Toxics最新文献

Differentiation of villous cytotrophoblasts into syncytiotrophoblasts is essential for placental endocrine function and estradiol production. Caffeine consumption has been linked to altered estradiol levels, but its effects on human trophoblast differentiation remain incompletely understood. This study investigated the effects of caffeine on biochemical differentiation of human trophoblasts using BeWo cells and human placental explants. Cell viability and apoptosis were assessed using CCK-8 and in situ TUNEL assays. Differentiation-associated changes were evaluated by measuring CYP19A1 and its placenta-specific promoter transcript CYP19 I.1, at the mRNA level, while aromatase protein expression and estradiol production were assessed by Western blotting and ELISA, respectively. Exposure to 2 mM caffeine reduced BeWo cell viability, whereas 1 mM caffeine had no detectable effects on cell viability or apoptosis. At non-cytotoxic concentrations, caffeine significantly increased CYP19A1 mRNA expression under both basal and forskolin-stimulated conditions and elevated estradiol production. Similar transcriptional and endocrine responses were observed in human placental explants. Pharmacological inhibition demonstrated that caffeine-induced CYP19A1 transcriptional upregulation was dependent on PKA signaling, but not on PKC or MAPK pathways. These results indicate that caffeine can modulate trophoblast biochemical differentiation via PKA-dependent regulation of placental aromatase expression and estradiol synthesis. While these findings provide mechanistic insight into caffeine-mediated effects on trophoblast endocrine function, their relevance to physiological exposure levels and in vivo placental development warrants cautious interpretation.

The pervasive toxicity of active aluminum (Al³⁺) in acidic red soils threatens agroecosystem sustainability, with conventional chemical stabilizers facing cost and secondary pollution constraints. This study evaluated rice husk/sawdust and their pyrolysis-derived biochar as stabilizers, focusing on microbial synergy. Results showed 3% rice husk biochar (RB) achieved 22.1 ± 1.1% stabilization efficiency within 180 days, outperforming sawdust biochar (12.1 ± 0.8%) and raw biomass. Biochar's alkalinity and porosity created neutral niches, enriching denitrifiers (Thiobacillus, Arthrobacter, Thermomonas) that elevated pH, promoted Al(OH)₃ precipitation, and enhanced oxygen-containing functional groups. This work valorizes agricultural waste for long-term Al³⁺ toxicity mitigation.

Based on continuous field observations conducted at the Shangdianzi Regional Atmospheric Background Station from 21 October to 20 November 2024 and from 1 December 2024, to 2 January 2025, this study systematically analyzed the concentration levels, seasonal variations, diurnal patterns, and ozone formation potential (OFP) of 24 carbonyl compounds (OVOCs) in the atmosphere during autumn and winter. Source apportionment was further investigated using characteristic ratios, correlation analysis, and multiple linear regression. The results indicate that the average concentration of Σ24OVOCs during the observation period was 2.70 ± 1.55 ppb. Formaldehyde, acetone, and acetaldehyde were the dominant species, accounting for 94.5% of the total concentration in this background area. A significant seasonal difference in carbonyl concentrations was observed, with the average concentration in autumn (3.68 ± 1.66 ppb) being approximately 2.1 times higher than that in winter (1.78 ± 0.58 ppb). The diurnal variation in most carbonyls exhibited a pattern of nighttime accumulation and daytime depletion, which was consistent with the trend of NO₂. The OFP results show that the average OFP of Σ24OVOCs was 30 ± 16 μg/m³, with formaldehyde contributing 86.9%, identifying it as a key precursor for ozone formation in the background region. Source analysis revealed that carbonyl compounds in autumn were influenced by combined natural, vehicular, and industrial sources, with significant secondary formation (27-36%) observed for C2 (acetaldehyde) and C3 (mainly acetone and propanal) species. In winter, anthropogenic contributions to carbonyls increased, with C2 and C3 species primarily originating from combustion sources, vehicle emissions, and industrial releases. This study provides the first insights into the pollution characteristics and source profiles of carbonyl compounds during autumn and winter at the Shangdianzi background site, offering a scientific basis for understanding regional atmospheric oxidative capacity and formulating integrated air pollution control strategies.

