Search Results (3,120)

Background: In patients with diabetes or hypertension, if appropriate intervention is not initiated early in the course of kidney disease, not only does the risk of progressing to end-stage renal failure increase, but mortality associated with vascular complications also rises as the disease progresses; therefore, there is an urgent need to develop urinary biomarkers that enable early diagnosis and prediction of disease progression. Methods: This two-year prospective observational study involved 185 outpatients. Patients were classified into two groups based on their baseline urinary L-FABP levels relative to the reference value of 8.4 μg/g·Cr at the start of the study. The rate of eGFR decline during the observation period was evaluated. Results: The results showed an interaction (synergistic effect) between urinary L-FABP and time in patients with diabetes or hypertension who had an eGFR of at least 60 mL/min/1.732 m²/kg/1.732 m². Patients with high urinary L-FABP levels (>8.4 μg/g·Cr) exhibited a notably faster eGFR decline compared with those with low levels (≤8.4 μg/g·Cr). This finding suggests the potential of urinary L-FABP as a predictor of renal function decline; we evaluated this utility using the area under the ROC curve (AUC) and logistic regression analysis. The results indicate that urinary L-FABP holds potential as a predictor of renal function decline in diabetic or hypertensive patients with preserved eGFR. Conclusions: Among the analysis groups in which the validation was conducted, it was demonstrated that urinary L-FABP holds potential as a predictor of renal function decline in patients with diabetes or hypertension who have a maintained eGFR. Given that urinary L-FABP is thought to reflect tubulointerstitial damage associated with renal microcirculatory impairment, its future utility as a urinary biomarker for the early diagnosis and prognosis of chronic kidney disease (CKD) is anticipated. Full article

The primal-dual hybrid gradient (PDHG) method is widely used for convex–concave saddle-point problems, yet its extrapolated variants are typically asymmetric because only one side is extrapolated. We propose a symmetry-preserving refinement, E-PDHG, which performs dual-side extrapolation followed by an explicit correction step. Under standard step-size conditions, we establish global convergence for all $\eta \in (-1,1)$ and derive a pointwise (non-ergodic) $O(1/\sqrt{t})$ rate for the last iterate. The method does not improve the asymptotic complexity order of PDHG; instead, it enlarges the practically stable parameter region while retaining the same per-iteration cost. Numerical experiments on image deblurring/inpainting and additional machine learning benchmarks (logistic regression and LASSO) demonstrate improved finite-iteration stability and efficiency. Full article

Background: Diabetic kidney disease is a major complication of type 2 diabetes mellitus (T2DM) and a leading cause of chronic kidney disease (CKD) worldwide. While diabetes duration is often considered a marker of cumulative metabolic exposure, its independent contribution to renal decline beyond aging and hypertension remains incompletely defined. Methods: We conducted a cross-sectional study including 250 adults with T2DM. Diabetes duration was analyzed both as a continuous variable and across four predefined strata (0–4, 5–9, 10–14, and ≥15 years). The primary endpoint was estimated glomerular filtration rate (eGFR), analyzed as a continuous outcome. Functional CKD was defined as eGFR < 60 mL/min/1.73 m². Linear and logistic regression models were constructed in unadjusted and adjusted forms (age, sex, BMI, hypertension, HbA1c). A sensitivity analysis modeling duration per 5-year increase was performed. Results: Mean eGFR declined significantly across duration strata (82.45, 84.27, 78.72, and 61.57 mL/min/1.73 m², respectively; p < 0.001). The prevalence of functional CKD increased markedly in patients with ≥15 years of diabetes (54.2%) compared with shorter-duration groups (~15–18%; p < 0.001). In linear regression, each additional year of diabetes was associated with a 1.32 mL/min/1.73 m² decline in eGFR (p < 0.001), remaining significant after adjustment (β = −0.85; p < 0.001). In logistic regression, each additional year was associated with a 10.7% increase in adjusted odds of CKD (OR = 1.11; 95% CI 1.04–1.17; p < 0.001). Each 5-year increment conferred a 66% increase in adjusted CKD risk (OR = 1.66; 95% CI 1.25–2.21; p < 0.001). Patients with ≥15 years of diabetes had nearly fourfold higher adjusted odds of CKD compared with those with 0–4 years (OR = 3.90; 95% CI 1.42–10.75; p = 0.008). Conclusions: Diabetes duration is strongly and independently associated with declining kidney function. Prolonged disease exposure confers a substantial increase in CKD risk, even after adjustment for age, hypertension, and metabolic factors. These findings highlight the progressive nephrotoxic impact of cumulative hyperglycemic exposure and underscore the need for early and sustained nephroprotective strategies in T2DM. Full article

