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BackgroundThe combined effect of depression and nutritional-inflammatory status on mortality in the chronic kidney disease (CKD) population is unclear.MethodsWe prospectively analyzed 3,934 (weighted population: 22,611,423) CKD participants from the National Health and Nutrition Examination Survey (2007–2018). Depression and nutritional-inflammatory status were assessed with Patient Health Questionnaire 9 (PHQ-9) and Advanced Lung Cancer Inflammation Index (ALI), respectively. Weighted multivariate COX regression models, restricted cubic splines (RCS) models, and stratified analyses were used to investigate the association of PHQ-9 scores and ALI with all-cause mortality.ResultsDuring a median follow-up of 5.8 years (interquartile range 3.4–8.6 years), a total of 985 patients died (25.0%). Each point increase in a patient’s PHQ-9 score increased the risk of all-cause mortality by 4% (HR, 1.04; 95% CI, 1.02–1.06; p 0.05 for interaction).ConclusionIn a nationally representative sample of US patients with CKD, coexisting depression and poorer nutrition-inflammation were associated with a significantly increased risk of all-cause mortality.

Background: Altered patterns of bile acids (BAs) are frequently present in liver fibrosis, and BAs function as signaling molecules to initiate inflammatory responses. Silybin meglumine (SLB-M) is widely used in treating various liver diseases including liver fibrosis. However, research on its effects on bile acid (BA) metabolism is limited. This study investigated the therapeutic effects of SLB-M on liver fibrosis and BA metabolism in a CCl4-induced murine model. Methods: A murine liver fibrosis model was induced by CCl4. Fibrosis was evaluated using HE, picrosirius red, and Masson’s trichrome staining. Liver function was assessed by serum and hepatic biochemical markers. Bile acid (BA) metabolism was analyzed using LC-MS/MS. Bioinformatics analyses, including PPI network, GO, and KEGG pathway analyses, were employed to explore molecular mechanisms. Gene expression alterations in liver tissue were examined via qRT-PCR. Results: SLB-M treatment resulted in significant histological improvements in liver tissue, reducing collagen deposition and restoring liver architecture. Biochemically, SLB-M not only normalized serum liver enzyme levels (ALT, AST, TBA, and GGT) but also mitigated disruptions in both systemic and hepatic BA metabolism by increased unconjugated BAs like cholic acid and chenodeoxycholic acid but decreased conjugated BAs including taurocholic acid and taurodeoxycholic acid, compared to that in CCl4-induced murine model. Notably, SLB-M efficiently improved the imbalance of BA homeostasis in liver caused by CCl4 via activating Farnesoid X receptor. Conclusions: These findings underscore SLB-M decreased inflammatory response, reconstructed BA homeostasis possibly by regulating key pathways, and gene expressions in BA metabolism.

This paper presents a novel two-stage optimization framework enhanced by deep learning-based robust optimization (GAN-RO) aimed at advancing the integration of photovoltaic (PV) systems into the power grid. Facing the challenge of inherent variability and unpredictability of renewable energy sources, such as solar and wind, traditional energy management systems often struggle with efficiency and grid stability. This research addresses these challenges by implementing a Generative Adversarial Network (GAN) to generate realistic and diverse scenarios of solar energy availability and demand patterns, which are integrated into a robust optimization model to dynamically adjust operational strategies. The proposed GAN-RO framework is demonstrated to significantly enhance grid management by improving several key performance metrics: reducing average energy costs by 20%, lowering carbon emissions by 30%, and increasing system efficiency by 8.5%. Additionally, it has effectively halved the operational downtime from 120 to 60 h annually. The scenario-based analysis further illustrates the framework’s capacity to adapt and optimize under varying conditions, achieving up to 96% system efficiency and demonstrating substantial reductions in energy costs across different scenarios. This study not only underscores the technical advancements in managing renewable energy integration, but also highlights the economic and environmental benefits of utilizing AI-driven optimization techniques. The integration of GAN-generated scenarios with robust optimization represents a significant stride towards developing resilient, efficient, and sustainable energy management systems for the future.

ObjectivesThe systemic immune-inflammation index (SII), a novel and systematic inflammatory biomarker that is associated with chronic kidney disease (CKD), has not received much attention. This study aimed to investigate the relationship between SII and CKD in the United States (U.S.) population.MethodsOur study ultimately included a nationally representative sample of 10,787 adults who participated in the 2007-2018 National Health and Nutrition Examination Survey. Weighted multivariate logistic regression was used to assess the correlation between SII and CKD, and a restricted cubic spline (RCS) model was subsequently used to explore the non-linear relationship between SII and CKD. Subgroup analyses were performed to further the effects of other covariates on the relationship between SII and CKD.ResultsFollowing confounder adjustment, a higher SII was related to the incidence of CKD (OR =1.36; 95% CI, 1.07–1.73; p =0.01), as validated by multivariable logistic regression. The RCS curve revealed a non-linear positive correlation between SII/1000 and CKD incidence (p for non-linear =0.0206). Additionally, subgroup analysis confirmed a stronger correlation for male participants (OR =2.628; 95% CI, 1.829-3.776) than for female participants (OR =1.733; 95% CI, 1.379-2.178) (p for interaction =0.046).ConclusionsSII is positively associated with the incidence of CKD among U.S. adults, especially in males. However, further studies are needed to confirm our findings and explore the causal factors that can contribute to the prevention and treatment of CKD.

Examining fuel droplet evaporation is crucial for enhancing fuel engine efficiency, conserving energy, and reducing emissions. This study utilizes experimental methods involving ultrasonic standing wave levitation and high-speed cameras to investigate the impact of temperatures and droplet properties, including initial diameter and composition, on the evaporation process. The evaporation behaviors of fuel droplets, like hexadecane and diesel, are documented across a temperature spectrum spanning 150 °C to 550 °C, with an initial droplet equivalent diameter ranging from 0.10 to 0.30 mm. The evaporation rate positively correlates with temperature and may vary by 15% to 71% between hexadecane and diesel droplets.