Source apportionment of soil PTE in a northern industrial county using PMF model: Partitioning strategies and uncertainty analysis.

Positive matrix factorization (PMF) has commonly been applied for source apportionment of potentially toxic elements (PTE) in agricultural soil, however, spatial heterogeneity of PTE significantly undermines the accuracy and reliability of PMF results. In this study, a representative industrial-agricultural hub in North China (Xuanhua district, Zhangjiakou City) was selected as the research subject, multiple partition processing (PP) strategies and uncertainty analyses were integrated to advance the PMF modeling and associated algorithm mechanisms were comparatively discussed. Specifically, we adopted three methods to split the research area into several subzones according to industrial density (PP-1), population density (PP-2), and the ecological risk index (PP-3) respectively, to rectify the spatial bias phenomenon of PTE concentrations and to achieve a more interpretable result. Our results indicated that the obvious enrichment of Cd, Pb, and Zn was found in the agricultural soil, with Hg and Cd accounted for 83.49% of the overall potential ecological risk. Combining proper PP with PMF can significantly improve the modelling accuracy. Uncertainty analysis showed that interval ratios of tracer species (Cd, Pb, Hg, and Zn) calculated by PP-3 were consistently lower than that of PP-1 and PP-2, indicating that PP-3 coupled PMF can afford the optimal modeling results. It suggested that natural sources, fertilizers and pesticides, atmosphere deposition, mining, and smelting were recognized as the major contributor for the soil PTE contamination. The contribution of anthropogenic activities, specifically fertilizers and pesticides, and atmosphere deposition, increased by 1.64% and 5.91% compared to PMF results. These findings demonstrate that integration of proper partitioning processing into PMF can effectively improve the accuracy of the model even at the case of soil PTE contamination with high heterogeneity, offering support to subsequently implement directional control strategies.

Toll-like receptor 4 promotes high glucose-induced catabolic and inflammatory responses in chondrocytes in an NF-κB-dependent manner.

Diabetes is an independent risk factor for knee osteoarthritis (OA), and hyperglycaemia-induced inflammation is considered to play an important role in their connection. The Toll-like receptor 4 (TLR4) regulates inflammatory responses in several pathological conditions including diabetes and OA. However, its role in diabetes-associated OA is poorly understood. In this study, we found that TLR4 expression was higher in OA cartilage from patients with type 2 diabetes mellitus (T2DM) than that from non-T2DM patients. Similarly, its expression was induced in primary mouse chondrocytes treated with high glucose, which suggests that TLR4 upregulation in T2DM-associated OA cartilage may originate from hyperglycaemia stimulation. We further discovered that TLR4 promoted high glucose-induced catabolic and inflammatory responses in chondrocytes, and mechanistically, these effects could be explained by the exacerbated activation of the transcription factor nuclear factor kappa B (NF-κB) pathway, since its inhibition by Bay 11-7082 abrogated TLR4 effects on high glucose-treated chondrocytes. Taken together, these findings may reveal a promotive role of TLR4 in regulating hyperglycaemia-induced catabolism and inflammation in T2DM-associated OA, and also implicate that TLR4 inhibition might be of therapeutic significance in treating T2DM-associated OA.