Toxics最新文献

The long-term inhalation of free silica dust causes silicosis-a prevalent occupational hazard-yet its systemic effect on male reproductive toxicity remains underexplored. Tetrandrine (Tet) is the only plant-derived anti-silicosis drug approved in China. This study investigates silica (SiO₂) -induced male reproductive damage and evaluates Tet's protective effects. Sixty male C57BL/6 mice (6-8 weeks) were divided into control, SiO₂ exposure, and SiO₂ + Tet groups. SiO₂ was administered via intranasal infusion and Tet via gavage. Mice were sacrificed at day 7 (male reproductive injury model corresponding to the pulmonary inflammation stage) and day 42 (male reproductive injury model corresponding to the pulmonary fibrosis stage). Analyses included sperm morphology, testicular transcriptome sequencing, RT-qPCR, and immunofluorescence. At day 7, SiO₂ exposure upregulated testicular inflammatory markers, which were partially mitigated by Tet. At day 42, SiO₂ increased sperm deformity and testicular fibrosis markers (fibronectin and vimentin); Tet intervention reduced these abnormalities. Transcriptome analysis revealed distinct gene expression patterns at day 7 versus day 42, indicating time-dependent injury mechanisms. Tetrandrine alleviates silica-induced reproductive damage in male mice, suggesting potential therapeutic applications for occupational silica exposure and expanding the understanding of silica toxicity beyond the respiratory system.

Eutrophication of water bodies and the bloom of toxin-producing cyanoprokaryotes raise health concerns. Various cyanoprokaryotes species, including Microcystis, Raphidiopsis, Nodularia, and Chrysosporum, release toxins into the aquatic environment, which can reach concentrations toxic to humans and animals. Rising temperatures and human activities are primary drivers behind the increasing frequency of toxic cyanobacterial blooms. The Word Health Organization (WHO) has established provisional guideline values for cyanotoxins in drinking water and water used for other purposes in daily human activities, and has published guidance for identifying hazards and managing risks posed by cyanobacteria and their toxins. There are currently no acceptable limit values for cyanotoxins. To address monitoring needs, contemporary strategies now incorporate molecular genetics, immunoassays, biochemical profiling, and emerging machine-learning frameworks. This paper reviews current early detection methods for harmful cyanobacterial blooms, highlighting their practical advantages and drawbacks.

Northern Thailand experiences recurrent seasonal haze driven by biomass burning (BB), which results in hazardous PM2.5 exposure and elevated environmental health risks. To address the need for timely and spatially resolved emission information, this study developed and evaluated an operational near-real-time (NRT) biomass-burning PM2.5 emission estimation system using the Fire INventory from NCAR version 2.5 (FINNv2.5). The objectives of this study are threefold: (1) to construct a high-resolution (≤1 km) NRT biomass-burning PM2.5 emission inventory for Northern Thailand; (2) to assess its temporal and spatial consistency with ground-based PM2.5 measurements and satellite fire observations; and (3) to examine its potential utility for informing environmental health risk management. The developed system captured short-lived, high-intensity burning episodes that defined the haze crisis, revealing a distinct peak period from late February to early April. Cumulative emissions from January to April 2024 exceeded 250,000 tons, dominated by Chiang Mai (25.8%) and Mae Hong Son (25.5%), which together contributed 51.3% of regional emissions. Strong correspondence with MODIS/VIIRS FRP (r = 0.79) confirmed the reliability of the NRT emission signal, while regression against observed PM2.5 concentrations indicated a substantial background burden (intercept = 40.41 μg m^-3) and moderate explanatory power (R² = 0.448), reflecting additional meteorological and transboundary influences. Translating these relationships into operational metrics, an Emission Control Threshold of 1518 tons day^-1 was derived to guide targeted burn permitting and reduce population exposure during peak-risk periods. This NRT biomass-burning PM2.5 emission estimation framework offers timely emissions information that may support decision makers in environmental health risk management, including the development of early warnings, adaptive burn-permit strategies, and more coordinated responses across Northern Thailand.

