Documentos donde el Autor es "Tutusaus, Kilian"

2026

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Butt, Naveed Anwer; Sarwat, Dilawaiz; Delgado Noya, Irene; Tutusaus, Kilian; Samee, Nagwan Abdel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, irene.delgado@uneatlantico.es, kilian.tutusaus@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2026) Benchmarking multiple instance learning architectures from patches to pathology for prostate cancer detection and grading using attention-based weak supervision. Scientific Reports. ISSN 2045-2322

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Elío Pascual, Iñaki; Tutusaus, Kilian; Eguren García, Imanol; Lasarte García, Álvaro; Ortega-Mansilla, Arturo; Prola, Thomas y Sumalla Cano, Sandra mail inaki.elio@uneatlantico.es, kilian.tutusaus@uneatlantico.es, imanol.eguren@uneatlantico.es, SIN ESPECIFICAR, arturo.ortega@uneatlantico.es, thomas.prola@uneatlantico.es, sandra.sumalla@uneatlantico.es (2026) Innovative Application of Chatbots in Clinical Nutrition Education: The E+DIEting_Lab Experience in University Students. Nutrients, 18 (2). p. 257. ISSN 2072-6643

2025

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés In the rapidly evolving landscape of artificial intelligence (AI) and the Internet of Things (IoT), the significance of device diagnostics and prognostics is paramount for guaranteeing the dependable operation and upkeep of intricate systems. The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. The wealth of information offered by telemetry data gathered from IoT devices presents an opportunity for diagnostics and prognostics applications. However, extracting valuable insights and making well-timed decisions from this extensive data reservoir remains a formidable challenge. This study proposes a novel AI-driven framework that integrates forward chaining and backward chaining algorithms to analyze telemetry data from IoT devices. The proposed methodology utilizes rule-based inference to detect real-time anomalies and predict potential future failures, providing a dual-layered approach for diagnostics and prognostics. The results show that the diagnostics engine using forward chaining detects real-time issues like “High Temperature” and “Low Pressure,” while the prognostics engine with backward chaining predicts potential future occurrences of these issues, enabling proactive prevention measures. The experimental results demonstrate that adopting this approach could offer valuable assistance to authorities and stakeholders. Accurate early diagnosis and prediction of potential failures have the capability to greatly improve maintenance efficiency, minimize downtime, and optimize cost. metadata Farooq, Muhammad Shoaib; Mir, Rizwan Pervez; Alvi, Atif; Tutusaus, Kilian; García Villena, Eduardo; Alrowais, Fadwa; Karamti, Hanen y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, kilian.tutusaus@uneatlantico.es, eduardo.garcia@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2025) Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices. Scientific Reports, 15 (1). ISSN 2045-2322

2024

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés In the last decades, the world population and demand for any kind of product have grown exponentially. The rhythm of production to satisfy the request of the population has become unsustainable and the concept of the linear economy, introduced after the Industrial Revolution, has been replaced by a new economic approach, the circular economy. In this new economic model, the concept of “the end of life” is substituted by the concept of restoration, providing a new life to many industrial wastes. Leaves are a by-product of several agricultural cultivations. In recent years, the scientific interest regarding leaf biochemical composition grew, recording that plant leaves may be considered an alternative source of bioactive substances. Plant leaves’ main bioactive compounds are similar to those in fruits, i.e., phenolic acids and esters, flavonols, anthocyanins, and procyanidins. Bioactive compounds can positively influence human health; in fact, it is no coincidence that the leaves were used by our ancestors as a natural remedy for various pathological conditions. Therefore, leaves can be exploited to manufacture many products in food (e.g., being incorporated in food formulations as natural antioxidants, or used to create edible coatings or films for food packaging), cosmetic and pharmaceutical industries (e.g., promising ingredients in anti-aging cosmetics such as oils, serums, dermatological creams, bath gels, and other products). This review focuses on the leaves’ main bioactive compounds and their beneficial health effects, indicating their applications until today to enhance them as a harvesting by-product and highlight their possible reuse for new potential healthy products. metadata Regolo, Lucia; Giampieri, Francesca; Battino, Maurizio; Armas Diaz, Yasmany; Mezzetti, Bruno; Elexpuru Zabaleta, Maria; Mazas Pérez-Oleaga, Cristina; Tutusaus, Kilian y Mazzoni, Luca mail SIN ESPECIFICAR, francesca.giampieri@uneatlantico.es, maurizio.battino@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, maria.elexpuru@uneatlantico.es, cristina.mazas@uneatlantico.es, kilian.tutusaus@uneatlantico.es, SIN ESPECIFICAR (2024) From by-products to new application opportunities: the enhancement of the leaves deriving from the fruit plants for new potential healthy products. Frontiers in Nutrition, 11. ISSN 2296-861X

