Search Results (21,391)

Background/Objectives: Postoperative atrial fibrillation (POAF) is a frequent complication after cardiac surgery and is associated with increased morbidity, prolonged hospitalization, and higher healthcare costs. Numerous multivariable prediction models have been developed to estimate individual risk; however, their methodological robustness, validation status, and clinical transportability remain uncertain. This systematic review aimed to critically evaluate the methodological quality, validation strategies, and predictive performance of multivariable prediction models developed to estimate the risk of postoperative atrial fibrillation (POAF) after cardiac surgery. Methods: In accordance with PRISMA 2020 guidelines, we conducted a comprehensive search of PubMed, Scopus, Web of Science, and Embase from inception to July 2025. Studies that developed or externally validated multivariable prediction models for POAF in adult patients undergoing cardiac surgery were eligible. Data extraction was performed using the CHARMS checklist, and methodological quality was assessed with PROBAST. Model performance was summarized descriptively, focusing on discrimination (C-statistic/AUC), calibration reporting, and validation strategies. Results: A total of 39 studies were included. Most models were based on logistic regression, whereas a minority employed Cox regression or machine learning techniques. Reported discrimination ranged from 0.60 to 0.98, demonstrating substantial heterogeneity in predictive performance. Calibration was inconsistently reported. Six studies performed external validation. According to PROBAST, 32 of 39 studies (82%) were rated at high risk of bias, predominantly within the analysis domain due to inadequate handling of overfitting, insufficient events-per-variable ratios, and limited validation procedures. Conclusions: Existing prediction models for POAF show variable discrimination but are frequently limited by high risk of bias, inadequate validation, and incomplete calibration assessment, thereby restricting their clinical applicability. Future research should prioritize rigorous external validation, transparent reporting in accordance with TRIPOD recommendations, and methodological strategies that enhance model generalizability and transportability across diverse surgical populations. Full article

Aims: This study aimed to evaluate the agreement of a frequency sampling method (FR) as a tool for species identification while measuring undisturbed sward height. Methods: The botanical composition of both grazing systems was evaluated during the pre-grazing and post-grazing periods using two methods: the Dry Weight Rank (DWR) and FR. A non-parametric Friedman test was applied to compare evaluation methods and grazing moments. Differences in detection frequencies between methods were assessed using McNemar’s test for paired binary data. Results: The evaluation method did not influence the relative abundance of the three most abundant plant species identified: U. decumbens, Paspalum genus, and Commelinaceae weeds. A high positive Lin’s concordance correlation coefficient (CCC) was observed between the two methods in U. decumbens, Paspalum genus, U. brizantha cv. Marandú, U. plantaginea, U. arrecta, and U. humidicola (CCC ≥ 0.70). We observed lower agreement for some functional groups, particularly Commelinaceae weeds (CCC = 0.38), narrow-leaf weeds (CCC = 0.46), and Cyperaceae weeds (CCC = 0.17). Canonical correlation analysis (CCA) between the chemical composition of leaves and the botanical composition estimated by the DWR revealed two significant canonical functions (p < 0.01), with canonical correlations of 0.692 and 0.478 for the first and second functions, respectively. When botanical composition estimated by the FR was used as a regressor for leaf chemical composition, three significant canonical functions (p < 0.01) were identified, with canonical correlations of 0.632, 0.529, and 0.425 for the first, second, and third functions, respectively. Conclusions: FR represents a practical and complementary approach for assessing botanical composition and plant diversity in heterogeneous tropical grasslands, particularly for the rapid monitoring of dominant species. However, lower agreement was observed for some low-abundance functional groups, indicating reduced FR sensitivity for certain plant types. Full article

