Galicia Sur Biomedical Foundation

Major depressive disorder (MDD) has been associated with an increased risk of cognitive decline and neurodegenerative disorders like Alzheimer's disease (AD), prompting interest in peripheral biomarkers related to amyloid metabolism as well as neuroaxonal and astroglial injury. However, evidence regarding circulating markers in MDD remains inconsistent. In this cross-sectional study, we simultaneously assessed plasma levels of amyloid-β peptides (Aβ40 and Aβ42), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) in MDD patients and healthy controls (HC) using ultrasensitive single-molecule array (SIMOA) technology. Associations with clinical and cognitive scales were examined. Plasma concentrations of Aβ40 and Aβ42 were significantly lower in MDD patients, whereas no group differences were observed for NfL and GFAP, after correcting for age and sex. However, both Aβ peptides were not significantly associated with depressive symptom severity, whereas the Aβ42/Aβ40 ratio was negatively associated with anhedonia. NfL and GFAP levels were primarily influenced by age. In the absence of a reduced Aβ42/Aβ40 ratio, these findings suggest that reduced plasma Aβ levels in MDD may reflect systemic or metabolic factors associated with MDD, including lifestyle or treatment-related effects. Therefore, these findings should be interpreted with caution and further examined in longitudinal studies to prevent potential confounding factors.

The application of bioactive nanoemulsions in the meat industry has attracted great interest due to their ability to improve the stability, bioavailability, and functionality of bioactive compounds, contributing to the extension of the shelf-life of highly perishable products, such as sausages and meat patties. Thus, this review provides a critical analysis of the application of nanoemulsions in sausages and meat patties, with emphasis on their mechanisms of action, formulation strategies, and performance in improving oxidative stability and microbial safety. Nanoemulsions, typically characterized by droplet sizes below 200 nm, increase interfacial area and penetration into meat matrices, resulting in reductions of 30–60% in lipid oxidation markers and decreases of 1–2 log CFU/g in spoilage and pathogenic microorganisms. Preparation and stabilization approaches, including high-energy and low-energy methods, are summarized, and the influence of nanoemulsion characteristics on texture, color, pH, and sensory perception is discussed. Particular attention is given to technological barriers, such as scale-up feasibility, stability during processing and storage, interactions with meat proteins, as well as regulatory and labeling considerations related to nano-enabled foods. Overall, the current evidence indicates that NEs represent a viable strategy to replace synthetic preservatives while supporting clean-label product development; however, further research on safety assessment, optimal dosing, and consumer acceptance is still required for broader industrial implementation.

Introduction: rhythmic gymnastics is a highly demanding aesthetic sport that has been associated with an increased risk of eating disorders, body dissatisfaction and nutritional deficits. Objectives: providing data on the dietary habits and body composition of Spanish amateur rhythmic gymnasts. Methods: descriptive transversal study on 56 female amateur rhythmic gymnasts practicing within clubs belonging to the Galician Gymnastics Federation, categorized into pre-teens (10-12 yrs) and teens (13-18 yrs). Anthropometric measurements were performed following ISAK protocols and complemented with bioimpedance. Dietary intake was qualitative and quantitatively determined through a three-day food record and a food frequency questionnaire. Physical activity level was estimated by an activity and training record. The Eating Attitudes Test (EAT-40) was used to identify disordered eating attitudes. Results: anthropometric data showed significantly lower z-scores, body fat percentage, triceps skinfold thickness, and waist-to-hip ratio in pre-teen gymnasts than in teens, and higher body water and mineral mass percentages. Pre-teens accumulated higher training load. Energy intake was lower than estimated energy expenditure and Spanish recommendations. The intake of fiber, legumes, fruit, vegetables, oils and fats did not reach the recommendations, while sugar and protein exceeded them. EAT-40 reflected no signs of eating disorders, with older gymnasts showing higher scores. Conclusions: suboptimal intake of energy and certain food groups such as legumes, vegetables, fruit and healthy fats, plus high training loads, could negatively impact health and performance of young female athletes. Considering the high nutritional and physical demands of rhythmic gymnastics, attention should be addressed to their diet and eating behaviors.

