Search Results (3,839)

Diabetic nephropathy affects approximately 30–40% of individuals with diabetes mellitus (DM) and is a major contributor to end-stage renal disease (ESRD). While angiotensin II receptor blockers (ARBs) have long served as a standard treatment, sodium-glucose cotransporter-2 inhibitors (SGLT2i) have recently gained attention for their renal and cardiovascular benefits. However, comparative real-world data on their long-term renal effectiveness remain limited. We conducted a retrospective, longitudinal study over a 2-year period to compare the impact of ARB monotherapy versus SGLT2i and angiotensin-converting enzyme inhibitor (ACEi) combination therapy on the progression of chronic kidney disease (CKD) in patients with DM. A total of 126 patients were included and grouped based on treatment regimen. Renal biomarkers were analyzed using t-tests and ANOVA (p < 0.01). Albuminuria was qualitatively classified via urinalysis as negative, level 1 (+1), level 2 (+2), or level 3 (+3). The ARB group demonstrated higher estimated glomerular filtration rate (eGFR) and lower serum creatinine (sCr) levels than the combination therapy group, with glycated hemoglobin (HbA1c), potassium (K⁺), and blood pressure remaining within normal limits in both cohorts. Albuminuria remained stable over time, with 60.8% of ARB users and 73.1% of combination therapy users exhibiting persistently or on-average negative results. Despite the expected additive benefits of SGLT2i/ACEi therapy, ARB monotherapy was associated with slightly more favorable renal function markers and a lower incidence of severe albuminuria. These findings suggest a need for further controlled studies to clarify the comparative long-term renal effects of these treatment regimens. Full article

Metabolic dysfunction-associated fatty liver disease (MASLD) is a chronic, non-communicable spectrum of diseases characterized by lipid accumulation. It is often asymptomatic, and its prevalence varies by region, age, gender, and economic status. It is estimated that 25% of the world’s population currently suffer from MAFLD, and 20 million patients will die from MAFLD-related diseases. In the last 20 years, tea and anti-obesity research have indicated that regularly consuming tea decreases the risk of cardiovascular disease, stroke, obesity, diabetes, and metabolic syndrome (MeS). In this review, we aimed to present studies concerning the influence of matcha extracts and epigallocatechin-3 gallate (EGCG) supplements on metabolic functions in the context of MAFLD in human and animal studies. The published data show promise. In both human and animal studies, the beneficial effects on body weight, cholesterol levels, and liver metabolism and function were noted, even in short-period experiments. The safety levels for EGCG and green tea extract consumption are marked. More experiments are needed to confirm the results observed in animal studies and to show the mechanisms by which green tea exerts its effects. The preliminary data from research concerning microbiota or epigenetic changes observed after polyphenols and green tea consumption need to be expanded. To improve the efficiency and availability of green tea or supplement consumption as a treatment for MAFLD patients, more research with larger groups and longer study durations is needed. Full article

This study evaluates the socio-economic impact of rural electrification through photovoltaic (PV) systems in Morocco. As part of the country’s broader energy transition strategy, decentralized renewable energy solutions like PV kits have been deployed to improve energy access in isolated rural areas. Using quasi-experimental econometric techniques, specifically propensity score matching (PSM) and estimation of the Average Treatment Effect on the Treated (ATT), the study measures changes in household income, expenditures, and economic activities resulting from PV electrification. The results indicate significant positive effects on household income, electricity spending, and productivity in agriculture and livestock. These findings highlight the critical role of decentralized renewable energy in advancing rural development and poverty reduction. Policy recommendations include expanding PV access with complementary support measures such as microfinance and technical training. Full article

