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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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Consumption of ultra-processed foods is associated with cognitive status in elderly patients

Background: Emerging evidence suggests that there might be an association between excess consumption of ultra-processed foods (UPFs) on cognitive health. UPF intake could promote systemic inflammation, oxidative stress phenomena, and metabolic dysregulation, contributing to neurodegeneration onset and cognitive decline in elderly population.Aim: The aim of this cross-sectional study was to examine the relation between UPF dietary pattern on MCI status in elderly patients taking into account the contribution of inflammatory markers.Design: The dietary intake was assessed using a validated food frequency questionnaire in ninety-two participants. All reported food items were categorized according to the NOVA system, classifying foods on the basis of the extent and purpose of industrial processing. Plasmatic concentrations of TGF-β1 and TNF-ɑ were measured by ELISA assay at the time of baseline neuropsychological evaluation. The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) were administered to evaluate the cognitive function in all participants. Non-parametric tests, correlation analysis, and logistic regression models were performed to assess the relations between variables of interest.Results: No significant associations were observed for unprocessed/minimally processed foods, culinary processed foods, or processed foods across the different regression models. In contrast, higher consumption of UPF was associated with increased odds of MCI (adjusted OR = 4.24, 95% CI: 1.05–17.13). However, after additional adjustment for inflammatory biomarkers (TGF-β and TNF-α), the association was attenuated and no longer statistically significant (OR = 4.79, 95% CI: 0.73–31.24), although the direction of the association remained positive.Conclusion: UPF consumption may be associated with increased likelihood of MCI, and inflammatory status may potentially play a role in this association.

Artículos y libros

Margherita Grasso mail , Francesca L’Episcopo mail , Marco Antonio Olvera-Moreira mail , Giuseppe Toscano mail , Stefano Muratore mail , Maria Angela Tripodi mail , Sabrina Musso mail , Veronica Bentivegna mail , Lucrezia Costanzo mail , Giusi Fatati mail , Melannie Toral-Noristz mail , Raynier Zambrano-Villacres mail , Lisandra León Brizuela mail , Raffaele Ferri mail , Giuseppe Lanza mail , Filippo Caraci mail ,

Grasso

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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.

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

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Environmental burden of fish in healthy and sustainable diets

Fish is widely promoted as part of healthy dietary patterns. The aim of this review was to summarise current literature on the environmental footprint of fish and its role within sustainable diets. Fish generally represents a minor share of total dietary environmental impacts, contributing to a smaller proportion of greenhouse-gas emissions (GHGe), land and water use than meat and other animal products. Several modelling studies showed that substituting meat with fish or increasing fish intake within optimised dietary patterns can reduce environmental impacts, although the magnitude varies by country, diet type, and fish species. However, some analyses reported increased GHGe associated with higher fish intake, especially in models ensuring nutritional quality. Overall, fish consumption is compatible with achieving nutritionally adequate and lower environmental impacts, although optimal match between environmental boundaries and nutritional needs is not always possible. These findings suggest that fish can play a constructive role in sustainable diets when integrated thoughtfully within broader dietary shifts.

Artículos y libros

Alberto Dolci mail , Alessandro Scuderi mail , Evelyn Frias-Toral mail , Leonardo de Jesús Hernández Cruz mail leonardo.hernandez@unib.org, Andrea Di Mauro mail , Fabrizio Furnari mail , Alice Rosi mail , Francesca Scazzina mail , Giuseppe Grosso mail ,

Dolci

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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