Documentos donde el Autor es "Gracia Villar, Santos"

Agrupar por: Fecha | Tipo de Documento | Sin Agrupar

Ir a: 2026 | 2025 | 2024 | 2023 | 2022 | 2021 | 2013

Número de documentos: 30.

2026

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
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Abbas, Ahmed; Rehman, Saif Ur; Mahmood, Khalid; Gracia Villar, Santos; Dzul López, Luis Alonso; Smerat, Aseel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2026) A novel approach for disease and pests detection in potato production system based on deep learning. Scientific Reports. ISSN 2045-2322

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
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés New energy vehicles (NEVs) has emerged as a sustainable alternative to conventional vehicles, however have unresolved reliability challenges due to their complex electronic systems and varying operating conditions. Faults in drivetrain and battery systems, occurring at rates up to 12% annually, present significant barriers to the widespread adoption of NEVs. This study proposes a robust fault detection framework that applies multiple machine learning and deep learning models to address these challenges. The research utilizes the benchmark NEV fault diagnosis dataset, which contains real-world sensor data from NEVs. The models tested include logistic regression, passive-aggressive classifier, ridge classifier, perceptron, gated recurrent unit (GRU), convolutional neural network, and artificial neural network. The proposed ensemble GRULogX model stands out among the implemented model, leveraging GRU with logistic regression and other key classifiers, and achieved 99% accuracy, demonstrating high precision and recall. Cross-validation and hyperparameter optimization were adopted to further ensure the model’s generalizability and reliability. This research enhances the fault detection capabilities of NEVs, thereby improving their reliability and supporting the wider adoption of clean energy transportation solutions. metadata Akhtar, Iqra; Nabeel, Mahnoor; Shahid, Umair; Munir, Kashif; Raza, Ali; Delgado Noya, Irene; Gracia Villar, Santos y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, irene.delgado@uneatlantico.es, santos.gracia@uneatlantico.es, SIN ESPECIFICAR (2026) Enhancing fault detection in new energy vehicles via novel ensemble approach. Scientific Reports, 16 (1). ISSN 2045-2322

2025

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
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease driven by persistent inflammation and oxidative stress. Ilex paraguariensis (yerba mate) contains bioactive compounds—particularly chlorogenic acids, quercetin, and rutin—with documented antioxidant and anti-inflammatory properties. Objectives: To systematically review the mechanistic and clinical evidence on Ilex paraguariensis and its main constituents in RA-relevant inflammatory, oxidative, and bone metabolic pathways. Methods: Following PRISMA 2020, PubMed/MEDLINE, LILACS, and SciELO were searched up to September 2025. Eligible studies included yerba mate preparations (last 10 years) or isolated compounds (last 5 years) assessing RA-relevant clinical, inflammatory, oxidative, or bone metabolic outcomes. Non-original studies were excluded. Owing to heterogeneity, findings were narratively synthesized, and risk of bias was evaluated using RoB 2, ROBINS-I, OHAT, and SYRCLE. Results: Twenty-three studies met inclusion criteria: 11 human (clinical or observational), 7 human-based in vitro, and 5 animal studies. Interventions with yerba mate infusions or standardized extracts suggest reductions in inflammatory markers (e.g., C-reactive protein, interleukin-6) and indicate improvements in glutathione-related oxidative balance. Evidence from isolated compounds, particularly quercetin and rutin, suggests comparable anti-inflammatory and antioxidant effects. Preclinical studies appear to indicate modulation of inflammatory and redox pathways relevant to RA. Conclusions: Yerba mate and its constituents show preliminary indications of anti-inflammatory and antioxidant effects with potential relevance to RA pathophysiology. However, in the absence of clinical trials in RA patients, conclusions remain tentative, constrained by small sample sizes, methodological heterogeneity, species differences, and internal validity concerns. Future research should include rigorously designed randomized trials and mechanistic studies using advanced human-relevant platforms, such as organoids and organ-on-chip systems. metadata Cassotta, Manuela; Cao, Qingwei; Hu, Haixia; Martinez, Carlos Rabeiro; Dzul López, Luis Alonso; Gracia Villar, Santos; Battino, Maurizio y Giampieri, Francesca mail manucassotta@gmail.com, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, luis.dzul@uneatlantico.es, santos.gracia@uneatlantico.es, maurizio.battino@uneatlantico.es, francesca.giampieri@uneatlantico.es (2025) Yerba Mate (Ilex paraguariensis) and Rheumatoid Arthritis: A Systematic Review of Mechanistic and Clinical Evidence. Nutrients, 17 (24). p. 3853. ISSN 2072-6643