Determining the concentration of fatty acid ethyl esters (FAEEs), ethyl sulfate (EtS), and ethyl glucuronide (EtG) is crucial for establishing the true scale of prenatal alcohol exposure (PAE) and enabling early diagnosis of fetal alcohol spectrum disorders. This study primarily aimed to compare two detection methods: retrospective maternal alcohol consumption surveys and chromatographic analysis of newborn meconium. Among 478 mothers, parallel survey data and meconium samples were collected. Nine FAEEs were measured by gas chromatography-mass spectrometry, and EtG and EtS by liquid chromatography-tandem mass spectrometry. The study also aimed to establish marker cut-offs and evaluate their clinical utility. While only 4% (approximately) of mothers reported alcohol consumption during pregnancy, the biomarker analysis suggested a significant underestimation of the actual PAE scale, highlighting the limitations of self-reported data. Analysis using the cumulative biomarker index for two biomarkers with a threshold of ≥5 indicated that alcohol consumption affected approximately 3% of the studied population, further demonstrating the low reliability of maternal self-reports. Ultimately, this study confirms that the combined EtG and EtS measurements provide the most reliable diagnostic information for PAE and underscores the necessity of objective meconium screening in clinical practice.

The ubiquity and environmental persistence of per- and polyfluoroalkyl substances (PFASs) have raised significant concerns about their detrimental effects on human health. Collective scientific efforts are increasingly focused on elucidating PFAS toxicity mechanisms and identifying potential low-impact PFAS structures that retain the exceptional properties of this chemical class. To advance the use of in silico methods in PFAS toxicity assessment, we developed a robust modelling framework for predicting PFAS acute oral toxicity class (high or low) in rats, leveraging the enhanced capabilities of the in-house Isalos Analytics Platform. The automated machine learning (autoML) functionality was employed to optimise four ML models-k-nearest neighbours (kNN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and fully connected neural network (NN)-using Mold2 molecular descriptors, and to identify the top-performing model through five-fold cross-validation. The selected kNN model (k = 3) was used for predictions on the held-out testing set, achieving an accuracy of 81.5%, while a Shapley values analysis provided valuable insights into the factors influencing toxicity predictions. Furthermore, the nearest-neighbour-based methodology enabled a read-across structural analysis of PFAS similarity groups consisting of each testing set instance and its three closest neighbours in the training set. This analysis revealed a consistent association between polyaromatic and heterocyclic structural features and high acute oral toxicity. The developed, thoroughly validated read-across model is freely accessible through the INSIGHT RatTox web application as well as the INSIGHT Cheminformatics Platform in Enalos Cloud, supporting high-throughput screening of PFAS compounds and investigation of structural similarities with their nearest neighbours for enriched structural interpretation.

Heavy metal pollution of farmland has emerged as a pressing global environmental challenge, which threatens food security, ecological integrity, and human health [...].

In industrial settings, one of the key environmental challenges is the remediation of soil contaminated by hydrocarbons. Washing the soil with surfactants mobilises and extracts these compounds, making them easier to treat. As it enables the recovery and reuse of soil within sustainable production processes, this technique is part of the circular economy. Soil-washing experiments using surfactants were carried out to determine whether a mixture of anionic and non-ionic surfactants could improve the remediation of soil contaminated by gasoline and diesel fuel compared to the use of a single surfactant. Four surfactants were used (non-ionic: polyoxyethylene lauryl ether and polyoxyethylene (80) sorbitan monooleate; anionic: sodium dodecylbenzenesulfonate and sodium dodecyl sulfate). The aliphatic and aromatic hydrocarbon fractions (C6-C8, C8-C10, C10-C12, C12-C16, C16-C21 and C21-C35) of gasoline and diesel fuel were analysed. Sodium dodecylbenzenesulfonate was selected for the purpose of preparing mixtures with the other two non-ionic surfactants, polyoxyethylene lauryl ether and polyoxyethylene (80) sorbitan monooleate. These surfactant mixtures demonstrated significantly higher removal rates than sodium dodecylbenzenesulfonate alone. Mixtures of sodium dodecylbenzenesulfonate and polyoxyethylene lauryl ether achieved hydrocarbon extraction of between 61% and 68%, while sodium dodecylbenzenesulfonate-polyoxyethylene (80) sorbitan monooleate mixtures obtained extraction of between 58% and 66%. Analysis of the gasoline and diesel hydrocarbon fractions indicated that smaller molecules desorb more easily than larger ones and that aromatics desorb more easily than aliphatics. Furthermore, the mixtures increased the extraction of both aliphatic and aromatic hydrocarbons, particularly the lighter compounds. The variation on removal rates within the hydrocarbon ranges may be related to the octanol-water partition coefficient (K_ow). These improvements with mixtures of anionic and non-ionic surfactants could be exploited to enhance the effectiveness of surfactant-flushing treatments and optimise the design of soil surfactant treatments.

