Clasificación de riesgos en el manejo y transporte de cargas para la prevención de los problemas osteomusculares en los trabajadores de exportadora de cacao Verapaz.

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Single-cell omics for nutrition research: an emerging opportunity for human-centric investigations

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.

Producción Científica

Manuela Cassotta mail manucassotta@gmail.com, Yasmany Armas Diaz mail , Danila Cianciosi mail , Bei Yang mail , Zexiu Qi mail , Ge Chen mail , Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Giuseppe Grosso mail , José L. Quiles mail , Jianbo Xiao mail , Maurizio Battino mail maurizio.battino@uneatlantico.es, Francesca Giampieri mail francesca.giampieri@uneatlantico.es,

Cassotta

<a class="ep_document_link" href="/17878/1/s13018-025-06422-7.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Shoulder ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization, and arthroscopic trillat for the treatment of shoulder instability: a systematic review of original studies on surgical techniques

Background Anterior shoulder instability is a common condition, especially among young and active individuals, often associated with both osseous and soft tissue injuries. Recent innovations have introduced various surgical options for managing critical and subcritical instability. Therefore, the primary objective of this systematic review was to collect, synthesize, and integrate international research published across multiple scientific databases on shoulder ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization (DAS), and arthroscopic Trillat techniques used in the treatment of shoulder instability. Method A structured search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the PICOS model, up to January 30, 2025, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus, and Scopus databases. The risk of bias was evaluated, and the PEDro scale was used to assess methodological quality. Results The initial search yielded a total of 964 articles. After applying the inclusion and exclusion criteria, the final sample consisted of 25 articles. These studies demonstrated a high standard of methodological quality. The review summarized the effects of ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization, and arthroscopic Trillat techniques in treating shoulder instability, detailing the sample population, immobilization period, frequency of instability episodes—including recurrent dislocations and subluxations—surgical methods, study designs, assessed variables, main findings, and reported outcomes. Conclusions Arthroscopic ligamentoplasty is advantageous in preserving the patient’s native anatomy, maintaining joint integrity, and allowing for alternative interventions in case of failure. The arthroscopic Trillat technique offers a minimally invasive solution for anterior instability without significant bone loss. The DAS technique utilizes the biceps tendon to provide dynamic stabilization, aiming to generate a sling effect over the subscapularis muscle. The Latarjet procedure remains the gold standard for managing anterior glenoid bone loss greater than 20%. Each surgical option for anterior shoulder instability carries specific implications, and treatment decisions should be tailored based on bone loss severity, capsuloligamentous quality, and the patient’s functional needs.

Producción Científica

Carlos Galindo-Rubín mail , Yehinson Barajas Ramón mail , Fernando Maniega Legarda mail , Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es,

Galindo-Rubín

<a href="/17862/1/sensors-25-06419.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2

Forest fires pose significant threats to ecosystems, human life, and the global climate, necessitating rapid and reliable detection systems. Traditional fire detection approaches, including sensor networks, satellite monitoring, and centralized image analysis, often suffer from delayed response, high false positives, and limited deployment in remote areas. Recent deep learning-based methods offer high classification accuracy but are typically computationally intensive and unsuitable for low-power, real-time edge devices. This study presents an autonomous, edge-based forest fire and smoke detection system using a lightweight MobileNetV2 convolutional neural network. The model is trained on a balanced dataset of fire, smoke, and non-fire images and optimized for deployment on resource-constrained edge devices. The system performs near real-time inference, achieving a test accuracy of 97.98% with an average end-to-end prediction latency of 0.77 s per frame (approximately 1.3 FPS) on the Raspberry Pi 5 edge device. Predictions include the class label, confidence score, and timestamp, all generated locally without reliance on cloud connectivity, thereby enhancing security and robustness against potential cyber threats. Experimental results demonstrate that the proposed solution maintains high predictive performance comparable to state-of-the-art methods while providing efficient, offline operation suitable for real-world environmental monitoring and early wildfire mitigation. This approach enables cost-effective, scalable deployment in remote forest regions, combining accuracy, speed, and autonomous edge processing for timely fire and smoke detection.

Producción Científica

Dilshod Sharobiddinov mail , Hafeez Ur Rehman Siddiqui mail , Adil Ali Saleem mail , Gerardo Méndez Mezquita mail , Debora L. Ramírez-Vargas mail debora.ramirez@unini.edu.mx, Isabel de la Torre Díez mail ,

Sharobiddinov

<a class="ep_document_link" href="/17863/1/v16p4316.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection

Breast cancer is a lethal carcinoma impacting a considerable number of women across the globe. While preventive measures are limited, early detection remains the most effective strategy. Accurate classification of breast tumors into benign and malignant categories is important which may help physicians in diagnosing the disease faster. This survey investigates the emerging inclination and approaches in the area of machine learning (ML) for the diagnosis of breast cancer, pointing out the classification techniques based on both segmentation and feature selection. Certain datasets such as the Wisconsin Diagnostic Breast Cancer Dataset (WDBC), Wisconsin Breast Cancer Dataset Original (WBCD), Wisconsin Prognostic Breast Cancer Dataset (WPBC), BreakHis, and others are being evaluated in this study for the demonstration of their influence on the performance of the diagnostic tools and the accuracy of the models such as Support vector machine, Convolutional Neural Networks (CNNs) and ensemble approaches. The main shortcomings or research gaps such as prejudice of datasets, scarcity of generalizability, and interpretation challenges are highlighted. This research emphasizes the importance of the hybrid methodologies, cross-dataset validation, and the engineering of explainable AI to narrow these gaps and enhance the overall clinical acceptance of ML-based detection tools.

Producción Científica

Alveena Saleem mail , Muhammad Umair mail , Muhammad Tahir Naseem mail , Muhammad Zubair mail , Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Shoaib Hassan mail , Imran Ashraf mail ,

Saleem

<a class="ep_document_link" href="/17849/1/1-s2.0-S2590005625001043-main.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence

Gender classification plays a vital role in various applications, particularly in security and healthcare. While several biometric methods such as facial recognition, voice analysis, activity monitoring, and gait recognition are commonly used, their accuracy and reliability often suffer due to challenges like body part occlusion, high computational costs, and recognition errors. This study investigates gender classification using gait data captured by Ultra-Wideband radar, offering a non-intrusive and occlusion-resilient alternative to traditional biometric methods. A dataset comprising 163 participants was collected, and the radar signals underwent preprocessing, including clutter suppression and peak detection, to isolate meaningful gait cycles. Spectral features extracted from these cycles were transformed using a novel integration of Feedforward Artificial Neural Networks and Random Forests , enhancing discriminative power. Among the models evaluated, the Random Forest classifier demonstrated superior performance, achieving 94.68% accuracy and a cross-validation score of 0.93. The study highlights the effectiveness of Ultra-wideband radar and the proposed transformation framework in advancing robust gender classification.

Producción Científica

Adil Ali Saleem mail , Hafeez Ur Rehman Siddiqui mail , Muhammad Amjad Raza mail , Sandra Dudley mail , Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Isabel de la Torre Díez mail ,

Saleem