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Construction, validation, and visualization tool development of a risk prediction model for prostate cancer biopsy decision-making
Longitudinal TyG index trajectories and renal function decline in a high cardiovascular-risk Chinese cohort: a sex-stratified analysis
Application research of DeepSeek in physical education teaching in colleges and universities: a case study of sports dance teaching
Graph-based community detection of molecular oil populations from cretaceous reservoirs of the Abadan Plain, SW Iran
Survival and associated factors among adult HIV/AIDS patients in a pastoralist area of Borena zone, Ethiopia
Prevalence of poor sleep quality among Palestinian breast cancer patients and the associated factors: a cross-sectional study
Explainable ensemble machine learning for dissolved oxygen prediction in a reservoir using SHAP and chord diagram analysis
Cardiorenal and survival outcomes of GLP-1 receptor agonist and SGLT-2 inhibitor therapy in diabetic kidney disease: two stage-stratified real-world comparisons
Abstract The comparative effectiveness of GLP-1 receptor agonists (GLP-1 RA) and SGLT-2 inhibitors (SGLT-2i), alone or in combination, for kidney protection in chronic kidney disease (CKD) remains unclear in real-world practice. We conducted a retrospective cohort study using the TriNetX global network from inception to up 11 th July 2025 including 736,212 adults with diabetes and CKD. We performed two separate, pre-specified comparisons stratified by baseline kidney function, each using 1:1 propensity score matching and each with its own comparator: in the preserved eGFR cohort, GLP-1 RA + SGLT-2i combination therapy versus SGLT-2i monotherapy (n = 142,372); and in the advanced CKD cohort, GLP-1 RA monotherapy versus standard care without either agent (n = 103,820). The two cohorts were analyzed independently and were not compared with each other; combination therapy was not evaluated in the advanced CKD cohort. Primary outcomes were major adverse kidney events (MAKE) and all-cause mortality. In the preserved eGFR cohort, combination therapy reduced MAKE by 27% (HR 0.73, 95% CI 0.69–0.77) and all-cause mortality by 46% (HR 0.54, 95% CI 0.50–0.58) compared to SGLT-2i monotherapy. The number needed to treat was 94 for MAKE prevention and 90 for mortality reduction. Combination therapy preserved 2.6 mL/min/1.73m 2 more eGFR at five years. In the advanced CKD cohort, GLP-1 RA monotherapy reduced all-cause mortality by 19% (HR 0.81, 95% CI 0.78–0.84) and major adverse cardiovascular events by 8% (HR 0.92, 95% CI 0.88–0.95) compared to standard care. Benefits were consistent across all subgroups (p-interaction = 0.18). Safety profiles favored GLP-1 RA-containing regimens with reduced amputation and pancreatitis risks. In two separate stage-stratified comparisons, GLP-1 RA plus SGLT-2i combination therapy was associated with greater kidney protection and lower mortality than SGLT-2i monotherapy in patients with preserved eGFR, and GLP-1 RA monotherapy was associated with lower mortality and fewer cardiovascular events than standard care in patients with advanced CKD. Because the two cohorts used different comparators and were not compared directly, these findings should not be interpreted as a single comparison of combination versus monotherapy across all stages of CKD.
Deep learning driven early detection of lung cancer from CT images using transfer learning approaches
Nanobionic enhancement of plant growth via copper nanoclusters in Raphanus sativus
Abstract Copper-based nanobionics offer a promising route to enhance photosynthetic efficiency and crop productivity. In this study the application of two photoluminescent copper nanomaterials, cysteine-stabilized copper nanoclusters (Cu-Cys) and copper-doped carbon nanoassemblies (Cu-CNAs) were used in studies of Raphanus sativus (radish), evaluating their uptake, physiological impact, and metabolomic response. Direct application of the optimal Cu-CNA concentration (250 mg·L⁻ 1 ) with seed priming resulted in a 63% increase in radish dry mass and a 31% increase in foliar dry mass, accompanied by a statistically significant 23% rise in chlorophyll absorbance (p* < 0.005) and a 67% increase in vitamin C concentration. ICP-MS confirmed up to 225% copper enrichment in foliage compared to the control, while CT imaging revealed a 49 Hounsfield Unit reduction in tissue density, indicative of accelerated cell expansion and increased porosity. MRI T₂ relaxometry showed stable hydration profiles, suggesting no adverse impact on water distribution. Untargeted metabolomics revealed upregulation of nicotinic acid, glycerophosphocholine, and stress-related amino acids such as alanine and pyroglutamyl-isoleucine. These metabolic shifts indicate a mild stress-induced reprogramming that coincides with the enhanced growth and structural improvements observed during both greenhouse trials. These findings demonstrate that Cu-CNAs can synergistically improve nutrient delivery and the crop growth rate, offering a sustainable and scalable strategy for photosynthetic enhancement.
Neandertal pelvis reveals specialized walking apparatus in human males
Machine learning and deep learning for predicting photocatalytic degradation efficiency of metronidazole via TiO2/ZnO nanocomposites: a response surface methodology approach
Abstract Inappropriate disposal of antibiotics in aquatic and soil environments makes bacteria resistant, which is a potential threat to humans and other organisms. Photocatalysis is a simple, inexpensive, and eco-friendly process that is considered an attractive option for degrading antibiotics. Here, we synthesized the TiO 2 /ZnO nanocomposite as a photocatalyst by the sol-gel method and characterized by EDX, TEM, FTIR, SEM, and XRD. Using the response surface methodology based on central composite design, the effect of different variables: pH, irradiation time, metronidazole (MNZ) concentration, and catalyst dose on the photodegradation of MNZ was investigated and optimized. Under most advantageous conditions, the synthesized nanocomposite is capable of degrading MNZ by a significant 94.92%. In this study, the recyclability, mechanism, and effect of light source intensity were also investigated. Machine learning and deep learning models were employed to predict the photocatalytic degradation efficiency of MNZ, guided by a response surface methodology (RSM). Among the evaluated models-support vector regression (SVR), artificial neural network (ANN), fully connected neural network (FCNN), and random forest (RF)- SVR demonstrated the highest predictive accuracy, SVR demonstrated the highest predictive accuracy, achieving R = 0.9495, R 2 = 0.8502, and the lowest MSE (7.2560) and RMSE (2.6937) among the evaluated models. Feature importance analysis revealed that reaction time and pH were the most influential parameters, followed by MNZ concentration, while catalyst dose had minimal impact. These findings underscore the effectiveness of kernel-based SVR in modeling complex photocatalytic systems with limited data and highlight the critical role of reaction conditions in optimizing degradation performance.