Anthropogenic noise can have diverse effects on natural ecosystems, but less is known about the degree to which noise can alter organisms in comparison to other disturbances. A variety of frequencies are produced by man-made objects, ranging from high to low frequencies, and we studied infrasound (<20 Hz) produced by wind turbines and trains. We estimated the number, mass and viability of seeds produced by flowers of Plains pricklypear (Opuntia polyacantha Haw.) that were left open to pollinators, hand-pollinated or bagged to exclude pollinators. Each pollination treatment was applied to plants at varying distances from wind turbines and railways (≤25 km). Self-pollinated Opuntia polyacantha and plants within the wind facility produced ≥1.6 times more seeds in the bagged treatments compared to more distant sites. Seed mass and the percent of viable seeds decreased with distance from infrasound. Viability of seeds was >70% for most treatments and sites. If wind facilities, railways and other man-made structures produce infrasound that increases self-pollination, crops and native plants near sources may produce heavier seeds with higher viability in the absence of pollinators, but genetic diversity of plants may decline due to decreased cross-pollination. Full article

Ozone is a powerful destructive agent in the oxidative process of polymer composites. The destructive ability of ozone depends primarily on its concentration, duration of exposure, the type of polymer, and its matrix structure. In this work, nonwoven PLA/NR fibers with natural rubber contents of 5, 10, and 15 wt.% were obtained, which were then subjected to ozone oxidation for 800 min. The effect of ozone treatment was estimated using various methods of physicochemical analysis. The visual effect was manifested in the form of a change in the color of PLA/NR fibers. The method of differential scanning calorimetry revealed a change in the thermophysical characteristics. The glass transition and cold crystallization temperatures of polylactide shifted toward lower temperatures, and the degree of crystallinity increased. It was found that in PLA/NR fiber samples, the degradation process predominates over the crosslinking process, as an increase in the melt flow rate by 1.5–1.6 times and a decrease in the correlation time determined by the electron paramagnetic resonance method were observed. The IR Fourier method recorded a change in the chemical structure during ozone oxidation. The intensity of the ether bond bands changed, and new bands appeared at 1640 and 1537 cm⁻¹, which corresponded to the formation of –C=C– bonds. Full article

This article presents a probabilistic methodology for assessing corrosion initiation in reinforced concrete structures exposed to chloride ingress. The approach addresses key limitations of conventional analytical models by accounting for non-diffusive barriers and incorporating a rigorous statistical treatment of censored data to mitigate biases introduced by limited simulation durations. A combination of analytical solutions for diffusion from opposite sides with time-dependent boundary conditions is also proposed and validated. The probabilistic study includes the depassivation assessment of a hollow pier section. The blocking effect caused by rebars is statistically characterised through correction factors derived from finite element simulations. These factors are used to adjust analytical solutions, which are computationally inexpensive. Results show that neglecting the rebar blocking effect can overestimate the mean corrosion initiation time by up to 42%, while the use of censored data reduces bias in lifetime estimates. The observed frequency of censored events reached up to 20% when simulations were truncated at 100 years. The corrected analytical models closely match the finite element results, statistically validating their application. The case study indicates premature corrosion initiation (less than 10 years to achieve target reliability), underscoring the need to better reconcile the desired levels of reliability with realistic input parameters for depassivation. Full article

Background: Despite significant advancements in diabetes care, many individuals with type 2 diabetes (T2D) do not receive optimal care and treatment. Digital interventions promoting behavioral changes have shown promising long-term results in supporting healthier lifestyles but are not implemented in most healthcare offerings, maybe due to lack of general practice support and collaboration. This study evaluates the efficacy of the Digital, Individualized, and Collaborative Treatment of T2D in General Practice Based on Decision Aid (DICTA), a randomized controlled trial integrating a patient-centered smartphone application for lifestyle support in conjunction with a clinical decision support (CDS) tool to assist general practitioners (GPs) in optimizing antidiabetic treatment. Methods: The present randomized controlled trial aims to recruit 400 individuals with T2D from approximately 70 GP clinics (GPCs) in Denmark. The GPCs will be cluster-randomized in a 2:3 ratio to intervention or control groups. The intervention group will receive one year of individualized eHealth lifestyle coaching via a smartphone application, guided by patient-reported outcomes (PROs). Alongside this, the GPCs will have access to the CDS tool to optimize pharmacological decision-making through electronic health records. The control group will receive usual care for one year, followed by the same intervention in the second year. Results: The primary outcome is the one-year change in estimated ten-year cardiovascular risk, assessed by SCORE2-Diabetes calculated from age, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, age at diabetes diagnosis, HbA1c, and eGFR. Conclusions: If effective, DICTA could offer a scalable, digital-first approach for improving T2D management in primary care by combining patient-centered lifestyle coaching with real-time pharmacological clinical decision support. Full article

