Rimexolone

Glucocorticoids (GCs) in the environment are of increasing concern due to the potent endocrine disrupting effects. However, most studies only explored their occurrences and behaviors in the aqueous phase, and few studies have investigated their concentrations and risks in soil. In this study, 61 natural and synthetic GCs were simultaneous analyzed, and their occurrences, spatial distribution and ecological risks in soil from 13 cities in Shandong Province, China were further discussed. Forty-three compounds were detected in the soil, with concentrations ranging from not detected (ND) to 40 ng/g dry weight. Natural metabolites, which are of less concern than synthetic compounds, were the dominant compounds and had higher concentrations (1.4-152 ng/g) and detection frequencies (>80 % for most compounds). Seventeen of the natural metabolites were found in soil for the first time. The concentrations and distribution of GCs exhibited significant correlations with the number of slaughtered sheep and goats, human population, oxidation reduction potential (ORP) and moisture content (MC) of the soil. Fluticasone propionate (FP), flumethasone (FM), triamcinolone acetonide (TLA), and budesonide (BD) were found to contribute most (57.4 %-99.9 %) to the total glucocorticoid receptor (GR) activity. Risk evaluation results suggested that terrestrial organisms were exposed to low risks posed by the detected GCs in soil in Shandong Province.

Based on high-resolution mass spectrometry (HRMS), nontarget analysis (NTA) can rapidly identify and characterize numerous hazardous substances in complex environmental samples. However, the intricate identification process often results in the underutilization of many mass spectrometry features. Even when chemical structures are identified, their toxicological effects and health outcomes may remain unknown. To address these challenges, this study introduces MSFragTox, a novel approach that leverages the rich fragmentation spectra inherent in high resolution tandem mass spectrometry (MS/MS) to directly predict toxicity. This method integrates MS/MS data with high-throughput screening (HTS) assays, focusing on seven endocrine disruption-related endpoints from Tox21, and uses MS-derived fingerprints: substructure fragmentation probability vectors to construct toxicity predictions using machine learning algorithms. The best model demonstrated robust performance with an average area under the receiver operating characteristic curve (AUROC) of 0.845 on the test set, outperforming models based on traditional molecular fingerprints and descriptors. Additionally, a web client (http://ms.envwind.site:8500) is provided for users to screen toxicity based on chemical MS/MS data. Furthermore, in-depth analyses of commonalities and differences in substructures reveal the mechanisms underlying across toxicity endpoints. Using MSFragTox, we validated the potential endocrine-disrupting effects of substances corresponding to MS/MS from real samples, highlighting the feasibility of directly studying toxicity through MS/MS and its potential applications in risk prediction and early warning for environmental samples.