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
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 > Biomedicina 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 Background Nowadays, there is no correlation between levels of cortisol and pain in the prehospital setting. The aim of this work was to determine the ability of prehospital cortisol levels to correlate to pain. Cortisol levels were compared with those of the numerical rating scale (NRS). Methods This is a prospective observational study looking at adult patients with acute disease managed by Emergency Medical Services (EMS) and transferred to the emergency department of two tertiary care hospitals. Epidemiological variables, vital signs, and prehospital blood analysis data were collected. A total of 1516 patients were included, the median age was 67 years (IQR: 51–79; range: 18–103) with 42.7% of females. The primary outcome was pain evaluation by NRS, which was categorized as pain-free (0 points), mild (1–3), moderate (4–6), or severe (≥7). Analysis of variance, correlation, and classification capacity in the form area under the curve of the receiver operating characteristic (AUC) curve were used to prospectively evaluate the association of cortisol with NRS. Results The median NRS and cortisol level are 1 point (IQR: 0–4) and 282 nmol/L (IQR: 143–433). There are 584 pain-free patients (38.5%), 525 mild (34.6%), 244 moderate (16.1%), and 163 severe pain (10.8%). Cortisol levels in each NRS category result in p < 0.001. The correlation coefficient between the cortisol level and NRS is 0.87 (p < 0.001). The AUC of cortisol to classify patients into each NRS category is 0.882 (95% CI: 0.853–0.910), 0.496 (95% CI: 0.446–0.545), 0.837 (95% CI: 0.803–0.872), and 0.981 (95% CI: 0.970–0.991) for the pain-free, mild, moderate, and severe categories, respectively. Conclusions Cortisol levels show similar pain evaluation as NRS, with high-correlation for NRS pain categories, except for mild-pain. Therefore, cortisol evaluation via the EMS could provide information regarding pain status. metadata López-Izquierdo, Raúl; Ingelmo-Astorga, Elisa A.; del Pozo Vegas, Carlos; Gracia Villar, Santos; Dzul López, Luis Alonso; Aparicio Obregón, Silvia; Calderón Iglesias, Rubén; Sanz-García, Ancor y Martín-Rodríguez, Francisco mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2025) Association between blood cortisol levels and numerical rating scale in prehospital pain assessment. Communications Medicine, 5 (1). ISSN 2730-664X

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Docencia > Materiales Docentes
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés The agriculture field is the basis of a country’s change and financial system. Crops are the main source of revenue for the people. One of the farmer’s most challenging problems is choosing the right crops for their land. This critical decision has a direct impact on productivity and profit. Wrong crop selection not only reduces yields but also causes food shortages, creating more problems for farmers. The best crop depends on many parameters such as illustration humidity, N, K, P, pH, rainfall, and temperature of the soil. Getting advice from experts is not an easy task. This requires intelligent models in crop recommendations that use machine-learning models to suggest suitable crops for soil and other environmental conditions. Temperature, humidity, and pH are important data for growing crops in agriculture. In this study, we gather and preprocess relevant data. To recommend the most suitable crop, we propose a novel ensemble learning approach called RFXG based on random forest (RF) and extreme gradient boosting (XGB) to suggest the best crop out of the twenty-two major crops. To measure the capability of the proposed approach, various machine learning models are utilized including extra tree classifier, multilayer perceptron, RF, decision trees, logistic regression, and XGB classifiers. To get the best performance, optimization of hyperparameter, and K-fold cross-validation procedures are performed. Experimental outcomes show that the proposed RFXG technique achieves a recommendation accuracy is 98%. Specifically, the proposed solution provides immediate recommendations to help farmers make timely decisions. metadata Afzal, Hadeeqa; Amjad, Madiha; Raza, Ali; Munir, Kashif; Gracia Villar, Santos; Dzul López, Luis Alonso y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, SIN ESPECIFICAR (2025) Incorporating soil information with machine learning for crop recommendation to improve agricultural output. Scientific Reports, 15 (1). ISSN 2045-2322