Background/Objectives: Fortified eggs have been proposed as food-based vehicles for delivering micronutrients and other bioactive compounds. However, human intervention evidence remains fragmented across nutrient targets, populations, intervention designs, and outcome domains. This review mapped human intervention studies and summarized the reported nutritional and health-related outcomes associated with fortified egg consumption. Methods: PubMed, Embase, Web of Science, the Cochrane Library, CNKI, Wanfang Data, and SinoMed were searched for studies published in English or Chinese from 1 January 2005 to 1 June 2025. Eligible studies were human intervention studies evaluating fortified, enriched, or bio-enhanced eggs and reporting at least one nutritional or health-related outcome. Owing to substantial heterogeneity in nutrient type, intervention design, comparator, duration, and outcome assessment, findings were synthesized narratively. Results: Thirty-seven human intervention studies met the eligibility criteria. Most evaluated eggs enriched with n-3 polyunsaturated fatty acids, followed by carotenoid-enriched eggs, whereas relatively few examined iodine or other fortified nutrients. Across studies, fortified egg consumption was generally associated with improvements in intake estimates and biomarkers of targeted nutrient status, including circulating fatty acids, serum carotenoids, and, in a limited number of studies, urinary iodine. Evidence for downstream health-related outcomes was more limited, inconsistent, and difficult to compare across nutrient categories and study populations. Conclusions: Fortified eggs may serve as useful food-based vehicles for improving the delivery of selected nutrients and short-term nutrient-status biomarkers. However, the evidence base remains heterogeneous and nutrient-specific, and it is still uncertain whether these changes translate into consistent, clinically meaningful health benefits. Larger, longer-term, and more rigorously reported trials, particularly in populations at nutritional risk, are needed. Full article

This study investigates the preparation and characterization of tin micro- and nanoparticles with an emphasis on phase-transformation-induced particle formation and chemical purity. Microparticles were generated through repeated phase transformations between β-Sn (white tin) and α-Sn (gray tin), exploiting the associated volumetric changes to induce fragmentation and particle size reduction. The evolution of particle size distribution was systematically analyzed as a function of transformation cycles. The data were analyzed using the modified Johnson–Mehl–Avrami–Kolmogorov equation, and the saturation particle size corresponds to the grain size of the original tin sheet. The phase transformation was induced homogeneously by α-Sn particles and heterogeneously by InSb, and the results were comparable. The influence of the surrounding atmosphere was studied. The increase in oxygen content during repeated phase transformation was measured. In parallel, tin nanoparticles were synthesized via a solution-based route using ammonium hexachlorostannate as a precursor. The nanoparticles precipitated from this solution at mild temperatures during the β-Sn to α-Sn transformation at 13.2 °C. Both micro- and nanoparticles were characterized in terms of morphology and size distribution. The results provide insight into the relationship between phase transformation and particle size reduction mechanisms, and offer a controllable pathway for the preparation of tin particles across micro- and nanoscale regimes. Full article

Coronary in-stent restenosis (ISR) remains a major contributor to repeat revascularisation despite advances in drug-eluting stent (DES) technology. Its persistence reflects a complex and heterogeneous interplay among mechanical, biological, and procedural factors, and understanding the dominant mechanism in each case is fundamental to effective treatment selection. This narrative review provides a contemporary, mechanism-guided approach to the practical management of coronary ISR. We summarise the definition, incidence, and classification of ISR—including the Mehran, Waksman, and SCAI 2023 time-based frameworks—and outline patient-related, procedural, anatomical, and stent-related risk factors. The pathophysiology of neointimal hyperplasia and neoatherosclerosis is discussed with reference to its clinical implications. Intracoronary imaging with intravascular ultrasound (IVUS) or optical coherence tomography (OCT) is central to ISR characterisation and treatment planning. Current international guidelines support imaging use in ISR management, though it is important to recognise that this recommendation is based largely on observational and surrogate-endpoint data rather than ISR-specific randomised trials demonstrating reductions in hard clinical outcomes, and practical barriers including cost, availability, and operator expertise must be acknowledged. Evidence-based treatment strategies—including drug-coated balloons (DCB), repeat DES implantation, lesion-modifying therapies, vascular brachytherapy, and coronary artery bypass grafting—are reviewed critically with reference to contemporary trial data and their specific clinical applicability. The choice between DCB and repeat DES is addressed with greater nuance, accounting for ISR type (BMS-ISR versus DES-ISR), lesion pattern, stent layering, and bleeding risk. Management considerations in complex subsets—chronic total occlusion ISR, left main ISR, saphenous vein graft ISR, and recurrent ISR—are also addressed. We propose a practical, substrate-driven management framework aligned with the 2024 ESC, 2021 ACC/AHA/SCAI, and 2018 JCS/JSCVS guidelines. Future research priorities include ISR-specific randomised trials with hard clinical endpoints, prospective validation of imaging-guided treatment algorithms, head-to-head comparisons of DCB platforms, and investigation of pharmacological strategies targeting neoatherosclerosis progression. Full article