2023

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Cerrado Inglés Inflammatory bowel disease (IBD) patients are at substantially higher risk of colorectal cancer (CRC) and IBD-associated CRC accounts for roughly 10-15% of the annual mortality in IBD patients. IBD-related CRC also affects younger patients if compared with sporadic CRC, with a 5-year survival rate of 50%. Regardless of medical therapies, the persistent inflammation state characterizing IBD raises the risk for precancerous changes and CRC, with additional input from several elements including genetic and environmental risk factors, IBD-associated comorbidities, intestinal barrier disfunction, and gut microbiota modifications. It is well known that nutritional habits and dietary bioactive compounds can influence IBD-associated inflammation, microbiome abundance and composition, oxidative stress balance, and gut permeability. In addition, in the last years, results from broad epidemiological and experimental studies have associated certain foods or nutritional patterns with the risk of colorectal neoplasia. Here we review the possible role of nutrition in the prevention of IBD-related CRC, focusing specifically on human studies. In conclusion it emerges that nutritional interventions based on healthy, nutrient-dense dietary patterns characterized by a high intake of fiber, vegetables, fruit, Omega-3 PUFAs, and low amount of animal proteins, processed foods and alcohol, combined with probiotic supplementation have the potential of reducing IBD-activity and preventing the risk of IBD-related CRC through different mechanisms, suggesting that targeted nutritional interventions may represent a novel promising approach for the prevention and management of IBD-associated CRC. metadata Cassotta, Manuela; Cianciosi, Danila; De Giuseppe, Rachele; Navarro-Hortal, Maria Dolores; Diaz, Yasmany Armas; Forbes-Hernández, Tamara Yuliett; Tutusaus, Kilian; Pascual Barrera, Alina Eugenia; Grosso, Giuseppe; Xiao, Jianbo; Battino, Maurizio y Giampieri, Francesca mail manucassotta@gmail.com, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, kilian.tutusaus@uneatlantico.es, alina.pascual@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, maurizio.battino@uneatlantico.es, francesca.giampieri@uneatlantico.es (2023) Possible role of nutrition in the prevention of Inflammatory Bowel Disease-related colorectal cancer: a focus on human studies. Nutrition. p. 111980. ISSN 08999007

Artículo Materias > Biomedicina
Materias > Ingeniería
Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Cerrado Inglés Obesity and overweight has increased in the last year and has become a pandemic disease, the result of sedentary lifestyles and unhealthy diets rich in sugars, refined starches, fats and calories. Machine learning (ML) has proven to be very useful in the scientific community, especially in the health sector. With the aim of providing useful tools to help nutritionists and dieticians, research focused on the development of ML and Deep Learning (DL) algorithms and models is searched in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol has been used, a very common technique applied to carry out revisions. In our proposal, 17 articles have been filtered in which ML and DL are applied in the prediction of diseases, in the delineation of treatment strategies, in the improvement of personalized nutrition and more. Despite expecting better results with the use of DL, according to the selected investigations, the traditional methods are still the most used and the yields in both cases fluctuate around positive values, conditioned by the databases (transformed in each case) to a greater extent than by the artificial intelligence paradigm used. Conclusions: An important compilation is provided for the literature in this area. ML models are time-consuming to clean data, but (like DL) they allow automatic modeling of large volumes of data which makes them superior to traditional statistics. metadata Ferreras, Antonio; Sumalla Cano, Sandra; Martínez-Licort, Rosmeri; Elío Pascual, Iñaki; Tutusaus, Kilian; Prola, Thomas; Vidal Mazón, Juan Luis; Sahelices, Benjamín y de la Torre Díez, Isabel mail SIN ESPECIFICAR, sandra.sumalla@uneatlantico.es, SIN ESPECIFICAR, inaki.elio@uneatlantico.es, kilian.tutusaus@uneatlantico.es, thomas.prola@uneatlantico.es, juanluis.vidal@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight. Journal of Medical Systems, 47 (1). ISSN 1573-689X

