Background: Maturation is a complex biological process affecting all tissues, organs, and systems, particularly during adolescence. The Peak Height Velocity (PHV) period, a hallmark of adolescent growth spurts, is associated with individual differentiations in the development of performance attributes amongst youth. Understanding the influence of sports participation on strength and power during the PHV period is essential for optimizing training outcomes and reducing injury risk. This scoping review synthesizes the literature on the strength and power development in athletes during the PHV period across various sports, highlighting the interaction between maturation, training, and performance outcomes. Methods: A systematic search of PubMed and Scopus, supplemented by manual searches, identified peer-reviewed studies from 2004 to 2025. The included longitudinal studies involved structured training and assessed strength-related performance during and around the PHV period. PRISMA-ScR guidelines were followed. Results: Twelve studies met the inclusion criteria. It is found that strength and power are significantly affected during the PHV period and participation in sports mitigates these effects. Training characteristics such as training frequency/volume and sport specificity were key factors. Early maturers often outperformed late maturers, though adolescent awkwardness temporarily reduced performance around PHV. Conclusions: Systematic participation in sports training can limit the phenomenon of adolescent awkwardness in the performance of strength and power. Hence, training programs should be tailored to maturity status, emphasizing skill development, strength training, and injury prevention. Future research should explore individualized training and the mechanisms underlying performance variability during the PHV period.

Stroke has become the first cause of functional disability and one of the leading causes of mortality worldwide. Therefore, it is of crucial importance to develop accurate biomarkers to assess stroke risk and prognosis. Emerging evidence suggests that neutrophil extracellular trap (NET) levels may serve as a valuable biomarker to predict stroke occurrence and functional outcome. NETs are known to create a procoagulant state by serving as a scaffold for tissue factor (TF) and platelets inducing thrombosis by activating coagulation pathways and endothelium. A literature search was conducted in two databases (MEDLINE and Scopus) to trace all relevant studies published between 1 January 2016 and 31 December 2022, addressing the potential utility of NETs as a stroke biomarker. Only full-text articles in English were included. The current review includes thirty-three papers. Elevated NET levels in plasma and thrombi seem to be associated with increased mortality and worse functional outcomes in stroke, with all acute ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage included. Additionally, higher NET levels seem to correlate with worse outcomes after recanalization therapies and are more frequently found in strokes of cardioembolic or cryptogenic origin. Additionally, total neutrophil count in plasma seems also to correlate with stroke severity. Overall, NETs may be a promising predictive tool to assess stroke severity, functional outcome, and response to recanalization therapies.

Maturation is a key factor in sports participation and often determines the young athletes’ characterization as a talent. However, there is no evidence of practical models for understanding the factors that discriminate children according to maturity. Hence, this study aims to deepen the understanding of the factors that affect maturity in 11-year-old Team Sports Athletes by utilizing explainable artificial intelligence (XAI) models. We utilized three established machine learning (ML) classifiers and applied the Sequential Forward Feature Selection (SFFS) algorithm to each. In this binary classification task, the logistic regression (LR) classifier achieved a top accuracy of 96.67% using the seven most informative factors (Sitting Height, Father’s Height, Body Fat, Weight, Height, Left and Right-Hand Grip Strength). The SHapley Additive exPlanations (SHAP) model was instrumental in identifying the contribution of each factor, offering key insights into variable importance. Independent sample t-tests on these selected factors confirmed their significance in distinguishing between the two classes. By providing detailed and personalized insights into child development, this integration has the potential to enhance the effectiveness of maturation prediction significantly. These advancements could lead to a transformative approach in young athletes’ pediatric growth analysis, fostering better sports performance and developmental outcomes for children.