Notable discrete probability laws appear as posterior distributions in the estimation of the common mean with heterogeneous variances. These probabilities, which are defined by an arbitrary set of distinct real numbers, also arise in seemingly unrelated areas of polynomial approximation and statistical physics. The corresponding orthogonal polynomials possess an interesting self-duality property, which invites the study of statistical distributions based on data parity. These distributions provide novel, intriguing formulas for the classical hypergeometric function along with several combinatorial identities. Full article

The performance of haul trucks in open-pit mining is strongly affected by haul-road geometry, surface condition, rolling resistance, and operational traffic regimes. However, existing studies often consider road-surface mapping, vehicle dynamic response, and onboard telemetry as separate information streams, which limits the reproducible assessment of how road-related factors are associated with VIMS-derived fuel-use proxy burden, mechanical dynamic response, and transport-cycle instability. This study proposes a field-based, segment-level multisensor framework that integrates unmanned aerial vehicle/light detection and ranging (UAV/LiDAR) road-surface reconstruction, global positioning system/inertial measurement unit (GPS/IMU) trajectory and vibration data, and Caterpillar Vial Information Management System (VIMS) telemetry into a unified spatiotemporal analytical dataset. The methodological contribution consists in the synchronization of heterogeneous data sources at the road-segment level, the calculation of interpretable road-condition and vehicle-response indicators, and the statistical assessment of road-related effects while explicitly accounting for confounding factors such as longitudinal grade, payload state, speed regime, truck class, and operational variability. Unlike studies that use LiDAR mapping, vibration monitoring, or onboard telemetry as separate diagnostic channels, the proposed approach introduces a segment-level analytical framework in which road morphology, truck response, and operational penalties are aligned within the same spatial unit, interpreted under confounder-aware conditions, and verified through repeat-pass reproducibility and robustness checks. The framework was tested on haul roads around the Ekibastuz open-pit coal mine. The field analysis identifies road segments where degraded surface morphology, increased waviness, unfavorable longitudinal profile, and higher rolling resistance coincide with increased mechanical dynamic response, VIMS-derived fuel-use proxy burden, braking instability, and travel-time variability. The results are interpreted as controlled field-supported associations rather than as isolated causal effects. The proposed maintenance ranking should therefore be regarded as a decision-support output, while the operational effectiveness of specific repair interventions requires future before–after validation. Full article

