Search Results (4,217)

Background/Objectives: Cytochrome P450 3A4 (CYP3A4) metabolizes approximately 30–50% of clinically used drugs; thus, accurate prediction of CYP3A4 inhibition is essential for early assessment of drug–drug interaction (DDI) risk and toxicity. This study evaluated an integrated artificial intelligence framework for predicting CYP3A4 inhibition (%) using a large, curated chemical dataset. Methods: A dataset of 23,713 compounds was compiled from the Korea Chemical Bank and multiple commercial and public databases. Vector-based machine learning (ML) models (LightGBM, XGBoost, CatBoost, and a weighted ML ensemble) and graph neural network (GNN) models (O-GNN with contrastive learning and manifold mixup (O-GNN + CL + Mixup), D-MPNN, GINE, and GATv2) were evaluated. Manifold mixup was applied during GNN training, and SMILES enumeration-based test-time augmentation was used at inference. The best-performing ML and GNN models were integrated using a weighted ensemble strategy. Model interpretability was examined using SHAP analysis for ML models and occlusion sensitivity analysis for O-GNN + CL + Mixup. Results: The weighted ML ensemble achieved the best performance among ML models (RMSE = 19.1031, Pearson correlation coefficient (PCC) = 0.7566); the O-GNN + CL + Mixup model performed the best among GNN models (RMSE = 20.1002, PCC = 0.7265). The hybrid model achieved improved predictive accuracy (RMSE = 19.0784, PCC = 0.7570). External validation on 100 newly generated experimental data points confirmed generalizability (Custom Metric = 0.8035). Conclusions: This study demonstrated that integrating ML and GNN models with data augmentation strategies improves the robustness and interpretability of CYP3A4 inhibition prediction and established a practical framework for metabolic screening and DDI risk assessment. Full article

Background/Objectives: Diabetes is major metabolic disorder and rapidly increasing public health problem globally. The greatest way to reduce diabetic complications is adequate knowledge about the condition. Hence, the primary objectives of this study were to evaluate the psychometric properties of the Simplified Diabetes Knowledge Test—Arabic version (SDKT-A) among Iraqi insulin-dependent diabetic patients. Additionally, the secondary objectives were to assess the associated independent variables and the risk of atherosclerosis and cardiovascular risk event by using atherogenic indices and lipid ratios with the SDKT-A. Methods: A cross-sectional, descriptive study was conducted in primary healthcare clinics. The SDKT was translated into Arabic using forward–backward translation, reconciliation, and pilot testing. Thereafter, psychometric properties of the SDKT-A were evaluated depending on different criteria. Atherogenic indices of Castelli risk indices I and II (CRI-I and II), triglyceride/HDL ratio, non-HDL-C ratio, atherogenic coefficient (AC), and triglyceride–total cholesterol–body weight index (TCBI) were calculated using specific formulas. Results: The SDKT-A questionnaire showed acceptable readability and validity. Cronbach’s alpha test (95% confidence interval) was 0.662 (0.59–0.73). The Pearson correlation coefficient of reliability for test–retest was found to be 0.659. The item difficulty index for most items was between 0.237 and 0.877. The point biserial correlation values ranged from 0.028 to 0.535 with Ferguson’s sigma value equal to 0.962. The content validation results showed a significant content validity ratio (CVR) value for most of the questions, ranging from 0.8 to 1. The content validity index (CVI) value for SDKT-A was found to be 0.98, which showed good agreement between experts. In addition, the exploratory factor analysis with promax rotation identified four domains for the final 20 items of the SDKT-A that explained 41.83% of the scale total variance. The mean score of the SDKT-A was 11.09 ± 3.40. The total score of the SDKT-A was positively and significantly correlated with education level (r = 0.322, p < 0.01). In addition, the total scores of the SDKT-A were negatively and significantly correlated with glycemic control, age, CRI-I, CRI-II, triglyceride/HDL ratio, AC, non-HDL-C ratio, and TCBI. Furthermore, the glycemic control (HbA1c) was positively and significantly correlated with the preventive measures factor (r = 0.175, p < 0.05), and were negatively and significantly correlated with the lifestyle and modification factor (r = −0.169, p < 0.05), diet and monitoring factor (r = −0.158, p < 0.05), and awareness factor (r = −0.149, p < 0.05). Conclusions: This study showed acceptable psychometric properties for the SDKT-A, with low levels of knowledge of diabetic disease in the sample population. Finally, comprehensive and interactive educational programs regarding lifestyle and modification, diet, and monitoring and awareness in primary healthcare centers in Iraq are warranted. Full article