2022

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients. metadata Shafi, Imran; Din, Sadia; Khan, Asim; Díez, Isabel De La Torre; Pali-Casanova, Ramón; Tutusaus, Kilian y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, ramon.pali@unini.edu.mx, kilian.tutusaus@uneatlantico.es, SIN ESPECIFICAR (2022) An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network. Cancers, 14 (21). p. 5457. ISSN 2072-6694

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The purpose of this article is to help to bridge the gap between sustainability and its application to project management by developing a methodology based on artificial intelligence to diagnose, classify, and forecast the level of sustainability of a sample of 186 projects aimed at local communities in Latin American and Caribbean countries. First, the compliance evaluation with the Sustainable Development Goals (SDGs) within the framework of the 2030 Agenda served to diagnose and determine, through fuzzy sets, a global sustainability index for the sample, resulting in a value of 0.638, in accordance with the overall average for the region. Probabilistic predictions were then made on the sustainability of the projects using a series of supervised learning classifiers (SVM, Random Forest, AdaBoost, KNN, etc.), with the SMOTE resampling technique, which provided a significant improvement toward the results of the different metrics of the base models. In this context, the Support Vector Machine (SVM) + SMOTE was the best classification algorithm, with accuracy of 0.92. Lastly, the extrapolation of this methodology is to be expected toward other realities and local circumstances, contributing to the fulfillment of the SDGs and the development of individual and collective capacities through the management and direction of projects. metadata García Villena, Eduardo; Pascual Barrera, Alina Eugenia; Álvarez, Roberto Marcelo; Dzul López, Luis Alonso; Tutusaus, Kilian; Vidal Mazón, Juan Luis; Miró Vera, Yini Airet; Brie, Santiago y López Flores, Miguel A. mail eduardo.garcia@uneatlantico.es, alina.pascual@unini.edu.mx, roberto.alvarez@uneatlantico.es, luis.dzul@uneatlantico.es, kilian.tutusaus@uneatlantico.es, juanluis.vidal@uneatlantico.es, yini.miro@uneatlantico.es, santiago.brie@uneatlantico.es, miguelangel.lopez@uneatlantico.es (2022) 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. Applied Sciences, 12 (21). p. 11188. ISSN 2076-3417

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés β-Thalassemia is one of the dangerous causes of the high mortality rate in the Mediterranean countries. Substantial resources are required to save a β-Thalassemia carriers’ life and early detection of thalassemia patients can help appropriate treatment to increase the carrier’s life expectancy. Being a genetic disease, it can not be prevented however the analysis of several indicators in parents’ blood can be used to detect disorders causing Thalassemia. Laboratory tests for Thalassemia are time-consuming and expensive like high-performance liquid chromatography, Complete Blood Count (CBC) with peripheral smear, genetic test, etc. Red blood indices from CBC can be used with machine learning models for the same task. Despite the available approaches for Thalassemia carriers from CBC data, gaps exist between the desired and achieved accuracy. Moreover, the data imbalance problem is studied well which makes the models less generalizable. This study proposes a highly accurate approach for β-Thalassemia detection using red blood indices from CBC augmented by supervised machine learning. In view of the fact that all the features do not carry predictive information regarding the target variable, this study employs a unified framework of two features selection techniques including Principal Component Analysis (PCA) and Singular Vector Decomposition (SVD). The data imbalance between β-Thalassemia carrier and non-carriers is handled by Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN). Extensive experiments are performed using many state-of-the-art machine learning models and deep learning models. Experimental results indicate the superiority of the proposed approach over existing approaches with an accuracy score of 0.96. metadata Rustam, Furqan; Ashraf, Imran; Jabbar, Shehbaz; Tutusaus, Kilian; Mazas Pérez-Oleaga, Cristina; Pascual Barrera, Alina Eugenia y de la Torre Diez, Isabel mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, kilian.tutusaus@uneatlantico.es, cristina.mazas@uneatlantico.es, alina.pascual@unini.edu.mx, SIN ESPECIFICAR (2022) Prediction β-Thalassemia carriers using complete blood count features. Scientific Reports, 12 (1). ISSN 2045-2322