In this study, magnesium-modified clinoptilolite (MZ) was successfully synthesized via precipitation and calcination to efficiently remove Pb(II) from aqueous solutions. The material was systematically characterized using BET, XRD, SEM-EDX, FT-IR, and XPS. Adsorption kinetics followed a pseudo-second-order model (R² = 0.9956), with MZ removing over 70% of Pb(II) within the first 3 h. Isotherm data were best described by the Langmuir model (R² = 0.9686), confirming monolayer chemical adsorption, with a maximum adsorption capacity (q_m) of 1656 mg/g. Notably, MZ maintained high adsorption capacity across a pH range of 3.0~5.5, and its performance was largely unaffected by the presence of high concentrations of competing ions (0.1~1.0 M NaNO₃). Mechanistic analysis revealed that the loaded MgO facilitates the chemical conversion of Pb(II) to hydroxycarbonate (Pb₃(CO₃)₂(OH)₂) via surface complexation, which constitutes the primary removal mechanism. These findings demonstrate that magnesium modification can transform natural zeolites into high-capacity, stable adsorbents, offering promising potential for the treatment of Pb(II)-contaminated water.

Individuals across their lifespan may experience hearing loss from medications or chemicals, prompting concern about ototoxic environmental exposures. This study applies computational modeling as a screening-level hazard identification and chemical prioritization approach and is not intended to constitute a human health risk assessment or to estimate exposure- or dose-dependent ototoxic risk. We evaluated in silico drug-induced ototoxicity models on 80 environmental chemicals, excluding 4 with known ototoxicity, and analyzed 76 chemicals using fingerprinting, similarity assessment, and machine learning classification. We compared predicted environmental ototoxicants with ototoxic drugs, paired select polychlorinated biphenyls with the antineoplastic drug mitotane, and used PCB 177 as a case study to construct an ototoxicity pathway. A systems biology framework predicted and compared molecular targets of mitotane and PCB 177 to generate a network-level mechanism. The consensus model (accuracy 0.95 test; 0.90 validation) identified 18 of 76 chemicals as potential ototoxicants within acceptable confidence ranges. Mitotane and PCB 177 were both predicted to disrupt thyroid-stimulating hormone receptor signaling, suggesting thyroid-mediated pathways may contribute to auditory harm; additional targets included AhR, transthyretin, and PXR. Findings indicate overlapping mechanisms involving metabolic, cellular, and inflammatory processes. This work shows that integrated computational modeling can support virtual screening and prioritization for chemical and drug ototoxicity risk assessment.

A correlation between the near-surface ozone concentration in the urban atmosphere and hospitalizations of community-acquired pneumonia patients has been analyzed based on a long-term (five years) series of observations in the warm season in Moscow, Russia. The study included hospitalization records for patients over 15 years old. One of the main goals was to reveal vulnerable groups of the urban population that react most strongly to increased ozone concentrations. It has been shown that increased near-surface ozone concentrations lead to increased hospitalizations. Older people (over 60 years old) are most sensitive to the negative impact of air pollution. Women in this age group are more sensitive to the effects of ozone air pollution than men. In the middle-aged group (31-60 years), the highest correlation between the number of community-acquired pneumonia cases and the ozone level in the atmospheric surface layer, conversely, was in men, but it was still lower than the rate in older people. The young people (15-30 years old) group turned out to be insensitive to the near-surface air pollution.

Bisphenol A (BPA) is a persistent environmental contaminant requiring effective removal strategies. Biofilms offer advantages over conventional activated sludge for refractory compound degradation, yet the specific microorganisms and mechanisms driving BPA removal in biofilms remain poorly understood. This study employed an integrated approach, combining ¹³C-DNA stable isotope probing (SIP) and metagenomics to identify BPA-assimilating microorganisms and elucidate their metabolic pathways in biofilms. Two moving bed biofilm reactors (MBBRs) were operated at contrasting BPA concentrations (500 μg/L and 10 mg/L) to enrich distinct microbial communities. Using DNA-SIP, we revealed differences in assimilating bacteria across diverse concentrations of BPA-enriched biofilms. Simultaneously, we reconstructed the genomes of these assimilating bacteria, dissecting the functional genes essential to the degradation process and identifying significant gene variations among different assimilating bacteria. By integrating these gene functions, we constructed the BPA metabolic pathway, which surprisingly comprised genes from various assimilating bacteria. This research significantly advances our understanding of BPA-assimilating bacteria within biofilms and provides valuable insights for refining biofilm technologies aimed at BPA removal from wastewater.