Background/Objectives: Patients admitted to intensive care units (ICUs) are confronted with complex clinical situations that impact their physical condition and psychological well-being. Psycho-emotional disorders such as pain, anxiety and post-traumatic stress are highly prevalent in this context, significantly affecting both the patient’s experience and the quality of care provided. Effective communication can help manage patients’ psycho-emotional states and prevent post-ICU disorders. To evaluate the effectiveness of the CONECTEM communicative intervention in improving the psycho-emotional well-being of critically ill patients admitted to the intensive care unit, regarding pain, anxiety, and post-traumatic stress symptoms. Methods: A quasi-experimental study employed a pre–post-test design with both a control group and an intervention group. The study was conducted in two ICUs in a tertiary Hospital in Spain. A total of 111 critically ill patients and 180 nurse–patient interactions were included according to the inclusion/exclusion criteria. Interactions were classified according to the level of the patient’s consciousness into three groups: G1 (Glasgow 15), G2 (Glasgow 14–9), and G3 (Glasgow < 9). Depending on the patient’s communication difficulties, nurses selected one of three communication strategies of the CONECTEM intervention (AAC low teach, pictograms, magnetic board, and musicotherapy). Pain was assessed using the VAS or BPS scale, anxiety using the STAI, and symptoms of PTSD using the IES-R. The RASS scale was utilized to evaluate the degree of sedation and agitation in critically ill patients receiving mechanical ventilation. Data analysis was performed using repeated ANOVA measures for the pre–post-test, as well as Pearson’s correlation test and Mann–Whitney U or Kruskal–Wallis statistical tests. Results: The results showed pre–post differences consistent with pain after the intervention in patients with Glasgow scores of 15 (p < 0.001) and 14–9 (p < 0.001) and in anxiety (p = 0.010), reducing this symptom by 50% pre-test vs. 26.7% post-test. Patients in the intervention group with levels of consciousness (Glasgow 15–9) tended to decrease their post-traumatic stress symptoms, with reductions in the mean IES scale patients with a Glasgow score of 15 [24.7 (±15.20) vs. 22.5 (±14.11)] and for patients with a Glasgow score of 14–9 [(Glasgow 14–9) [30.2 (±13.56) 27.9 (±11.14)], though this was not significant. Given that patients with a Glasgow score below 9 were deeply sedated (RASS-4), no pre–post-test differences were observed in relation to agitation levels. Conclusions: The CONECTEM communication intervention outcomes differed between pre- and post-intervention assessments in patients with a Glasgow Coma Scale score of 15–9 regarding pain. These findings are consistent with a potential benefit of the CONECTEM communication intervention, although further studies using designs that allow for stronger causal inference are needed to assess its impact on the psycho-emotional well-being of critically ill patients. Full article

The contribution of non-exhaust emissions (NEEs) to particle number concentration (PNC) remains insufficiently quantified, particularly across different urban environments. In this study, we address this gap by quantifying the contribution of NEEs to airborne nanoparticles in urban areas. Using positive matrix factorisation (PMF), conditional probability function analysis, Pearson correlation, and source identification, we identified five source factors contributing to PNC at two sites in London: a traffic site and a background site. Five source factors were resolved at both sites: Aitken-mode traffic exhaust particles, nucleation-mode exhaust emission, secondary aerosol, non-exhaust emission, and regional background accumulation. Interestingly, the contribution of NEEs differed between the two sites. At the traffic site, NEEs contributed 14.9%, while at the background site, their contribution was higher at 28.5%, likely due to the favourable summer dispersion conditions. However, the contribution of nucleation-mode exhaust emission also showed significant differences: 26.6% at the traffic site and only 9.9% at the background site. Based on these findings, we propose that air quality policies should integrate NEEs into regulations, improve road maintenance, and use PNC-based along with metal tracers to identify and control PNC. This study offers valuable insights for developing strategies to manage urban nanoparticle pollution. Full article