Artículo Materias > Biomedicina 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 The aim of the present work was to determine the correlation between the State-Trait Anxiety Inventory (STAI) score and pupillary diameter and whether this correlation exists to develop a predictive model of anxiety with the pupillary diameter of students exposed to high-fidelity clinical simulation. This was a randomized, blinded, simulation-based clinical trial. The study was conducted at the Advanced Clinical Simulation Center, Faculty of Medicine, Valladolid University (Spain), from February 1 to April 15, 2023, and involved volunteer sixth-year undergraduate medical students. The STAI score, vital signs (oxygen saturation, perfusion index, blood pressure, heart rate, and temperature), and pupillary response were assessed. The primary outcomes were the delta (pre/postsimulation) of the state STAI and the delta of the pupillary diameter. Sixty-one sixth-year students fulfilled the inclusion criteria. There was no difference regarding the clinical scenario. There was a statistically significant correlation between the state STAI score and pupillary diameter. The predictive model had an AUC of 0.876, with the delta diameter of the pupillary being the only statistically significant variable for anxiety prediction. Our results showed that both the pupillary response and the STAI score allowed the identification of students with disabling anxiety. These results could pave the way for appropriate protocol development that allows for personalized tutoring of students with elevated anxiety levels. metadata Martín-Rodríguez, Francisco; Martín-Sánchez, Rafael; del Pozo Vegas, Carlos; Lopez-Izquierdo, Raúl; Martín-Conty, José Luis; Silva Alvarado, Eduardo René; Gracia Villar, Santos; Dzul López, Luis Alonso; Aparicio Obregón, Silvia; Calderón Iglesias, Rubén; Sanz-García, Ancor y Castro Villamor, Miguel Ángel mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, eduardo.silva@funiber.org, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2025) Pupilometer efficacy in monitoring anxiety in undergraduate medical students during high-fidelity clinical simulation. Scientific Reports, 15 (1). ISSN 2045-2322

2024

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés This paper studies the latest and state-of-the-art underwater thermal energy harvesting algorithms and techniques designed in the latest decade (2014-2024). The techniques are classified based on their unique operations for energy harvesting. This classification includes thermal energy harvesting using a phase change material (PCM), thermoelectric generator (TEG) and multi-source harvesting. Every class of techniques is described by its operation using a schematic diagram and a mathematical model to fully understand its working principle. Moreover, every individual technique is also described in terms of its operation, amount of harvested energy/power and the aspect(s) where margin of further improvement exists. Also, a comparative analysis of the classified algorithms is performed with each other as well as with other underwater energy harvesting techniques (solar, piezoelectric, wave) to highlight their effectiveness and feasibility in a diverse set of underwater and various other applications. The classified techniques are also compared in terms of harvested output to indicate their harvesting efficiency. Furthermore, the publications made in the latest decade in terms of thermal energy harvesting using PCM, TEG and multi-source methods are also graphically depicted. Such a description of the studied techniques and classified methods is unique from the already existing underwater energy harvesting reviews in literature where an in-depth and thorough analysis is absent, rather only marginal description is given. The harvesting results indicate that hybrid (multi-source) and PCM methods have the greatest amount of harvested power and energy, respectively. Finally, the research challenges in underwater thermal energy harvesting are specified and areas of further research are highlighted for future investigation. metadata Khan, Anwar; Gracia Villar, Santos; Dzul López, Luis Alonso; Almaleh, Abdulaziz; Alqahtani, Abdullah M. y Alnaimi, Raja’A mail SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Underwater Thermal Energy Harvesting: Frameworks, Challenges, Applications, and Future Investigation. IEEE Access, 12. pp. 174371-174386. ISSN 2169-3536

Artículo Materias > Biomedicina
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 Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relies on manual techniques and expertise, often resulting in time-consuming processes and variability in diagnoses. Virtual histopathology offers a more consistent, and automated approach, employing techniques like machine learning, deep learning, and image processing to simulate staining and enhance tissue analysis. This review explores the strengths, limitations, and clinical applications of these methods, highlighting recent advancements in virtual histopathological approaches. In addition, important areas are identified for future research to improve diagnostic accuracy and efficiency in clinical settings. metadata Imran, Muhammad Talha; Shafi, Imran; Ahmad, Jamil; Butt, Muhammad Fasih Uddin; Gracia Villar, Santos; García Villena, Eduardo; Khurshaid, Tahir y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, eduardo.garcia@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Virtual histopathology methods in medical imaging - a systematic review. BMC Medical Imaging, 24 (1). ISSN 1471-2342

