Using the super-learner to predict the chemical acute toxicity on rats.

With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD50) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relationship models relying on single algorithm always lack interpretability and precision, given the complexity of the mechanisms underlying acute toxicity. To address these issues, this study has developed a predictive framework using an ensemble learning model based on Super-learner. Particularly, we first obtained LD50 data for 9843 compounds and constructed 16 meta models using 4 molecular descriptors and machine learning algorithms. The Super-learner model performed well, achieving R² values of 0.61 and 0.64 in five-fold cross-validation and test sets, respectively, with corresponding root mean square errors of 0.55 and 0.64, significantly outperforming the results of individual model. Additionally, we incorporated data filtering and applicability domain methods, which demonstrated that the Super-learner can mitigate the impact of dataset noise to some extent. The model achieved an R² of 0.76 within an applicability domain, ensuring prediction accuracy within the chemical space. Compared to previous studies, the model developed here using Super-learner generally achieved better performance across a larger applicability domain. Finally, we has launched an online tool (http://sltox.hhra.net), allowing users to quickly predict LD50 of compounds, greatly simplifying the chemical safety assessment process. This study not only provides an effective and cost-efficient method for predicting chemical toxicity but also offers technical support and data for risk assessments of chemicals.

Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective.

The massive production and application of nanomaterials (NMs) have raised concerns about the potential adverse effects of NMs on human health and the environment. Evaluating the adverse effects of NMs by laboratory methods is expensive, time-consuming, and often fails to keep pace with the invention of new materials. Therefore, in silico methods that utilize machine learning techniques to predict the toxicity potentials of NMs are a promising alternative approach if regulatory confidence in them can be enhanced. Previous reviews and regulatory OECD guidance documents have discussed in detail how to build an in silico predictive model for NMs. Nevertheless, there is still room for improvement in addressing the ways to enhance the model representativeness and performance from different angles, such as data set curation, descriptor selection, task type (classification/regression), algorithm choice, and model evaluation (internal and external validation, applicability domain, and mechanistic interpretation, which is key to ensuring stakeholder confidence). This review explores how to build better predictive models; the current state of the art is analyzed via a statistical evaluation of literature, while the challenges faced and future perspectives are summarized. Moreover, a recommended workflow and best practices are provided to help in developing more predictive, reliable, and interpretable models that can assist risk assessment as well as safe-by-design development of NMs.

Alleviative effects of C60 fullerene nanoparticles on arsenate transformation and toxicity to Danio rerio.

Widely-used C60 fullerene nanoparticles (C60) result in their release into the aquatic environment, which may affect the distribution and toxicity of pollutants such as arsenic (As), to aquatic organism. In this study, arsenate (As(V)) accumulation, speciation and subcellular distribution was determined in Danio rerio (zebrafish) intestine, head and muscle tissues in the presence of C60. Meanwhile we compared how single-walled carbon nanotubes (SWCNTs), multi-walled carbon nanotubes (MWCNTs), graphene oxide (GO) and graphene (GN) nanoparticles alter the behaviors of As(V). Results showed that C60 significantly inhibited As accumulation and toxicity in D. rerio, due to a decrease in total As and monomethylarsonic acid (MMA) and As(V) species concentrations, a lower relative distribution in the metal-sensitive fraction (MSF). It was attributed that C60 may coat As(V) ion channels and consequently, affect the secretion of digestive enzymes in the gut, favoring As excretion and inhibiting As methylation. Similarly, MWCNTs reduced the species concentration of MMA and As(V) in the intestines, low GSH (glutathione) contents in the intestine. Due to the disparity of other carbon-based nanomaterial morphologies, SWCNTs, GO and GN exhibited the various effects on the toxicity of As(V). In addition, the possible pathway of arsenobetaine (AsB) biosynthesis included migration from the intestine to muscle in D. rerio, with the precursor of AsB likely to be 2-dimethylarsinylacetic acid (DMAA). The results of this study suggest that C60 is beneficial for controlling As(V) pollution and reducing the impact of As(V) biogeochemical cycles throughout the ecosystem.

Hydrogen nanobubbles and oxidative windows: Investigating how varying hydrogen concentrations influence seed germination and stress defense.

