Search Results (2,320)

Background: Creatine supplementation is widely used to enhance exercise performance, mainly resistance training adaptations, yet its differential effects on upper- and lower-body strength and muscular power remain unclear across populations. Objective: This systematic review and meta-analysis aimed to quantify the effects of creatine supplementation in studies that included different exercise modalities or no exercise on upper- and lower-body muscular strength and power in adults. Methods: A comprehensive search of PubMed, Scopus, and Web of Science was conducted through 21 September 2024 to identify randomized controlled trials evaluating the effects of creatine supplementation on strength (bench/chest press, leg press, and handgrip) and power (upper and lower body). Weighted mean differences (WMDs) and 95% confidence intervals (CIs) were calculated using random-effects modeling. Subgroup analyses examined the influence of age, sex, training status, dose, duration, and training frequency. Results: A total of 69 studies with 1937 participants were included for analysis. Creatine plus resistance training produced small but statistically significant improvements in bench and chest press strength [WMD = 1.43 kg, p = 0.002], squat strength [WMD = 5.64 kg, p = 0.001], vertical jump [WMD = 1.48 cm, p = 0.01], and Wingate peak power [WMD = 47.81 Watts, p = 0.004] when compared to the placebo. Additionally, creatine supplementation combined with exercise training revealed no significant differences in handgrip strength [WMD = 4.26 kg, p = 0.10] and leg press strength [WMD = 3.129 kg, p = 0.11], when compared with the placebo. Furthermore, subgroup analysis based on age revealed significant increases in bench and chest press [WMD = 1.81 kg, p = 0.002], leg press [WMD = 8.30 kg, p = 0.004], and squat strength [WMD = 6.46 kg, p = 0.001] for younger adults but not for older adults. Subgroup analyses by sex revealed significant increases in leg press strength [WMD = 9.79 kg, p = 0.001], squat strength [WMD = 6.43 kg, p = 0.001], vertical jump [WMD = 1.52 cm, p = 0.04], and Wingate peak power [WMD = 55.31 Watts, p = 0.001] in males, but this was not observed in females. Conclusions: This meta-analysis indicates that creatine supplementation, especially when combined with resistance training, significantly improves strength in key compound lifts such as the bench or chest press and squat, as well as muscular power, but effects are not uniform across all measures. Benefits were most consistent in younger adults and males, while older adults and females showed smaller or non-significant changes in several outcomes. No overall improvement was observed for handgrip strength or leg press strength, suggesting that the ergogenic effects may be more pronounced in certain multi-joint compound exercises like the squat and bench press. Although the leg press is also a multi-joint exercise, results for this measure were mixed in our analysis, which may reflect differences in study design, participant characteristics, or variability in testing protocols. The sensitivity of strength tests to detect changes appears to vary, with smaller or more isolated measures showing less responsiveness. More well-powered trials in underrepresented groups, particularly women and older adults, are needed to clarify population-specific responses. Full article

