Toxics最新文献

Non-animal assessment of skin sensitization is a global trend. Recently, scientific efforts have been focused on the integration of multiple evidence for decision making with the publication of OECD Guideline No. 497 for defined approaches to skin sensitization. The integrated testing strategy (ITS) methods reported by the guideline integrates in chemico, in vitro, and in silico testing to assess both hazard and potency of skin sensitization. The incorporation of in silico methods achieved comparable performance with fewer experiments compared to the traditional two-out-of-three (2o3) method. However, the direct application of current ITSs to agrochemicals can be problematic due to the lack of agrochemicals in the training data of the incorporated in silico methods. To address the issue, we present ITS-SkinSensPred 2.0 for agrochemicals and agrochemical formulations using a reconfigured in silico model SkinSensPred for pesticides. Compared to ITSv2, the proposed ITS-SkinSensPred 2.0 achieved an 11% and 16% improvement in the accuracy and correct classification rate for hazard identification and potency classification, respectively. In addition, an online ITS tool was implemented and available on the SkinSensDB website. The tool is expected to be useful for evaluating skin sensitization of substances.

Heavy metal contamination in agricultural soils has garnered increasing attention, yet research on the spatiotemporal trends of heavy metal pollution in tropical regions with multiple annual crop harvests remains limited. This study examines data from 41 studies published between 2000 and 2024, including 206 records from 4122 sampling points on Hainan Island in China, to investigate the spatial distribution and temporal trends of heavy metal pollution. The results reveal that the average concentrations of Cd, Pb, As, Cr, and Hg in surface soil samples from agricultural lands on Hainan Island are 0.12, 28.28, 4.36, 63.98, and 0.075 mg/kg, respectively, all below the risk screening thresholds set by the Soil Pollution Risk Control Standard for Agricultural Land (GB 15618-2018). Spatially, heavy metal concentrations exhibit considerable regional variation. Cd levels are lower in the central region but higher in the northern and southern parts of the island. Both the cumulative pollution index and potential ecological risk index are elevated at the northern and southern ends, indicating more severe pollution in these areas. Pb and As show similar spatial patterns, with higher concentrations in the west and lower concentrations in the east. Conversely, Cr has higher concentrations in the northeast and lower concentrations in the southwest. Hg levels are elevated at the northern and southern ends of the island, though the overall pollution and ecological risk in these areas remain relatively low. Temporally, the concentration of heavy metals in agricultural soils has increased overall over the past two decades, with peak values occurring between 2017 and 2023. From 2002 to 2013, the variation was modest, while the largest fluctuations occurred between 2014 and 2016. Among the metals, Cr exhibited the most significant increase, indicating the most severe pollution, followed by Cd and Hg. As and Pb showed relatively lower levels of contamination. Regarding exceedance rates, the exceedances were evaluated against the thresholds established in GB15618-2018 and GB15618-1995. Cd's exceedance rate increased from approximately 1% between 2002 and 2014 to between 7.78% and 20.93% in the following years, peaking in 2017. The exceedance rate for As rose slightly from 0% to 0.83%, with sporadic exceedances starting in 2015. Although these were relatively minor, a severe pollution point for As was observed in 2019. Exceedance rates for Pb and Cr increased significantly, from 0.75% and 7.50% in 2019 to 1.94% and 9.44% in 2023, reflecting increases of 4.8 to 10 times. These findings underscore the need for strengthened monitoring and management of heavy metal pollution in agricultural soils on Hainan Island to safeguard land quality and ensure the sustainability of local agricultural practices.

In this study, gaseous element mercury (GEM) and gaseous oxidized mercury (GOM) in the atmosphere were continuously observed at a minute resolution from 1 April 2019 to 31 December 2020 in urban Xi'an, the largest central city in Northwestern China. The concentrations of GEM and GOM drastically fluctuated within the ranges of 0.022-297 ng/m³ and 0.092-381 pg/m³, showing average values of 5.78 ± 7.36 ng/m³ and 14.2 ± 20.8 pg/m³, respectively. GEM and GOM showed a decreasing trend of 0.121 ng/m³ and 0.472 pg/m³ per month, respectively, which we believe was mainly caused by anthropogenic sources, especially by a reduction in coal-fired emissions, rather than meteorological factors. The significant positive correlation between GEM and PM_2.5, SO₂, NO₂, and CO, as well as Cr, As, and Pb in PM_2.5 also proves that. GEM showed a higher concentration at nighttime than daytime, while an M-shaped diurnal trend was observed for GOM. The hazard quotient of GEM for both males and females decreased at a rate of 0.003 per month, and children aged 2-5 were more sensitive to non-carcinogenic health risks. The changing trends, controlling factors, and human health risks of Hg in the atmosphere are necessary and crucial to study for improving our understanding of the impacts of Hg in Northwestern China.

Background: A limited number of studies have investigated the role of environmental chemicals in the etiology of mild cognitive impairment (MCI). We performed a cross-sectional study of the association between exposure to selected trace elements and the biomarkers of cognitive decline.

