Search Results (43,981)

To address the worsening energy crisis from rapid fossil fuel consumption, this study synthesized Ce-FeWO₄ composites and hydrophilic carbon nanotubes. XRD and other characterizations showed all intermediates had rough, porous nanosheet morphology; Ce-doping formed disordered porous structure in FeWO₄, increasing its specific surface area. Three-electrode tests confirmed optimal parameters: 0.5% Ce-doping and 12 h growth. Ce-FeWO₄ exhibited a specific capacity of 1875 ± 28 F/g at 1 A/g (based on five parallel samples), and retained 1807 F/g after 3000 cycles (exceeding previous studies) with excellent stability. The Ce-FeWO₄//CNTs asymmetric supercapacitor achieved 152 F/g specific capacity, 81.4 Wh/g energy density, and 768 W/kg power density. The simple, efficient, eco-friendly preparation process and the material’s high capacitance and stability offer broad application prospects in the electrode field. Full article

For effective water purification, the combination of membrane separation and catalytic degradation technologies not only permits continuous pollutant degradation but also successfully reduces membrane fouling. In recent years, catalytic membranes (CMs) have garnered a lot of interest in the water treatment industry. The main benefits of CMs are methodically explained in this review, emphasizing the synergistic effect of membrane separation and catalysis. These benefits include stable catalyst loading achieved through membrane structure manipulation, nanoconfinement, and effective degradation of organic pollutants. The application of catalytic membranes in water treatment is then thoroughly summarized, and they are separated into five main groups based on their unique catalytic reaction mechanisms: ozone catalytic membranes, photocatalytic membranes, electrocatalytic membranes, Fenton-type catalytic membranes, and persulfate catalytic membranes. The mechanisms and performance characteristics of each kind of CM are looked at in greater detail. Finally, research directions and future prospects for water treatment using catalytic membranes are proposed. This review provides recommendations for future research and development to ensure the effective use of catalytic membranes in water treatment, in addition to providing a thorough examination of the advancements made in their application in the treatment of various wastewaters. Full article

Background/Objectives: To determine whether adolescent (T1) mental health, quality of life, and adjustment to celiac disease (CeD) are associated with college-age (T2) perceived physical and mental health and gluten-free diet (GFD) adherence. Methods: In 2015, adolescents with CeD (n = 101, T1) completed standardized surveys assessing mental health (CSI-4), quality of life (PedsQL), and adjustment to CeD (CDDUX). Five years later, participants ≥18 years self-reported GFD adherence and physical and mental health perception in college (n = 59, T2). Patients who were current or recent college students that provided complete data at both time points were analyzed (n = 43) using Kendall’s tau to test: concurrent associations among T2 perceived physical/mental health and GFD adherence; and prospective associations between T1 measures and T2 perceived outcomes. Results: Higher T1 CSI-4 and PedsQL scores were negatively correlated with T2 perceived physical health (τ = −0.31, p = 0.02 and τ = −0.28, p = 0.04, respectively). There was trending association between T1 PedsQL and T2 mental health perception (τ = -0.23, p = 0.06). T2 physical and mental health perception were positively correlated (τ = 0.41, p = 0.001). No significant associations emerged between T1 measures and T2 GFD adherence, nor between T2 health perception and GFD adherence, although T2 physical health perception positively trended with GFD adherence (p = 0.78). Conclusions: Adolescents with CeD reporting more depressive symptoms or lower quality of life feel less physically and mentally healthy when in college. In college, feeling physically healthy aligns with feeling mentally healthy, although neither clearly predicts GFD adherence. Early mental health screening in adolescents with CeD may support transitions to independent dietary management. Full article

