Search Results (1,052)

Background: The four-compartment (4C) model is a criterion method for evaluating body composition tools like bioelectrical impedance analysis (BIA). This systematic review assessed the clinical equivalence of BIA devices compared to the 4C model and explored limitations in using the 4C model as a criterion method. Methods: Twelve cross-sectional and baseline longitudinal studies involving healthy, weight-stable, non-athlete, non-pregnant adults were included. The primary outcome was a Bland–Altman analysis, with bias, limits of agreement, and proportional bias extracted from each paper. The study quality was evaluated using the AXIS tool. Due to the high variability across studies, a meta-analysis was not performed. Results: BIA devices generally performed poorly against the 4C model estimates of percentage body fat and fat-free mass. Across the 12 studies, mean bias for percentage body fat between BIA and the 4C model ranged from −3.5% to +4.4%, with limits of agreement typically spanning 15 to 20 percentage points. For fat-free mass, mean bias ranged from −3.9 kg to +1.8 kg, with limits of agreement often exceeding ±6 kg. These wide limits indicate non-equivalence at the individual level despite small mean differences. Differences in both BIA device design and variations in 4C methodology across studies may have contributed to these discrepancies. Conclusions: BIA estimates of percentage body fat and fat-free mass were overall not equivalent to the 4C model. Alternative criterion methods, such as MRI, and use of raw BIA data are recommended. Standardization of BIA devices is also needed for improved clinical and research use. Full article

This study explores the feasibility of estimating tibiofemoral joint contact forces using deep learning models trained on in vivo biomechanical data. Leveraging the comprehensive CAMS-Knee datasets, we developed and evaluated two machine learning network architectures, a bidirectional Long Short-Term-Memory Network with a Multilayer Perceptron (biLSTM-MLP) and a Temporal Convolutional Network (TCN) model, to predict medial and lateral knee contact forces (KCFs) across various activities of daily living. Using a leave-one-subject-out validation approach, the biLSTM-MLP model achieved root mean square errors (RMSEs) as low as 0.16 body weight (BW) and Pearson correlation coefficients up to 0.98 for the total KCF (${\mathrm{F}}_{\mathrm{tot}}$) during walking. Although the prediction of individual force components showed slightly lower accuracy, the model consistently demonstrated high predictive accuracy and strong temporal coherence. In contrast to the biLSTM-MLP model, the TCN model showed more variable performance across force components and activities. Leave-one-feature-out analyses underscored the dominant role of lower-limb kinematics and ground reaction forces in driving model accuracy, while EMG features contributed only marginally to the overall predictive performance. Collectively, these findings highlight deep learning as a scalable and reliable alternative to traditional musculoskeletal simulations for personalized knee load estimation, establishing a foundation for future research on larger and more heterogeneous populations. Full article

Background/Objectives: Accurate estimates of variance components are essential in breeding programs. In this context, the main objective of this study was to estimate variance components for growth traits in the Montana Composite^® beef population, which was developed in Brazil by crossing various taurine and indicine breeds. After 30 years of selection, the impact of recombination, heterosis, and inbreeding may have influenced the genetic background of the population. Methods: We analyzed data of birth weight, weaning weight, post-weaning weight gain, and yearling weight using 124,255 phenotypic records, 193,129 pedigree records, and 3911 genotyped individuals. Ten single-trait animal models (M1–M10) were compared, differing in the relationship matrix (pedigree- or genome-based relationships) and the inclusion of direct/maternal breed composition, heterosis, and recombination effects. Results: Models incorporating genomic information consistently yielded better fit and lower residual variances than pedigree-based models, highlighting the advantage of genomic information in capturing Mendelian sampling and realized genetic relationships. The inclusion of heterosis effects improved model fit and led to a partial reallocation of genetic variance from additive to non-additive components. In contrast, the inclusion of recombination effects in the models minimally influenced variance component estimates. Nevertheless, more complex models affected animal rankings and altered the breed composition of top-ranked selection candidates, with selection overlap between pedigree- and genomic-based evaluations ranging from moderate to high. Conclusions: Overall, genome-based models accounting for breed composition, heterosis, and recombination provided the most robust variance component estimates and the best support for long-term selection goals in the studied tropical composite beef cattle population. Full article