Free-space optical (FSO) communication enables high-bandwidth license-free data transmission and is particularly attractive for maritime point-to-point links. However, FSO performance is strongly affected by atmospheric conditions. This work presents a semi-empirical model quantifying the impact of fine particulate matter (PM2.5) on received optical power in a maritime FSO link. The model is derived from long-term experimental measurements collected over a 2.96 km horizontal optical path above the sea surface, combining received signal strength indicator (RSSI) data with co-located PM2.5 observations. Statistical analysis reveals a strong negative correlation between PM2.5 concentration and received optical power (Pearson coefficient −0.748). Using a logarithmic attenuation formulation, the PM2.5-induced attenuation is estimated to increase by approximately 0.0026 dB/km per µg/m³ of PM2.5 concentration. A second-order semi-empirical model captures the observed nonlinear attenuation behavior with a coefficient of determination of R² = 0.57. The proposed model provides a practical tool for link budgeting, performance forecasting, and adaptive design of maritime FSO systems operating in aerosol-rich environments. Full article

Introduction: Diabetes mellitus (DM) is a prevalent metabolic disorder frequently leading to serious renal complications, particularly diabetic nephropathy. This retrospective case–control study investigated the levels and associations of commonly used enzymatic (serum creatinine and urea) and physiological (glomerular filtration rate [GFR]) markers of kidney function in diabetic patients compared to non-diabetic controls. Methodology: A total of 237 participants were enrolled, comprising 81 diabetic cases and 156 non-diabetic controls. Creatinine and urea levels were determined using enzymatic methods, measuring optical density, whereas GFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, based on creatinine, age, and sex. Statistical comparisons include p-value, Pearson correlation, etc. Results: The diabetic group exhibited significantly higher mean levels of serum creatinine (2.08 ± 2.26 mg/dL) and urea (57.71 ± 38.75 mg/dL) and a significantly lower mean GFR (59.59 ± 34.16 mL/min/1.73 m²) compared to the non-diabetic control group (0.95 ± 0.69 mg/dL, 31.79 ± 20.49 mg/dL, and 96.72 ± 23.77 mL/min/1.73 m², respectively; all comparisons with p < 0.005). Correlation analysis revealed a more scattered positive association between creatinine and urea, and a pronounced inverse correlation between GFR and both creatinine and urea in the diabetic cases, suggesting a compromised renal function profile. Conclusions: Our findings demonstrate a significant association between diabetes and impaired renal function, as evidenced by elevated creatinine and urea levels and reduced GFR. These readily available biomarkers are crucial prognostic indicators for the early detection and effective management of diabetic nephropathy, emphasizing the importance of rigorous metabolic and blood pressure control to mitigate disease progression. Full article

Accurate prediction, forecasting and interpretability of air pollutant concentrations are important for sustainable environmental management and protecting public health. An integrated artificial intelligence (AI) framework is proposed to predict, forecast and analyse six major air pollutants, such as particulate matter concentrations (PM_2.5 and PM₁₀), ground-level ozone (O₃), carbon monoxide (CO), nitrogen dioxide (NO₂), and sulphur dioxide (SO₂), using a combination of ensemble and deep learning models. Five years of hourly air quality and meteorological data are analysed through correlation and Granger causality tests to uncover pollutant interdependencies and driving factors. The results of the Pearson correlation analysis reveal strong positive associations among primary pollutants (PM_2.5–PM₁₀, CO–nitrogen oxides NO_x and VOCs) and inverse correlations between O₃ and NO_x (NO and NO₂), confirming typical photochemical behaviour. Granger causality analysis further identified NO₂ and NO as key causal drivers influencing other pollutants, particularly O₃ formation. Among the 23 tested AI models for prediction, XGBoost, Random Forest, and Convolutional Neural Networks (CNNs) achieve the best performance for different pollutants. NO₂ prediction using CNNs displays the highest accuracy in testing (R² = 0.999, RMSE = 0.66 µg/m³), followed by PM_2.5 and PM₁₀ with XGBoost (R² = 0.90 and 0.79 during testing, respectively). The Air Quality Index (AQI) analysis shows that SO₂ and PM₁₀ are the dominant contributors to poor air quality episodes, while ozone peaks occur during warm, high-radiation periods. The interpretability analysis based on Shapley Additive exPlanations (SHAP) highlights the key influence of relative humidity, temperature, solar brightness, and NO_x species on pollutant concentrations, confirming their meteorological and chemical relevance. Finally, a deep-NARMAX model was applied to forecast the next horizons for the six air pollutants studied. Six formulas were elaborated using input data at times (t, t − 1, t − 2, …, t − n) to forecast a horizon of (t + 1) hours for single-step forecasting. For multi-step forecasting, the forecast is extended iteratively to (t + 2) hours and beyond. A recursive strategy is adopted for this purpose, whereby the forecast at (t + 1) is fed back as an input to generate the forecasts at (t + 2), and so forth. Overall, this integrated framework combines predictive accuracy with physical interpretability, offering a powerful data-driven tool for air quality assessment and policy support. This approach can be extended to real-time applications for sustainable environmental monitoring and decision-making systems. Full article