Background/Objectives: This study evaluated the association between nutrient intake adequacy during early pregnancy and gestational diabetes mellitus (GDM) risk through a prospective cohort study of Korean pregnant women. Methods: A total of 2227 singleton pregnant women were included in this study. Dietary assessment was conducted once during early pregnancy enrollment using the 24 h dietary recall method. The collected dietary data were analyzed using the CAN-Pro Korean food composition database to calculate nutrient-specific intake levels. Nutrient Adequacy Ratio (NAR) for each nutrient and Mean Adequacy Ratio (MAR), which integrates individual NARs, were calculated based on intake levels. GDM diagnosis was determined using oral glucose tolerance test (OGTT) results conducted between 24 and 28 weeks of gestation. Relative risk (RR) for each individual nutrient was calculated, and logistic regression analysis was performed to estimate odds ratios (OR) for GDM risk according to MAR quartiles. Results: Among 2227 participants, 157 women developed GDM. Compared to the highest MAR quartile, women in the lowest quartile showed significantly increased GDM risk (OR = 1.82, 95% CI: 1.10–2.99), with the second lowest quartile demonstrating similarly elevated risk (OR = 1.75, 95% CI: 1.06–2.88). Among individual nutrients, inadequate vitamin D intake was associated with the highest GDM risk (RR = 3.84), followed by insufficient intakes of vitamin K (RR = 1.89), vitamin B₆ (RR = 1.62), niacin (RR = 1.54), and calcium (RR = 1.39). Body mass index-stratified analysis revealed that the association between low nutritional adequacy and GDM risk was particularly pronounced in women with BMI ≥ 25.0 kg/m², showing up to a four-fold increased risk in the lowest adequacy groups. Conclusions: These findings suggest that low overall dietary adequacy and nutritional imbalance during early pregnancy are associated with increased GDM risk. The results underscore the importance of ensuring adequate and balanced nutrition during early pregnancy for GDM prevention. Full article

Background: Pediatric heart failure (PHF) is a heterogeneous syndrome with high morbidity, but existing classification systems inadequately capture its developmental and pathophysiological complexity due to reliance on adult-centric parameters. Using machine learning, we aimed to identify clinically distinct PHF phenotypes with unique outcomes and therapeutic implications. Methods: In this multicenter retrospective study, we analyzed 2903 consecutive PHF patients (≤18 years) from 30 Chinese tertiary centers from 20 provinces (2013–2022). Unsupervised machine learning (k-means clustering with PCA) evaluated 99 clinical, biomarker, and echocardiographic variables to derive phenotypes, which were compared for mortality, hospitalization, and treatment responses. Results: Three phenotypically distinct clusters emerged. Cluster 1 (Chronic Hypertensive and Cardiorenal Profile, 30.1%) predominantly affected older children (78%) with hypertension (54.4%), renal dysfunction (creatinine 45.8 μmol/L), and ventricular tachycardia (53.8%). This cluster showed the lowest in-hospital mortality (2.5%) but frequent 7–14 day hospitalizations (35.8%) and the highest beta-blocker use (54.5%). Cluster 2 (Preterm and CHD-Associated HF, 43.4%) comprised preterm infants (71.4%) with congenital heart disease (72.2%) and preserved LVEF (67%), demonstrating the highest mortality (5.1%) and prolonged stays (>30 days: 10.6%) with predominant diuretic (40.6%) and antibiotic use (54.3%). Cluster 3 (Fulminant Myocarditis Profile, 26.5%) exhibited cardiogenic shock with severely reduced LVEF (33%) and elevated BNP (3234 pg/mL), showing bimodal outcomes (4.8% LOS < 3 days vs. 32.2% LOS 15–30 days) and the highest IVIG utilization (46.5%) with intermediate mortality (3.8%). The majority of between-group differences were statistically significant (p < 0.001). Conclusions: Machine learning identified three PHF phenotypes with distinct in-hospital risk profiles and therapeutic implications, challenging current classification systems. These findings highlight the potential for phenotype-specific management strategies and provide a rationale for future research into arrhythmia prevention in hypertensive profiles and early immunomodulation in fulminant myocarditis, while highlighting the need for specialized care pathways for preterm/CHD patients. Prospective validation is warranted to translate this framework into clinical practice. Full article