Methods: During 2019-2021, we recruited 128 newly diagnosed patients with MCI from two Neurology Clinics in Northern Italy, i.e., Modena and Reggio Emilia. At baseline, we measured serum and cerebrospinal fluid (CSF) concentrations of cadmium, copper, iron, manganese, and zinc using inductively coupled plasma mass spectrometry. With immuno-enzymatic assays, we estimated concentrations of β-amyloid 1-40, β-amyloid 1-42, Total Tau and phosphorylated Tau181 proteins, neurofilament light chain (NfL), and the mini-mental state examination (MMSE) to assess cognitive status. We used spline regression to explore the shape of the association between exposure and each endpoint, adjusted for age at diagnosis, educational attainment, MMSE, and sex.

Results: In analyses between the serum and CSF concentrations of trace metals, we found monotonic positive correlations between copper and zinc, while an inverse association was observed for cadmium. Serum cadmium concentrations were inversely associated with amyloid ratio and positively associated with Tau proteins. Serum iron concentrations showed the opposite trend, while copper, manganese, and zinc displayed heterogeneous non-linear associations with amyloid ratio and Tau biomarkers. Regarding CSF exposure biomarkers, only cadmium consistently showed an inverse association with amyloid ratio, while iron was positively associated with Tau. Cadmium concentrations in CSF were not appreciably associated with serum NfL levels, while we observed an inverted U-shaped association with CSF NfL, similar to that observed for copper. In CSF, zinc was the only trace element positively associated with NfL at high concentrations.

Conclusions: In this cross-sectional study, high serum cadmium concentrations were associated with selected biomarkers of cognitive impairment. Findings for the other trace elements were difficult to interpret, showing complex and inconsistent associations with the neurodegenerative endpoints examined.

Amines are widespread environmental pollutants that may pose health risks. Specifically, the N-dealkylation of amines mediated by cytochrome P450 enzymes (P450) could influence their metabolic transformation safety. However, conventional experimental and computational chemistry methods make it difficult to conduct high-throughput screening of N-dealkylation of emerging amine contaminants. Machine learning has been widely used to identify sources of environmental pollutants and predict their toxicity. However, its application in screening critical biotransformation pathways for organic pollutants has been rarely reported. In this study, we first constructed a large dataset comprising 286 emerging amine pollutants through a thorough search of databases and literature. Then, we applied four machine learning methods-random forest, gradient boosting decision tree, extreme gradient boosting, and multi-layer perceptron-to develop binary classification models for N-dealkylation. These models were based on seven carefully selected molecular descriptors that represent reactivity-fit and structural-fit. Among the predictive models, the extreme gradient boosting shows the highest prediction accuracy of 81.0%. The SlogP_VSA2 descriptor is the primary factor influencing predictions of N-dealkylation metabolism. Then an ensemble model was generated that uses a consensus strategy to integrate three different algorithms, whose performance is generally better than any single algorithm, with an accuracy rate of 86.2%. Therefore, the classification model developed in this work can provide methodological support for the high-throughput screening of N-dealkylation of amine pollutants.

Bacteria of the genus Pseudomonas are the most studied microorganisms that biodegrade persistent perfluoroorganic pollutants, and the research of their application for the remediation of environmental sites using biotechnological approaches remains relevant. The aim of this study was to investigate the ability of a known destructor of perfluorooctane sulfonic acid from the genus Pseudomonas to accelerate and enhance the destruction of long-chain perfluorocarboxylic acids (PFCAs), specifically perfluorooctanoic acid and perfluorononanoic acid, in water and soil in association with the strain P. mosselii 5(3), which has previously confirmed genetic potential for the degrading of PFCAs. The complete genome (5.86 million base pairs) of the strain 2,4-D, probably belonging to a new species of Pseudomonas, was sequenced, assembled, and analyzed. The genomes of both strains contain genes involved in the defluorination of fluorinated compounds, including haloacetate dehalogenase H-1 (dehH1) and haloalkane dehalogenase (dhaA). The strain 2,4-D also has a multicomponent enzyme system consisting of a dioxygenase component, an electron carrier, and 2-halobenzoate 1,2-dioxygenase (CbdA) with a preference for fluorides. The strain 2,4-D was able to defluorinate PFCAs in an aqueous cultivation system within 7 days, using them as the sole source of carbon and energy and converting them to perfluorheptanoic acid. It assisted strain 5(3) to convert PFCAs to perfluoropentanoic acid, accelerating the process by 24 h. In a model experiment for the bioaugmentation of microorganisms in artificially contaminated soil, the degradation of PFCAs by the association of pseudomonads also occurred faster and deeper than by the individual strains, achieving a degree of biodestruction of 75% over 60 days, with the perfluoropentanoic acid as the main metabolite. These results are of great importance for the development of methods for the biological recultivation of fluorinated organic pollutants for environmental protection and for understanding the fundamental mechanisms of bacterial interactions with these compounds.