The hydrogen molecule can effectively regulate plant growth and development, improving plant resistance to abiotic stresses. However, studies regarding the optimal concentration of hydrogen and the associated mechanisms of action in organisms are lacking. This study showed that the maximum germination rate of radish seeds decreased from 90 % to 50 % under the stress of cadmium ions (Cd2+), and hydrogen nanobubble (NB) water significantly alleviated the stress effect of Cd2+ on radish seed germination. A hydrogen concentration of 0.8 ppm had the best effect, reducing Cd2+ accumulation in radish seeds by 63.23 % and increasing the maximum germination rate from 50 % to 65 %. At concentrations exceeding 1.2 ppm, the beneficial effect of hydrogen was weakened or even reversed. Consequently, we integrated the concept of the oxidative window into a REDOX balance model and demonstrated that an appropriate hydrogen concentration can effectively maintain the REDOX state within organisms. Transcriptome sequencing analysis revealed that hydrogen NB water modulated Cd2+ absorption and accumulation in seeds by regulating cell wall components, alleviating oxidative stress through oxidoreductase activity, and enhancing nutrient synthesis and metabolism. This collectively alleviated the inhibitory effect of Cd2+ on seed germination. This study is helpful for further understanding the effect of hydrogen concentration on the REDOX balance of seed germination, providing a theoretical basis for selecting hydrogen concentration to improve its effectiveness in agricultural fields.

Strong cation-exchange combined with mass spectrometry reveals the glycoform heterogeneity of sialylated glycoproteins.

Sialylation is an important modification of proteins, related to protein life and bioactivity. However, the evaluation of sialylation is only based on the average molecular composition by peptide mapping and glycan profiling because sialylated proteins are usually too heterogeneous to obtain good quality mass spectra by conventional intact mass analysis methods. In this study, a simple strong cation exchange-mass spectroscopy (SCX-MS) method was developed for intact mass analysis of sialylated glycoproteins. The developed SCX-MS method provided good separation for sialylated glycoproteins and had an inherent characteristic of native MS. Thus, the intact mass analysis of highly heterogeneous glycoprotein, which cannot be obtained by reversed-phase liquid chromatography (RPLC)-MS and size exclusion chromatography (SEC)-MS methods, can be well analyzed using the current SCX-MS method. First, the method was developed and optimized using the etanercept monomer. Conditions including MS parameters, flow rate, and gradient were investigated. Then, the developed method was used to analyze a new recombinant vaccine, protein 1. Similar to the etanercept monomer, the intact molecular information of protein 1, which cannot be obtained by RPLC-MS and SEC-MS, can be achieved using SCX-MS. Combined with information obtained on peptide mapping and glycan profiles obtained by LC-MS, the new vaccine was well characterized. Finally, the SCX-MS method was used to quickly evaluate the batch-to-batch reproducibility of protein 1. It was much faster than peptide mapping and glycan profiling methods and can provide information complementary to these strategies. It should be useful for many applications where speed and comprehensive characterization are required, such as recombinant sialylated vaccines and fusion proteins.

Methodological Validation of Sedimentation Velocity Analytical Ultracentrifugation Method for Adeno-Associated Virus and Collaborative Calibration of System Suitability Substance.

Currently, adeno-associated virus (AAV) is one of the primary gene delivery vectors in gene therapy, facilitating long-term in vivo gene expression. Despite being imperative, it is incredibly challenging to precisely assess AAV particle distribution according to the sedimentation coefficient and identify impurities related to capsid structures. This study performed the systematic methodological validation of quantifying the AAV empty and full capsid ratio. This includes specificity, accuracy, precision, linearity, and parameter variables involving the sedimentation velocity analytical ultracentrifugation (SV-AUC) method. Specifically, SV-AUC differentiated among the empty, partial, full, and high sedimentation coefficient substance (HSCS) AAV particles while evaluating their sedimentation heterogeneity. The intermediate precision analysis of HE (high percentage of empty capsid) and HF (high percentage of full capsid) samples revealed that the specific species percentage, such as empty or full, was more significant than 50%. Moreover, the relative standard deviation (RSD) could be within 5%. Even for empty or partially less than 15%, the RSD could be within 10%. The accuracy recovery rates of empty capsid were between 103.9% and 108.7% across three different mixtures. When the measured percentage of specific species was more significant than 14%, the recovery rate was between 77.9% and 106.6%. Linearity analysis revealed an excellent linear correlation between the empty, partial, and full in the HE samples. The AAV samples with as low as 7.4 × 1011 cp/mL AAV could be accurately quantified with SV-AUC. The parameter variable analyses revealed that variations in cell alignment significantly affected the overall results. Still, the detection wavelength of 235 nm slightly influenced the empty, partial, and full percentages. Minor detection wavelength changes showed no impact on the sedimentation coefficient of these species. However, the temperature affected the measured sedimentation coefficient. These results validated the SV-AUC method to quantify AAV. This study provides solutions to AAV empty and full capsid ratio quantification challenges and the subsequent basis for calibrating the AAV empty capsid system suitability substance. Because of the AAV structure and potential variability complexity in detection, we jointly calibrated empty capsid system suitability substance with three laboratories to accurately detect the quantitative AAV empty and full capsid ratio. The empty capsid system suitability substance could be used as an external reference to measure the performance of the instrument. The results could be compared with multiple QC (quality control) laboratories based on the AAV vector and calibration accuracy. This is crucial for AUC to be used for QC release and promote gene therapy research worldwide.