Accurate land cover mapping is essential for environmental monitoring and agricultural management. Sentinel-2 imagery, with high spatial resolution and open access, provides valuable opportunities for operational classification. Convolutional neural networks (CNNs) have demonstrated state-of-the-art results, yet their adoption is limited by high computational demands and limited methodological transparency. This study proposes a lightweight CNN for soil–vegetation classification, in Dolj County, Romania. The architecture integrates three convolutional blocks, global average pooling, and dropout, with fewer than 150,000 trainable parameters. A fully documented workflow was implemented, covering preprocessing, patch extraction, training, and evaluation, addressing reproducibility challenges common in deep leaning studies. Experiments on Sentinel-2 imagery achieved 91.2% overall accuracy and a Cohen’s kappa of 0.82. These results are competitive with larger CNNs while reducing computational requirements by over 90%. Comparative analyses showed improvements over an NDVI baseline and a favorable efficiency–accuracy balance relative to heavier CNNs reported in the literature. A complementary ablation analysis confirmed that the adopted three-block architecture provides the optimal trade-off between accuracy and efficiency, empirically validating the robustness of the proposed design. These findings highlight the potential of lightweight, transparent deep learning for scalable and reproducible land cover monitoring, with prospects for extension to multi-class mapping, multi-temporal analysis, and fusion with complementary data such as SAR. This work provides a methodological basis for operational applications in resource-constrained environments. Full article

In the field of acoustic wave detection, optical sensors have significant potential applications in numerous civilian and military fields due to their high sensitivity and immunity to electromagnetic interference. This study designed an undercoupled silica optical waveguide resonator (OWR) with a 2% refractive index contrast. Mode spot converters were introduced at both ends of the straight waveguide to achieve efficient optical transmission between the fiber and the waveguide. The resonator was fabricated using plasma-enhanced chemical vapor deposition (PECVD) and inductively coupled plasma (ICP) etching technologies. The results show that the quality factor (Q-factor) of the resonator reached 2.75 × 10⁶. Compared with a resonator with a refractive index difference of 0.75%, the Q-factor remained at the same order of magnitude while the sensor size was significantly reduced. To achieve high-sensitivity acoustic wave detection, this study employed an intensity demodulation method to realize acoustic wave detection with the resonator. Test results demonstrate that the OWR can detect acoustic signals in the frequency range of 25 Hz to 20 kHz, with a minimum detectable sound pressure of 1.58 μPa/Hz^1/2 @20 kHz and a sensitivity of 1.492 V/Pa @20 kHz. The sensor exhibits a good signal-to-noise ratio and stability. The proposed method shows broad application prospects in the field of acoustic sensing and is expected to enable large-scale applications in scenarios such as communication, biomedical monitoring, and precision industrial sensing. Full article

Background and Objectives: Postoperative atrial fibrillation (POAF) remains one of the most frequent complications after cardiac surgery, increasing the risk of morbidity, prolonged hospitalization, and adverse long-term outcomes. Although several clinical and echocardiographic factors have been associated with POAF, the integrated contribution of atrial conduction delay, biatrial mechanics, and atrioventricular coupling to arrhythmogenesis remains unclear. Materials and Methods: This retrospective study included 131 adult patients undergoing coronary artery bypass grafting and/or aortic valve replacement. Preoperative echocardiography within one week before surgery provided detailed assessment of atrial phasic function, valvular motion, and total atrial conduction time (TACT). Univariate analysis was followed by multivariable modeling using penalized logistic regression (Elastic Net) to identify the most robust predictors of POAF. Discriminative performance and calibration were evaluated via receiver operating characteristic (ROC) and calibration analysis. An exploratory Extreme Gradient Boosting (XGBoost) model with SHapley Additive exPlanations (SHAP) analysis was used to confirm the stability and directionality of nonlinear feature interactions. Results: POAF occurred in 47 (36%) patients. The Elastic Net model identified prolonged TACT, reduced right atrial active emptying fraction (RAAEF), increased indexed minimal left atrial volume (MIN LA/BSA), and lower tricuspid annular plane systolic excursion (TAPSE) as the most informative predictors. The model demonstrated excellent internal discrimination (AUC = 0.95; 95% CI 0.91–0.99) and satisfactory calibration (Hosmer–Lemeshow p = 0.41). Exploratory XGBoost analysis yielded concordant feature hierarchies, confirming the physiological consistency of the results. Conclusions: POAF arises from an identifiable electromechanical substrate characterized by atrial conduction delay, biatrial mechanical impairment, and reduced atrioventricular coupling. A parsimonious, regularized statistical model accurately delineated this profile, while complementary machine-learning analysis supported its internal validity. These findings underscore the potential of echocardiographic electromechanical parameters for refined preoperative risk stratification, pending prospective multicenter validation. Full article