The purpose of this study was to compare the internal and external load in continuous and intermittent small-sided games (SSG) formats. Eight semi-professional soccer players participated in the study, and they completed three protocols: (a) I-intermittent SSG protocol (Int-I, 4 sets of 4 min with a 3 min recovery); (b) Continuous SSG protocol (Con, 2 sets of 8 min with a 3 min recovery); (c) II-SSG protocol (Int-II, 4 sets of 4 min, where each set includes 1 min of exercise with varying recovery periods (10, 20, 30 s), with a 3 min recovery period between sets). A one-way analysis of variance (ANOVA) was used to analyze the dependent variables, with significance determined at p < 0.05. The three protocols differed in total distance covered and in distance covered at speeds >19 km/h, with the Int-II protocol resulting in the greatest distance covered (p < 0.05). Additionally, players in the Con protocol exercised at a higher percentage of their maximum heart rate (%HRmax) (p < 0.05), while the highest RPE value was observed in the Int-I interval protocol (p < 0.05). The external load experienced by players in intermittent SSG protocols is higher, while internal load (%HRmax) remains relatively low. This effect is especially notable in the new intermittent exercise model proposed in this study, which incorporates progressively increasing recovery times within each exercise set. Coaches can use this model to increase players’ external load without causing a heightened perception of fatigue.

Objectives: The early detection of low bone mineral density (BMD) is essential for preventing osteoporosis and related complications. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its cost and limited availability restrict its use in large-scale screening. This study investigated whether raw bioelectrical impedance analysis (BIA) data combined with explainable machine learning (ML) models could accurately classify osteopenia in women aged 40 to 55. Methods: In a cross-sectional design, 138 women underwent same-day BIA and DXA assessments. Participants were categorized as osteopenic (T-score between −1.0 and −2.5; n = 33) or normal (T-score ≥ −1.0) based on DXA results. Overall, 24.1% of the sample were classified as osteopenic, and 32.85% were postmenopausal. Raw BIA outputs were used as input features, including impedance values, phase angles, and segmental tissue parameters. A sequential forward feature selection (SFFS) algorithm was employed to optimize input dimensionality. Four ML classifiers were trained using stratified five-fold cross-validation, and SHapley Additive exPlanations (SHAP) were applied to interpret feature contributions. Results: The neural network (NN) model achieved the highest classification accuracy (92.12%) using 34 selected features, including raw impedance measurements, derived body composition indices such as regional lean mass estimates and the edema index, as well as a limited number of categorical variables, including self-reported physical activity status. SHAP analysis identified muscle mass indices and fluid distribution metrics, features previously associated with bone health, as the most influential predictors in the current model. Other classifiers performed comparably but with lower precision or interpretability. Conclusions: ML models based on raw BIA data can classify osteopenia with high accuracy and clinical transparency. This approach provides a cost-effective and interpretable alternative for the early identification of individuals at risk for low BMD in resource-limited or primary care settings.

Background/Objectives: Master athletes in team sports represent a growing population of individuals who continue to engage in structured training and competition. Among these sports, basketball has primarily been investigated in older athletes; however, age-related effects on training load within the broader master athlete population remain largely unexplored. This study aimed to examine the age-related differences in workloads among master basketball athletes and determine whether game participation can facilitate the achievement of the recommended physical activity level. Methods: A total of 178 male athletes were divided into three age groups (35–45, 46–60, and ≥60 years) and participated in a national tournament. External load metrics, including accumulated acceleration load (AAL), mechanical load (ML), and jump load (JL), were recorded using tri-axial microsensors. Internal load (IL) was assessed via session ratings of perceived exertion (sRPE). Physical activity levels were categorized into light, moderate, and vigorous intensity using accelerometry-derived metabolic equivalents. Results: Significant age-related differences (p < 0.01) were observed in AAL, ML, and JL, with the youngest group showing the highest values. Likewise, the time spent in moderate-to-vigorous physical activity (MVPA) displayed an age-dependent manner and decreased with age. Older athletes spent more time in low-intensity activities and employed energy-conserving strategies, avoiding high-speed and high-impact actions. Despite these variations, sRPE ratings were similar among groups. Conclusions: In conclusion, age is a crucial regulator of training load and physical activity and should be considered by practitioners and coaches who design training and physical activity plans for master basketball athletes. Despite the age-related differences, participation in basketball matches provides a substantial opportunity for increasing daily MVPA.