Swim bladders in some teleost fish can act as gas-filled cavities that oscillate under acoustic pressure and transfer the sound energy to the inner ears. Quantifying the resonance frequency and damping of these oscillations is useful for linking swim bladder mechanics to hearing-related and behavioural questions, but many established direct-measure approaches have relied on open-water deployments and careful avoidance of boundary reflections, making experiments logistically demanding and difficult to reproduce (e.g., requiring deep-water sites, careful control of surface/boundary reflections, and complex deployment geometries). This study presents a compact laboratory methodology for estimating swim bladder resonance properties using a closed, fully water-filled, stainless-steel impedance tube. Broadband pseudorandom excitation is applied via an end-plate shaker, and the acoustic response of the system is recorded using wall-mounted hydrophones. Resonance peaks are identified using power spectral estimates of recorded signals, allowing resonance frequency and quality factor to be extracted from the peak location and −3 dB bandwidth. The approach is first established using inflated latex balloons as surrogate encapsulated gas cavities, providing a controlled benchmark for repeatability and interpretation. It is then applied to recently euthanised brown trout (Salmo trutta), where clear resonance features attributable to the swim bladder are observed and show systematic variation with body size. A coupled finite element model reproduces the principal resonance behaviour under the experimental loading and supports interpretation of the measured peaks as swim bladder resonance. The results provide a validated foundation for subsequent non-invasive measurements on live, free-swimming fish, as well as for future applications where swim bladder condition may be relevant to management or conservation. Full article

This study examines information recovery under structured partial observation in multi-informant questionnaire systems. Rather than predicting an external ground truth, we evaluate the recoverability of an operational full-information decision rulewhen only partial informant views are available. In the empirical SNAP-IV calibration study, this reference is intentionally defined as a deterministic function of the combined informant views, so the combined-view result is treated only as an oracle-style ceiling and the substantive analysis concerns how single-view recovery degrades when one informant is withheld. To examine whether a similar qualitative pattern extends beyond this calibration setting, we additionally evaluate a latent-state simulation in which the reference decision is generated from an unobserved latent state and informant views are noisy observations. Across both settings, single-view recoverability declines as inter-rater disagreement increases, whereas combined-view representations remain more stable. In the empirical study, combined-view models achieved near-ceiling recovery performance (e.g., 90.9% for Logistic Regression and 91.3% for MLP), while Teacher-only recovery dropped from approximately 78% to 63% under higher disagreement ($p=0.0005$, Cohen’s $d=1.9$). Additional non-learned single-rater score-threshold baselines exhibited the same qualitative degradation pattern, indicating that the effect is not specific to fitted machine learning models. Importantly, this work is methodological: it does not propose new learning algorithms or clinical prediction models, but instead presents a conceptual–methodological framework, together with model-agnostic recoverability quantities, for quantifying missing-view information loss under incomplete, heterogeneous observations. Full article

Background and Objectives: Chronic adenoiditis is a major contributor to persistent middle ear dysfunction (PMED) in children; however, clinical evolution varies considerably despite similar anatomical obstruction. This study aimed to identify inflammatory endotypes of chronic adenoiditis using unsupervised clustering and to evaluate their association with PMED through mechanistic and predictive modeling. Materials and Methods: A retrospective cohort of 236 children (3–12 years) with chronic adenoiditis and otitis media with effusion was analyzed. Clinical, endoscopic, audiological, and hematologic inflammatory parameters (eosinophils, NLR, ELR, CRP, IgE) were included. K-means clustering identified inflammatory endotypes. Associations with PMED at six months were evaluated using multivariate logistic regression and mediation analysis. Predictive performance was compared using logistic regression, random forest, and gradient boosting models, with SHAP-based interpretability and decision curve analysis. Results: Three distinct endotypes were identified: eosinophilic (28%), neutrophilic (41%), and fibrotic–obstructive (31%). PMED occurred in 44% of the fibrotic endotype compared with 22% in the eosinophilic group (p < 0.001). In multivariate analysis, the fibrotic endotype independently predicted PMED (OR = 3.48, 95% CI 1.92–6.31), alongside PTA > 30 dB (OR = 2.91) and NLR > 3.5 (OR = 2.36). Mediation analysis showed that hearing impairment accounted for 34% of the effect of anatomical obstruction on persistence. Gradient boosting achieved superior discrimination (AUC = 0.90) and demonstrated the highest net clinical benefit. Conclusions: Chronic adenoiditis comprises biologically distinct inflammatory endotypes with differential risk of persistent middle ear dysfunction. Integrating inflammatory profiling with machine learning enhances mechanistic understanding and risk stratification, supporting precision-based management in pediatric otorhinolaryngology. Full article

