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Efficient decontamination of radioactive Ba2+ is of great significance to human health and environmental safety. Herein, an adsorbent of sulfonic acid functionalized Zr-MOF has been successfully developed, which could efficiently...

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This work has focused on the cluster state of B‐site ions in the rare‐earth perovskite REFepCr1−pO3, and GdFe0.5Cr0.5O3 is taken for instance. This analysis indicates a paramagnetic behavior of the system, which originates from the paramagnetic contribution of the Gd3+. An internal‐field model is used to simplify the superexchange interaction between the A/B‐sites. Furthermore, the cluster state of B‐site ions is computed using the average number of nearest‐neighborhoods. Using Marine Predator Algorithm to fit the experiment data, it is found that Fe–Cr interaction does not conform to the case of ferromagnetic superexchange; therefore, there is no spin glass behavior in this system. Via fitting the Mossbauer spectrum at low temperatures (120 and 12 K), the calculations described above are verified. The calculation gives the hyperfine‐field intervals of 2.47 and 0.92 T at 120 and 12 K. It is found that B‐site ions tend to form clusters. The arrangement of the ions is random and without the tendency of orderly interval occupation of ions (like double perovskite). The study sheds light on the mechanism of single‐ion clustering and introduces new methods for calculating single‐ion clustering states. It can also be applied to the case of other A‐site ions in perovskite system and is not just limited to p = 0.5 in REBpB’1−pO3.

Introduction: The glomerular filtration rate (GFR) is crucial for chronic kidney disease (CKD) diagnosis and therapy. Various studies have sought to recognize ideal endogenous markers to improve the estimated GFR (eGFR) for clinical practice. To screen out potential novel metabolites related to GFR (mGFR) measurement in CKD patients from the Chinese population, we identified more biomarkers for improving GFR estimation. Methods: Fifty-three CKD participants were recruited from the third affiliated hospital of Sun Yat-sen University in 2020. For each participant, mGFR was evaluated by utilizing the plasma clearance of iohexol and collecting serum samples for untargeted metabolomics analyses by Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC–MS/MS). All participants were divided into four groups according to mGFR. The metabolite peak area data were uploaded to MetaboAnalyst5.0 for one-way ANOVA, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) and confirmed the metabolites whose levels increased or decreased with mGFR and Variable Importance in Projection (VIP) values>1. Metabolites were ranked by correlation with the original values of mGFR, and metabolites with a correlation coefficient>0.8 and VIP >2 were identified. Results: We screened out 198 metabolites that increased or decreased with mGFR decline. After ranking by correlation with mGFR, the top 50 metabolites were confirmed. Further studies confirmed the 10 most highly correlated metabolites. Conclusion: We screened out the metabolites that increased or decreased with mGFR decline in CKD patients from the Chinese population, and 10 of them were highly correlated. They are potential novel metabolites to improve GFR estimation.

Background: With the development of chronic kidney disease (CKD), there are various changes in metabolites. However, the effect of these metabolites on the etiology, progression and prognosis of CKD remains unclear.Objective: We aimed to identify significant metabolic pathways in CKD progression by screening metabolites through metabolic profiling, thus identifying potential targets for CKD treatment.Methods: Clinical data were collected from 145 CKD participants. GFR (mGFR) was measured by the iohexol method and participants were divided into four groups according to their mGFR. Untargeted metabolomics analysis was performed via UPLC-MS/MSUPLC–MSMS/MS assays. Metabolomic data were analyzed by MetaboAnalyst 5.0, one-way ANOVA, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) to identify differential metabolites for further analysis. The open database sources of MBRole2.0, including KEGG and HMDB, were used to identify significant metabolic pathways in CKD progression.Results: Four metabolic pathways were classified as important in CKD progression, among which the most significant was caffeine metabolism. A total of 12 differential metabolites were enriched in caffeine metabolism, four of which decreased with the deterioration of the CKD stage, and two of which increased with the deterioration of the CKD stage. Of the four decreased metabolites, the most important was caffeine.Conclusion: Caffeine metabolism appears to be the most important pathway in the progression of CKD as identified by metabolic profiling. Caffeine is the most important metabolite that decreases with the deterioration of the CKD stage.