Background/Objectives: Chronic inflammation and oxidative stress are key contributors to colorectal cancer (CRC) development. However, prospective evidence in Asian populations remains limited. This study aimed to investigate the associations between circulating inflammatory cytokines, oxidative markers, and CRC risk in a Korean population. Methods: We conducted a case–cohort study nested within the Korean National Cancer Community (KNCCC) Cohort to investigate associations between inflammatory cytokines, oxidative stress markers, and CRC risk. A total of 128 incident CRC cases and 822 subcohort participants were included. Serum levels of interleukin (IL)-6, tumor necrosis factor-α (TNF-α), IL-1β, interferon-γ (IFN-γ), IL-10, reactive oxygen species (ROS), and nitric oxide (NO) were measured. Hazard ratios (HRs) were estimated using the Cox proportional hazards models with Barlow’s weighting. Results: Higher serum IL-6 levels were strongly associated with increased CRC risk, with HRs of 6.20 (95% CI: 2.38–16.19), 8.31 (3.24–21.33), and 10.22 (3.95–26.46) for the second through fourth quartiles, compared to the lowest. Detectable levels of IL-1β and IFN-γ were also significantly associated with CRC risk (HRs: 2.16 and 1.53, respectively). Stratified analysis showed that IL-6 and IL-1β were associated with CRC risk in both obese and non-obese participants, while TNF-α, IL-10, and NO were associated with increased risk only among obese individuals. No significant associations were observed for ROS. Conclusions: Elevated levels of inflammatory cytokines (IL-6, IL-1β, IFN-γ) and NO were associated with higher CRC risk, suggesting their potential as early biomarkers. Obesity may modify the associations between certain markers and CRC risk. These findings highlight the role of systemic inflammation and oxidative stress in colorectal carcinogenesis. Full article

Portfolio management (PM) is a broad investment strategy aimed at risk mitigation through diversified financial product investments. Acknowledging the significance of dynamic adjustments after establishing a portfolio to enhance stability and returns, we propose employing reinforcement learning (RL) to address dynamic decision-making challenges. However, traditional RL methods often struggle to adapt to significant market volatility, primarily by focusing on adjusting existing asset weights. Different from traditional RL methods, the multi-agent reinforcement learning correctable strategy (MAC) developed in this study detects and replaces potentially harmful assets with familiar alternatives, ensuring a resilient response to market crises. Utilizing the multi-agent reinforcement learning model, MAC empowers individual agents to maximize portfolio returns and minimize risk separately. During training, MAC strategically replaces assets to simulate market changes, allowing agents to learn risk-identification through uncertainty estimation. During testing, MAC detects potentially harmful assets and replaces them with more reliable alternatives, enhancing portfolio stability. Experiments conducted on a real-world US Exchange-Traded Fund (ETF) market dataset demonstrate MAC’s superiority over standard RL-based PM methods and other baselines, underscoring its practical efficacy for real-world applications. Full article

Background/Objectives: Avocados are nutrient-dense fruits rich in monounsaturated fats, fiber, and key micronutrients. Although avocado purchases increased in recent years, comprehensive national data on consumption patterns remains limited. Thus, this study aimed to characterize the prevalence, quantity, and context of avocado intake among the US population and to evaluate its contribution to daily nutrient intake. Methods: Day 1 24-h dietary recall data were analyzed from 19,086 participants aged ≥1 year in NHANES 2017–March 2020 and August 2021–August 2023. Avocado intake was identified using consumption data coded as avocado, avocado for use on a sandwich, guacamole, and guacamole with tomatoes. Weighted estimates described consumption prevalence, amount, form, self-selected eating occasion, location, and source. Nutrient contributions from avocado were calculated overall and by sex, age, and race/ethnicity. Results: 5.8% of participants consumed avocado. Prevalence was highest among women (6.9%), adults aged 19–50 years (7.4%), and Hispanic individuals (9.1%). Among consumers, the mean intake was 67.0 g/day. Plain avocado was the most common form (54.8%), and most intake occurred at dinner (43.4%) and at home (67.7%). Avocado was an important contributor to the mean daily intake among consumers for beta-cryptoxanthin (33.4%), alpha-carotene (27.0%), dietary fiber (19.9%), monounsaturated fatty acids (19.7%), and other nutrients. Conclusions: Although avocado consumption remains relatively modest, it contributes meaningfully to nutrient intake among avocado consumers. These findings provide descriptive insight into avocado consumption patterns and nutrient contributions in the U.S. population. Full article