Background: Pan-drug-resistant (PDR) Acinetobacterinfections are an escalating ICU threat, demanding both patient-level triage and facility-wide forecasting. Objective: The aim of this study was to build a dual-scale AI framework that (i) predicts PDR status at infection onset and (ii) forecasts hospital-level PDR burden through 2027. Methods: We retrospectively analyzed 270 Acinetobacter infection episodes (2020–2024) with 65 predictors spanning demographics, timelines, infection type, resistance-class flags, and a 25-drug antibiogram. TabTransformer and XGBoost were trained on 2020–2023 episodes (n = 210), evaluated by stratified 5-fold CV, and externally tested on 2024 episodes (n = 60). Metrics included AUROC, AUPRC, accuracy, and recall at 90% specificity; AUROC was optimism-corrected via 0.632 + bootstrap and DeLong-tested for drift. SHAP values quantified feature impact. Weekly PDR incidence was forecast with an attention–LSTM model retrained monthly (200 weekly origins, 4-week horizon) and benchmarked against seasonal-naïve, Prophet, and SARIMA models (MAPE and RMSE). Quarterly projections (TFT-lite) extended forecasts to 2027. Results: The CV AUROC was 0.924 (optimism-corrected 0.874); an ensemble of TabTransformer + XGBoost reached 0.958. The 2024 AUROC fell to 0.586 (p < 0.001), coinciding with a PDR prevalence drop (75→38%) and three covariates with PSIs > 1.0. Isotonic recalibration improved the Brier score from 0.326 to 0.207 and yielded a net benefit equivalent to 26 unnecessary isolation-days averted per 100 ICU admissions at a 0.20 threshold. SHAP highlighted Ampicillin/Sulbactam resistance, unknown acquisition mode, and device-related infection as dominant drivers. The attention–LSTM achieved a median weekly MAE of 0.10 (IQR: 0.028–0.985) vs. 1.00 for the seasonal-naïve rule, outperforming it on 48.5% of weeks and surpassing Prophet and SARIMA (MAPE = 6.2%, RMSE = 0.032). TFT-lite projected a ≥ 25% PDR tipping point in 2025 Q1 with a sustained rise in 2027. Conclusions: The proposed framework delivers explainable patient-level PDR risk scores and competitive 4-week and multi-year incidence forecasts despite temporal drift, supporting antimicrobial stewardship and ICU capacity planning. Shrinkage and bootstrap correction were applied to address the small sample size (EPV = 2.1), which poses an overfitting risk. Continuous recalibration and multi-center validation remain priorities. Full article

Purpose: This study aimed to develop and validate a new machine learning (ML)-based screening tool for a two-step prediction of the need for and type of nutritional therapy (enteral, parenteral, or combined) using Nutrition Risk Screening 2002 (NRS-2002) and other demographic parameters from the Optimal Nutrition Care for All (ONCA) national cohort data. Methods: This multicenter retrospective cohort study included 191,028 patients, with data on age, gender, body mass index (BMI), NRS-2002 score, presence of cancer, and hospital unit type. In the first step, classification models estimated whether patients required nutritional therapy, while the second step predicted the type of therapy. The dataset was divided into 60% training, 20% validation, and 20% test sets. Random Forest (RF), Artificial Neural Network (ANN), deep learning (DL), Elastic Net (EN), and Naive Bayes (NB) algorithms were used for classification. Performance was evaluated using AUC, accuracy, balanced accuracy, MCC, sensitivity, specificity, PPV, NPV, and F₁-score. Results: Of the patients, 54.6% were male, 9.2% had cancer, and 49.9% were hospitalized in internal medicine units. According to NRS-2002, 11.6% were at risk of malnutrition (≥3 points). The DL algorithm performed best in both classification steps. The top three variables for determining the need for nutritional therapy were severe illness, reduced dietary intake in the last week, and mild impaired nutritional status (AUC = 0.933). For determining the type of nutritional therapy, the most important variables were severe illness, severely impaired nutritional status, and ICU admission (AUC = 0.741). Adding gender, cancer status, and ward type to NRS-2002 improved AUC by 0.6% and 3.27% for steps 1 and 2, respectively. Conclusions: Incorporating gender, cancer status, and ward type into the widely used and validated NRS-2002 led to the development of a new scale that accurately classifies nutritional therapy type. This ML-enhanced model has the potential to be integrated into clinical workflows as a decision support system to guide nutritional therapy, although further external validation with larger multinational cohorts is needed. Full article

