Amitriptylinoxide

Peroxisome proliferator-activated receptor gamma (PPARγ), a critical nuclear receptor, plays a pivotal role in regulating metabolic and inflammatory processes. However, various environmental contaminants can disrupt PPARγ function, leading to adverse health effects. This study introduces a novel approach to predict the inhibitory activity (IC50 values) of 140 chemical compounds across 13 categories, including pesticides, organochlorines, dioxins, detergents, flame retardants, and preservatives, on PPARγ. The predictive model, based on the light-gradient boosting machine (LightGBM) algorithm, was trained on a dataset of 1804 molecules showed r² values of 0.82 and 0.59, Mean Absolute Error (MAE) of 0.38 and 0.58, and Root Mean Square Error (RMSE) of 0.54 and 0.76 for the training and test sets, respectively. This study provides novel insights into the interactions between emerging contaminants and PPARγ, highlighting the potential hazards and risks these chemicals may pose to public health and the environment. The ability to predict PPARγ inhibition by these hazardous contaminants demonstrates the value of this approach in guiding enhanced environmental toxicology research and risk assessment.

Effluent wastewater from conventional wastewater treatment plants (WWTPs) is a source of environmental micropollutants. This study investigated temporal trends of organic micropollutants in effluent wastewater, aiming to identify underlying drivers and their implications for treatment efficiency. From September to December 2022, we collected 168 effluent and 10 influent samples. These samples were concentrated using a three-layer solid-phase extraction method and analyzed by liquid chromatography-high resolution mass spectrometry (LC-HRMS). Both targeted and suspect screening approaches were employed, allowing for the full quantification of 64 micropollutants and the identification of 90 additional compounds through suspect screening. Correlations revealed distinct groups of micropollutants with similar temporal trends, indicating common sources or behaviors during treatment. Notably, caffeine and paracetamol showed strong correlations with influent flow rates, indicating their removal efficiency is significantly influenced by hydraulic conditions. PFAS compounds, tire-wear chemicals, and biocides correlated with rain events. Micropollutants were categorized into nine groups based on their temporal trends, linking them to sources and persistence in the WWTP. Industrial discharges significantly contributed to spikes in pharmaceuticals like amitriptyline and citalopram. Metabolite analysis effectively distinguishing between sources of consumption and industrial discharge. These findings underscore the need for regulatory frameworks addressing a broader range of micropollutants. Key events such as rain and industrial discharges impact micropollutant composition and concentrations in effluent wastewater. Our study provides insights into their dynamics within WWTPs, informing improved treatment strategies.