Background/Objectives: Sugar-sweetened beverage (SSB) consumption has been linked to obesity, metabolic diseases, and rising healthcare costs. This study aimed to assess the impact of a 20% excise tax on SSBs in Brazil on obesity/overweight prevalence, seven musculoskeletal and cardiovascular diseases, and related healthcare costs, with their associated impacts on health inequalities. Methods: Using 2017/2018 Brazilian Household Budget Survey data for baseline consumption and own- and cross-price elasticities for taxed beverages, we estimated changes in caloric consumption for the entire population and for lower- and upper-income quartiles. The PRIMEtime dynamic individual-level simulation model projected body weight changes, lifetime Quality-Adjusted Life-Years (QALYs), healthcare costs (discounted at 5%), and disease cases (20-year horizon). Results: A 20% excise SSB tax was projected to reduce obesity prevalence by 1.7 percentage points in men and 1.5 percentage points in women, from baseline rates of 19.8% and 23.6%, respectively. Lifetime gains were estimated at 17,878 QALYs per million men and 12,181 per million women, alongside healthcare cost savings of Int$520 million. Impacts varied by income, with smaller health gains in the lowest quartile and higher among the wealthiest. Over 20 years, the tax could avert 1784 cases of type 2 diabetes mellitus/100,000 adults (52% in men) and 1070 cases of ischemic heart disease/100,000 adults (80% in men). Conclusions: A 20% excise SSB tax in Brazil could yield large health and cost benefits. With the recent approval of the Selective Tax under Complementary Law 214/2025, Brazil has a timely opportunity to translate these projected benefits into effective public health policy. Full article

Background/Objectives: This study aimed to analyze the relationship between various anthropometric measurements (Body Mass Index (BMI), Clínica Universidad de Navarra-Body Adiposity Estimator (CUNBAE), hip and waist circumference (WC), weight, and height) and Triple-Negative Breast Cancer (TNBC) according to menopausal status. Methods: A total of 113 TNBC cases and 226 matched controls from the MCC-Spain study were included. Controls were matched by age, educational level, family history, and province. Conditional logistic regression models, stratified by menopausal status, were used to estimate adjusted Odds Ratios (aORs) and their 95% Confidence Intervals (95% CIs) for the association between anthropometric measures and TNBC risk. Results: A divergent non-significant trend was observed: compared to their respective controls, premenopausal cases tended to have lower mean anthropometric measurements (except height), while postmenopausal cases showed higher means. No statistically significant associations were observed for individual measures derived from logistic regressions. However, when comparing women with normal BMI and normal WC (the reference group), a non-significant association of risk was found in those premenopausal women who were centrally obese (normal weight/high WC) (aOR = 1.79; 95% CI = 0.17–18.29), but the combination of overweight and a large WC showed an aOR of 0.22 (95% CI = 0.03–1.68) before menopause. In contrast, the combination of overweight and a high WC showed a statistically significant adjusted OR of 3.28 in postmenopausal women (95% CI = 1.10–9.81). Conclusions: Our findings suggest that the relationship between adiposity and TNBC is inverse in premenopausal women and direct in postmenopausal women, highlighting the importance of considering both body fat distribution and menopausal status when evaluating TNBC. However, our findings are limited by low statistical power, which may have led to a lack of statistical significance, and there is a need for larger, collaborative studies. Full article