Background: Chronic Obstructive Pulmonary Disease (COPD) represents a significant global health burden, characterized by complex diagnostic and management challenges. Artificial Intelligence (AI) presents a powerful opportunity to enhance clinical decision-making and improve patient outcomes by leveraging complex health data. Objectives: This scoping review aims to systematically map the existing literature on AI applications in COPD. The primary objective is to identify, categorize, and summarize research into three key domains: (1) Diagnosis, (2) Clinical Symptoms, and (3) Clinical Outcomes. Methods: A scoping review was conducted following the Arksey and O’Malley framework. A comprehensive search of major scientific databases, including PubMed, Scopus, IEEE Xplore, and Google Scholar, was performed. The Population–Concept–Context (PCC) criteria included patients with COPD (Population), the use of AI (Concept), and applications in healthcare settings (Context). A global search strategy was employed with no geographic restrictions. Studies were included if they were original research articles published in English. The extracted data were charted and classified into the three predefined categories. Results: A total of 120 studies representing global distribution were included. Most datasets originated from Asia (predominantly China and India) and Europe (notably Spain and the UK), followed by North America (USA and Canada). There was a notable scarcity of data from South America and Africa. The findings indicate a strong trend towards the use of deep learning (DL), particularly Convolutional Neural Networks (CNNs) for medical imaging, and tree-based machine learning (ML) models like CatBoost for clinical data. The most common data types were electronic health records, chest CT scans, and audio recordings. While diagnostic applications are well-established and report high accuracy, research into symptom analysis and phenotype identification is an emerging area. Key gaps were identified in the lack of prospective validation and clinical implementation studies. Conclusions: Current evidence shows that AI offers promising applications for COPD diagnosis, outcome prediction, and symptom analysis, but most reported models remain at an early stage of maturity due to methodological limitations and limited external validation. Future research should prioritize rigorous clinical evaluation, the development of explainable and trustworthy AI systems, and the creation of standardized, multi-modal datasets to support reliable and safe translation of these technologies into routine practice.

ABSTRACT Physical activity is key to overall well-being, but the barriers to participation differ by age and context. This systematic review aimed to identify and evaluate the psychometric properties of questionnaires assessing barriers to physical activity across different age groups and to analyze their cross-cultural adaptation processes. A systematic review was conducted following PRISMA and COSMIN guidelines, searching three electronic databases up to February 2025. Measurement properties were classified as sufficient, insufficient, inconsistent, or indeterminate, and the quality of evidence was assessed using the COSMIN-modified GRADE approach. A total of 39 studies evaluating 23 different questionnaires were included. Seven instruments targeted children and adolescents, seven focused on adults, three on older adults, and five covered wider age ranges. Structural validity and internal consistency were the most frequently assessed properties, while responsiveness and criterion validity were rarely evaluated. Overall, 55% of the measurement property results were rated as sufficient, and 77% of methodological quality ratings were high. Although various questionnaires are available to assess barriers to physical activity, their psychometric properties and cross-cultural adaptation processes remain insufficiently explored, particularly in adults and older adults. This review highlights the need for further validation of existing instruments and the development of culturally sensitive tools with strong methodological rigour to improve research and public health interventions promoting physical activity.

Background and Objectives: Spirometry is the most widely used pulmonary function test for diagnosing respiratory diseases. Its progressive incorporation into non-specialized settings, such as primary care, raises challenges for ensuring the reliability of results. In this context, tools based on artificial intelligence (AI) techniques have emerged as promising solutions to support quality control in spirometry. This systematic review aims to synthesize the available evidence on their application in this field. Methods: A systematic search was conducted in PubMed and IEEE Xplore to identify peer-reviewed original studies, published between 2014 and June 2025, that applied AI to spirometry quality control. The search and data extraction followed the PRISMA guidelines. Results: Six studies met the inclusion criteria. Four analyzed the acceptability and usability of the maneuver, and two focused on detecting errors committed during test performance. The most widely used models were convolutional neural networks, used in four studies, whereas two studies employed other conventional machine learning models. Three models reported area under the ROC curve values higher than 0.88. Conclusions: AI-based tools show great potential to assist in spirometry quality control, both in determining acceptability and in detecting errors. However, current studies remain scarce and highly heterogeneous in both objectives and methods. Broader, multicenter research, including validation in non-specialized settings, is required to confirm their clinical utility and facilitate their implementation in clinical practice.