Artículo Materias > Biomedicina 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 Emergency medical services (EMSs) face critical situations that require patient risk classification based on analytical and vital signs. We aimed to establish clustering-derived phenotypes based on prehospital analytical and vital signs that allow risk stratification. This was a prospective, multicenter, EMS-delivered, ambulance-based cohort study considering six advanced life support units, 38 basic life support units, and four tertiary hospitals in Spain. Adults with unselected acute diseases managed by the EMS and evacuated with discharge priority to emergency departments were considered between January 1, 2020, and June 30, 2023. Prehospital point-of-care testing and on-scene vital signs were used for the unsupervised machine learning method (clustering) to determine the phenotypes. Then phenotypes were compared with the primary outcome (cumulative mortality (all-cause) at 2, 7, and 30 days). A total of 7909 patients were included. The median (IQR) age was 64 (51–80) years, 41% were women, and 26% were living in rural areas. Three clusters were identified: alpha 16.2% (1281 patients), beta 28.8% (2279), and gamma 55% (4349). The mortality rates for alpha, beta and gamma at 2 days were 18.6%, 4.1%, and 0.8%, respectively; at 7 days, were 24.7%, 6.2%, and 1.7%; and at 30 days, were 33%, 10.2%, and 3.2%, respectively. Based on standard vital signs and blood test biomarkers in the prehospital scenario, three clusters were identified: alpha (high-risk), beta and gamma (medium- and low-risk, respectively). This permits the EMS system to quickly identify patients who are potentially compromised and to proactively implement the necessary interventions. metadata López-Izquierdo, Raúl; del Pozo Vegas, Carlos; Sanz-García, Ancor; Mayo Íscar, Agustín; Castro Villamor, Miguel A.; Silva Alvarado, Eduardo René; Gracia Villar, Santos; Dzul López, Luis Alonso; Aparicio Obregón, Silvia; Calderón Iglesias, Rubén; Soriano, Joan B. y Martín-Rodríguez, Francisco mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, eduardo.silva@funiber.org, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs. npj Digital Medicine, 7 (1). ISSN 2398-6352

2023

Artículo Materias > Biomedicina
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 Abierto Inglés A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions. metadata Siddiqui, Hafeez Ur Rehman; Saleem, Adil Ali; Raza, Muhammad Amjad; Gracia Villar, Santos; Dzul Lopez, Luis; Diez, Isabel de la Torre; Rustam, Furqan y Dudley, Sandra mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence. Diagnostics, 13 (18). p. 2881. ISSN 2075-4418

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 Abierto Inglés Agriculture is a critical domain, where technology can have a significant impact on increasing yields, improving crop quality, and reducing environmental impact. The use of renewable energy sources such as solar power in agriculture has gained momentum in recent years due to the potential to reduce the carbon footprint of farming operations. In addition to providing a source of clean energy, solar tracking systems can also be used for remote weather monitoring in the agricultural field. The ability to collect real-time data on weather parameters such as temperature, humidity, and rainfall can help farmers make informed decisions on irrigation, pest control, and other crop management practices. The main idea of this study is to present a system that can improve the efficiency of solar panels to provide constant power to the sensor in the agricultural field and transfer real-time data to the app. This research presents a mechanism to improve the arrangement of a photovoltaic (PV) array with solar power and to produce maximum energy. The proposed system changes its direction in two axes (azimuth and elevation) by detecting the difference between the position of the sun and the panel to track the sun using a light-dependent resistor. A testbed with a hardware experimental setup is designed to test the system’s capability to track according to the position of the sun effectively. In the end, real-time data are displayed using the Android app, and the weather data are transferred to the app using a GSM/WiFi module. This research improves the existing system, and results showed that the relative increase in power generation was up to 52%. Using intelligent artificial intelligence techniques with the QoS algorithm, the quality of service produced by the existing system is improved. metadata Kanwal, Tabassum; Rehman, Saif Ur; Ali, Tariq; Mahmood, Khalid; Gracia Villar, Santos; Dzul Lopez, Luis y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@unini.edu.mx, SIN ESPECIFICAR (2023) An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field. Agriculture, 13 (8). p. 1600. ISSN 2077-0472

Artículo Materias > Biomedicina
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 Abierto Inglés In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented. metadata Salinari, Alessia; Machì, Michele; Armas Diaz, Yasmany; Cianciosi, Danila; Qi, Zexiu; Yang, Bei; Ferreiro Cotorruelo, Maria Soledad; Gracia Villar, Santos; Dzul López, Luis Alonso; Battino, Maurizio y Giampieri, Francesca mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, maurizio.battino@uneatlantico.es, francesca.giampieri@uneatlantico.es (2023) The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment. Diseases, 11 (3). p. 97. ISSN 2079-9721