Background: Spinal metastases can cause significant impairment of neurological function and quality of life. Hence, personalized clinical decision-making based on prognosis and likely outcome is desirable. The effectiveness of AI in predicting complications and treatment outcomes for patients with spinal metastases is assessed. Methods: A thorough search was carried out through the PubMed, Scopus, Web of Science, Embase, and Cochrane databases up until 27 January 2025. Included were studies that used AI-based models to predict outcomes for adult patients with spinal metastases. Three reviewers independently extracted the data, and screening was conducted in accordance with PRISMA principles. AUC results were pooled using a random-effects model, and the PROBAST program was used to evaluate the study’s quality. Results: Included were 47 articles totaling 25,790 patients. For training, internal validation, and external validation, the weighted average AUCs were 0.762, 0.876, and 0.810, respectively. The Skeletal Oncology Research Group machine learning algorithms (SORG-MLAs) were the ones externally validated the most, continuously producing AUCs > 0.84 for 90-day and 1-year mortality. Models based on radiomics showed promise in preoperative planning, especially for outcomes of radiation and concealed blood loss. Most research concentrated on breast, lung, and prostate malignancies, which limited its applicability to less common tumors. Conclusions: AI models have shown reasonable accuracy in predicting mortality, ambulatory status, blood loss, and surgical complications in patients with spinal metastases. Wider implementation necessitates additional validation, data standardization, and ethical and regulatory framework evaluation. Future work should concentrate on creating multimodal, hybrid models and assessing their practical applications. Full article

Background/Objectives: Empathy is a core competency in nursing, contributing to patient care quality and professional resilience. This study investigated empathy levels among Greek undergraduate nursing students at the University of Peloponnese and examined the personal and educational factors that contribute to empathic development. Methods: A cross-sectional survey was conducted with 144 students from all academic years using the Jefferson Scale of Physician Empathy—Health Professions (JSPE-HP) and the SF-12 Health Survey. Data were analyzed using ANOVA and stepwise multiple linear regression. Results: Mean empathy scores were relatively high (M = 110.31, SD = 10.52). Empathy increased significantly with academic progression (p < 0.001), and higher scores were associated with parental status (p = 0.030) and better mental health (p = 0.044). Conversely, students with a chronically ill close contact reported lower empathy (p = 0.018). Regression analysis identified having children and exposure to chronic illness as significant predictors. Conclusions: Educational progression, life experience, and well-being are key contributors to empathy development. These insights support strategies to enhance empathy through curriculum design, student support, and wellness programs. Integrating empathy training into management policy can foster professional growth, reduce burnout, and improve patient care and workforce sustainability. Full article

Monitoring water quality in narrow tributary bays of large river-type reservoirs is hindered by sparse sampling and cloud-limited imagery. This study develops a Trophic State Index (TSI) inversion for Xiangxi Bay, a major tributary bay of the Three Gorges Reservoir, using Landsat data and a backpropagation (BP) neural network, with hyperparameters tuned via a grid search algorithm (GSA). Environmental drivers such as water temperature, solar radiation, and photosynthetically active radiation were combined with Landsat spectral bands. Eleven sites measured monthly in 2009 yielded 98 samples after preprocessing, and training achieved R² = 0.94. Predictions for 2009 show clear spatiotemporal heterogeneity: those for April and September (TSI = 48–59) exceeded those for July and October (46–56), with mid–lower reaches (52–59) being higher than mid–upper reaches (47–54). Out-of-period predictions for April/June 2019 and August/November 2020 were consistent with seasonal expectations, with higher spring–summer TSIs (2019: 50–57; 2020 August: 45–55) than in November 2020 (37–47). Key limitations include the small sample size, cloud-related data gaps, and sensitivity to evolving reservoir operations. This framework demonstrates a practical route to the satellite-based monitoring and mapping of trophic status in narrow reservoir tributaries. Full article

To enhance the understanding and management of fire risks in subway stations, this study aims to identify critical fire risk transmission paths using an integrated PSR–DEMATEL–ISM approach. A comprehensive evaluation framework is first constructed based on the Pressure–State–Response (PSR) model, systematically categorizing 22 influencing factors into three dimensions: pressure, state, and response. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is then employed to analyze the causal relationships and centrality among these factors, distinguishing between cause and effect groups. Subsequently, Interpretive Structural Modeling (ISM) is applied to organize the factors into a multi-level hierarchical structure, enabling the identification of risk propagation pathways. The analysis reveals five high-centrality and high-causality factors: fire safety education and training, completeness of fire management rules and regulations, fire smoke detection and firefighting capability, operational status of monitoring equipment, and effectiveness of emergency response plans. Based on these key drivers, six major transmission paths are derived, reflecting the internal logic of fire risk evolution in subway environments. Among them, chains originating from Fire Safety Education and Training (S6), Architectural Fire Protection Design (S7), and Completeness of Fire Management Rules and Regulations (S16) exhibit the most significant influence on system-wide safety performance. This study provides theoretical support and practical guidance for proactive fire prevention and emergency planning in urban rail transit systems, offering a structured and data-driven approach to identifying vulnerabilities and improving system resilience. Full article

