Search Results (19,390)

Background: Atopic dermatitis (AD) is a chronic inflammatory disease requiring topical and systemic treatments. This study examines healthcare service utilization and medication use in children with AD in a large healthcare organization during the year 2024. Methods: A cross-sectional case-control study was conducted comparing 128,239 children with AD to 128,239 matched controls regarding healthcare utilization and medication use. Multivariate analysis assessed differences between the groups. Results: Children with AD had increased healthcare utilization compared to the control group, with higher rates of visits to pediatricians, general practitioners, and dermatologists. A total of 144 children (0.11%) with AD were treated by immunosuppressive drugs, as compared to 78 children (0.06%) in the control group (OR 1.8, 95% CI 1.4–2.4, p-value < 0.001). A total of 410 children (0.32%) were treated with biologic drugs as compared to 12 children (0.01%) in the control group (OR 34.3, 95% CI 19.3–60.9, p-value < 0.001). A total of 34 children (0.03%) were treated with Janus kinase (JAK) inhibitors as compared to 2 children (0.002%) in the control group (OR 17, 95% CI 4.1–70.8, p-value < 0.001). Conclusions: Increased utilization of healthcare services was observed in pediatric patients with AD compared to the control group. As only a small proportion of the children with AD received immunosuppressants, biologic treatments, and JAK inhibitors, we suggest that the use of systemic medications should be strongly considered in pediatric patients with moderate to severe AD. Full article

Adult granulosa cell tumors (AGCT) are rare ovarian neoplasms with typically indolent behavior but potential for late recurrence. This study aimed to evaluate long-term outcomes and identify clinicopathological predictors of disease-free survival (DFS) in patients with AGCTs. This retrospective cohort study included patients with histologically confirmed AGCTs who were treated or followed at Ege University Faculty of Medicine between January 2012 and 2023. Survival outcomes were analyzed using Kaplan–Meier and Cox regression methods. Among 55 patients with a median follow-up of 113.7 months, the median DFS was 92.3 months, and the median overall survival (OS) was 113.7 months. The 5-year DFS and OS rates were 84.5% and 93.9%, respectively. Recurrence occurred in 23.6% of patients and was significantly linked to advanced FIGO stage, atypical endometrial pathology, and bleomycin–etoposide–cisplatin (BEP)/etoposide–cisplatin (EP)-based adjuvant chemotherapy. Larger tumor size (>10 cm) and stage III disease were also associated with shorter DFS. Univariate analysis showed that stage III disease (HR 7.14, p = 0.006) and tumor size >10 cm (HR 3.59, p = 0.025) were associated with significantly shorter DFS, while absence of endometrial pathology was protective (HR 0.34, p = 0.022). In multivariate analysis, stage III disease remained the only independent predictor of recurrence (HR 4.45, p = 0.046). Advanced-stage disease is an independent predictor of recurrence and should be considered a high-risk feature requiring prolonged follow-up. Full article

Background/Objectives: Bronchopulmonary dysplasia (BPD) frequently affects preterm infants and is associated with lasting morbidity. Early prediction remains challenging. The present study investigated whether hematological inflammatory markers—platelet-to-lymphocyte ratio (PLR), red cell distribution width (RDW), and red cell distribution width-to-platelet ratio (RPR)—can predict the development of BPD in preterm neonates. Methods: We performed a retrospective cohort study involving 100 infants born at less than 32 weeks’ gestation. Complete blood count (CBC) parameters were collected at birth, 72 h, 1 week, and 2 weeks of life. Associations between PLR, RDW, RPR, and BPD development were analyzed. Multivariate regression and receiver operating characteristic (ROC) curve analyses were carried out to evaluate the predictive performance of the markers. Results: Forty-nine percent of infants developed BPD. Those with BPD had significantly higher RDW, PLR, and RPR values, and lower lymphocyte and platelet counts at various time points. Gestational age, respiratory distress syndrome, and hematological indices independently predicted BPD. ROC analysis showed that RDW ≥ 67.2 and PLR ≥ 98.13 at 72 h, and RPR ≥ 0.3 at 7 and 14 days had good predictive performance. A combined scoring system, including clinical and hematological markers, achieved high sensitivity and specificity. Conclusions: Hematological inflammatory markers, especially RPR, PLR, and RDW, derived from routine CBC tests may serve as accessible, cost-effective tools for early BPD risk stratification in preterm infants. Additional studies are needed to confirm these results and better define their relevance in clinical practice. Full article