2022

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés To address the current pandemic, multiple studies have focused on the development of new mHealth apps to help in curbing the number of infections, these applications aim to accelerate the identification and self-isolation of people exposed to SARS-CoV-2, the coronavirus known to cause COVID-19, by being in close contact with infected individuals. The main objectives of this paper are: (1) Analyze the current status of COVID-19 apps available on the main virtual stores: Google Play Store and App Store for Spain, and (2) Propose a novel mobile application that allows interaction and doctor-patient follow-up without the need for real-time consultations (face-to-face or telephone). In this research, a search for eHealth and telemedicine apps related to Covid-19 was performed in the main online stores: Google Play Store and App Store, until May 2021. Keywords were entered into the search engines of the online stores and relevant apps were selected for study using a PRISMA methodology. For the design and implementation of the proposed app named COVINFO, the main weaknesses of the apps studied were taken into account in order to propose a novel and useful app for healthcare systems. The search yielded a total of 50 apps, of which 24 were relevant to this study, of which 23 are free and 54% are available for Android and iOS operating systems (OS). The proposed app has been developed for mobile devices with Android OS being compatible with Android 4.4 and higher. This app enables doctor-patient interaction and constant monitoring of the patient's progress without the need for calls, chats or face-to-face consultation in real time. This work addresses design and development of an application for the transmission of the user's symptoms to his regular doctor, based on the fact that only 16.6% of existing applications have this functionality. The COVINFO app offers a novel service: asynchronous doctor-patient communication, as well as constant monitoring of the patient’s condition and evolution. This app makes it possible to better manage the time of healthcare personnel and avoid overcrowding in hospitals, with the aim of preventing the collapse of healthcare systems and the spread of the coronavirus. metadata Herrera Montano, Isabel; Pérez Pacho, Javier; Gracia Villar, Santos; Aparicio Obregón, Silvia; Breñosa, Jose y de la Torre Díez, Isabel mail SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, silvia.aparicio@uneatlantico.es, josemanuel.brenosa@uneatlantico.es, SIN ESPECIFICAR (2022) Descriptive Analysis of Mobile Apps for Management of COVID-19 in Spain and Development of an Innovate App in that field. Scientific Reports, 12 (1). ISSN 2045-2322

Artículo Materias > Ciencias Sociales 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 This article proposes a discussion on the form of coexistence of local Development Agencies in Uruguay, with local governments in the face of the new scenarios marked by the decentralization process, initiated in the country with the Constitutional Reform of 1996 and culminating in February 2009, with the Law of Political Decentralization and Citizen Participation. The discussion applies in particular to the local development agency of the city of Rivera (ADR), located in the northeast of the country. A descriptive, mixed, bibliographic, documentary investigation was carried out with primary data collection to internal and external references to ADR. The results show that the coexistence of both institutions has been difficult, without defining clear roles. Promoting dialogue to define the role of each seems to be the great challenge facing the sustainability of the agency metadata Garat de Marin, Mirtha Silvana; Soriano Flores, Emmanuel; Rodríguez Velasco, Carmen Lilí; Silva Alvarado, Eduardo; Calderón Iglesias, Rubén; Álvarez, Roberto Marcelo y Gracia Villar, Santos mail silvana.marin@uneatlantico.es, emmanuel.soriano@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR, ruben.calderon@uneatlantico.es, roberto.alvarez@uneatlantico.es, santos.gracia@uneatlantico.es (2022) Development Agencies and Local Governments—Coexistence within the Same Territory. Social Sciences, 11 (9). p. 398. ISSN 2076-0760

2021

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Background: To address the current pandemic, multiple studies have focused on the development of new mHealth applications to help curb the number of infections, these applications aim to accelerate the identification and self-isolation of people exposed to SARS-CoV- 2, the coronavirus known to cause COVID-19, by being in close contact with infected individuals. Objective: The main objectives of this paper are: 1)To analyze the current status of COVID-19 apps available the main virtual stores: Google Play Store and App Store, and 2)To propose a novel mobile application based on the limitations of the analyzed apps. Methods: The search for apps in this research was carried out in the main virtual stores: Google Play Store and App Store, until May 2021. After the analysis of the selected apps, a novel app is proposed whose main function will be the multiple transmission of information about the patient's symptoms from the application, without the need for phone calls or chat in real time. For its development, the flowchart shown in this session is followed. Results: The search yielded a total of 50 apps, of which 24 were relevant to this study. It is important to note that 23 of the apps analyzed are free. Of the total number of apps, 54% are available for Android and iOS operating systems. 50% of the apps have more than 5 thousand downloads. This means that Covid-19 related apps are in high demand among mobile device users today. The developed app is called COVINFO and its name comes from the union of the words COVID-19 and information, inserted in such a way that the user can get an idea of the app's functionality just by listening or reading the resulting name. The application has been created for mobile devices with Android operating system, being compatible with Android 4.4 and higher. Conclusions: Of the apps found, 37.5% only offer information about the virus and the necessary measures to avoid infection. During the analysis it was detected that 12.5% of the apps are focused on locating outbreaks and that none of them have been successful for the following reasons: not being interconnected to share data; and the request for access to the user's geolocation, generating distrust on the part of the user who, consequently, rejects them. This work addresses the development of an application for the transmission of the user's symptoms to his regular doctor, based on the fact that only 16.6% of the existing applications have this functionality. The COVINFO app offers a service that no other application on the market has: doctor-patient interaction without the need for calls or chat in real time for constant monitoring by the doctor of the patient's condition and evolution. metadata Herrera Montano, Isabel; Pérez Pacho, Javier; Gracia Villar, Santos; Aparicio Obregón, Silvia; Breñosa, Jose y de la Torre Díez, Isabel mail SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, silvia.aparicio@uneatlantico.es, josemanuel.brenosa@uneatlantico.es, SIN ESPECIFICAR (2021) Analysis of mobile apps for information, prevention and monitoring of covid-19 and proposal of an innovative app in this field. JMIR Preprints. (En Evaluación)

Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Cancer stem cells (CSCs) are a rare tumor subpopulation with high differentiation, proliferative and tumorigenic potential compared to the remaining tumor population. CSCs were first discovered by Bonnet and Dick in 1997 in acute myeloid leukemia. The identification and isolation of these cells in this pioneering study were carried out through the flow cytometry, exploiting the presence of specific cell surface molecular markers (CD34+/CD38−). In the following years, different strategies and projects have been developed for the study of CSCs, which are basically divided into surface markers assays and functional assays; some of these techniques also allow working with a cellular model that better mimics the tumor architecture. The purpose of this mini review is to summarize and briefly describe all the current methods used for the identification, isolation and enrichment of CSCs, describing, where possible, the molecular basis, the advantages and disadvantages of each technique with a particular focus on those that offer a three-dimensional culture. metadata Cianciosi, Danila; Ansary, Johura; Forbes-Hernandez, Tamara Y.; Regolo, Lucia; Quinzi, Denise; Gracia Villar, Santos; Garcia Villena, Eduardo; Tutusaus Pifarre, Kilian; Alvarez-Suarez, José M.; Battino, Maurizio y Giampieri, Francesca 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) The Molecular Basis of Different Approaches for the Study of Cancer Stem Cells and the Advantages and Disadvantages of a Three-Dimensional Culture. Molecules, 26 (9). p. 2615. ISSN 1420-3049

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

Artículo Materias > Biomedicina Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Low energy pulsed electromagnetic signals (PEMS) therapy, in the field of bioelectronics, has been suggested as a promising analgesic therapy with special interest in treating conditions with poor response to pharmacotherapy. This study evaluated the effectiveness of PEMS therapy on the treatment of chronic low back pain patients with a neuropathic component. A group of 64 individuals with such condition was allocated to a 2-week treatment period (10 twenty-minute sessions on consecutive days) with an active PEMS therapy device or an inactive device in random order. The pain was assessed on a visual analog scale, and the functional status was assessed using the SF-12 questionnaire. The visual analog scale scores were lower after treatment than at baseline but only in the group treated with the active device. According to the DN4 score, neuropathic pain decreased in both experimental groups with respect to baseline, but this was only significant for the group treated with the active device. Similarly, an improvement in the SF-12 and Medical Outcomes Study (MOS) sleep scale components was reported. The study demonstrated that low-energy PEMS therapy was efficient in reducing pain and improving function in chronic low back pain patients with a neuropathic component. metadata de Teresa, Carlos; Varela-López, Alfonso; Rios-Álvarez, Susana; Gálvez, Rafael; Maire, Coralie; Gracia Villar, Santos; Battino, Maurizio y Quiles, José L. mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, maurizio.battino@uneatlantico.es, jose.quiles@uneatlantico.es (2021) Evaluation of the Analgesic Efficacy of a Bioelectronic Device in Non-Specific Chronic Low Back Pain with Neuropathic Component. A Randomized Trial. Journal of Clinical Medicine, 10 (8). p. 1781. ISSN 2077-0383

