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

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

Background: Recovery after a training session or match is a key factor in injury prevention and sports performance. The purpose of this systematic review was to analyze and consolidate the available scientific evidence from the main databases on the use of infrared thermography in the assessment of fatigue, injury risk factors, and recovery in soccer players.Methods: The literature search was conducted following the PRISMA guidelines and the PICOS model until June 30, 2025, in the main scientific databases (ScienceDirect, EMBASE, Web of Science (WOS), Cochrane Library, SciELO, MEDLINE/PubMed, SPORTDiscus, and Scopus). The risk of bias and methodological quality were assessed using the Cochrane Handbook guidelines and the PEDro scale.”Results: The initial literature search yielded a total of 510 records. After applying the inclusion and exclusion criteria, the final sample consisted of 20 studies, which were of high methodological quality. The results showed the effects of infrared thermography in assessing fatigue, identifying injury risk factors, and monitoring recovery processes in soccer players. The studies also systematically reported the characterization of the population, the assessment methods used, the variables analyzed, the methodological design, the main results, and the effects of the intervention.Conclusions: Infrared thermography shows promise as a valid, reliable, and non-invasive tool for assessing skin temperature, reflecting temperature changes in response to physiological processes. It allows for the analysis of structural or metabolic fatigue and thermal asymmetries. Therefore, thermography could be used to design individualized recovery protocols.

Producción Científica

Yehinson Barajas Ramón mail , Julio Calleja-González mail , José Luaces-Carreño mail , Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es,

Barajas Ramón

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

Opuntia ficus-indica peel is known to possess antioxidant, anti-inflammatory, and anticancer activities and currently is discarded or used for animal feeding. Within this context, the aim of this work was to evaluate the antiproliferative and pro-apoptotic effect of purple prickly pear peel extract (PPE) on the human colon adenocarcinoma cancer cell line (HTC116). The methanolic extract of PPE was characterized in terms of betalain and polyphenols as well as total antioxidant capacity. Cell viability, apoptosis induction, cell cycle arrest, and reactive oxygen species (ROS) production assays were performed. Important proteins and genes related to proliferation and apoptosis were determined. PPE represents a good source of bioactive compounds with a high antioxidant capacity. Cell viability was reduced gradually by PPE treatments, with lower effects in nontumorigenic cells. Compared to the control group, a significant induction of apoptosis as well as cell cycle arrest in the sub-G1 phase and ROS production was observed in PPE-treated cells. Furthermore, the treatment induced the overexpression of p53 at protein levels and upregulated the mRNA expression of pro-apoptotic BAX, CASP9, BID, and CYCS, along with the significant decrease of anti-apoptotic BCL2 gene expression. Simultaneously, cyclin D1 and CDK4 gene expression were significantly decreased, while p21 increased considerably. The treatment also induced the downregulation of Her2 and PI3K at protein levels and caused the suppression of PI3KCA and mTOR expression at gene levels. Overall, these findings suggested that PPE has potential anticancer effects against human colon adenocarcinoma progression.

Producción Científica

Yasmany Armas Diaz mail , Zexiu Qi mail , Bei Yang mail , Danila Cianciosi mail , Luca Mazzoni mail , Massimiliano Gasparrini mail , Tamara Y. Forbes-Hernandez mail tamara.forbes@unini.edu.mx, José L. Quiles mail jose.quiles@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Francesca Giampieri mail francesca.giampieri@uneatlantico.es,

Armas Diaz

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Liquorice alters adipocyte–breast cancer cell crosstalk by modulating oxidative stress and suppressing aromatase and renin–angiotensin signalling

Obesity is recognised to be a risk factor for breast cancer since adipose tissue influences the tumour microenvironment. This study aims to investigate the effect of the secretome of 3T3-L1 adipocytes untreated or treated with liquorice root extract (LRE), containing flavonoids, phenolic acids, and saponins on MCF-7 breast cancer cells. By treating adipocytes with LRE, the secretion of certain pro-tumorigenic factors like IGFBP-6, resistin, and VEGF was reduced. MCF-7 cells exposed to conditioned medium from LRE-treated adipocytes exhibited an increase in reactive oxygen species levels, downregulation of the Nrf2 antioxidant pathway, and increased autophagy. Those conditions reduced cell viability, migration, and colony formation. Additionally, there was downregulation of genes associated with oestrogen signalling and tumour-related processes, including CYP19A1 (aromatase), ERα, Her2, and components of the renin–angiotensin system (RAS). These findings suggest that LRE can modulate the adipocyte secretome to influence breast cancer cell behaviour under obesity-related in vitro conditions.

Producción Científica

Danila Cianciosi mail , Yasmany Armas Diaz mail , Bei Yang mail , Zexiu Qi mail , Ge Chen mail , José L. Quiles mail jose.quiles@uneatlantico.es, Massimiliano Gasparrini mail , Manuela Cassotta mail manucassotta@gmail.com, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Francesca Giampieri mail francesca.giampieri@uneatlantico.es,

Cianciosi

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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.

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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

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Enhanced weather classification using xception with SENet and attention mechanisms

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

Producción Científica

Gunjan Shandilya mail , Sheifali Gupta mail , Abdul Khader Jilani Saudagar mail , Sunnia Ikram mail , Ateeq Ur Rehman mail , Isabel De la Torre Díez mail , Heba G. Mohamed mail , Ramón Pali-Casanova mail ramon.pali@unini.edu.mx, Ángel Gabriel Kuc Castilla mail angel.kuc@uneatlantico.es, Upinder Kaur mail ,

Shandilya