Background: Renal dysfunction remains a frequent complication after transcatheter aortic valve implantation (TAVI). Although contrast exposure and baseline renal impairment are established risk factors, the influence of structural valve characteristics, including valve diameter and prosthesis platform, on early renal outcomes is not well defined. This study evaluated whether valve size and valve platform independently affect early post-procedural renal function. Methods: This retrospective cohort study included 410 consecutive patients undergoing TAVI between 2018 and 2021 with complete pre- and post-procedural renal biomarker data. Of these, 371 patients with complete covariate data were analyzed in multivariable models. Serum creatinine and estimated glomerular filtration rate (eGFR) were assessed within 72 h before and after TAVI. Renal function change was calculated as absolute differences. Acute kidney injury (AKI) was defined according to KDIGO criteria. Correlation analyses and multivariable linear and logistic regression models were performed. Results: Median valve diameter was 26 mm (IQR 26–29). Renal function remained largely stable, with a median creatinine change of −0.06 mg/dL and median eGFR change of +4.0 mL/min/1.73 m². Valve diameter was not associated with creatinine change (ρ = −0.047, p = 0.330) or eGFR change (ρ = 0.053, p = 0.278). KDIGO-defined AKI occurred in 56 patients (13.7%) and did not differ by valve platform (p = 0.719) or diameter tertiles (p = 0.204). Conclusions: Valve diameter and platform were not independently associated with early renal outcomes after TAVI. Baseline renal function and contrast exposure were the principal determinants of post-procedural renal trajectory. Full article

Background/Objectives: Acute kidney injury (AKI) frequently occurs in the outpatient setting and is associated with adverse renal and survival outcomes. However, there is no established definition of outpatient AKI, and the risk factors, especially seasonal variation, remain limited. This study aimed to investigate seasonal variation in the risk of outpatient AKI. Methods: This retrospective observational study used routinely collected clinical laboratory data from a single hospital in Japan between 2007 and 2022. Outpatient AKI was defined as ≥35% relative decline in estimated glomerular filtration rate (eGFR) compared with a preceding outpatient measurement obtained within 14–90 days. Monthly and seasonal variations in outpatient AKI risk in patients with chronic kidney disease (CKD) were evaluated using logistic regression models. Subgroup analyses were performed according to AKI stage, age group, and CKD stage. Results: A total of 203,853 outpatient records were analyzed. The incidence of outpatient AKI was highest in August and lowest in November. Analyses demonstrated significantly increased odds ratios of outpatient AKI in January, February, July, and August. Seasonally, the risk was significantly higher during the summer. Stage-specific analyses showed that AKI stage 1 was more frequent in the summer, whereas AKI stage 2 tended to increase during the winter. Conclusions: Outpatient AKI exhibits distinct seasonal patterns, with increased risk during both summer and winter and differential associations according to AKI severity and baseline kidney function. Recognition of these patterns may help identify vulnerable populations and inform targeted preventive strategies for outpatient AKI. Full article