This study evaluates temporal variability and algorithm differences in soil moisture estimates over Europe using the European Center for Medium-range Weather Forecasts (ECMWF) operational analysis and the passive Soil Moisture Active Passive (SMAP) soil moisture product. While models and satellite retrievals have improved in capturing the timing of soil moisture dynamics, absolute accuracy and temporal variability magnitudes still diverge. This study compares the representation of short-term and seasonal variability of soil moisture in absolute and normalized terms over two different hydrometeorological growing periods (2021 and 2022). Both datasets exhibit intermediate to high temporal correlations with in situ measurements at selected stations (median Pearson correlation coefficients of all stations range between 0.65 and 0.79), confirming previous studies. However, they overestimate the magnitude of absolute soil moisture variability at most stations (median interquartile range of all stations at 0.085 (0.10) m³m⁻³ for ECMWF and 0.072 (0.079) m³m⁻³ for SMAP opposed to 0.063 (0.072) m³m⁻³ for in situ in 2021 (2022)) due to an overestimation of short-term fluctuations, especially at dry stations in southern France and Eastern Europe. The soil wetness index is underestimated, particularly within SMAP estimates. The performance of both is sensitive to hydrometeorological conditions, with the 2022 European drought causing strong seasonal and weak short-term fluctuations. This is easier to capture than conditions with pronounced short-term and weaker seasonal fluctuations, as in 2021. Overall, SMAP and ECMWF time series show considerable coincident timing, whereas the magnitude of temporal variability and accuracy depend on site-specific characteristics and the pre-processing of the data. Full article

Small ruminants, such as newborn lambs, rely on timely colostrum intake to acquire passive immunity through the absorption of immunoglobulin (Ig). Evaluating Ig transfer is important for ensuring lamb health and survival. However, current methods such as enzyme-linked immunosorbent assay (ELISA) and radial immunodiffusion (RID) are widely used but remain costly and require specialized facilities. The immunocrit assay has been proposed as a lower-cost alternative for evaluating serum Ig concentrations. This study aimed to evaluate the immunocrit method in lambs by comparing it with ELISA, RID, and total serum protein. Serum was collected from 135 Katahdin-Easycare lambs 24–36 h after birth. Samples were analyzed using sheep immunoglobulin G ELISA, Sheep immunoglobulin G RID, serum protein, and the immunocrit method. Pearson’s correlation was used to assess linear relationships between the methods, and Receiver Operating Characteristics (ROC) analysis was used to evaluate test accuracy, with RID as the gold standard (15 mg/mL cutoff). The immunocrit showed a high correlation with RID (r = 0.870), moderate correlation with serum protein (r = 0.725), and good correlation with ELISA (r = 0.607). The ROC analysis showed that the immunocrit had a sensitivity of 100% at a cutoff of 4.34%. These results indicate that the immunocrit method provides comparable accuracy to RID and serum protein, and could serve as a reliable, practical, and inexpensive tool for on-farm evaluation of passive transfer in Katahdin-Easycare crossbred lambs between 24 and 36 h after birth. Full article