This study assesses the presence and distribution of per- and polyfluoroalkyl substances (PFAS) and polybrominated diphenyl ethers (PBDEs) in the surface waters of the Itupararanga Reservoir and the Sorocaba River, Brazil. Samples collected during the dry and rainy seasons were analyzed to determine their composition, spatial distribution, and seasonal variability. Results indicate the ubiquitous presence of PFAS, with significantly higher concentrations in the dry season, suggesting point sources of contamination, such as industrial and domestic discharges. Perfluorobutanoic acid (PFBA), Perfluorooctane sulfonate (PFOS), and Perfluorooctanoic acid (PFOA) were the predominant compounds, while 6:2 Fluorotelomer sulfonate (6-2FTS) stood out for its abundance in areas with industrial activity. For PBDEs, marked seasonal variability was observed, with higher concentrations during the rainy season, suggesting the mobilization of these compounds by surface runoff. BDE-209 was the most abundant congener, representing over 58% of the total concentration of PBDEs detected. Concentrations of PFAS and PBDEs in the study area are comparable to those reported globally, although there are differences associated with industrial practices and local environmental dynamics. The increased presence of short-chain PFAS and Deca-BDEs highlights the need for ongoing monitoring and the implementation of regulatory measures to mitigate contamination in water sources used for human consumption.

Antibiotic resistance gene (ARG) monitoring in environmental systems increasingly relies on DNA-based molecular approaches; however, the extent to which DNA extraction strategies bias downstream resistome interpretation remains insufficiently understood. This study systematically evaluated the effects of single versus successive DNA extraction on DNA recovery, microbial community composition, and the abundance and diversity of 385 genes related to antibiotic resistance including ARGs and mobile genetic elements (MGEs) across three contrasting matrices: water, sediment, and fish intestinal tissue. Successive extraction markedly increased DNA yield and detection of functional genes in water and sediment, particularly for low-abundance and particle-associated taxa. Enhanced recovery resulted in higher richness and abundance of ARGs and MGEs and strengthened correlations between intI1, ARGs, and bacterial taxa, indicating that single-cycle extraction may underestimate resistome magnitude and potential host associations in complex matrices. Conversely, fish intestinal tissue, used here as a representative biological matrix, showed limited benefit or even reduced gene abundance with repeated extraction, likely due to rapid depletion of extractable nucleic acids and DNA degradation. While successive extraction improves recovery efficiency, the potential inclusion of extracellular or relic DNA suggests caution in interpreting inflated ARG abundance. Overall, our findings demonstrate that DNA extraction is a matrix-dependent methodological driver that can reshape both quantitative outcomes and ecological inference. Matrix-specific optimization and careful protocol selection are therefore essential for improving data comparability and minimizing methodological underestimation in environmental resistome assessments.

(1) Background: Hot pot bases are susceptible to phthalate (PAE) contamination due to their high lipid content. Standard risk models often fail to capture extreme values, leading to biased exposure estimates. This study characterized dibutyl phthalate (DBP) and bis(2-ethylhexyl) phthalate (DEHP) contamination using a hybrid modeling framework to ensure precise risk profiling. (2) Methods: A total of 91 samples were analyzed via GC-MS. Concentration data were fitted using traditional parametric, extreme value mixture (EVMM), and finite mixture models. Probabilistic dietary risks were assessed for Chinese demographic groups using 10,000-iteration Monte Carlo simulations. (3) Results: DEHP (detection rate: 55%) and DBP (32%) were best modeled by a two-component Gamma mixture and a Lognormal-Generalized Pareto distribution, respectively. These advanced models significantly outperformed conventional distributions in capturing upper-tail extremes. Crucially, all hazard quotients (HQs) remained below the safety threshold of 1, indicating acceptable risk, although children aged 7-13 exhibited the highest calculated risk (Max DEHP HQ = 0.68). (4) Conclusions: Although current exposure levels are within safe limits, the heavy-tailed distributions identify potential sporadic high-exposure events that traditional models overlook, specifically highlighting the relative vulnerability of children aged 7-13. This study validates that hybrid statistical approaches offer superior precision for analyzing skewed contamination data. Consequently, these findings provide a critical scientific basis for refining regulatory monitoring and implementing targeted source-tracking measures to mitigate long-tail food safety risks.