Artículo Materias > Biomedicina
Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Alzheimer's is a chronic degenerative disease of the central nervous system considered the leading cause of dementia in the world. It is characterized by two etiopathological events related to oxidative stress: the aggregation of β-amyloid peptide and the formation of neurofibrillary tangles of hyperphosphorylated Tau protein in the brain. The incidence of this disease increases with age and has been associated with inadequate lifestyles. Some natural compounds have been shown to improve the hallmarks of the disease. However, despite its potential, there is no scientific evidence about Manuka honey (MH) in this regard. In the present work we evaluated the effect of MH on the toxicity induced by Aβ aggregation and Tau in a Caenorhabditis elegans model. Our results demonstrated that MH was able to improve indicators of oxidative stress and delayed Aβ-induced paralysis in the AD model CL4176 through HSP-16.2 and SKN-1/NRF2 pathways. Nevertheless, its sugar content impaired the indicators of locomotion (an indicator of tau neurotoxicity) in both the transgenic strain BR5706 and in the wild-type N2 worms. metadata Navarro-Hortal, María D.; Romero-Márquez, Jose M.; Muñoz-Ollero, Pedro; Jiménez-Trigo, Victoria; Esteban-Muñoz, Adelaida; Tutusaus, Kilian; Giampieri, Francesca; Battino, Maurizio; Sánchez-González, Cristina; Rivas-García, Lorenzo; Llopis, Juan; Forbes-Hernández, Tamara Y. y Quiles, José L. mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, kilian.tutusaus@uneatlantico.es, francesca.giampieri@uneatlantico.es, maurizio.battino@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, jose.quiles@uneatlantico.es (2022) 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. Food & Function. ISSN 2042-6496

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Hafnia alvei is receiving increasing attention from both a medical and veterinary point of view, but the diversity of molecules it produces has made the interest in this bacterium extend to the field of probiotics, the microbiota, and above all, to its presence and action on consumer foods. The production of Acyl Homoserine Lactones (AHLs), a type of quorum-sensing (QS) signaling molecule, is the most often-studied chemical signaling molecule in Gram-negative bacteria. H. alvei can use this communication mechanism to promote the expression of certain enzymatic activities in fermented foods, where this bacterium is frequently present. H. alvei also produces a series of molecules involved in the modification of the organoleptic properties of different products, especially cheeses, where it shares space with other microorganisms. Although some strains of this species are implicated in infections in humans, many produce antibacterial compounds, such as bacteriocins, that inhibit the growth of true pathogens, so the characterization of these molecules could be very interesting from the point of view of clinical medicine and the food industry. Lastly, in some cases, H. alvei is responsible for the production of biogenic amines or other compounds of special interest in food health. In this article, we will review the most interesting molecules that produce the H. alvei strains and will discuss some of their properties, both from the point of view of their biological activity on other microorganisms and the properties of different food matrices in which this bacterium usually thrives. metadata Ramos Vivas, Jose; Tapia Martínez, Olga; Elexpuru Zabaleta, Maria; Tutusaus, Kilian; Armas Diaz, Yasmany; Battino, Maurizio y Giampieri, Francesca mail jose.ramos@uneatlantico.es, olga.tapia@uneatlantico.es, maria.elexpuru@uneatlantico.es, kilian.tutusaus@uneatlantico.es, SIN ESPECIFICAR, maurizio.battino@uneatlantico.es, francesca.giampieri@uneatlantico.es (2022) The Molecular Weaponry Produced by the Bacterium Hafnia alvei in Foods. Molecules, 27 (17). p. 5585. ISSN 1420-3049

