This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI’s ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as ‘artificial intelligence’, ‘machine learning’, ‘sports injury’, and ‘risk prediction’. While AI’s predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges.

Probiotics and postbiotics have gained increasing attention in microbiome research due to their potential roles in maintaining gut homeostasis and supporting human health. While probiotics are defined as live microorganisms that confer health benefits when administered in adequate amounts, postbiotics represent preparations of inanimate microorganisms and/or their components that also exert biological effects on the host. This narrative review provides an updated overview of the current knowledge on probiotics and postbiotics, with a particular focus on their mechanisms of action, production strategies, and emerging applications in human health. The review discusses key mechanisms through which probiotics and postbiotics interact with the host, including modulation of the gut microbiota, enhancement of epithelial barrier integrity, immune system regulation, metabolic modulation, and systemic signaling pathways. Advances in production technologies, ranging from conventional fermentation to innovative inactivation and stabilization approaches, are also examined, alongside challenges related to yield optimization, stability, safety, and standardization. Although a growing body of evidence suggests potential benefits of probiotics and postbiotics in metabolic, inflammatory, gastrointestinal, and immune-related conditions, much of the available data is derived from preclinical studies or small-scale clinical trials. Consequently, their clinical efficacy, optimal dosing, and long-term safety require further validation. By integrating current findings and highlighting existing gaps in the literature, this review aims to clarify the therapeutic potential of probiotics and postbiotics and to support the development of more robust, evidence-based strategies for their application in functional foods, supplements, and future biotherapeutic interventions.

Plantar pressure and foot load distribution are essential parameters in evaluating postural alignment, neuromuscular balance, and the risk of musculoskeletal disorders. This cross-sectional study analyzed static plantar pressure in 113 healthy young adults (18–35 years) using the Spine 3D Sensor Medica platform. Contact area, load, average pressure, and maximum peak pressure were measured bilaterally in forefoot and hindfoot regions. Strong correlations were found between body weight, BMI, and plantar load, particularly in the hindfoot, while peak pressures were influenced more by individual biomechanical factors than anthropometry. Women demonstrated greater inter-limb asymmetries compared to men. These findings provide reference values for static plantar pressure and highlight their importance for clinical musculoskeletal rehabilitation. By identifying early postural imbalances and abnormal load distributions, baropodometric assessments can guide targeted interventions, improve pain management, and optimize functional recovery. The study supports incorporating advanced plantar pressure analysis in personalized rehabilitation programs for preventing and managing musculoskeletal dysfunctions.

Background : Youth football players experience rapid physical and biological changes while being exposed to high training loads, increasing performance demands and musculoskeletal injury risk. Current evaluations often analyze anthropometric, physiological, and biological domains separately, and few studies integrate these dimensions using machine-learning (ML) approaches. Objective: To provide a multidimensional assessment of elite youth football players and investigate how anthropometric, physical, and biological markers jointly relate to performance through classical statistics and ML. Methods: One hundred elite players (14–18 years) underwent standardized anthropometric, physical, and laboratory assessments. Analyses included descriptive statistics, ANOVA/MANOVA, PCA, factor analysis, composite biological indices, and ML models (linear regression, SVR) with 5-fold cross-validation. K-means clustering explored hidden adaptation phenotypes. Results: Older players showed higher weight and BMI, physical testing revealed consistent limb asymmetry (~5%), and biological markers remained within reference ranges. PCA and factor analysis extracted latent structural and metabolic domains. Linear regression predicted performance with R² ≈ 0.59, while SVR underperformed. K-means identified three adaptation phenotypes. Conclusions: Performance and resilience arise from interactions between structural, functional, and biological domains. Interpretable ML methods enhance individualized monitoring, early risk detection, and evidence-based injury-prevention strategies.

Fermented plant-based beverages represent promising functional foods due to their content of bioactive compounds (polyphenols, prebiotics) and viable probiotic microorganisms. Sprouted buckwheat is a rich source of bioactives and nutrients, which makes it a promising ingredient for the development of synbiotic formulations. This study aimed to optimize the fermentation process of a plant-based beverage composed of germinated buckwheat, honey, inulin, and Lactiplantibacillus plantarum (Lpb. plantarum), using Box–Behnken experimental design (BBD) and Response Surface Methodology (RSM) tools. The influence of three independent variables (inulin, honey, and inoculum concentration) was evaluated on five key response variables: total polyphenol content, flavonoid content, antioxidant activity (RSA%), pH, and starter culture viability. The optimal formulation—comprising 3% inulin, 10% honey, and 6.97 mg/100 mL inoculum—demonstrated functional stability over 21 days of refrigerated storage (4 °C), maintaining high levels of antioxidants and probiotic viability in the fermented beverage. Kinetic analysis of the fermentation process confirmed the intense metabolic activity of Lpb. plantarum, as evidenced by a decrease in pH, active consumption of reducing sugars, and organic acids accumulation.