In this article, we report a comprehensive analysis of the chemical composition and biological activity of Dysphania ambrosioides essential oil (DA-EO) originating from Bulgaria. Gas chromatography–mass spectrometry (GC–MS) analysis led to the identification of 53 constituents, revealing a complex phytochemical profile. The results classify the investigated oil as a thymol–carvacrol chemotype, dominated by oxygenated monoterpenes (56.79%), with thymol (19.45%) and carvacrol (14.30%) as the major components. This compositional profile differs markedly from the ascaridole-rich chemotypes commonly reported in the literature. The biological activity of DA-EO was evaluated through its antimicrobial, antioxidant, and anti-inflammatory properties. The oil exhibited broad-spectrum antimicrobial activity against pathogenic microorganisms such as S. aureus, E. coli, and L. monocytogenes. Antioxidant assays (HPSA, HRSA) indicated moderate activity, closely associated with the terpenoid composition of the oil. The anti-inflammatory potential, assessed via inhibition of albumin denaturation (IAD), was analyzed using nonlinear four-parameter (4PL) and five-parameter (5PL) logistic models. The obtained IC₅₀ values (67.0–77.0 µg/mL) were comparable to those of the reference drug ibuprofen, highlighting the significant potential of DA-EO as a natural therapeutic agent. Full article

Fear of falling (FoF) is common in older adults and can reduce physical activity, mobility, and independence. As assistive technologies become more common, understanding how attitudes towards technology influence FoF is important. This study examined indoor FoF and its association with technology acceptance among 500 community-dwelling Austrian adults aged 65–85 via a cross-sectional web survey. Indoor FoF was assessed using the Falls Efficacy Scale–International (FES-I) indoor items. Technology acceptance was measured using the TechPH questionnaire, which captured TechEnthusiasm and TechAnxiety. Logistic regression models were used to analyse associations with FoF, dichotomised at the median. The mean age was 74 years, and 55% of participants were female. Overall, indoor FoF was low. Adjusted models indicated that older age (OR = 1.08; 95% CI: 1.04–1.12) and female sex (OR = 1.55; 95% CI: 1.01–2.38) were linked to higher FoF. Greater TechEnthusiasm was associated with lower FoF (OR = 0.65; 95% CI: 0.50–0.85), while higher TechAnxiety (i.e., less confidence with technology) was linked to higher FoF (OR = 1.79; 95% CI: 1.40–2.27). The TechEnthusiasm-FoF association was stronger among women. Promoting enthusiasm for technology may reduce FoF, but potential acceptance barriers must be addressed, especially among higher-risk individuals. Full article

Bio-CO₂ is part of the natural carbon cycle and represents a sustainable carbon source for the production of Renewable Fuels of Non-Biological Origin (RFNBOs), such as synthetic methanol. This study addresses the critical knowledge gap in aligning diverse biogenic CO₂ sources with e-methanol requirements in the EU by providing harmonized mapping, based on datasets, literature sources, and reported industrial statistics at the sectoral and country level. Bio-CO₂ streams from biogas and biogas upgrading, biomass combustion, pulp and paper, bioethanol production, and the food and beverage sector are evaluated for total emissions, CO₂ concentrations and purity, the geographical distribution, seasonality, and impurity profiles. Results show that approximately 350 Mtpa of bio-CO₂ are emitted across the EU, with highly heterogeneous characteristics. Biogas upgrading and fermentation-based processes generate highly pure CO₂ streams (>98–99%), yet their small and dispersed nature complicates logistics. In contrast, biomass-combustion and pulp and paper sectors provide large volumes (around 214.6–298.2 Mtpa and 73.9 Mtpa CO₂, respectively), but in diluted streams (typically 3–15% and 10–20%). Replacing just 10% of the EU maritime fuel demand with e-methanol would require 53.6 Mtpa of bio-CO₂ and 58 GW of electrolyzer capacity, a stark contrast to the current operational 385 MW. The findings highlight the need for infrastructure planning and aggregation hubs to enable the large-scale deployment of RFNBO methanol in the maritime sector. Full article

Sentiment classification plays a crucial role in analyzing customer feedback to identify market trends, enhance product recommendations, and improve customer satisfaction. This study focuses on sentiment analysis of Amazon reviews using two major datasets—Fine Food Reviews and Unlocked Mobile Reviews—which exhibit label imbalance. To address this challenge, both oversampling and undersampling techniques were applied to balance the datasets. Various machine learning (ML) algorithms, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and Gradient Boosting Machine (GBM), as well as deep learning (DL) models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer-based models like RoBERTa, were implemented. After data cleaning and preprocessing, models were trained, and performance was evaluated. The results indicate that oversampling significantly enhances classification accuracy, particularly for the Fine Food dataset. Among ML models, Random Forest achieved the highest accuracy due to its ensemble approach and robustness in handling high-dimensional data. DL models, particularly RoBERTa, also demonstrated superior performance owing to their capacity to capture contextual dependencies. The findings emphasize the importance of data balancing for optimal sentiment analysis and contribute valuable insights toward advancing automated opinion classification in e-commerce applications. Full article