Background/Objectives: Pregnant women with obesity may experience a substantial burden of sleep disruption and obstetric risk, but prospective data integrating non-respiratory sleep symptoms, prescribed nutritional supplement exposure, and routinely collected hematological measures are limited. This single-center prospective pilot study aimed to describe longitudinal changes in anthropometry, non-respiratory sleep symptoms, prescribed nutritional supplement exposure, routine hematological and blood-count-derived inflammatory indices, and obstetric and neonatal outcomes, and to document study-accrual and data completeness metrics for a future controlled cohort. Methods: Seventeen adult women with singleton pregnancies and first-trimester body mass index (BMI) ≥ 30 kg/m² reached the sleep unit, and all consented to participate. Protocol-based assessments were performed in the first and third trimesters, whereas routine laboratory and treatment data were extracted from medical records. Nutritional exposure was defined solely by prescription status in routine records; doses, formulations, adherence, dietary intake, and serum micronutrient markers were unavailable. Analyses were endpoint-specific complete-case analyses. Results: Study accrual at the sleep unit occurred from 27 July 2023 to 14 March 2024 (17 participants over 7.6 months; approximately 2.2 participants/month). The number informed in Obstetrics and the number not reaching the sleep unit were not recorded. Paired anthropometry was available for 15/17 participants, paired Epworth and Insomnia Severity Index scores for 14/17, paired restless-legs scores for 13/17, and delivery outcomes for 16/17. Median paired changes were 6.0 kg (IQR 1.4–7.9) for weight and 2.3 kg/m² (IQR 0.5–3.1) for BMI. Hemoglobin decreased, and selected blood-count-derived indices varied across routine trimester measurements. Sleep scores showed no clear longitudinal change, although nocturnal awakenings and subclinical insomnia were frequent. Type III polygraphy was attempted in all 17 participants at T1 and was valid in 16; one participant declined a repeat study after a technically invalid recording. Polygraphy was valid in 15 participants at T3; a respiratory event index (REI) ≥5 events/h was observed in 6/16 (37.5%) and 8/15 (53.3%), respectively. Conclusions: This pilot provides feasibility, data completeness, and variability estimates for a larger controlled cohort. The small uncontrolled sample, non-standardized laboratory timing, treatment heterogeneity, and missing upstream referral logs prevent obesity-specific, mechanistic, or clinically actionable conclusions. Full article

Accurate rice yield estimation is essential for food security. Two key factors affecting estimation accuracy are the long-term upward trend in yield over time and regional heterogeneity across space. Current studies predominantly employ statistical detrending methods (e.g., moving averages, linear regression) to isolate temporal trends. However, such methods rely on prior assumptions about the time–yield relationship and may introduce systematic bias when these assumptions break down. Meanwhile, the individual contributions of temporal and spatial information, and their interactive effects, have not been systematically evaluated within a unified framework. We selected 112 rice-growing counties across six U.S. states (2000–2021), using vegetation index (Normalized Difference Vegetation Index), meteorological indicators (growing degree days, killing degree days, and cumulative precipitation), and spatiotemporal variables (year, longitude, and latitude). We designed six input configurations to compare conventional detrending against direct temporal variable inclusion, testing across four model architectures (Long Short-Term Memory, Random Forest, XGBoost, and Transformer). Results showed that: (1) directly inputting year significantly outperformed detrending across all models, with the combined spatiotemporal configuration achieving the best performance (LSTM R² = 0.61 vs. 0.54 for detrending); (2) year was the most important predictor in SHAP analysis, with spatiotemporal variables ranking higher than most meteorological and remote sensing variables; (3) spatial information consistently improved accuracy and mitigated systematic bias for extreme yield regions; (4) the combined configuration performed best across different states, years (including extreme climate events), and yield levels, achieving near-end-of-season accuracy at the grain-filling stage (1.5–2 months before harvest). This study demonstrates that integrating raw spatiotemporal data directly into deep learning models is more effective than statistical detrending, offering a simpler and more robust approach for large-scale crop yield estimation. Full article