Background: Aedes albopictus, the tiger mosquito, is an invasive exotic species native to Southeast Asia, currently established in Europe, including Spain and the region of Navarre. This vector poses an emerging public health threat due to its ability to transmit dengue, Zika, and chikungunya viruses, which cause diseases in humans. This study presents novel findings by documenting the progression of the invasion of Aedes albopictus in the Navarre region in northern Spain, tracing its status from initial absence to its definitive establishment in certain areas. Methods: Surveillance in Navarre within the LIFE-IP NAdapta-CC project was conducted through a network of strategically placed ovitraps and adult traps to collect eggs and adult mosquitoes. Awareness campaigns and outreach events were organized to inform local authorities and the public about monitoring results and preventive measures. Results: Monitoring confirms Aedes albopictus’ expansion across Navarre despite training, information dissemination, and control efforts, including entomological containment in targeted areas. Conclusions: Eliminating breeding sites remains the most effective strategy to limit its spread. Complete eradication is unlikely given its invasive nature, and the species is expected to expand and colonize at least part of the region in the coming years. Full article

This study examines the feminization of poverty in Indonesia, focusing on the distinct vulnerabilities faced by female-headed households. Utilizing data from the 2023 National Socio-Economic Survey (SUSENAS) involving 291,231 households, this study applies a logistic regression model to investigate gender-specific determinants of household poverty. This research finds that education, digital literacy, financial inclusion, and the employment sector are significant factors influencing poverty status, with female-headed households facing disproportionately higher risks. These gaps are mainly attributed to systemic barriers in financial access, digital literacy gaps, and limited labor market opportunities for women. This study emphasizes the importance of implementing gender-responsive policy measures, including targeted education, enhanced digital literacy training, and inclusive financial programs. By presenting empirical evidence from Indonesia, this study contributes to the discourse on gender and poverty, offering actionable insights for the development of inclusive poverty alleviation strategies. Full article

Background/Objectives: Chemotherapy is an aggressive form of oncological treatment often accompanied by numerous adverse effects. A patient’s baseline status significantly influences the course of therapy, its efficacy, quality of life, and overall survival. This review aims to analyze the published peer-reviewed studies in this area and to assess whether they permit the formulation of preliminary recommendations for future prehabilitation protocols. Methods: An integrative review was conducted due to the limited number of relevant studies. Four databases—MEDLINE/PubMed (Medical Literature Analysis and Retrieval System Online/National Library of Medicine), Scopus, Cochrane, and Web of Science—were systematically searched for English-language articles published between 2010 and 13 January 2025, using the terms “prehabilitation,” “chemotherapy,” “drug therapy,” and “neoadjuvant.” A total of 162 records were retrieved. After duplicate removal, titles and abstracts were screened. The remaining papers were subjected to detailed analysis, resulting in ten studies with diverse methodologies being included. Results: We reviewed ten (n = 10) studies, most of which were reviews focused on breast cancer, indicating variation in the state of knowledge across different cancer types. A protein intake of 1.4 g/kg body mass helps preserve fat-free mass, with whey being more effective than casein. Omega-3 fatty acid supplementation at a dose of 2.2 g/kg may prevent chemotherapy-related neurotoxicity and support appetite and weight maintenance. Physical activity, especially when it includes strength training, improves VO₂max, preserves fat-free mass, and may reduce stress and anxiety. We identified one randomized controlled trial in which a single exercise session before the first dose of doxorubicin resulted in a smaller reduction in cardiac function. Continuous psychological support should be available. A combined behavioural and pharmacological approach appears to be the most effective strategy for smoking cessation. Conclusions: No official guidelines exist for prehabilitation before chemotherapy, and the availability of studies on this topic is very limited. The pre-treatment period represents a critical window for interventions. Further research is needed to evaluate the effectiveness and applicability of particularly single-component interventions. Full article

