Toxicology letters最新文献

Background: Exposure to heavy metals in the environment has always been the focus of public concern. More and more evidence suggests that heavy metal exposure may lead to bone degeneration and an increased risk of pathological fractures. In this study, we analyzed the data of NHANES (National Health and Nutrition Survey) and applied nine machine learning models to check the relationship between heavy metal exposure and osteoporosis.

Methods: The data originates from NHANES conducted during the periods of 2003-2004,2005-2006,2007-2008,2009-2010,2013-2014 and 2017-2018 and is utilized for the development of machine learning models. The Spearman Correlation analysis was employed to identify the relationships among all independent variables, while the Boruta algorithm was utilized for feature selection. The chosen data was equilibrated with SMOTE and partitioned into training and testing sets in a 7:3 ratio. Support Vector Machine, Gradient Boosting Machine, Neural Network, Random Forest, XGBoost, K-Nearest Neighbors, AdaBoost, LightGBM, and CatBoost were employed to construct machine learning models. The optimum model was chosen for further research based on area under the curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score. The Shapley additive explanation (SHAP) method was employed to elucidate the contribution of variables to the machine learning model.

Results: The XGBoost model among nine machine learning models demonstrated the best and most balanced performance in evaluating the correlation between heavy metal exposure and osteoporosis (AUC value of 0.834), significantly outperforming the other eight models. It achieved an accuracy of 0.822, sensitivity of 0.709, specificity of 0.830. Age was identified as the primary influencing factor in this machine learning model (mean |SHAP| = 0.30). Based on SHAP feature importance, the metals were ranked (descending) as Tl, Pb, Cd, Ba, Mo, Sb, Cs, Co and Tu, with Tl showing the strongest contribution to osteoporosis prediction.SHAP dependency plots and waterfall plots further illustrate the decision-making mechanism of the model.

Conclusions: In this study, the XGBoost model showed better performance than the other eight models. Among the nine types of urine metals, thallium (Tl) is the most important variable in the prediction of osteoporosis in machine learning models. Among all independent variables, age and gender are considered the most important components of the model. Subsequent research should develop more sophisticated algorithms to authenticate these findings and adjust relevant parameters to improve the model's precision.

Flocoumafen (FCF), a second-generation anticoagulant rodenticide (SGAR), has limited toxicological data regarding its effects on aquatic organisms. In this study, we exposed zebrafish embryos to FCF solutions at concentrations of 0.2, 0.4, and 0.8mg/L until 120hours post-fertilization (hpf). The results revealed a decrease in survival rates. Notably, FCF exposure significantly reduced the frequency of spontaneous tail coils at 24 hpf, while shortened body length and induced spinal curvature at 120 hpf. Furthermore, zebrafish larvae exhibited craniofacial abnormalities and incomplete bone mineralization at 120 hpf following FCF exposure. In Tg(HuC:EGFP) transgenic strains, neuronal loss was observed. Additionally, FCF-exposed zebrafish larvae showed a marked reduction in locomotor ability, activity levels, and turning capacity. The qPCR and enzyme activity assays revealed significant changes in gene expression associated with the Notch signaling pathway, accompanied by increased levels of reactive oxygen species (ROS), superoxide dismutase (SOD), catalase (CAT), malondialdehyde (MDA). Astaxanthin (ASTA) partially alleviated the toxicities induced by FCF. These findings suggest that FCF may induce skeletal and neurological toxicities by affecting oxidative stress, disrupting the normal expression of skeletal and nervous system-related genes in the Notch signaling pathway, and ultimately leading to behavioral abnormalities. Our findings may provide new insights into a comprehensive evaluation of FCF toxicology in aquatic organisms, and may assist the government in formulating and implementing regulatory policies regarding the application of FCF.

The triclosan (TCS) has been widely used as an antimicrobial and antibacterial agent in personal care products, medical, acrylic, veterinary, and household items. Owing to its capacity to interfere with hormone-regulated pathways, it has been evidenced as a potential endocrine-disrupting chemical. This review summarizes the existing data on the mechanistic basis of TCS-induced endocrine disruption, emphasizing its impacts on steroidogenesis, receptor-based signalling, thyroid hormone regulation, crosstalk with metabolic hormones, and transformation product hazards. Consolidating molecular and cellular studies, this review highlights TCS-altered major endocrine functions and identifies areas of concern requiring further investigation for risk assessment and regulatory decisions.