Accurate prediction of individual treatment effects in survival analysis is often complicated by the presence of competing risks and the inherent unobservability of counterfactual outcomes. While machine learning models offer improved discriminative power, they typically lack rigorous guarantees for uncertainty quantification, which are essential for safety-critical clinical decision-making. In this paper, we introduce CONFIDE (CONFormal Inference for Distribution-free Estimation), a novel framework that bridges causal inference and conformal prediction to construct valid prediction sets for cause-specific cumulative incidence functions. Unlike traditional confidence intervals for population-level parameters, CONFIDE provides individual-level prediction sets for time-to-event outcomes, which are more clinically actionable for personalized treatment decisions by directly quantifying uncertainty in future patient outcomes rather than uncertainty in population averages. By integrating semi-parametric hazard estimation with targeted bias correction strategies, CONFIDE generates calibrated prediction sets that cover the true potential outcome with a user-specified probability, irrespective of the underlying data distribution. We empirically validate our approach on four diverse medical datasets, demonstrating that CONFIDE achieves competitive discrimination (C-index up to 0.83) while providing robust finite-sample marginal coverage guarantees (e.g., 85.7% coverage on the Bone Marrow Transplant dataset). We note two key limitations: (1) coverage may degrade under heavy censoring (>40%) unless inverse probability of censoring weighted (IPCW) conformal quantiles are used, as demonstrated in our sensitivity analysis; (2) while the method guarantees marginal coverage averaged over the covariate distribution, conditional coverage for specific covariate values is theoretically impossible without structural assumptions, though practical approximations via locally-adaptive calibration can improve conditional performance. Our framework effectively enables trustworthy personalized risk assessment in complex survival settings. Full article

Background/Objectives: Vitamin D is a nutrient involved not only in bone metabolism but also in metabolic functions, and deficiency is common during adolescence. This study aimed to describe the distribution of serum 25-hydroxyvitamin D levels among Korean adolescents and to examine their associations with metabolic syndrome and its individual components. Methods: We analyzed data from the 2022–2023 Korea National Health and Nutrition Examination Survey. Adolescents aged 10–18 years with serum 25-hydroxyvitamin D measurements were included (unweighted N = 880). Weighted analyses were performed by categorizing serum 25-hydroxyvitamin D levels into quartiles. Associations between vitamin D quartiles and anthropometric and metabolic parameters were examined using complex-sample general linear models, and odds ratios for metabolic syndrome and its individual components according to vitamin D deficiency were estimated using complex-sample logistic regression models. Results: Weighted prevalence of vitamin D deficiency (<20 ng/mL) was 62.4%, higher in females than males. Higher 25(OH)D quartiles were inversely associated with obesity-related indices, including BMI, waist circumference, and waist-to-height ratio, after full adjustment (p for trend < 0.05). No significant associations were observed for blood pressure, fasting glucose, or lipid parameters. In dichotomous analyses (<20 vs. ≥20 ng/mL), vitamin D deficiency was associated with higher odds of waist circumference ≥ 90th percentile (OR 2.59), waist-to-height ratio > 0.5 (OR 2.63), and BMI ≥ 95th percentile (OR 1.89), while metabolic syndrome was not significant. Conclusions: Vitamin D appears to play an important role in metabolic health in adolescents and was particularly associated with general and central obesity. Full article

Non-communicable diseases, particularly cardiovascular diseases (CVD) and metabolic syndrome (MS), remain a major challenge for primary health care (PHC). This study aimed to assess cardiometabolic risk and health behaviours in adult PHC patients using routine preventive screening. This prospective observational study included 506 adults attending routine consultations in an urban PHC centre in Poland. Preventive assessment included anthropometric measurements (body weight, height, BMI, and waist circumference), blood pressure, lipid profile, and fasting glucose levels. Health behaviours were recorded using the standardised NFZ CHUK questionnaire. The 10-year CVD risk was estimated using the SCORE2 algorithm. Multivariable logistic regression was used to identify independent factors associated with high cardiovascular risk (SCORE2 ≥ 5%) and of a composite endpoint defined as the presence of any non-optimal biochemical parameter. Nearly half of the participants had excess body weight (overweight or obesity), and more than half met criteria for central obesity. Borderline or elevated total cholesterol was found in 47% of patients, abnormal LDL in 27%, low HDL-C (<40 mg/dL) in 80% (84% when applying sex-specific cut-offs), and impaired fasting glucose or diabetes in about 12%. High SCORE2 risk (≥5%) was observed in approximately 9% of the cohort. In multivariable models, SCORE2 components (age, sex, and smoking) were, as expected, associated with high SCORE2 risk, and obesity (BMI ≥ 30 kg/m²)—a factor not included in SCORE2—was additionally associated with higher risk. Additionally, age, male sex, and obesity also predicted the presence of at least one non-optimal biochemical marker. The prevalence of high SCORE2 risk increased from 1.2% in patients with 0–1 modifiable risk factor to 25.7% in those with 4–5 factors. Lower educational attainment was associated with a higher proportion of high-risk individuals in univariate analysis. Routine preventive activities in PHC enable the identification of important lipid and glucose abnormalities and the clustering of modifiable risk factors, even in a relatively young, highly educated population. Systematic cardiovascular screening and a focus on patients with accumulated risk factors should remain a priority in PHC to enable early identification of high-risk patients and timely implementation of lifestyle and therapeutic interventions. Full article

