Exposure to three primary xenoestrogens (XEs), including phthalates, parabens, and phenols, has been strongly associated with chronic kidney disease (CKD). An interpretable machine learning (ML) model was developed to predict CKD using data from the ...
The increasing prevalence of environmental pollutants has raised public concern about their potential role in diseases such as atherosclerosis (AS). Existing studies suggest that chemicals, including bisphenol S (BPS), may adversely affect cardiovasc...
Contaminants capable of crossing the placental barrier (PB) adversely affect female reproduction and fetal development. The rapid identification of PB-permeable contaminants is urgently needed due to the inefficiencies of conventional cell-based tran...
Respiratory toxicity of chemicals is a common clinical and environmental health concern. Currently, most in silico prediction models for chemical respiratory toxicity are often based on a single or vague toxicity endpoint, and machine learning models...
Organophosphorus pesticides (OPPs) are widely used in agriculture but pose significant ecological and human health risks due to their persistence and toxicity in the environment. While microbial degradation offers a promising solution, gaps remain in...
Emerging environmental contaminants (EECs) such as pharmaceuticals, pesticides, and industrial chemicals pose significant challenges for detection and identification due to their structural diversity and lack of analytical standards. Traditional targ...
Polycyclic aromatic hydrocarbons (PAHs) are persistent pollutants with well-known genotoxic and mutagenic effects, posing risks to ecosystems and human health. Their hydrophobic nature promotes accumulation in soils and aquatic environments, increasi...
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with strong carbon‑fluorine (CF) bonds that contribute to bioaccumulation and long-term environmental and health risks. Traditional PFAS detection and treatment meth...
Heavy metal concentration in pregnant women affects neurocognitive and behavioral development of their infants and children. The majority of existing research focusing on pregnant women's heavy metal concentration has considered individual environmen...
Application of machine learning-based methods to identify novel bacterial enzymes capable of degrading a wide range of xenobiotics offers enormous potential for bioremediation of toxic and carcinogenic recalcitrant xenobiotics such as pesticides, pla...