Human-powered electricity generation (HPEG) systems offer promising sustainable energy solutions, yet existing deterministic models fail to capture the inherent variability in human biomechanical performance. This study develops a comprehensive stochastic framework integrating advanced Monte Carlo uncertainty quantification with multi-component fatigue modeling and Pareto optimization. The framework incorporates physiological parameter vectors, kinematic variables, and environmental factors through multivariate distributions, addressing the complex stochastic nature of human power generation. A novel multi-component efficiency function integrates biomechanical, coordination, fatigue, thermal, and adaptation effects, while advanced fatigue dynamics distinguish between peripheral muscular, central neural, and substrate depletion mechanisms. Experimental validation (623 trials, 7 participants) demonstrates RMSE of 3.52 W and CCC of 0.996. Monte Carlo analysis reveals mean power output of 97.6 ± 37.4 W (95% CI: 48.4–174.9 W) with substantial inter-participant variability (CV = 37.6%). Pareto optimization identifies 19 non-dominated solutions across force-cadence space, with maximum power configuration achieving 175.5 W at 332.7 N and 110.4 rpm. This paradigm shift provides essential foundations for next-generation HPEG implementations across emergency response, off-grid communities, and sustainable infrastructure applications. The framework thus delivers dual contributions: advancing stochastic uncertainty quantification methodologies for complex biomechanical systems while enabling resilient decentralized energy solutions critical for sustainable development and climate adaptation strategies. Full article

Background/Objectives: The objective of this study is to investigate pregnancy and neonatal outcomes in women with pre-gestational diabetes (PGDM) in the Korean population and compare outcomes according to glycosylated hemoglobin (HbA1c) levels in the third trimester. Methods: Singleton pregnant women with PGDM, with follow-up data, and who delivered at 16 Korean tertiary institutions between 2010 and 2023 were included for analysis. Eligible patients were divided into two groups according to HbA1c levels (47.5 mmol/mol, 6.5%) in the third trimester (well-controlled and poorly controlled group). Adverse pregnancy and neonatal outcomes between the two groups were compared. The primary outcome was the composite neonatal adverse outcome and the secondary outcome was pregnancy-related hypertension. Results: In 416 pregnancies, the mean HbA1c in the third trimester was 45 mmol/mol (6.26%). Of these, 296 (71.2%) women were included in the well-controlled group and 120 (28.8%) in the poorly controlled group. Between these, the poorly controlled group showed a significantly higher risk of composite neonatal adverse outcome (57.8% vs. 79.2%, p < 0.001) and pregnancy-related hypertension (14.5% vs. 24.2%, p = 0.022). In multivariate analysis, HbA1c > 6.5% in the third trimester was associated with higher risk of composite neonatal adverse outcome and pregnancy-related hypertension. HbA1c ROC curves for the third trimester that predicted composite neonatal adverse outcomes had an AUC of 0.66; HbA1c of 43.7 mmol/mol (6.15%) had a sensitivity of 52.3% and specificity of 73.5% (p < 0.001). Conclusions: In PGDM, HbA1c > 47.5 mmol/mol (6.5%) in the third trimester was significantly associated with a higher risk of adverse neonatal and pregnancy outcomes and could be a predictive factor for composite neonatal adverse outcomes and pregnancy-related hypertension. Maintenance of HbA1c levels below 43.7 mmol/mol (6.15%) in the third trimester might decrease the risk of adverse neonatal outcomes. Full article