Background/Objectives: Type 2 diabetes mellitus (T2DM) significantly elevates the risk of coronary artery disease (CAD), particularly in Asian populations where both conditions are epidemic. While shared genetic factors contribute to this comorbidity, evidence from Asian cohorts remains fragmented, with limited focus on population-specific variants. This meta-analysis synthesizes evidence on genetic variants associated with CAD risk in Asian patients with T2DM. Methods: We systematically searched several databases according to the PRISMA statement and checklist. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated using random-effects models, with heterogeneity assessed via I² and Cochran’s Q, and publication bias via funnel plots and Egger’s test. Results: In total, data on 11,268 subjects were reviewed, including 4668 cases and 6600 controls. Among 950 identified studies, 18 met eligibility criteria, and 14 studies provided sufficient data for the meta-analysis. The random-effects pooled estimate across all studied variants was not statistically significant (OR = 1.16 [95% CI: 0.68–2.00]; z = 0.56, p = 0.58). However, analysis of individual loci revealed gene-specific associations with CAD among this population: PCSK1 gene (OR = 2.12 [95% CI: 1.26–3.52]; p < 0.05; weight = 8.77%), GLP1R gene (OR = 2.25 [95% CI: 1.27–3.97]; p < 0.01; weight = 8.62%). ADIPOQ gene (OR = 8.00 [95% CI: 2.34–27.14]; p < 0.01; weight = 6.35%). Several genes were associated with an elevated risk of CAD: PCSK1 gene (OR = 2.12 [95% CI: 1.26–3.52]; p < 0.05; weight = 8.77%), GLP1R gene (OR = 2.25 [95% CI: 1.27–3.97]; p < 0.01; weight = 8.62%) and ADIPOQ gene (OR = 8.00 [95% CI: 2.34–27.14]; p < 0.01; weight = 6.35%). Several genes were associated with possible protective effects: ACE gene (OR = 0.41 [95% CI: 0.23–0.73]; p < 0.01; weight = 8.57%), Q192R gene (OR = 0.20 [95% CI: 0.08–0.52]; p < 0.001; weight = 7.41%). Heterogeneity was substantial (τ² = 0.78; I² = 81.95%; Q (13) = 64.67, p < 0.001). Conclusions: This first meta-analysis of genetic variants associated with CAD in Asian populations with T2DM identified specific locus-level associations implicating lipid metabolism, incretin signaling, and oxidative stress pathways. The lack of a significant pooled effect, alongside high heterogeneity, underscores the complexity and population-specific nature of this genetic architecture. These findings suggest that effective precision risk stratification may depend more on specific variants than on a broad polygenic signal, highlighting the need for further research in a larger, distinct sample size. Full article

Forest canopy near-infrared reflectance and mass-based canopy nitrogen concentration (canopy %N) have been shown to be positively correlated. While the mechanisms underpinning this relationship remain unresolved, the broad range of wavelengths involved points to structural properties that influence scattering and covary with %N. Despite this, efforts that have focused on commonly measured structural properties such as leaf area index (LAI) have failed to identify a causal mechanism. Here, we sought to understand how lidar-derived canopy traits related to additional properties of foliar arrangement and structural complexity modulate the effects of leaf spectra and leaf area index (LAI) on canopy reflectance. We developed a leaf layer spectra model to explore how canopy reflectance would change if complex foliage arrangements were removed, compressing the canopy into optically dense, uniform stacked layers while maintaining the same leaf area index. Model results showed that LAI-weighted leaf reflectance saturates at a leaf area index of approximately two for needleleaf species and four for broadleaf species. When upscaled to estimate plot-level canopy reflectance in the absence of structural complexity (NIRr_LAI), results showed a strong positive relationship with canopy %N (r² = 0.86), despite a negative relationship for individual leaves or “big-leaf” canopies with an LAI of one (NIRrL, r² = 0.78). This result implies that the relationship between canopy near-infrared reflectance and canopy %N results from the integrated effects of canopy complexity acting on differences in leaf-level optical properties. We introduced an index of relative reflectance (IRr) that shows that the relative contribution of structural complexity to canopy near-infrared reflectance (NIRr_C) is related to canopy %N (r² = 0.55), with a three-fold reduction from potential canopy near-infrared reflectance observed in stands with low %N compared to a two-fold reduction in stands with high %N. These findings support the hypothesis that the correlation between canopy %N and canopy reflectance is the result of interactions between leaf traits and canopy structural complexity. Full article