Background/Objectives: Selective serotonin reuptake inhibitors (SSRIs), widely prescribed for anxiety disorders, may negatively impact the gut microbiota, contributing to dysbiosis. Considering the gut–brain axis’s importance in mental health, probiotics could represent an effective adjunctive strategy. This study evaluated the effects of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 on microbiota composition, metabolic activity, and immune markers in fecal samples from patients with anxiety on SSRIs, using the SHIME^® (Simulator of the Human Intestinal Microbial Ecosystem) model. Methods: The fecal microbiotas of four patients using sertraline or escitalopram were inoculated in SHIME^® reactors simulating the ascending colon. After stabilization, a 14-day probiotic intervention was performed. Microbial composition was assessed by 16S rRNA sequencing. Short-chain fatty acids (SCFAs), ammonia, and GABA were measured, along with the prebiotic index (PI). Intestinal barrier integrity was evaluated via transepithelial electrical resistance (TEER), and cytokine levels (IL-6, IL-8, IL-10, TNF-α) were analyzed using a Caco-2/THP-1 co-culture system. The statistical design employed in this study for the analysis of prebiotic index, metabolites, intestinal barrier integrity and cytokines levels was a repeated measures ANOVA, complemented by post hoc Tukey’s tests to assess differences across treatment groups. For the 16S rRNA sequencing data, alpha diversity was assessed using multiple metrics, including the Shannon, Simpson, and Fisher indices to evaluate species diversity, and the Chao1 and ACE indices to estimate species richness. Beta diversity, which measures microbiota similarity across groups, was analyzed using weighted and unweighted UniFrac distances. To assess significant differences in beta diversity between groups, a permutational multivariate analysis of variance (PERMANOVA) was performed using the Adonis test. Results: Probiotic supplementation increased Bifidobacterium and Lactobacillus, and decreased Klebsiella and Bacteroides. Beta diversity was significantly altered, while alpha diversity remained unchanged. SCFA levels increased after 7 days. Ammonia levels dropped, and PI values rose. TEER values indicated enhanced barrier integrity. IL-8 and TNF-α decreased, while IL-6 increased. GABA levels remained unchanged. Conclusions: The probiotic combination of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 modulated gut microbiota composition, metabolic activity, and inflammatory responses in samples from individuals with anxiety on SSRIs, supporting its potential as an adjunctive strategy to mitigate antidepressant-associated dysbiosis. However, limitations—including the small pooled-donor sample, the absence of a healthy control group, and a lack of significant GABA modulation—should be considered when interpreting the findings. Although the SHIME^® model is considered a gold standard for microbiota studies, further clinical trials are necessary to confirm these promising results. Full article

In this paper, preliminary gate reliability of p-GaN HEMTs under high positive gate bias is studied. Gate robustness is of great interest both from an academic and industrial point of view; in fact, different tests and models can be explored to estimate the device lifetime, which must meet some minimum product requirements, as specified by international standards (AEC Q101, JESD47, etc.). However, reliability characterizations are usually time-consuming and are performed in parallel on multiple packaged devices. Therefore, it would be useful to have a faster method to screen out weaker gate trials, already on-wafer, before reaching the packaging step. For this purpose, a room-temperature stress procedure is presented and described in detail. Then, this screening test is applied to devices with a reference gate process, and, as a result, high gate leakage degradation is observed. Afterwards, a different process implementing a dielectric layer between p-GaN and gate metal is evaluated, highlighting the improved behavior during the stress test. However, it is also observed that devices with this process suffer from very high drain leakage, and this effect is then studied and understood through TCAD (technology computer-aided design) simulations. Finally, the effect of a surface treatment performed on the p-GaN is analyzed, showing improved gate pre-reliability while maintaining low drain leakage. Full article