Background/Objectives: Multidisciplinary approaches spanning the physical, cognitive, and social domains of geriatric evaluation are essential to promote functional well-being and reduce the aversive consequences of aging. The main objective of the pilot study, “Multidomain Interventions to improve the COgnitive and fUNctional well-being of elderly individuals in residential sTructures” (I-COUNT), is to assess the feasibility of a 6-month multidomain intervention performed on older adults in Long-Term Care Facilities (LTCFs), compared with a group of residents following a traditional care approach. Methods: The intervention will involve two LTCFs in Italy and will include physical exercise and cognitive training, administered and monitored using wearable technologies, a nutritional program based on the Mediterranean diet enriched with selected functional foods, and the administration of the vaccinations recommended in the national vaccination plan. The I-COUNT study will assess the feasibility and acceptability of the defined protocol and provide information to determine the sample size for a definitive study. In relation to the potential health impact of multidomain interventions on older people living in LTCFs, the primary outcome will consider the change in microbiota composition assessed 3 months after the start of interventions, while secondary outcomes will include the evaluation of changes in selected biomarkers, physical performance, psychological health, cognitive functioning, and nutritional status at 6- and 9-month follow-up points. Conclusions: The I-COUNT study will allow us to assess the feasibility of delivering a multidomain intervention on elderly people. Exploratory findings on potential health effect will support the development of a larger-scale randomized controlled trial. Trial registration number: ClinicalTrials.gov ID NCT06820710. Full article

Tumor-initiating cells (TICs) constitute a subpopulation of cancer cells with stem-like properties contributing to tumorigenesis, progression, recurrence, and therapeutic resistance. Despite their biological importance, their molecular signatures that distinguish them from non-TICs remain incompletely characterized. This study aimed to comprehensively analyze transcriptomic differences between TICs and non-TICs, identify TIC-specific gene expression patterns, and construct a machine learning-based classifier that could accurately predict TIC status. RNA sequencing data were obtained from four human cell lines representing TIC (TS10 and TS32) and non-TIC (32A and Epi). Transcriptomic profiles were analyzed via principal component, hierarchical clustering, and differential expression analysis. Gene-Ontology and Kyoto-Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted for functional interpretation. A logistic-regression model was trained on differentially expressed genes to predict TIC status. Model performance was validated using synthetic data and external projection. TICs exhibited distinct transcriptomic signatures, including enrichment of non-coding RNAs (e.g., MIR4737 and SNORD19) and selective upregulation of metabolic transporters (e.g., SLC25A1, SLC16A1, and FASN). Functional pathway analysis revealed TIC-specific activation of oxidative phosphorylation, PI3K-Akt signaling, and ribosome-related processes. The logistic-regression model achieved perfect classification (area under the curve of 1.00), and its key features indicated metabolic and translational reprogramming unique to TICs. Transcriptomic state-space embedding analysis suggested reversible transitions between TIC and non-TIC states driven by transcriptional and epigenetic regulators. This study reveals a unique transcriptomic landscape defining TICs and establishes a highly accurate machine learning-based TIC classifier. These findings enhance our understanding of TIC biology and show promising strategies for TIC-targeted diagnostics and therapeutic interventions. Full article