Background/Objectives: Capsular contracture remains the most common complication after breast augmentation. The Baker score is a classical, yet subjective method to assess the contracture, therefore more objective and reproducible measures are necessary to not only evaluate but also to predict a capsule formation. The aim of the prospective study was to assess the predictive value of sonoelastography, tonometry and physical examination at different time points after the surgery and with association with Baker score, since this early period is considered critical for the initiation of capsular formation. Methods: 28 patients (range of age 21.0–40.0) after breast augmentation completed the study protocol. A total of 56 breasts underwent clinical (modified Baker score), tonometric, and elastographic evaluation before surgery, on postoperative days 7 and 14, and after one year. Measurements were taken at the boundaries of the four breast quadrants and included adipose, glandular, muscular and fascial tissues. Results: At long-term follow-up, tonometric and elastographic values were lower than on postoperative days 7 and 14. Early measurements in certain quadrants and tissue regions showed some association with Baker scores at one year. All tissues increased in stiffness postoperatively and remained stiffer at the 1-year follow-up. No significant differences in tissue stiffness were observed between days 7 and 14, except for higher values in adipose tissue in the inner quadrant and lower values at the peri-implant region in the outer quadrant. Conclusions: Sonoelastography is a precise and objective tool for detecting capsular changes after breast augmentation and may improve early prediction of contracture. Full article

Background and Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of liver disease in the United States and frequently coexists with other liver diseases. Despite growing interest, the presence of MASLD in patients with primary sclerosing cholangitis (PSC) remains underexplored. This study aimed to assess the prevalence and characteristics of the MASLD-PSC overlap syndrome, with a specific focus on patient-reported outcomes such as pruritus and fatigue. Methods: A cross-sectional analysis was performed within a prospective cohort of patients with PSC enrolled in the Autoimmune Liver Diseases Registry at a United States tertiary medical center (2018–2024). MASLD overlap was established based on evidence of hepatic steatosis on liver imaging or biopsy, combined with at least one cardiometabolic risk factor. Fatigue and pruritus were assessed using the Chronic Liver Disease Questionnaire (CLDQ) and the 5D Itch Scale. Ordinal logistic regression models were used to explore the potential impact of MASLD overlap on fatigue and pruritus severity. Results: Among 103 PSC patients, 33% had MASLD overlap. These patients were older (55 vs. 46 years, p = 0.006), had a higher BMI (30 vs. 25 kg/m², p < 0.001), and were more likely to have small bile duct involvement (43% vs. 12%, p = 0.002). A history of liver transplantation (LT) was noted in 18% of PSC-only patients, compared to 3% of those with PSC/MASLD (p = 0.055). MASLD overlap was significantly associated with higher pruritus intensity (OR 3.09, 95% CI 1.02–9.28, p = 0.044), but was paradoxically linked to lower fatigue levels (OR 0.37, 95% CI 0.16–0.85, p = 0.020). Conclusions: Patients with PSC/MASLD exhibit distinct clinical features. MASLD overlap was found to significantly impact patient-reported outcomes, with lower fatigue intensity but increased pruritus severity, suggesting a role for metabolic or inflammatory pathways, warranting further investigation. Full article

Phosphate ore is essential for global food security and industry. However, the depletion of high-grade deposits necessitates processing complex low-grade ores, posing significant separation challenges. Flotation, the main beneficiation method, exploits minor differences in surface properties, yet conventional approaches offer limited molecular-level insight, resulting in inefficiency, high reagent use, and pollution. Molecular simulation has emerged as a transformative solution, integrating quantum chemistry, molecular dynamics, and mesoscale modeling to accurately predict electronic structures and optimize flotation systems. This review systematically examines its applications in phosphate ore processing, highlighting four key advances: a multi-scale framework linking atomic mechanisms to macro-performance; structure–activity models for rational reagent design; insights into interfacial micro-environments for intelligent control; and machine learning integration for high-throughput screening. Key challenges such as force field accuracy and simulation scalability are addressed, along with emerging directions like in situ dynamic simulation and integration with process engineering. This review aims to support the development of efficient, sustainable, and intelligently optimized phosphate beneficiation technologies. Full article