This study investigates the implications of sewage contamination in the coastal and riverine environments of Hout Bay, Cape Town, South Africa. Chemical analyses were applied to quantify the presence of pollutants such as pharmaceutical and personal care products (PPCPs) in sentinel marine organisms such as mussels, as well as microbial indicators of faecal contamination in river water and seawater, for estimating the extent of impact zones in the coastal environment of Hout Bay. This research investigated the persistent pharmaceuticals found in marine outfall wastewater effluent samples in Hout Bay, examining whether these substances were also detectable in marine biota, specifically focusing on Mytilus galloprovincialis mussels. The findings reveal significant levels of sewage-related pollutants in the sampled environments, with concentrations ranging from 32.74 to 43.02 ng/g dry weight (dw) for acetaminophen, up to 384.96 ng/g for bezafibrate, and as high as 338.56 ng/g for triclosan. These results highlight persistent PPCP contamination in marine organisms, with increasing concentrations observed over time, suggesting a rise in population and pharmaceutical use. Additionally, microbial analysis revealed high levels of E. coli in the Hout Bay River, particularly near stormwater from the Imizamo Yethu settlement, with counts exceeding 8.3 million cfu/100 mL. These findings underscore the significant impact of untreated sewage on the environment. This study concludes that current sewage treatment is insufficient to mitigate pollution, urging the implementation of more effective wastewater management practices and long-term monitoring of pharmaceutical levels in marine biota to protect both the environment and public health. Full article

Crossover trials are specifically designed to evaluate treatment effects within individual participants through within-subject comparisons. In a standard AB/BA crossover trial, participants are randomly allocated to one of two treatment sequences: either the AB sequence (where patients receive treatment A first and then cross over to treatment B after a washout period) or the BA sequence (where patients receive B first and then cross over to A after a washout period). Asymptotic and approximate unconditional test procedures, based on two Wald-type statistics, the likelihood ratio statistic, and the score test statistic for the odds ratio (OR), are developed to evaluate the equality of treatment effects in this trial design. Additionally, confidence intervals for OR are constructed, accompanied by an approximate sample size calculation methodology to control the interval width at a pre-specified precision. Empirical analyses demonstrate that asymptotic test procedures exhibit robust performance in moderate to large sample sizes, though they occasionally yield unsatisfactory type I error rates when the sample size is small. In such cases, approximate unconditional test procedures emerge as a rigorous alternative. All proposed confidence intervals achieve satisfactory coverage probabilities, and the approximate sample size estimation method demonstrates high accuracy, as evidenced by empirical coverage probabilities aligning closely with pre-specified confidence levels under estimated sample sizes. To validate practical utility, two real examples are used to illustrate the proposed methodologies. Full article

Social adversity is linked to poorer physical health in breast cancer survivors, highlighting the urgency of addressing health equity. Simultaneously, identifying individual-level factors that mitigate these effects may provide more immediate relief for survivors. This study examined whether four modifiable psychosocial factors—emotion dysregulation, physical activity, sleep disturbance, and social support—moderate the relationship between place-based social adversity and physical health in 255 breast cancer survivors (Mage = 56.03, 74.5% non-Hispanic White) within six months post-treatment. Linear regression analyses with 5000 bootstrapped estimates revealed that sleep disturbance significantly moderated the relationship between place-based social adversity and physical health (B = −0.014, SE = 0.001, bootstrapped 95% CI = −0.027, −0.001). Specifically, greater place-based social adversity was associated with poorer physical health at high levels of sleep disturbance (B = −0.22, p = 0.004), but not at low (B = 0.01, p = 0.94) or average (B = −0.10, p = 0.07) levels. Emotion dysregulation, physical activity, and social support did not moderate this relationship. Findings suggest that improving sleep quality may buffer the negative impact of social adversity on physical health, identifying sleep as a potential target for interventions aimed at reducing disparities among breast cancer survivors. Full article