Background: Post-stroke spasticity is a frequent and disabling consequence of stroke, including when affecting the lower limbs, where it may impair stance, gait, balance, postural control, functional independence and quality of life. Botulinum toxin type A (BoNT-A) is widely used as a focal neuromodulatory treatment for post-stroke spasticity. However, the relationship between BoNT-A-induced reduction in muscle hypertonia, objective changes in spastic muscle’s biomechanical properties, and functional outcomes such as mobility and balance remains insufficiently clarified. This systematic review aimed to synthesize recent evidence regarding the non-invasive assessment of spastic muscle properties following BoNT-A administration in post-stroke patients, with emphasis on mobility and balance outcomes. Methods: A systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search was performed in international electronic databases and included studies published between 1 January 2023 and 31 December 2025. The search strategy used specific keywords and keyword combinations/syntaxes, contextually, related to the topic of interest. Results: A total of 32 studies met the eligibility criteria and were included in the final data analysis and synthesis, comprising 13 primary clinical studies—6 randomized or controlled interventional studies and 7 observational studies—together with 12 reviews or evidence syntheses, 3 technical or clinical framework papers, and 4 survey, epidemiological, health-services or health-economic studies. Overall, the included articles addressed BoNT-A treatment in post-stroke spasticity, with partial focus on muscle properties, gait, mobility, and functional outcomes. However, only a limited number of studies investigated objective non-invasive assessment methods, and few directly related muscle-property changes in balance and mobility outcomes. Formal risk-of-bias assessment and quantitative synthesis were not performed because of the substantial heterogeneity of the included evidence, with only two studies being potentially suitable for pooling and these addressing different muscle groups, interventions, and outcome domains. Discussion and Conclusions: The reviewed literature confirms the clinical relevance of BoNT-A in the management of post-stroke spasticity. However, most studies assess treatment effects mainly through clinical scales, while objective evaluation of muscle stiffness, elasticity, viscoelastic properties, and their relationship with mobility and balance remains limited. Although some studies address gait, functional recovery, or muscle-related changes, the combined use of BoNT-A treatment, myotonometric assessment, and proprioceptive–stabilometric evaluation is largely absent. Therefore, current evidence highlights an important research gap and supports the need for future longitudinal studies integrating non-invasive biomechanical and balance assessment tools to better monitor treatment response and guide individualized neurorehabilitation in post-stroke patients. Full article

Digital inclusive finance (DIF) is gradually becoming an important driver of the high-quality development of rural industries. Based on panel data from 30 provinces in China from 2013 to 2023, building the high-quality development of rural industries index system and the digital new quality productivity (DNQP) index system, this study tests the impact and mechanisms of DIF on the high-quality development of rural industries. The main results show that DIF significantly promotes the high-quality development of rural industries. Furthermore, DIF facilitates this development by fostering DNQP. The study also identifies threshold effects, with DIF and DNQP serving as threshold variables. Additionally, the study examines the heterogeneity of the empowerment effect, demonstrating that this promoting effect is more pronounced in non-major grain-producing areas and regions with high-level fiscal support for agriculture. The findings provide theoretical and empirical evidence to support sustainable rural industrial development and offer policy implications for leveraging digital finance in rural contexts. Full article

This study evaluates an Adaptive Monte Carlo Localization-Extended Kalman Filter (AMCL-EKF) pose-estimation stack for repeatable 2D LiDAR SLAM in GPS-denied indoor inspection scenarios. AMCL was used as an online map-referenced correction source fused with LiDAR odometry and Inertial Measurement Unit (IMU) data, and the resulting pose estimate was supplied online to three SLAM backends: Cartographer, GMapping, and SLAM Toolbox. Experiments were performed with a wheeled Husarion Panther and a quadruped Boston Dynamics Spot in three indoor environments of different geometric complexity, producing 720 SLAM executions. Trajectory repeatability was assessed using SE(2)-aligned pairwise and centroid-based ATE-style dispersion and translational RPE, while map repeatability was evaluated with occupied-cell IoU. Accordingly, the metrics were used to quantify between-run dispersion rather than absolute accuracy against external ground-truth data. The results show that AMCL-EKF fusion is highly dependent on the environment, platform, and SLAM backend. AMCL improved selected configurations, especially for Spot in structured environments and for Panther map consistency, but degraded others in geometrically repetitive corridors and mixed-structure spaces. The study also shows that the presence of AMCL-assisted odometry correction alone does not determine final trajectory repeatability, because each SLAM backend incorporates the supplied fused pose estimate differently. The findings support confidence-aware AMCL integration and motivate integrated SLAM architectures resistant to over-correction. These results provide guidance for robust autonomous mapping and inspection with heterogeneous mobile robotic platforms in real environments. Full article