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Age-related macular degeneration (AMD) is a serious degenerative disease affecting the eyes, and is the main cause of severe vision loss among people >55 years of age in developed countries. Its onset and progression have been associated with several genetic and lifestyle factors, with diet appearing to play a pivotal role in the latter. In particular, dietary eating patterns rich in plant foods have been shown to lower the risk of developing the disease, and to decrease the odds of progressing to more advanced stages in individuals already burdened with early AMD. We systematically reviewed the literature to analyse the relationship between the adherence to a Mediterranean diet, a mainly plant-based dietary pattern, and the onset/progression of AMD. Eight human observational studies were analysed. Despite some differences, they consistently indicate that higher adherence to a Mediterranean eating pattern lowers the odds of developing AMD and decreases the risk of progression to more advanced stages of the disease, establishing the way for preventative measures emphasizing dietary patterns rich in plant-foods metadata Gastaldello, Annalisa; Giampieri, Francesca; Quiles, José L.; Navarro-Hortal, María D.; Aparicio Obregón, Silvia; García Villena, Eduardo; Tutusaus, Kilian; De Giuseppe, Rachele; Grosso, Giuseppe; Cianciosi, Danila; Forbes-Hernández, Tamara Y.; Nabavi, Seyed M. y Battino, Maurizio mail SIN ESPECIFICAR, francesca.giampieri@uneatlantico.es, jose.quiles@uneatlantico.es, SIN ESPECIFICAR, silvia.aparicio@uneatlantico.es, eduardo.garcia@uneatlantico.es, kilian.tutusaus@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, maurizio.battino@uneatlantico.es (2022) Adherence to the Mediterranean-Style Eating Pattern and Macular Degeneration: A Systematic Review of Observational Studies. Nutrients, 14 (10). p. 2028. ISSN 2072-6643

2021

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Cerrado Inglés BACKGROUND:In Spain, there are several studies published on the relationship between eating habits and lifestyle among university students; but only a few of them are focused on online postgraduate students. OBJECTIVE:Herein we aimed to evaluate the degree of adherence to the Mediterranean diet pyramid (2010 edition), non-communicable diseases (NCDs), and lifestyle in online postgraduate students aged 20–65 years belonging to the food area of the Fundación Universitaria Iberoamericana (FUNIBER). METHODS:We performed a descriptive cross-sectional study including 100 online post-graduate students aged 20–65 years who were recruited by an accidental non-probabilistic sampling method consisting of a questionnaire on their sociodemographic characteristics, NCDs, lifestyle, and a 3-day food intake record (3-d). RESULTS:The profile of the students was 74% women, with a mean age of 36.6 (±10.5) years and body mass index (BMI) of 22.6 kg / m2 (±3.3). 71% of the volunteers presented normal weight, while 20% were overweight. Indeed, only a low percentage of the volunteers presented hypertension (1%), cardiovascular disease (0%), diabetes mellitus 1 (2%), diabetes mellitus 2 (3%), hypercholesterolemia (9%), and hyperuricemia (2%). Concerning lifestyle, (77%) of students were non-smokers, (78%) consumed beverages with caffeine, (51%) did not consume alcoholic beverages, and nearly all of them (84%) frequently (3 times /week) practiced physical activity. 68% of the recruited students exhibited adherence to “Medium diet quality diet (4–7)” followed by (26%) with a “Poor diet quality (<3)” and “Optimal diet quality” (6%). CONCLUSIONS:Spanish postgraduate students of the nutritional area, have good health and a healthy lifestyle but are moving away from the MD model, should be established campaigns for the improvement of eating habits of the postgraduate university population. metadata Elío Pascual, Iñaki; Jarrin, Sandra; Elexpuru Zabaleta, Maria; Crespo-Álvarez, Jorge; Dominguez Azpíroz, Irma; Tutusaus, Kilian; Ruiz Salces, Roberto; Calderón Iglesias, Rubén y Sumalla Cano, Sandra mail inaki.elio@uneatlantico.es, sandra.jarrin@uneatlantico.es, maria.elexpuru@uneatlantico.es, jorge.crespo@uneatlantico.es, irma.dominguez@uneatlantico.es, kilian.tutusaus@uneatlantico.es, roberto.ruiz@uneatlantico.es, ruben.calderon@uneatlantico.es, sandra.sumalla@uneatlantico.es (2021) Adherence to the pyramid of the Mediterranean diet (2010), non-communicable diseases and lifestyle in online postgraduate Spanish students in the food area. Mediterranean Journal of Nutrition and Metabolism, 14 (2). pp. 191-205. ISSN 1973798X

