Background Albuminuria is a hallmark of diabetic kidney disease (DKD) that promotes its progression, leading to renal fibrosis. Renal macrophage function is complex and influenced by macrophage metabolic status. However, the metabolic state of diabetic renal macrophages and the impact of albuminuria on the macrophage metabolic state are poorly understood. Methods Extracellular vesicles (EVs) from tubular epithelial cells (HK-2) were evaluated using transmission electron microscopy, nanoparticle tracking analysis and western blotting. Glycolytic enzyme expression in macrophages co-cultured with HSA-treated HK-2 cell-derived EVs was detected using RT-qPCR and western blotting. The potential role of EV-associated HIF-1α in the mediation of glycolysis was explored in HIF-1α siRNA pre-transfected macrophages co-cultured with HSA-treated HK-2 cell-derived EVs, and the extent of HIF-1α hydroxylation was measured using western blotting. Additionally, we injected db/db mice with EVs via the caudal vein twice a week for 4 weeks. Renal macrophages were isolated using CD11b microbeads, and immunohistofluorescence was applied to confirm the levels of glycolytic enzymes and HIF-1α in these macrophages. Results Glycolysis was activated in diabetic renal macrophages after co-culture with HSA-treated HK-2 cells. Moreover, HSA-treated HK-2 cell-derived EVs promoted macrophage glycolysis both in vivo and in vitro. Inhibition of glycolysis activation in macrophages using the glycolysis inhibitor 2-DG decreased the expression of both inflammatory and fibrotic genes. Mechanistically, EVs from HSA-stimulated HK-2 cells were found to accelerate macrophage glycolysis by stabilizing HIF-1α. We also found that several miRNAs and lncRNAs, which have been reported to stabilize HIF-1α expression, were increased in HSA-treated HK-2 cell-derived EVs. Conclusion Our study suggested that albuminuria induced renal macrophage glycolysis through tubular epithelial cell-derived EVs by stabilizing HIF-1α, indicating that regulation of macrophage glycolysis may offer a new treatment strategy for DKD patients, especially those with macroalbuminuria.

Objective Recent studies have found that polycyclic aromatic hydrocarbons (PAHs) exposure may increase the risk of cardiovascular disease. The present study aimed to explore the association between PAHs exposure and severe abdominal aortic calcification (AAC) in adults. Methods Data were collected from the 2013–2014 National Health and Nutrition Examination Survey. PAHs exposure was analyzed from urinary mono hydroxylated metabolites of PAHs. Logistic regression models and subgroup analysis were performed to explore the association of PAHs exposure with severe AAC prevalence. Results A total of 1,005 eligible individuals were recruited into the study. After adjusting for confounding factors, those with the highest quartiles of 1-hydroxynaphthalene (1-NAP: OR 2.19, 95% CI 1.03–4.68, Pfor trend < 0.001), 2-hydroxynaphthalene (2-NAP: OR 2.22, 95% CI 1.04–4.64, Pfor trend < 0.001) and 1-hydroxypyrene (1-PYR: OR 2.15, 95% CI 1.06–4.33, Pfor trend < 0.001) were associated with an increased prevalence of severe AAC in the adults compared to those who in the lowest quartile. Conclusion This study found that urinary 1-NAP, 2-NAP and 1-PYR were positively associated with severe AAC prevalence in adults.

No abstract

Anthocyanins are severity indicators for apple mosaic disease and can be used to monitor tree health. However, most of the current studies have focused on healthy leaves, and few studies have estimated the anthocyanin content in diseased leaves. In this study, we obtained the hyperspectral data of apple leaves with mosaic disease, analyzed the spectral characteristics of leaves with different degrees of Mosaic disease, constructed and screened the spectral index sensitive to anthocyanin content, and improved the estimation model. To improve the conciseness of the model, we integrated Variable Importance in Projection (VIP), Partial Least Squares Regression (PLSR), and Akaike Information Criterion (AIC) to select the optimal PLSR model and its independent variables. Sparrow Search Algorithm-Random Forest (SSA-RF) was used to improve accuracy. Results showed the following: (1) anthocyanin content increased gradually with the aggravation of disease. The reflectance of the blade spectrum in the visible band increased, the red edge moved to short wave, and the phenomenon of “blue shift of spectrum” occurred. (2) The VIP-PLSR-AIC selected 17 independent variables from 21 spectral indices. (3) Variables were used to construct PLSR, Back Propagation (BP), Support Vector Machine (SVM), Random Forest (RF), and SSA-RF to estimate anthocyanin content. Results showed the estimation accuracy and stability of the SSA-RF model were better than other models. The model set determination coefficient (R2) was up to 0.955, which is 0.047 higher than that of the RF model and 0.138 higher than that of the SVM model with the lowest accuracy. The model was constructed at the leaf scale and can provide a reference for other scale studies, including a theoretical basis for large-area, high-efficiency, high-precision anthocyanin estimation and monitoring of apple mosaics using remote sensing technology.