Estimating heterogeneous treatment effects (HTEs) across multiple correlated outcomes poses significant challenges due to complex dependency structures and diverse data types. In this study, we propose a novel deep learning framework integrating empirical copula transformations with a CNN-LSTM (Convolutional Neural Networks and Long Short-Term Memory networks) architecture to capture nonlinear dependencies and temporal dynamics in multivariate treatment effect estimation. The empirical copula transformation, a rank-based nonparametric approach, preprocesses input covariates to better represent the underlying joint distributions before modeling. We compare this method with a baseline CNN-LSTM model lacking copula preprocessing and a nonparametric tree-based approach, the Causal Forest, grounded in generalized random forests for HTE estimation. Our framework accommodates continuous, count, and censored survival outcomes simultaneously through a multitask learning setup with customized loss functions, including Cox partial likelihood for survival data. We evaluate model performance under varying treatment perturbation rates via extensive simulation studies, demonstrating that the Empirical Copula CNN-LSTM achieves superior accuracy and robustness in average treatment effect (ATE) and conditional average treatment effect (CATE) estimation. These results highlight the potential of copula-based deep learning models for causal inference in complex multivariate settings, offering valuable insights for personalized treatment strategies. Full article

Background/Objectives: Patients with stage IV gastric cancer who develop chronic intestinal failure require home parenteral nutrition (HPN). This study aimed to evaluate the prognostic relevance of nutritional and immune–inflammatory biomarkers and to construct an individualised survival prediction model using machine learning techniques. Methods: A secondary analysis was performed on a cohort of 410 patients with TNM stage IV gastric adenocarcinoma who initiated HPN between 2015 and 2023. Nutritional and inflammatory indices, including the Controlling Nutritional Status (CONUT) score and lymphocyte-to-monocyte ratio (LMR), were assessed. Independent prognostic factors were identified using Cox proportional hazards models. A Random Survival Forest (RSF) model was constructed to estimate survival probabilities and quantify variable importance. Results: Both the CONUT score and LMR were independently associated with overall survival. In multivariate analysis, higher CONUT scores were linked to increased mortality risk (HR = 1.656, 95% CI: 1.306–2.101, p < 0.001), whereas higher LMR values were protective (HR = 0.632, 95% CI: 0.514–0.777, p < 0.001). The RSF model demonstrated strong predictive accuracy (C-index: 0.985–0.986) and effectively stratified patients by survival risk. The CONUT score exerted the greatest prognostic influence, with the LMR providing additional discriminatory value. A gradual decline in survival probability was observed with an increasing CONUT score and a decreasing LMR. Conclusions: The application of machine learning to immune–nutritional data offers a robust tool for predicting survival in patients with advanced gastric cancer requiring HPN. This approach may enhance risk stratification, support individualised clinical decision-making regarding nutritional interventions, and inform treatment intensity adjustment. Full article

Accurate estimation aboveground biomass (AGB) in winter wheat is crucial for yield assessment but remains challenging to achieve non-destructively. Unmanned aerial vehicle (UAV)-based remote sensing offers a promising solution at the plot level. Traditional field sampling methods, such as random plant selection or full-quadrat harvesting, are labor intensive and may introduce substantial errors compared to the canopy-level estimates obtained from UAV imagery. This study proposes a novel method using Fractional Vegetation Coverage (FVC) to adjust field-sampled AGB to per-plant biomass, enhancing the accuracy of AGB estimation using UAV imagery. Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. The aim was to evaluate the performance of multimodal data in estimating winter wheat leaves, spikes, stems, and total AGB. Results demonstrated that (1) FVC-adjusted per-plant biomass significantly improved correlations with most indicators, particularly during the filling stage, when the correlation between leaf biomass and NDVI increased by 56.1%; (2) RF and NN models outperformed SVM, with the optimal accuracies being R² = 0.709, RMSE = 0.114 g for RF, R² = 0.66, RMSE = 0.08 g for NN, and R² = 0.557, RMSE = 0.117 g for SVM. Notably, the RF model achieved the highest prediction accuracy for leaf biomass during the flowering stage (R² = 0.709, RMSE = 0.114); (3) among different water treatments, the R² values of water and drought treatments were higher 0.723 and 0.742, respectively, indicating strong adaptability. This study provides an economically effective method for monitoring winter wheat growth in the field, contributing to improved agricultural productivity and fertilization management. Full article