2013

Artículo Materias > Ciencias Sociales Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Español El objetivo de este estudio fue presentar cómo aplicar un modelo de ciclo de contra-inteligencia empresarial (CCIE) en la dirección estratégica, de forma que se puedan tomar medidas sobre la protección contra el espionaje cibernético en las tecnologías de la información y las comunicaciones (TICs) en las organizaciones. Los datos se obtuvieron de las recomendaciones de tres estudios relacionados con el espionaje cibernético en las TICS. El método de investigación se fundamentó en el análisis cualitativo de las recomendaciones de estos tres documentos y se clasificaron de acuerdo a las etapas del modelo CCIE recomendado por Lauria (2008). Los resultados demostraron que el 73% de las recomendaciones de los documentos analizados se relacionó con la definición de requisitos de protección, evaluación de vulnerabilidades, procesar, analizar y difundir los resultados. El 22% se relacionó con evaluar las amenazas de la competencia, desarrollar y utilizar protección. Estos resultados sugieren deficiencias en el desarrollo de contramedidas, lo cual afecta el ciclo completo. Se analizaron los documentos en el año 2013. metadata Valdés Ortíz, Francisco; Hidalgo González, Cristina; Gracia Villar, Santos y Domingo Soriano, Saúl mail SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, saul_domingo@funiber.org (2013) Aplicación de contra-inteligencia empresarial: análisis sobre la protección contra el espionaje cibernético en las organizaciones. Scientific International Journal, 10 (3). pp. 19-31. ISSN 1554-6349

Este listado fue generado el Tue Jun 9 00:04:18 2026 UTC.

open

Association between socioeconomic and health variables and community-acquired pneumonia mortality rates in Chile from 1990 to 2021

Objectives To describe long-term trends in mortality attributed to community-acquired pneumonia (CAP) in Chile from 1990 to 2021, stratified by age group, and to evaluate associations with selected socioeconomic and demographic indicators. Study design Ecological, observational, longitudinal study using national secondary data. Methods CAP mortality rates were analyzed for the total population and by age group. Associations with the Human Development Index (HDI), poverty rate, aging index, and life expectancy at birth were examined using a hierarchical analytical approach. This included Spearman's rank correlation for initial exploration, multivariable linear regression to assess adjusted associations, and Prais–Winsten generalized least squares regression to account for first-order autocorrelation and shared temporal trends. Stationarity was evaluated using augmented Dickey–Fuller tests, with supplementary analyses using first-differenced variables. Missing data were imputed using time-based regression or interpolation, with sensitivity analyses performed. Results CAP mortality declined substantially across all age groups over the study period. Strong bivariate correlations were observed between mortality and all socioeconomic indicators; however, these associations were attenuated after adjustment for confounding and temporal autocorrelation. In multivariable and time-series models, HDI and the aging index remained significantly associated with CAP mortality in children (0–9 years) and older adults (≥65 years), whereas associations in intermediate age groups were not robust after accounting for shared secular trends. Poverty and life expectancy did not demonstrate independent associations in adjusted models. Conclusions CAP mortality in Chile has decreased markedly over the past three decades. Associations with socioeconomic indicators are strongest at the extremes of age and persist after accounting for temporal structure, although the ecological design precludes causal inference. These findings highlight the importance of considering demographic and socioeconomic context in population-level analyses of infectious disease outcomes.

Artículos y libros

Italo Salvador López Muñoz mail italo.lopez@doctorado.unini.edu.mx, Maria Loreto Romero Ladrón de Guevara mail , Christian R. Mejia mail , Shyla Del-Aguila-Arcentales mail , Aldo Alvarez-Risco mail , Neal M. Davies mail , Jaime A. Yáñez mail ,

López Muñoz

open

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

This study presents a novel integrative approach for the analysis of high-dimensional gene expression data, leveraging the complementary strengths of unsupervised clustering and supervised classification. Using K-means clustering, the dataset is stratified into three distinct clusters, revealing intrinsic biological patterns and relationships. The resulting cluster assignments are subsequently employed as pseudo-labels to train machine learning models, including support vector machines, random forest, and a stacking ensemble classifier. To validate and enhance the robustness of clustering, complementary methodologies such as hierarchical clustering and DBSCAN are employed, with results visualized through PCA-driven dimensionality reduction. The high predictive accuracy achieved by the classifiers underscores the separability and reliability of the identified clusters. Furthermore, feature importance analysis highlighted key genetic determinants within each cluster, offering actionable insights into potential biomarkers and critical genomic features. This framework bridges the gap between exploratory unsupervised learning and predictive supervised modeling, providing a scalable and interpretable methodology for analyzing complex genomic datasets. Its applicability extends to biomarker discovery, patient stratification, and other precision medicine applications, emphasizing its utility in advancing genomic research and clinical practice.

Producción Científica

Eshmal Iman mail , Sohail Jabbar mail , Shabana Ramzan mail , Ali Raza mail , Farwa Raoof mail , Stefanía Carvajal-Altamiranda mail stefania.carvajal@uneatlantico.es, Vivian Lipari mail vivian.lipari@uneatlantico.es, Imran Ashraf mail ,

Iman

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.