Background: The Meet-URO score combines the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria with neutrophil-to-lymphocyte ratio (NLR) and bone metastasis status. Although developed in immune checkpoint inhibitor (ICI) cohorts, its performance among patients receiving first-line tyrosine kinase inhibitor (TKI) monotherapy is uncertain. Methods: We performed a multicenter, retrospective cohort study of 301 adults with histologically confirmed metastatic renal cell carcinoma (mRCC) treated with first-line TKI monotherapy (sunitinib, pazopanib, or cabozantinib) between 2008 and 2025 across five tertiary centers in Turkey. The primary endpoint was overall survival (OS). Meet-URO was calculated at treatment start and analyzed as prespecified risk strata (0–4, 5–8, 9). Kaplan–Meier estimates, Cox models, and Harrell’s C-index assessed discrimination, with bootstrapped 95% CIs. Results: Median follow-up was 40 months; median OS (mOS) was 25 months (95% CI, 21–29) and median progression-free survival was 10 months (95% CI, 8–12). Meet-URO stratified OS: 41 months for scores 0–4, 21 months for 5–8, and 7 months for 9 (log-rank p < 0.001). In multivariable analysis, Meet-URO remained independently prognostic (HR 1.73 for 5–8 vs. 0–4; HR 3.57 for 9 vs. 0–4; both p < 0.001). Discrimination was modest (C-index 0.722) and slightly lower than IMDC (C-index 0.745). NLR ≥ 3.2 was associated with inferior OS (19 vs. 37 months; p < 0.001). Bone metastasis was not significantly associated with OS (p = 0.27). Conclusions: Meet-URO is a valid prognostic tool for mRCC patients treated with first-line TKIs and identifies an ultra-high-risk subgroup (score = 9) with poor survival. While not superior to IMDC, Meet-URO may offer complementary risk information to support clinical monitoring and trial referral, particularly in settings where ICI combinations are restricted. Full article

Emerging evidence suggests a potential role of gut dysbiosis in neurodegenerative disorders and, in particular, Alzheimer’s disease (AD) pathology and cognitive decline. However, the role of gut microbiome in the early prodromal stages of AD and particularly in mild cognitive impairment (MCI) remains understudied and has been mostly explored in Asian populations with no representation of European populations. To address this research gap in the literature and to suggest novel microbiome features associated with MCI, we conducted a cross-sectional study in a European population sample and profiled gut microbiota in 99 individuals without dementia through 16s ribosomal RNA (rRNA) sequencing. Individuals were categorized by cognitive status based on standard clinical criteria to cognitively normal (n = 49) or individuals with MCI (n = 50). Differential abundance through Microbiome Multivariable Associations with Linear model (MaAsLin2) and elastic net logistic regression analyses were used to identify gut microbiome features associated with MCI. MCI group was older than the CN group and age was used as covariate in the differential abundance analysis. No differences in alpha and beta diversity were found between the two groups (p > 0.05). At false discovery rate (FDR) < 0.05, we identified specific genera associated with MCI, mostly linked to short chain fatty acids (SCFAs) production (e.g., Candidatus_Soleaferrea q = 0.027, MaAsLin2 coefficient = 1.65, Sellimonas q = 0.017, MaAsLin2 coefficient = −4.45), while we highlight nominal (p < 0.05, q > 0.05) correlations of genera (e.g., Hydrogenoanaerobacterium, Subdoligranulum) with metrics of cognitive assessment. Microbiota was shown to have a fairly good discriminative capacity for MCI status (area under the curve AUC = 0.77), with Rothia genus found as the top predictor for MCI (beta coefficient [95% confidence intervals] = 0.224 [0.216–0.233]). Overall, our findings add to current knowledge reporting gut microbiome alterations in MCI by suggesting novel associated microbiome features; however, larger scale longitudinal studies are needed to further elucidate the underlying biological pathways linked to the disease. Full article