Background/Objective: Hand hygiene is a cornerstone of infection prevention; however, compliance is inconsistent in intensive care units (ICUs), particularly in resource-constrained settings. This study evaluated whether a low-cost, multimodal quality improvement intervention could improve process-level hand hygiene compliance using routine, episode-based audits embedded in the ICU practice. Methods: We conducted a single-cycle Plan-Do-Study-Act quality improvement project in a 12-bed mixed medical–surgical ICU in Pakistan (December 2023–January 2024). Hand hygiene performance was assessed using the unit’s routine weekly episode-based audit protocol, aligned with the WHO Five Moments framework. A targeted multimodal intervention comprising education, point-of-care visual reminders, audit feedback, and leadership engagement was implemented between the pre- and post-intervention phases (four weeks each). Non-applicable moments were scored as “compliant by default” according to the institutional protocol. A sensitivity analysis was performed excluding these moments to calculate pure adherence. Compliance proportions were summarized using exact 95% Clopper–Pearson confidence intervals without inferential testing. Results: A total of 942 audit episodes (471 per phase) generated 4710 moment-level assessments were generated. Composite hand hygiene compliance increased from 63.1% pre-intervention to 82.0% post-intervention [absolute increase: 18.9 percentage points (pp)]. Sensitivity analysis excluding non-applicable moments demonstrated pure adherence improvement from 54.2% to 82.5% (+28.3 pp), confirming a genuine behavioral change rather than a measurement artifact. Compliance improved across all five WHO moments, with the largest gains in awareness-dependent moments targeted by the intervention: before touching the patient (+27.0 pp) and after touching patient surroundings (+40.0 pp). Week-by-week compliance remained stable within both phases, without immediate post-intervention decay. Conclusions: A pragmatic, low-cost multimodal intervention embedded in routine ICU workflows was associated with substantial short-term improvements in hand hygiene compliance over a four-week observation period, particularly for awareness-dependent behaviors. Episode-based audit systems can support directional process monitoring in resource-limited critical care settings without the need for electronic surveillance. However, its long-term sustainability beyond one month and generalizability to other settings remain unknown. Sensitivity analyses are essential when using “compliant by default” scoring to distinguish adherence patterns from measurement artifacts. Full article

In regions lacking sufficient data, remote sensing (RS) offers a reliable alternative for precipitation estimation, enabling more effective drought management. This study comprehensively evaluates four commonly used RS datasets—Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS), Tropical Applications of Meteorology using Satellite data (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) against ground-based data—with respect to their performance in detecting precipitation and drought patterns in the Great Ruaha River Basin (GRRB), Tanzania (1983–2020). Statistical metrics including the Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and bias were employed to assess the performance at daily, monthly, seasonal (wet/dry), and annual timescales. Most of the RS products exhibited lower correlations (r < 0.5) at daily timestep and low RMSE, bias, and ME. Monthly performance improved substantially (r > 0.8 at most stations) particularly during the wet season (r = 0.52–0.82) while annual and dry-season performance declined (r < 0.5 and r < 0.3, respectively). Performance under RMSE, bias, and ME declined at higher timescales, particularly during the wet season and annually. CHIRPS, MSWEP, and PERSIANN generally overestimated precipitation while TAMSAT consistently underestimated it. Spatially, CHIRPS and MSWEP reproduced coherent basin-scale patterns of drought persistence, with longer dry-spells concentrated in the northern, central, and western parts of the basin and shorter dry-spells in the eastern and southern regions. Trend analysis further revealed that most products captured consistent large-scale changes in dry-spell characteristics, although localized drought events were more variably detected. CHIRPS and MSWEP showed superior performance especially in capturing monthly precipitation patterns and major drought events in the basin. Most products struggled to detect extreme dry conditions with the exception of CHIRPS and MSWEP at certain stations and periods. Based on these findings, CHIRPS and MSWEP are recommended for drought monitoring and water resource planning in the GRRB. Their appropriate use can help water managers make informed decisions, promote sustainable resource use, and strengthen resilience to extreme weather events. Full article

Sustainable management of indoor and outdoor air quality is essential for protecting public health, enhancing well-being, and supporting resilient urban environments. Low-cost air quality sensors enable continuous, real-time monitoring of key pollutants and, when combined with data analytics, provide scalable and cost-effective insights for smart building operation and environmental decision-making. This pilot study evaluates an indoor–outdoor air quality monitoring system deployed at the Faculty of Electrical Engineering and Information Technologies in Skopje, with a focus on: (i) PM2.5 and PM10 concentrations and their relationship with meteorological conditions and human occupancy; (ii) sensor responsiveness and reliability in an educational setting; and (iii) implications for sustainable building operation. From January to March 2025, two indoor sensors (a classroom and a faculty hall) and two outdoor rooftop sensors continuously measured PM2.5 and PM10 at one-minute intervals. All sensors were calibrated against a reference instrument prior to deployment, while meteorological data were obtained from a nearby station. Time-series analysis, Pearson correlation, and multiple regression were applied. Indoor particulate levels varied strongly with occupancy and ventilation status, whereas outdoor concentrations showed weak to moderate correlations with meteorological variables, particularly atmospheric pressure. Moderate correlations between indoor and outdoor PM suggest partial pollutant infiltration. Overall, this pilot study demonstrates the feasibility of low-cost sensors for long-term monitoring in educational buildings and highlights the need for adaptive, context-aware ventilation strategies to reduce indoor exposure. Full article