The Estimated Time of Arrival (ETA) of vessels is a vital operational indicator for voyage planning, fleet deployment, and resource allocation. However, most existing studies focus on short-distance liner services with fixed routes, while ETA prediction for long-distance tramp bulk carriers remains insufficiently accurate, often resulting in operational inefficiencies and charter party disputes. To fill this gap, this study proposes a data-driven stacking ensemble learning framework that integrates Light Gradient-Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) as base learners, combined with a Linear Regression meta-learner. This framework is specifically tailored to the unique complexities of tramp shipping, advancing beyond traditional single-model approaches by incorporating systematic feature engineering and model fusion. The study also introduces the construction of a comprehensive multi-dimensional AIS feature system, incorporating baseline, temporal, speed-related, course-related, static, and historical behavioral features, thereby enabling more nuanced and accurate ETA prediction. Using AIS trajectory data from bulk carrier voyages between Weipa (Australia) and Qingdao (China) in 2023, the framework leverages multi-feature fusion to enhance predictive performance. The results demonstrate that the stacking model achieves the highest accuracy, reducing the Mean Absolute Error (MAE) to 3.30 h—a 74.7% improvement over the historical averaging benchmark and an 11.3% reduction compared with the best individual model, XGBoost. Extensive performance evaluation and interpretability analysis confirm that the stacking ensemble provides stability and robustness. Feature importance analysis reveals that vessel speed, course stability, and remaining distance are the primary drivers of ETA prediction. Additionally, meta-learner weighting analysis shows that LightGBM offers a stable baseline, while systematic deviations in XGBoost predictions act as effective error-correction signals, highlighting the complementary strengths captured by the ensemble. The findings provide operational insights for maritime logistics and port management, offering significant benefits for port scheduling and maritime logistics management. Full article

Background: Teicoplanin is widely used for the empirical and targeted treatment of febrile neutropenia in patients with hematological malignancies. However, the pathophysiological alterations typical of this population may substantially affect drug exposure. The aim of this study was to develop and validate a population pharmacokinetic (PopPK) model of teicoplanin in adult hematological patients and to propose individualized dosing strategies. Methods: A retrospective, single-center study including 151 patients and 263 serum concentrations was conducted, with participants assigned to development (n = 100) and validation (n = 51) cohorts. Concentrations were quantified using a turbidimetric immunoassay, and the PopPK model was developed in NONMEM using FOCE-I. Results: Teicoplanin pharmacokinetics were described by a one-compartment model with first-order elimination. Ideal body weight, estimated glomerular filtration rate, and age were identified as significant predictors of clearance. Internal and external validation confirmed the robustness and predictive performance of the model. Monte Carlo simulations showed that conventional regimens (6 mg/kg every 12 h for three loading doses, followed by 6 mg/kg once-daily, or 600 mg every 12 h for three loading doses, followed by 600 mg once-daily) are insufficient to achieve therapeutic trough concentrations (≥15–20 mg/L) within the first 72 h, particularly in patients with preserved renal function or higher body weight. An intensified regimen consisting of five loading doses of 12 mg/kg every 12 h, followed by 12 mg/kg once-daily, enabled rapid attainment and maintenance of trough concentrations ≥ 20 mg/L in patients with lower to intermediate ideal body weight. Conclusions: These findings underscore the importance of intensified dosing strategies and covariate-guided individualization supported by therapeutic drug monitoring to achieve optimal teicoplanin exposure in this vulnerable patient group. Full article