Producción Científica

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

open

Concern for mpox infection in Latin America

Background Mpox arrived in Latin America and quickly began to replicate, so it is important to measure the concern it generates among residents. The study aims to assess whether country or other factors are associated with concern about mpox infection in Latin America. Methods The study uses a cross-sectional, multicenter design. Sampling was conducted using non-random snowball sampling. From August to September 2022, concern about being infected with mpox was assessed using a previously validated questionnaire (Cronbach's Alpha: 0.85); it was divided into nine countries and other social variables. Results From 1404 respondents, the majority of respondents were female (60.3%) and young (median age 25 years); also, a few reported that it was a significant problem (6% almost all the time and 11% often) and were concerned (6% almost all the time and 11% often) about the possibility of mpox infection. In multivariate analysis, men (aPR: 0.85; 95% CI: 0.73–0.99; p-value=0.046), younger (aPR: 0.98; 95% CI: 0.97–0.99; p-value<0.001), single (aPR: 0.78; 95% CI: 0.62–0.99; p-value=0.042) and, compared to Peru, those living in Colombia (aPR: 0.75; 95% CI. 0.58–0.97; p-value=0.027) and Costa Rica (aPR: 0.65; 95% CI: 0.44–0.96; p-value=0.032) reported the lowest concern; also, Bolivia (aPR: 1.16; 95% CI: 0.94–1.43; p-value=0.176) and Honduras (aPR: 1.01; 95% CI: 0.80–1.27; p-value=0.943) reported that their concerns tend to be higher. Conclusions There were evident differences across respondents' countries; these baseline results show that the first report was made in many countries that were also significantly affected by mpox and now face a new epidemic threatening public health.

Artículos y libros

Christian R. Mejia mail , Aldo Alvarez-Risco mail , Luciana Daniela Garlisi-Torales mail , Telmo Raúl Aveiro mail , Jamil Cedillo-Balcázar mail , Néstor Valentin Rocha-Saravia mail , Andrea Retana-González mail , Medally C. Paucar mail , Beatriz Mejia Raudales mail , Jose Armada mail , Shyla Del-Aguila-Arcentales mail , Neal M. Davies mail , Jaime A. Yáñez mail jaime.yanez@unini.edu.mx,

Mejia

open

Fish consumption and brain structure: a comprehensive systematic review of observational studies

Background Age-related structural changes in the human brain, including cortical atrophy, reductions in grey and white matter volumes, and the accumulation of small vessel–related lesions such as white matter hyperintensities (WMH) and cerebral microbleeds, represent critical biological substrates underlying cognitive decline and dementia. Fish consumption has been associated with slower cognitive decline and reduced risk of dementia, but a comprehensive evaluation of its relation with brain structures is lacking. Aims The aim of this study was to systematically review current scientific literature providing evidence of relation between fish intake and brain structures in human studies. Methods Studies indexed in two major electronic databases have been screened based on a combination of keywords and MeSH terms. Studies were eligible whether they assessed fish consumption in relation to brain structures in the adult populations. Results A total of 24 studies conducted predominantly on older adults met inclusion criteria. Most brain volume measures were obtained via magnetic resonance imaging (MRI) procedures. Higher fish consumption was associated with reduced severity of white matter hyperintensities (a biomarker of cerebral small vessel disease and white matter damage) and cerebral micro-bleed, preservation of certain brain areas volumes (i.e., hippocampus, temporal lobe and periventricle white matter) and cortical thickness of specific areas (i.e., precuneus, parietal, and cingulate grey matter), among others, compared to lower intake. Some analyses found no association and isolated findings suggested possible adverse associations that were not consistently replicated. Studies reporting null findings may underline the possible relevance of the overall diet (i.e., adherence to the Mediterranean diet). Conclusions Inclusion of fish in a healthy and balanced diet is associated with better white matter grades on MRI and slower progression of white matter hyperintensities and reduction of vascular-related lesions of the aging brain, suggesting a potential role in preventing neurocognitive deterioration. Heterogeneity across studies underscores the need for additional studies.

Producción Científica

Justyna Godos mail , Giuseppe Caruso mail , Agnieszka Micek mail , Alberto Dolci mail , Zoltan Ungvari mail , Andrea Lehoczki mail , Lisandra León Brizuela mail , Evelyn Frias-Toral mail , Andrea Di Mauro mail , Mario Siervo mail , Michelino Di Rosa mail , Giuseppe Grosso mail ,

Godos

Enlaces de interés

Enlaces de interés

Documentos donde el Autor es "Gracia Villar, Santos"

2026

2025

2024

2023

2022

2021

2013

TEMÁTICA

ACCESO

IDIOMA

Filtros

Exportar como	Atom RSS 1.0 RSS 2.0