Background: Evidence linking adenoid hypertrophy (AH) and atopy is conflicting. We examined whether Cassano-graded AH severity is more closely associated with inflammatory markers than with IgE-mediated sensitization. Methods: We retrospectively included children aged 3–12 years diagnosed with AH between December 2022 and December 2025. AH was graded according to the Cassano classification and dichotomized as advanced AH (Stage III–IV). Atopic features were evaluated separately as clinical atopy, IgE-mediated sensitization, elevated total IgE, and eosinophilia. Multivariable logistic regression analyses were performed to assess factors associated with clinical atopy, sensitization, and advanced AH. Results: Among 426 children, clinical atopy was present in 28.2%, sensitization in 23.0%, elevated total IgE in 16.4%, and eosinophilia in 27.7%; 39.2% had advanced AH. In multivariable analysis, clinical atopy was independently associated with family history of atopy (aOR 13.9; 95% CI 7.9–24.4), elevated total IgE (aOR 3.86; 95% CI 2.10–7.08), and passive smoking exposure (aOR 1.73; 95% CI 1.07–2.79). Sensitization was independently associated only with family history of atopy (aOR 4.99; 95% CI 1.99–12.53). Advanced AH was independently associated only with eosinophilia (aOR 2.07; 95% CI 1.30–3.29). Conclusions: AH severity was associated with eosinophilia rather than classical IgE-mediated sensitization. Assessment of eosinophilia may aid routine severity evaluation in children with AH. Full article

E-commerce has been a rapidly growing sales channel in recent years, with a strong trend toward further expansion. However, logistics companies face significant challenges in the preparation and sorting of orders when delivering shipments purchased through e-commerce platforms. In this process, order pickers play a pivotal role, as their efficiency directly impacts both the operational performance of logistics companies and the quality of service provided to customers. During peak periods of high order volumes, it is common for order pickers to exceed the prescribed work norm, making them eligible for performance-based bonuses. This study aims to develop a model for evaluating order picker efficiency, ranking them, and determining the optimal allocation of bonuses. It addresses a critical gap in the existing literature, as only a handful of studies have explored this issue in depth. To assess the efficiency of 56 order pickers, the DEA method was applied, incorporating three input and five output variables. The analysis identified 18 order pickers as fully efficient. These individuals were then ranked using the IMF SWARA and COPRAS methods, where IMF SWARA was employed to determine the weights of nine evaluation criteria, while COPRAS was used for the final ranking process. Based on the ranking results, a structured bonus allocation model was developed, encompassing four distinct scenarios. Furthermore, a sensitivity analysis and model validation were conducted to ensure the robustness and reliability of the proposed approach. Full article

Background: The COVID-19 pandemic revealed persistent gaps in local health department (LHD) health informatics capacity. This study examines organizational characteristics of LHDs associated with the adoption of six health information systems: electronic case reporting (eCR), electronic disease reporting systems (EDRS), electronic health records (EHR), electronic lab reporting (ELR), health information exchange (HIE), and immunization registries (IR). Methods: We used a mixed-methods design, including multinomial or binary logistic regression analyses of quantitative data from the 2022 NACCHO National Profile of Local Health Departments (n = 441) and thematic analysis of semi-structured interviews with five LHD staff members. Results: About half (49.9%) of LHDs had implemented eCR, while higher proportions had implemented EDRS (78.0%), EHR (62.4%), ELR (57.2%), HIE (92.6%), and IR (92.6%). Workforce size was associated with the implementation of eCR, EHR, and IR. The number of vacant staff positions was associated with a lower odds of IR implementation; compared with medium-sized LHDs, both small and large LHDs had higher odds of IR implementation. Shared-governance LHDs had higher odds of adopting ELR and HIE than state-governed LHDs. Qualitative themes highlighted challenges, including staff burnout, high turnover, pay inequities, role ambiguity, political pressures, rapid changes in informatics, and interoperability problems. Conclusions: Findings underscore the need to improve LHD workforce capacity and governance structures to support a resilient public health informatics infrastructure. Full article