging the gap between visual data and clinical text, focusing on their potential for automating the generationof pathology reports. Additionally, multi-agent systems, which leverage specialized artificial intelligence (AI) agentsto collaboratively perform diagnostic tasks, are explored for their contributions to improving diagnostic accuracy andscalability. Through a synthesis of recent studies, this review highlights the successes, challenges, and future direc-tions of these AI technologies in pathology diagnostics, offering a comprehensive foundation for the development ofintegrated, AI-driven diagnostic workflows.
Usama Ali mail , Imran Shafi mail , Jamil Ahmad mail , Arlette Zárate Cáceres mail , Thania Chio Montero mail , Hafiz Muhammad Raza ur Rehman mail , Imran Ashraf mail ,
Ali
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Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners
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.
Saliha Ejaz mail , Soyiba Javed mail , Imran Shafi mail , Jamil Ahmad mail , Samuel Allende Monje mail samuel.allende@uneatlantico.es, Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Jin-Ghoo Choi mail , Imran Ashraf mail ,
Ejaz
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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.
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