Background/Objectives: This pilot study employed a quasi-experimental, single-group, pre-post design to examine the acute effects of single music lessons on executive function and to explore whether affective changes are associated with cognitive improvement in trained musicians. Drawing on Fredrickson’s broaden-and-build theory and Eysenck’s processing efficiency theory, we hypothesized that changes in positive affect and state anxiety would be statistically associated with cognitive outcomes. Methods: Using purposive sampling, 60 musicians (34 female, 26 male; Mage = 26.0, SD = 9.8; range: 16–58 years) completed assessments before and after a 45–60 min instrumental lesson (guitar, n = 20; violin, n = 20; piano, n = 20). Executive function was measured using the Stroop Color-Word Test (Golden version, Greek-validated). Affective states were assessed using the Positive and Negative Affect Schedule (PANAS; 20 items) and State-Trait Anxiety Inventory-State (STAI-S; 20 items). Data were analyzed using paired t-tests, Pearson correlations, path analysis, and bootstrap mediation analysis (5000 resamples). Results: Music lessons were associated with improved executive function (Stroop interference: d = 0.59, p < 0.001), increased positive affect (d = 1.87, p < 0.001), and reduced negative affect (d = −2.34, p < 0.001) and state anxiety (d = −2.64, p < 0.001). Path analysis demonstrated excellent model fit (CFI = 1.00; RMSEA = 0.00), with affective changes associated with 61.3% of the total effect on cognitive improvement. Conclusions: Single music lessons were associated with both cognitive and affective benefits, with affective changes statistically linked to cognitive outcomes. As a pilot study, these exploratory findings require replication using controlled designs before generalization. Future research should incorporate neuroimaging methods and cross-cultural validation. Full article

Background: Vascular disease is a known risk factor for the development of leukoaraiosis. Assessment of cerebral blood flow (CBF) was performed at baseline and after acetazolamide (AZM) challenge to evaluate for vascular reserve and steal within the brain. Little has been reported on the physiological reserve in the non-flow-limited hemispheres. This study attempts to evaluate for vascular steal in areas commonly involved in leukoaraiosis, in the setting of pharmaceutically induced states of increased CBF. Methods: Patients who underwent AZM challenge MRI from 2014 to 2021 and a cerebral angiogram within one year were included. Patients with bilateral disease or non-diagnostic imaging artifacts were excluded. MRIs were obtained after 1 g of AZM was administered 5 and 10 min prior to acquisition. Augmentation and steal maps were generated. Regression analysis, Pearson correlation coefficient, two-sample t-test, Spearman and Mann–Whitney U analyses were utilized for statistical evaluation. Results: A total of 38 patients with unilateral cerebral vaso-occlusive disease underwent the AZM challenge. Vascular steal and T2 hyperintensities were assessed in non-flow-limited hemispheres (NFLH) and flow-limited hemispheres (FLH). A moderate correlation was demonstrated between NFLH steal and NFLH T2 hyperintensities (rs = 0.48, p = 0.0020). A weak correlation without statistical significance was demonstrated between ipsilateral T2 and contralateral T2 hyperintensities (rs = 0.27, p = 0.10). Conclusions: The vascular steal phenomenon was demonstrated in the distal cerebral vasculature of cerebral white matter even in the absence of upstream flow-limiting stenosis, suggesting an inherent vulnerability of these structures to hemodynamic fluctuations and possiblly contributing etiology to leukoaraiosis. Full article

Background: This study aimed to examine correlation between nurses’ assessments of health literacy in older adults, communication skills, and ageism, as well as whether these factors could be key predictors of nurse burnout. Methods: To determine which factors predict burnout among clinical nurses, a structured questionnaire was distributed to 269 clinical nurses. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson’s correlation coefficients, and multiple regression analysis. Results: Elderly patients’ health literacy assessed by nurses showed significant correlations with communication skills, ageism, and burnout. Communication skills were negatively correlated with ageism and burnout, whereas ageism showed a strong positive correlation with burnout. Multiple regression analysis revealed that ageism (β = 0.287), communication skills (β = −0.251), female gender (β = 0.139), and aging anxiety (β = −0.181)were significant predictors of burnout, collectively explaining 29.3% of the variance in burnout. Conclusions: Ageism was the strongest predictor of burnout among clinical nurses, followed by communication skills. Strategies reducing ageism and enhancing communication competencies are essential for mitigating burnout in geriatric nursing practice. These findings highlight the need for systematic educational interventions related to the elderly tailored for both nursing students and clinical nurses. Full article