Artículo Materias > Educación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The purpose of this article was to evaluate the level of satisfaction of a sample of graduates in relation to different online postgraduate programs in the environmental area, as part of the process of continuous improvement in which the educational institution was immersed for the renewal of its accreditation before the corresponding official bodies. Based on the bibliographic review of a series of models and tools, a Likert scale measurement instrument was developed. This instrument, once applied and validated, showed a good level of reliability, with more than three quarters of the participants having a positive evaluation of satisfaction. Likewise, to facilitate the relational study, and after confirming the suitability of performing a factor analysis, four variable grouping factors were determined, which explained a good part of the variability of the instrument’s items. As a result of the analysis, it was found that there were significant values of low satisfaction in graduates from the Eurasian area, mainly in terms of organizational issues and academic expectations. On the other hand, it was observed that the methodological aspects of the “Auditing” and “Biodiversity” programs showed higher levels of dissatisfaction than the rest, with no statistically significant relationships between gender, entry profile or age groups. The methodology followed and the rigor in determining the validity and reliability of the instrument, as well as the subsequent analysis of the results, endorsed by the review of the documented information, suggest that the instrument can be applied to other multidisciplinary programs for decision making with guarantees in the educational field metadata García Villena, Eduardo; Pueyo Villa, Silvia; Delgado Noya, Irene; Tutusaus, Kilian; Ruiz Salces, Roberto y Pascual Barrera, Alina Eugenia mail eduardo.garcia@uneatlantico.es, silvia.pueyo@uneatlantico.es, irene.delgado@uneatlantico.es, kilian.tutusaus@uneatlantico.es, roberto.ruiz@uneatlantico.es, alina.pascual@unini.edu.mx (2021) Instrumentalization of a Model for the Evaluation of the Level of Satisfaction of Graduates under an E-Learning Methodology: A Case Analysis Oriented to Postgraduate Studies in the Environmental Field. Sustainability, 13 (9). p. 5112. ISSN 2071-1050

Artículo Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés In recent times, scientific attention has been paid to different foods and their bioactive components for the ability to inhibit the onset and progress of different types of cancer. Nigella sativa extract, powder and seed oil and its main components, thymoquinone and α-hederin, have showed potent anticancer and chemosensitizing effects against various types of cancer, such as liver, colon, breast, renal, cervical, lung, ovarian, pancreatic, prostate and skin tumors, through the modulation of various molecular signaling pathways. Herein, the purpose of this review was to highlight the anticancer activity of Nigella sativa and it constitutes, focusing on different in vitro, in vivo and clinical studies and projects, in order to underline their antiproliferative, proapoptotic, cytotoxic and antimetastatic effects. Particular attention has been also given to the synergistic effect of Nigella sativa and it constitutes with chemotherapeutic drugs, and to the synthesized analogs of thymoquinone that seem to enhance the chemo-sensitizing potential. This review could be a useful step towards new research on N. sativa and cancer, to include this plant in the dietary treatments in support to conventional therapies, for the best achievement of therapeutic goals. metadata Ansary, Johura; Giampieri, Francesca; Forbes-Hernandez, Tamara Y.; Regolo, Lucia; Quinzi, Denise; Gracia Villar, Santos; Garcia Villena, Eduardo; Tutusaus, Kilian; Alvarez-Suarez, José M.; Battino, Maurizio y Cianciosi, Danila mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, eduardo.garcia@uneatlantico.es, kilian.tutusaus@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2021) 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. Molecules, 26 (8). p. 2108. ISSN 1420-3049