Background: Taekwondo (TKD) athletes’ nutritional and health statuses and body composition are critical to their physical performance and overall fitness. In Lebanon, TKD is widely practiced; however, there is a significant gap in the literature regarding the nutritional and health profiles of its athletes. This study aimed to assess the nutritional status, anemia prevalence, body composition, and other health-related characteristics, among Lebanese TKD athletes. Additionally, it explored the determinants of normal hemoglobin (Hb) levels, blood pressure, normal muscle mass, and normal fat mass. Methods: A cross-sectional study was conducted between January and July 2023, involving 110 TKD athletes. Hemoglobin and hematocrit levels were measured to assess anemia, while body composition was evaluated using a bioelectrical impedance analyzer. Blood pressure was also recorded. Household dietary diversity was assessed using the Food Consumption Score, and additional data on sociodemographic factors, training frequency, and supplement or medication use were gathered through a structured questionnaire. Logistic regression models were applied to identify predictors of normal Hb levels, hypertension, and optimal muscle and fat mass. Results: Results showed that male athletes had significantly higher rates of normal Hb (p-value = 0.013) and muscle mass percentages (p-value < 0.001), while females had higher rates of normal blood pressure (p-value = 0.002) and were more likely to use iron supplements (p-value = 0.002) and painkillers (p-value = 0.041). Normal fat mass was positively associated with normal Hb levels (aOR: 11.98, p-value = 0.033). Female gender was linked to a lower likelihood of normal muscle mass (aOR: 0.13, p-value < 0.001) and hypertension (aOR: 0.19, p-value = 0.009). Higher training duration (10 h or more per week) (aOR: 3.46, p-value = 0.04) and normal BMI (aOR: 4.93, p-value = 0.003) were positively associated with normal muscle mass. Normal BMI (aOR: 14.68, p-value < 0.001) was positively associated with normal fat mass. Conclusion: These findings underscore the importance of individualized dietary interventions to enhance athletes’ overall health and performance, through the optimization of athletes’ body composition, and the prevention of deficiencies, especially iron deficiency. Full article

Multi-label text classification is a fundamental and important task in natural language processing, with widespread applications in specialized domains such as sentiment analysis, legal document classification, and medical coding. However, real-world applications often face challenges such as high annotation costs, data scarcity, and long-tailed label distributions. These issues are particularly pronounced in professional fields like healthcare and law, significantly limiting the performance of classification models. This paper focuses on the topic of few-shot multi-label text classification and provides a systematic survey of current research progress and mainstream techniques. From multiple perspectives, including modeling under few-shot settings, research status, technical approaches, commonly used datasets, and evaluation metrics, this study comprehensively reviews the existing literature and advances. At the technical level, the methods are broadly categorized into data augmentation and model training. The latter includes paradigms such as transfer learning, prompt learning, metric learning, meta-learning, graph neural networks, and attention mechanisms. In addition, this survey explores the research and progress of specific tasks under few-shot multi-label scenarios, such as multi-label aspect category detection, multi-label intent detection, and hierarchical multi-label text classification. In terms of experimental resources, this review compiles commonly used datasets along with their characteristics and categorizes evaluation metrics that are widely adopted in few-shot multi-label classification settings. Finally, it discusses the key research challenges and outlines future directions, offering insights to guide further investigation in this field. Full article

Background: Monitoring autonomic balance provides valuable insights into recovery status and physiological readiness, both of which are essential for performance optimization in athletes. The Stress Score (SS) and Sympathetic–Parasympathetic Ratio (SPS), derived from Poincaré plot heart rate variability (HRV) indices, have been proposed as practical markers of sympathetic activity and overall autonomic balance. However, these traditional calculations often require lengthy recordings and specialized software, limiting their feasibility in field settings. This study introduces modified versions of these metrics derived from ultra-short-term (1 min) time–domain HRV recordings: the Modified Stress Score (MSS) and Modified Sympathetic–Parasympathetic Ratio (MSPS). Methods: Competitive male athletes (n = 20, age = 21.2 ± 2.1 year, height = 183.6 ± 8.9 cm, weight = 79.2 ± 10.3 kg) completed a maximal exercise test with HRV recorded before and after exercise. Results: Following natural log-transformation, MSS and MSPS demonstrated strong correlations with SS and SPS across all time points (r = 0.87–0.94, all p < 0.01) and displayed the expected physiological responses to exercise and recovery. Conclusions: These findings suggest that MSS and MSPS are practical, accessible tools for assessing autonomic balance in athletes. Their application may enhance our ability to monitor recovery status, guide individualized training strategies, and optimize performance in applied sport settings. Full article