The study examines the relationship between water surface loss and deforestation in the Brazilian Amazon, focusing on the expansion of farming (crops and agriculture, as well as pasture and livestock) and the impact of inadequate legislation from 1985 to 2023. The Amazon biome is vital for the global hydrological cycle and is home to about 10% of the known species. Data from MapBiomas and multivariate statistical techniques revealed that forest and water surface areas decreased significantly while pasture and agricultural regions increased. Environmental legislation has shown progress, with Center and Left-leaning governments implementing environmental protection measures. In contrast, Center–Right and Right-leaning governments prioritized economic interests, resulting in significant setbacks in forest protection and increased deforestation. The study further highlights the importance of developing integrated and sustainable strategies that balance economic development and environmental conservation in the Amazon biome. Full article

Background: Hip fractures in geriatric patients represent a major public health burden, with a clinically important subset presenting with concomitant upper extremity (UE) fractures. The independent impact of these dual injuries on clinical outcomes remains incompletely characterized. Methods: A retrospective study of patients aged ≥65 years who underwent surgical treatment for hip fracture at tertiary medical center, between January 2010 and January 2024. Patients were stratified based on the presence of a UE fracture sustained at the same time as the hip fracture. Multivariable regression models were used to assess outcomes, adjusting for age, sex, hip fracture type, and comorbidity burden. Primary outcomes were hospital length of stay and mortality at 30 days and 1 year. Secondary outcomes included readmission rates, revision surgery, and infection complications. Results: Of 7488 patients, 251 (3.4%) had concomitant upper extremity (UE) fractures. These patients had a longer mean hospital stay compared with isolated hip fractures (20.2 vs. 17.5 days, p = 0.047), with no significant difference in 30-day mortality (p = 0.439) and a trend toward lower 1-year mortality (p = 0.058). In the concomitant UE fracture group, operative treatment was associated with longer hospitalization (26.2 vs. 19.2 days, p = 0.05) and higher revision surgery rates (14.0% vs. 3.1%, p = 0.01). Subgroup analyses by fracture type showed similar trends, with longer hospital stays observed in intracapsular fractures with concomitant injury (p = 0.05). Subgroup analysis by UE fracture location showed significantly longer stays for distal radius fractures compared with isolated hip fractures, whereas no significant differences were observed for proximal humerus or other UE fracture locations. Conclusions: Concomitant UE fractures in geriatric hip fracture patients are associated with prolonged hospitalization. Operative management of UE fractures results in longer hospital stays and an increased risk of revision surgery. These findings highlight the importance of tailored perioperative planning and resource allocation for this vulnerable patient group. Full article

Background: There is little evidence available regarding the long-term tumor response after transarterial radioembolization (TARE) for hepatocellular carcinoma (HCC). Aim: To identify preprocedural predictive factors for achieving a 5-year complete response (CR) following TARE in patients with HCC. Methods: This retrospective study included 37 patients with treatment-naïve HCC who underwent TARE between January 2016 and December 2019 and were followed for at least 5 years. Tumor characteristics—including maximum diameter, number of main lesions, presence of satellite nodules, and portal vein thrombosis—were evaluated using preprocedural liver dynamic magnetic resonance imaging. Treatment response was assessed according to the modified Response Evaluation Criteria in Solid Tumors. Multivariate logistic regression analyses were performed to identify factors associated with tumor response following TARE. Results: Thirty-seven patients (median age, 64 years) were categorized into two groups: (1) the CR group (n = 9), consisting of patients without tumor recurrence for 5 years and without additional treatment; and (2) the non-CR group (n = 28), consisting of patients who required additional treatment because of residual or recurrent viable tumors. Tumors in the non-CR group had significantly larger diameters compared with those in the CR group (9.8 cm vs. 5.9 cm, p = 0.006). In multivariable analysis, a tumor diameter > 7 cm was the only factor significantly associated with tumor recurrence (odds ratio = 21.277, p = 0.010). Portal vein thrombosis did not reach statistical significance (odds ratio = 9.779, p = 0.063). Conclusions: Tumor diameter > 7 cm is a significant predictor of tumor recurrence within 5 years after TARE for HCC. This finding may support a more individualized post-TARE management approach, potentially allowing clinicians to avoid overtreatment and adopt a watchful waiting strategy for selected patients. Full article