Persistent urban–rural inequality remains a major challenge for sustainable regional development, especially in countries where rural communities still face limited access to markets, employment, and public services. This study examines whether rural road network density promotes inclusive regional growth in China. Using county-level panel data from 2013 to 2024, we construct an inclusive regional growth index that combines economic output, nighttime-light-measured economic activity, rural income, and the urban–rural income gap. rural road network density is measured by the length of county, township, and village roads per 100 square kilometers. Two-way fixed-effects models, mechanism tests, robustness checks, instrumental-variable estimation, and heterogeneity analysis are employed. The results show that rural road network density significantly improves inclusive regional growth. Dimensional analysis indicates that higher rural road network density increases real GDP per capita, strengthens nighttime-light-measured economic activity, raises rural income, and reduces the urban–rural income gap. Mechanism analysis shows that these effects operate through labor mobility, market access, and non-agricultural industrial development. The results remain robust to alternative road measures, lagged specifications, outlier treatment, sample restrictions, and instrumental-variable estimation. Heterogeneity analysis further shows that the effects are larger in central-western counties, low-accessibility counties, and less-developed counties. These findings suggest that rural road network density is not only a transport infrastructure indicator but also a key spatial condition for promoting sustainable and inclusive regional development. Full article

Enhanced Recovery After Surgery (ERAS) pathways have improved perioperative outcomes by standardizing evidence-based interventions across the surgical continuum. However, substantial variability in postoperative recovery persists, even within well-implemented ERAS programs. This heterogeneity reflects differences in clinical risk, functional reserve, biological response to surgical stress, treatment responsiveness, and contextual factors that are not fully captured by uniform protocols. Precision medicine provides a potential framework for refining ERAS by integrating patient-specific data into perioperative risk assessment, intervention selection, patient monitoring, and recovery planning. Nevertheless, most precision medicine tools remain insufficiently validated for routine ERAS implementation, and their clinical utility is limited by heterogeneous evidence, data integration challenges, costs, workflow complexity, and equity concerns. Future progress will require prospective validation, pragmatic implementation studies, interoperable data systems, and evaluation of patient-centered outcomes. This narrative review examines the emerging role of precision medicine tools in perioperative practice and proposes an idealized conceptual model of “precision ERAS” in which standardized evidence-based care is preserved as the foundation, while selected interventions are adapted according to individual risk, biological phenotype, and recovery trajectory. Full article

Background: Hydrogen breath testing is commonly used to assess intestinal fermentation and diagnose small intestinal bacterial overgrowth (SIBO). However, it remains unclear whether hydrogen production reflects systemic inflammatory or metabolic status. This study evaluated the relationship between hydrogen production dynamics and systemic biomarkers using a data-driven analytical approach. Methods: This cross-sectional study included 162 adults undergoing lactulose hydrogen breath testing. Hydrogen production was characterized using continuous measures, including area under the curve (AUC), early and late hydrogen responses, and unsupervised clustering-derived hydrogen response groups. Associations with serum 25-hydroxyvitamin D, C-reactive protein (CRP), leukocyte count, and interleukin-6 (IL-6) were assessed using multivariable regression models adjusted for age and body mass index (BMI). Results: Hydrogen production showed substantial interindividual variability. Unsupervised analysis identified low-, intermediate-, and high-hydrogen response groups. Differences between groups were driven mainly by overall fermentation intensity rather than distinct temporal response profiles. No significant associations were observed between hydrogen production metrics and systemic biomarkers. Hydrogen-related variables were not independently associated with vitamin D, CRP, leukocyte count, or IL-6 concentrations. In contrast, BMI was consistently associated with inflammatory markers, particularly CRP and IL-6. Correlation analyses demonstrated strong relationships among hydrogen-derived variables but weak associations with systemic parameters. Conclusions: Data-driven analysis revealed marked heterogeneity in intestinal hydrogen production but no detectable association with systemic inflammatory or metabolic markers within the present cohort. These findings suggest that hydrogen breath test metrics primarily reflect local intestinal fermentation rather than systemic physiological status. Hydrogen breath testing remains useful for assessing gastrointestinal function, but no evidence supporting its value as a marker of systemic inflammation was identified in the present cohort. Full article