Environmental pesticide exposure has been linked to adverse health effects, and residential proximity to agricultural land is commonly used as a proxy for exposure; however, the contribution of non-agricultural biomes remains insufficiently explored. This study examined whether the proximity and area of different biomes are associated with the detection of selected pesticides in human urine in Latvia. Urine samples were collected from 202 participants (101 adults and 101 children) within the Human Biomonitoring for Europe (HBM4EU) study during the winter and summer seasons of 2020. A suspect screening approach using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) was applied and 23 pesticides were detected (8 insecticides, 12 fungicides, 2 herbicides and triclosan, an antimicrobial ingredient used in cleaning agents). Geospatial data were analysed in Quantum Geographic Information System (QGIS) to derive biome proximity and area within a 1000 m residential buffer; associations were assessed using generalized linear mixed-effects models. Agricultural land was present within 1000 m of 93.1% of residences, yet neither its distance nor area was consistently associated with pesticide detection. Boscalid was detected in 18.4% of samples and was positively associated with wetland area across seasons (p < 0.001), while fludioxonil (14.7%) showed weak and heterogeneous spatial associations and pirimiphos-methyl (10.2%) showed no significant patterns. Overall, pesticide exposure was substance-specific and influenced by landscape characteristics beyond agricultural proximity, highlighting the need to integrate non-agricultural biomes into human biomonitoring in low-intensity pesticide-use settings.

Diabetes develops through a mix of clinical, metabolic, lifestyle, demographic, and environmental factors. Most current classification models focus on traditional biomedical indicators and do not include environmental exposure biomarkers. In this study, we develop and evaluate a supervised machine learning classification framework that integrates heterogeneous demographic, anthropometric, clinical, behavioral, and environmental exposure features to classify physician-diagnosed diabetes using data from the National Health and Nutrition Examination Survey (NHANES). We analyzed NHANES 2017-2018 data for adults aged ≥18 years, addressed missingness using Multiple Imputation by Chained Equations, and corrected class imbalance via the Synthetic Minority Oversampling Technique. Model performance was evaluated using stratified ten-fold cross-validation across eight supervised classifiers: logistic regression, random forest, XGBoost, support vector machine, multilayer perceptron neural network (artificial neural network), k-nearest neighbors, naïve Bayes, and classification tree. Random Forest and XGBoost performed best on the balanced dataset, with ROC AUC values of 0.891 and 0.885, respectively, after imputation and oversampling. Feature importance analysis indicated that age, household income, and waist circumference contributed most strongly to diabetes classification. To assess out-of-sample generalization, we conducted an independent 80/20 hold-out evaluation. XGBoost achieved the highest overall accuracy and F1-score, whereas random forest attained the greatest sensitivity, demonstrating stable performance beyond cross-validation. These results indicate that incorporating environmental exposure biomarkers alongside clinical and metabolic features yields improved classification performance for physician-diagnosed diabetes. The findings support the inclusion of chemical exposure variables in population-level diabetes classification and underscore the value of integrating heterogeneous feature sets in machine learning-based risk stratification.

Microplastics (MPs) and antibiotics have emerged as contaminants of global concern, posing potential threats to ecosystem security and organismal health. To investigate the individual and combined toxicity of microplastics (PS-MPs) and sulfamethoxazole (SMX), we conducted a 120 h acute exposure experiment using embryo-larval zebrafish as a toxicological model. Our findings demonstrate that both PS-MPs and SMX can induce neurodevelopmental toxicity in embryo-larval zebrafish during embryonic development. Notably, PS-MPs and SMX exerted a significant synergistic effect. PS-MPs 1 µm in diameter were restricted to the chorion surface of pre-hatching zebrafish, whereas post-hatching, PS-MPs accumulated mainly in the gut and gills, with accumulation levels increasing progressively with exposure duration. Individual exposure to PS-MPs or SMX reduced spontaneous locomotion, decreased heart rate, and shortened body length in embryo-larval zebrafish. In addition to exacerbating these effects, coexposure further increased the incidence of malformations such as pericardial effusion and spinal curvature. PS-MPs and SMX significantly decreased the levels of dopamine (DA), serotonin (5-HT), and γ-aminobutyric acid (GABA) in zebrafish while also suppressing acetylcholinesterase (AChE) activity and increasing acetylcholine (ACh) levels. Moreover, upon coexposure at high concentrations, PS-MPs and SMX acted synergistically to reduce the levels of DA and GABA. The downregulation of key neurodevelopmental genes (elavl3, gap43, and syn2a) and related neurotransmitter pathway genes indicates that PS-MPs and SMX impaired structural development and functional regulation of the nervous system. An integrated biomarker response (IBR) index confirmed that PS-MPs and SMX significantly enhanced developmental neurotoxicity during early neurodevelopment in embryo-larval zebrafish through synergistic effects. Our study provides critical toxicological evidence for the scientific assessment of the ecological risks posed by microplastic-antibiotic cocontamination.