The increasing need for effective sludge management has positioned hydrothermal liquefaction (HTL) as a viable solution, harnessing its capability to transform organic materials into renewable resources under elevated temperature and pressure conditions. This research seeks to assess the performance of HTL in processing high-solid organic sludge by examining the removal efficiencies of chemical oxygen demand (COD), total solids (TS), and suspended solids (SS), together with improvements in biogas potential (BGP) and hydrogen yield. Experimental procedures were carried out within a temperature range of 100–210 °C and pressure levels of 20–80 kg/cm², using a hydrogen-producing microbiome (HMb) and anaerobically digested sludge as inoculants for anaerobic fermentation. Multivariate analysis was applied to investigate the influence of temperature and pressure on COD, TS, and SS removal rates as well as BGP, while a series of batch tests further confirmed the effects of these parameters on fermentation outcomes. Findings revealed that COD, SS, and TS removal efficiencies reached 90.6%, 91.5%, and 87.4%, respectively, under conditions of 100 °C and 60 kg/cm². The maximum biogas potential (BGP) of approximately 500 mL was attained at 180 °C, whereas hydrogen production demonstrated substantial enhancement within the HTL pressure range of 40–60 kg/cm², decreasing beyond this range. Additionally, total dissolved solids (TDS) reached a peak concentration of 389 g/L under conditions of 180 °C and 40 kg/cm², emphasizing HTL’s positive impact on enhancing methane fermentation efficiency. These findings demonstrate that HTL pretreatment, when operated under optimized temperature and pressure conditions, offers a promising approach for enhancing both waste reduction and bioenergy recovery from high-solid organic sludge. Full article

This paper proposes a framework that improves classification performance for multivariate time series data while providing an objective assessment of each variable’s influence, including interaction effects. While convolutional neural networks (CNNs) offer significant advantages in analyzing multivariate time series data, the structural limitations of CNNs have restricted their ability to detect statistical interactions. Our approach creatively modifies convolutional filters and layer structures, enabling feature extraction that captures the influence of pairwise interactions. These extracted features are processed by interpretable models to calculate feature importance, enabling in-depth causal analysis by quantifying both individual and pairwise variable effects. In addition, the proposed method enhances the overall classification performance of multivariate time series data. Synthetic data experiments verified that the proposed method effectively extracted relevant features that explain pairwise interactions. In addition, in the multivariate time series classification experiments using real data, the proposed method demonstrated superior performance compared to baseline methods. These results suggest that the proposed approach is a practical and interpretable solution for multivariate time series classification tasks in domains where variable interactions play a decisive role, such as healthcare, finance, and manufacturing. Full article

This study proposes a physiologically interpretable framework for stress state classification using respiratory signals. The framework aims to assess whether integrating physiologically meaningful features with an interpretable model can enhance both the accuracy and interpretability of stress state classification. First, a 16-parameter feature set was constructed by extracting rhythm, depth, and nonlinear characteristics of respiratory signals. Subsequently, feature correlations and group differences across stress states were analyzed via heatmaps, multivariate analysis of variance (MANOVA), and box plots. A stacking ensemble model was then designed for three-state classification (normal/stress/meditation). Finally, Shapley additive explanations (SHAP) values were used to quantify feature contributions to classification outcomes. The leave-one-subject-out (LOSO) cross-validation results show that on the wearable stress and affect detection (WESAD) dataset, the model achieves an accuracy of 92.33% and a precision of 93.54%. Furthermore, initial validation shows key respiratory features like breath rate, inspiration time ratio, and expiratory variability coefficient align with autonomic regulation. Key respiratory metrics in other areas like rapid shallow breathing index also play an important role in the stress classification. Notably, increased respiratory depth under a stress state needs further study to clarify its physiological reasons. Overall, this framework enhances physiological interpretability while maintaining competitive performance, offering a promising approach for future applications in multimodal stress monitoring and clinical assessment. Full article