As new pollutants, microplastics (MPs) have attracted much attention worldwide because they cause serious environmental pollution and pose potential health risks to humans. However, the toxic effects of MPs are still unclear. In this study, we analysed the inflammatory effects of 0.1 μm polystyrene microplastics (PS-MPs) on mouse and human liver cell lines. After 28 days of exposure to PS-MPs, the mice presented decreased liver index values and increased AST/ALT values. HL7702 and HepG2 were treated with PS-MPs for 24 h, and the cytotoxicity, the expression levels of inflammatory factors, and the phosphorylation of proteins in inflammation related pathways were confirmed. Compared with the control, the cell viability of these two cells significantly decreased after exposure to the PS-MPs at 1000 μm/cm², and the BMD model also exhibited a similar dose. LDH leakage and AST also increased in a dose-dependent increase after PS-MPs exposure. The relative levels of chemokines such as GM-CSF, IL-6, IL-8, and IL-12p70 were significantly greater than those in the control. Furthermore, the PS-MPs can increase the expression levels of TLR4, MyD88, and NF-κB and activate the phosphorylation of NF-κB and STATs. Based on these results, exposure to PS-MPs can stimulate liver inflammation and activate the TLR4/MyD88/NF-κB and JAK-STAT pathways.

Acute pancreatitis (AP), induced by tetracycline, a widely used antibiotic, poses significant clinical and toxicological challenges, yet its molecular mechanisms remain unclear. This study aims to promote drug toxicology strategies for the effective investigation of the putative toxicity and potential molecular mechanisms of antibiotic drugs through the study of tetracycline in AP. Using the SwissTargetPrediction, SEA Search, Super-PRED, GeneCards, Drugbank, Online Mendelian Inheritance in Man (OMIM), and Therapeutic Target Database (TTD), we identified 259 potential targets associated with tetracycline exposure and AP. Further refinement via the STRING database and Cytoscape (version 3.10.1) software highlighted 22 core targets, including TP53, TNF, and AKT1. Functional enrichment via the Database for Annotation, Visualization, and Integrated Discovery (DAVID) identified pathways through Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, highlighting PI3K-Akt, MAPK, HIF-1, and AGE-RAGE as critical mediators in tetracycline-induced AP. Molecular docking confirmed the strong binding between tetracycline and the core targets. Overall, these findings suggest that tetracycline may affect the occurrence and progression of pancreas-related inflammation by regulating pancreatic cell apoptosis and proliferation, activating inflammatory signaling pathways, and regulating lipid metabolic pathways. This study provides a theoretical basis for understanding the molecular mechanism of tetracycline-induced AP and lays the foundation for the prevention and treatment of digestive system diseases associated with excessive exposure to tetracycline antibiotics and certain tetracyclines. In addition, our network toxicology approach has accelerated the elucidation of toxic pathways in antibiotic drugs that lack specific characteristics.

Dichlorvos (2,2-dichlorovinyl dimethyl phosphate, DDVP) is a highly toxic organophosphorus insecticide, and its persistence in air, water, and soil poses potential threats to human health and ecosystems. Covalent triazine frameworks (CTFs), with their sufficient visible-light harvesting capacity, ameliorated charge separation, and exceptional redox ability, have emerged as promising candidates for the photocatalytic degradation of DDVP. Nevertheless, pure CTFs lack effective oxidative active sites, resulting in elevated reaction energy barriers during the photodegradation of DDVP. In this work, density functional theory (DFT) calculations were employed to investigate the impact of various oxygen-containing acid groups (-COOH, -HSO₃, -H₂PO₃) on DDVP photodegradation performance. First, simulations of the structure and optical properties of modified CTFs reveal that oxygen-containing acid groups induce surface distortion and result in a redshift in the absorption edge. Subsequently, analysis of the density of states, frontier molecular orbitals, surface electrostatic potential, work function, and dipole moment demonstrates that oxygen-containing acid groups enhance CTF polarization, facilitate charge separation, and ameliorate their oxidative capability. Additionally, the free-energy diagram of DDVP degradation uncovers that oxygen-containing acid groups lower the energy barrier by elevating the adsorption and activation capability of DDVP. Notably, -H₂PO₃ presents optimal potential for the photodegradation of DDVP by unique electronic structure and activation capability. This work offers a valuable reference for the development of oxygen-containing acid CTF-based photocatalysts applied in degrading toxic organophosphate pesticides.

The species sensitivity distribution (SSD) analysis for aquatic ecosystems has been increasingly used in risk assessment. However, existing analyses of the impact of trace metals in lake sediments on aquatic organisms often neglect the spatiotemporal variability of trace metal release. This oversight can result in ecological risk assessments that lack specificity. To address this gap, we collected 32 core sediment samples from Lake Chaohu to systematically investigate the ecological toxicological risks posed by the release of eight trace metal indicators into the overlying water column under four hydrological scenarios throughout the year. Results indicated that only Cu, Pb, and Zn exhibit persistent toxicological risks. The comprehensive ecological toxicological risk of sediment trace metals showed spatial differences, increasing from the western region to the eastern region, i.e., western region < central region < eastern region. Seasonally, the risk levels are ordered as follows: May < September < November to April of the following year < June to August. The eastern region in summer (June to August) was identified as the high-risk area and period for trace metal pollution in sediments. Based on these conclusions, it is recommended to implement pollution control and environmental monitoring measures in the eastern region during the summer to effectively control the pollution and ecological risks of trace metals.