Background: Epidemiological evidence has established associations between heavy metal exposure and increased risks of various cancers. However, causality remains difficult to establish in conventional observational studies due to residual confounding and reverse causation. Mendelian randomization (MR) could overcome these limitations by using genetic variants as instrumental variables to strengthen causal inference. This study employs a two-sample MR approach to investigate the potential causal effects of serum heavy metal concentrations on multiple cancer types.

Methods: Genetic instruments for heavy metal exposure were obtained from genome-wide association study (GWAS) data. The primary causal estimates were derived using the inverse variance weighted (IVW) method, with robustness assessed and pleiotropy addressed through supplementary analyses, including weighted median, MR-Egger regression, and MR-PRESSO. All analyses accounted for multiple testing via Bonferroni correction.

Results: Mendelian randomization analysis revealed that genetically predicted serum nickel conferred an elevated risk of cervical cancer (OR = 1.473, 95 % CI: 1.102-1.969, P = 0.009), while serum manganese exhibited a protective effect against acute lymphoblastic leukemia (OR = 0.399, 95 % CI:0.173-0.922, P = 0.032). Additionally, serum lead was associated with an increased risk of biliary tract cancer (OR = 1.315, 95 % CI: 1.04-1.661, P = 0.022), and serum copper was causally linked to higher overall cancer incidence (OR = 1.052, 95 % CI: 1.004-1.102, P = 0.032).

Conclusion: This study provides solid evidence for causal effects of heavy metal exposure on cancer development. The findings compel further investigation into the biological mechanisms involved and highlight the urgency of developing targeted public health interventions to mitigate these risks.

Bisphenol S (BPS) and bisphenol F (BPF) have gained attention as endocrine disruptors that affect brain functions, with their specific impact on the cerebellum being less thoroughly explored. Estrogen receptors (ERs) are crucial in maintaining neuronal health in the cerebellum, which participates in motor coordination. Additionally, bone morphogenetic proteins (BMPs) participate in various neuronal processes and are influenced by ER signaling. This study investigated how environmentally relevant doses of BPS and BPF (0.4 to 40 μg/kg) affect cerebellar functions in adult female rats, with a particular emphasis on ER and BMP signaling. Our assessments of motor coordination, conducted using rota-rod and grip strength tests, indicated that exposure to BPS/BPF significantly compromised balance and muscle strength. Investigation of cerebellar tissue revealed decreased levels of estrogen receptors ERα and ERβ, along with reduced BMP2 at both mRNA and protein levels. BMP2 signaling was also diminished, marked by lower BMPR2 and downstream components like p-Smad. Notably, ER agonists PPT and DPN restored BMP2 levels, while BMP2 administration enhanced ERα and ERβ levels, highlighting a reciprocal relationship between these signaling pathways. Moreover, treatments with PPT, DPN, and BMP2 led to improvements in neuronal survival, NeuN levels, and overall motor coordination performance. In conclusion, BPS and BPF disrupt cerebellar functions and neuronal health by interfering with the interplay between ER and BMP2 signaling, suggesting that targeting these pathways may help mitigate the adverse effects of these chemicals.

Diclofop (DF) is the main chiral metabolite of diclofop-methyl (DFM), a widely used herbicide for grass weed control. While DFM toxicity is documented, little is known about DF, despite its persistence. This study evaluated the enantioselective inhibition by racemic DF and its enantiomers over the major cytochrome P450 (CYP) isoforms present in human liver microsomes. The inhibition screening showed that (+)-DF preferentially inhibited CYP1A2, CYP2C9, CYP2E1, and CYP3A4/5, whereas (-)-DF was more active against CYP2C19 and CYP3A4/5. IC₅₀ and kinetic studies confirmed moderate inhibition without time-dependent effect. DF competitively inhibited CYP2C9 with a K_i value of 2.13 µmol L^-1. Finally, R₁ factor estimations indicated that in vivo inhibition is unlikely even at exposure levels up to 100-fold above the acceptable daily intake. These results highlight the enantioselective inhibitory potential of a pesticide metabolite and its relevance for pesticide-drug interactions and human toxicity.