Background: Autonomic dysfunction is commonly encountered after acute ischemic stroke (AIS) and may influence both functional recovery and survival. Heart rate variability (HRV) provides a non-invasive measure of autonomic balance, but its temporal evolution and prognostic significance in AIS remain insufficiently evaluated. Methods: In this prospective observational study, 148 AIS patients (mean age of 65.93 ± 9.19 years) underwent HRV assessment at baseline, one month, and three months follow-up, between January 2022 and October 2024. Time and frequency domain parameters, including Standard Deviation of NN intervals (SDNN), Low-Frequency (LF) power, High-Frequency (HF) power, and LF/HF ratio, were analyzed. Functional outcome was assessed using the modified Rankin Scale (mRS), with a good outcome defined as mRS ≤ 2. Multivariable logistic regression identified independent predictors of poor outcome (mRS > 2) at each time point. Mortality was recorded at one and three months, and potential predictors were evaluated. Results: Over three months, SDNN increased by 34.84% (p < 0.001), HF power rose by 22.26% (p < 0.001), LF power decreased by 21.61% (p < 0.001), and LF/HF ratio declined by 35.41% (p < 0.001), indicating a shift toward parasympathetic predominance. Higher SDNN correlated strongly with better functional status and was an important predictor of favorable outcome at all time points (p < 0.001). Higher LF/HF ratio predicted poor outcome at baseline (p < 0.01) and three months (p < 0.001). At three months, mortality reached 12.2%, with significant predictors including coronary artery disease (CAD), heart failure (HF), chronic kidney disease (CKD), and altered HRV parameters. Conclusions: Post-stroke recovery is characterized by the progressive restoration of autonomic balance, with higher SDNN and lower LF/HF associated with improved functional recovery and survival. HRV analysis offers valuable prognostic insight and may aid in risk stratification after AIS. Full article

This work presents a proof of concept including simulation and experimental validations of acoustic gas sensor prototypes for trace CO₂ detection up to 1 ppm. For the detection of lower gas concentrations especially, the dependency of acoustic resonances on the molecular weights and, consequently, the speed of sound of the gas mixture, is exploited. We explored two resonator types: a cylindrical acoustic resonator and a Helmholtz resonator intrinsic to the MEMS microphone’s geometry. Both systems utilized mass flow controllers (MFCs) for precise gas mixing and were also modeled in COMSOL Multiphysics 6.2 to simulate resonance shifts based on thermodynamic properties of binary gas mixtures, in this case, N₂-CO₂. We performed experimental tracking using Zurich Instruments MFIA, with high-resolution frequency shifts observed in µHz and mHz ranges in both setups. A compact and geometry-independent nature of MEMS-based Helmholtz tracking showed clear potential for scalable sensor designs. Multiple experimental trials confirmed the reproducibility and stability of both configurations, thus providing a robust basis for statistical validation and system reliability assessment. The good simulation experiment agreement, especially in frequency shift trends and gas density, supports the method’s viability for scalable environmental and industrial gas sensing applications. This resonance tracking system offers high sensitivity and flexibility, allowing selective detection of low CO₂ concentrations down to 1 ppm. By further exploiting both external and intrinsic acoustic resonances, the system enables highly sensitive, multi-modal sensing with minimal hardware modifications. At microscopic scales, gas detection is influenced by ambient factors like temperature and humidity, which are monitored here in a laboratory setting via NDIR sensors. A key challenge is that different gas mixtures with similar sound speeds can cause indistinguishable frequency shifts. To address this, machine learning-based multivariate gas analysis can be employed. This would, in addition to the acoustic properties of the gases as one of the variables, also consider other gas-specific variables such as absorption, molecular properties, and spectroscopic signatures, reducing cross-sensitivity and improving selectivity. This multivariate sensing approach holds potential for future application and validation with more critical gas species. Full article