Ozone in the atmosphere strongly absorbs deep ultraviolet light with wavelengths between 200 and 280 nm. Therefore, this characteristic is advantageous and promising for unperturbed, non-disturbed information transmission in fields such as secure communications when deep ultraviolet light is employed. However, existing optical communication systems utilizing deep ultraviolet light are characterized by substantial size, which presents significant challenges in terms of local transferability. This paper employs an array of 275 nm deep ultraviolet light-emitting diodes (LEDs) connected in series, paired with photomultiplier tubes (PMTs) as transmitters and receivers. The system is encapsulated with a visual tracking module and mounted on drones and vehicles, achieving mobile duplex real-time communication under sunlight. The communication distance reaches 30 m with a packet loss rate of 1.36%. This work enables rapid and flexible deployment of deep ultraviolet optical communication systems, offering broad application prospects. Full article

Background/Objectives: Severe knee osteoarthritis (KOA) is a degenerative disease that significantly affects quality of life (QoL). Although intra-articular (IA) injections of plasma rich in growth factors (PRGF) have proven effective, the subchondral bone plays a crucial role in pathogenesis. The objective of this study was to evaluate the efficacy of intraosseous (IO) PRGF infiltrations in comparison with a saline placebo, followed by the conventional standard treatment of three IA PRGF injections, in enhancing clinical outcomes in patients suffering from severe KOA. Methods: A prospective, randomized, double-blind, multicenter clinical trial was conducted. Eighty-six patients with Kellgren–Lawrence grade III-IV KOA were randomly assigned to two groups: one received an IO infiltration of PRGF and the other received an IO saline solution. Both groups subsequently received three IA PRGF injections. Clinical outcomes were assessed using the KOOS and WOMAC scales at baseline and at 3, 6, and 12 months. Results: Both groups showed a statistically significant improvement in all KOOS and WOMAC subscales at all follow-up points compared to their baseline values. However, the group that received the IO PRGF infiltration demonstrated significantly greater improvements in nearly all domains of the KOOS and WOMAC scales (pain, symptoms, function, and quality of life) at 3, 6, and 12 months compared to the saline group (p < 0.05). No serious adverse events were recorded. Conclusions: The combination of intraosseous and intra-articular PRGF infiltrations is a superior therapeutic strategy to the combination of intraosseous saline solution and intra-articular PRGF for treating severe KOA. These findings suggest that treating the subchondral bone directly with PRGF has a significant and clinically relevant therapeutic effect, resulting in greater pain reduction and functional improvement at one-year follow-up. Full article

Objectives: To investigate the association between Type-D personality and pain-related outcomes in patients undergoing spine surgery, and to discuss implications for the management of chronic pain conditions. Methods: A prospective cohort of 200 patients scheduled for elective spine surgery was assessed for Type-D personality using the DS14 scale. Postoperative outcomes including pain intensity (VAS), functional recovery (ODI), complication rates, and patient satisfaction (PSI) were measured preoperatively and at 3, 6, and 12 months. Multivariate regression analyses adjusted for age, sex, surgical approach, comorbidities, and baseline health status. Results: Type-D personality was identified in 30% of patients. These individuals reported significantly higher postoperative pain, slower functional recovery, higher complication rates, and lower overall satisfaction compared to non-Type-D patients. Compared with non-Type-D patients, Type-D patients reported higher pain and slower functional recovery at 12 months (VAS β = 0.34, 95% CI 0.18–0.52, p = 0.004, Cohen’s d = 0.61; ODI β = 0.31, 95% CI 0.12–0.48, p = 0.006, d = 0.58), and lower satisfaction (PSI β = −0.36, 95% CI −0.49 to −0.20, p < 0.001, d = 0.66). Conclusions: Type-D personality is associated with worse postoperative pain and recovery. Preoperative psychological assessment and tailored interventions may improve outcomes. These findings highlight the importance of integrating psychosocial screening into pain management strategies for both spine surgery and chronic pain populations. Full article