Aging effects of titanium dioxide on Cu toxicity to Daphnia magna: Exploring molecular docking and significance of surface properties.

Cosmetics and personal care products containing titanium dioxide nanoparticles (TiO2 NPs) may enter aquatic environments, where the surface coatings of TiO2 NPs may change with aging due to environmental factors such as light, and potentially affect their bioaccumulation and toxicity. This study examined how aging impacted the physicochemical properties of three commercially available TiO2 NPs and subsequent influence on the bioaccumulation and toxicity of copper (Cu) in Daphnia magna (D. magna). We demonstrated that aging significantly affected the hydrophobicity of TiO2 NPs, which affected their binding to water molecules and adsorption of Cu. Changes of bioaccumulation of TiO2 NPs and Cu in D. magna ultimately affected the activities of intracellular antioxidant enzymes such as SOD, CAT, GSH-Px, and the transmembrane protein Na+/K+-ATPase. Molecular docking calculations demonstrated that changes of activities of these biological enzymes were due to the interaction between TiO2 NPs, Cu, and amino acid residues near the sites with the lowest binding energy and active center of the enzyme. Such effect was closely related to the hydrophobicity of TiO2 NPs. Our study demonstrated the close relationship between surface properties of TiO2 NPs and their biological effects, providing important evidence for understanding the behavior of nanomaterials in aquatic environments.

Generation and utilization of endostatin-sensitive cell lines for assessing the biological activity of endostatin.

Recombinant proteins are gaining increasing popularity for treating human diseases. The clinical effectiveness of recombinant proteins is directly related to their biological activity, which is an important indicator in drug development and quality control. However, certain recombinant proteins have unclear or complex signal pathways, making detecting their activity in vitro difficult. For instance, recombinant human endostatin (endostatin), a new antitumor drug developed in China, lacks a sensitive and stable assay for its biological activity since being market approval. To address this issue, we performed a genome-wide screening of immortalized human umbilical vein endothelial cells (HUVECs) using a CRISPR/Cas9 knockout library containing 20,000 targeted genes. We identified two potential endostatin-resistant genes, NEPSPP and UTS2, and successfully constructed a highly sensitive cell line, HUVEC-UTS2-3#, by knocking down the UTS2 gene. Based on the optimized parameters of HUVEC-UTS2-3# cells, we established a new method for detecting the biological activity of endostatin. The method was validated, and it produced results consistent with primary HUVEC cells but with higher sensitivity and more stable data. The use of gene-editing technology provides a novel solution for detecting the biological activity of recombinant proteins that other methods cannot detect.

Investigating spectroscopic and copper binding characteristics of dissolved organic matter in wastewater using EEMs with two-dimensional Savitzky-Golay second-order differentiation-PARAFAC.

Dissolved organic matter (DOM) in wastewater interacts with heavy metal particles in aquatic environments, which changes their dynamics and bioavailability. For quantifying the DOM, an excitation-emission matrix (EEM) paired alongside parallel factor analysis (PARAFAC) is typically employed. However, a drawback of PARAFAC has been revealed in recent studies, i.e., the rise of overlapping spectra or wavelength shifts in fluorescent components. Here, traditional EEM-PARAFAC and, for the first time, two-dimensional Savitzky-Golay second-order differential-PARAFAC (2D-SG-2nd-df-PARAFAC) were used to study the DOM-heavy metal binding. The samples from four treatment units of a wastewater treatment plant, i.e., influent, anaerobic, aerobic, and effluent, underwent the process of fluorescence titration with Cu2+. Four components were separated with dominant peaks in regions I, II, and III (proteins and fulvic acid-like) through PARAFAC and 2D-SG-2nd-df-PARAFAC. A single peak was observed in region V (humic acid-like) by PARAFAC. In addition, Cu2+-DOM complexation indicated clear differences in DOM compositions. The binding strength increased between Cu2+ and fulvic acid-like components in contrast to protein-like components from influent to the effluent, and increasing fluorescence intensity with the addition of Cu2+ in the effluent indicated changes in their structural composition. Moreover, when comparing both methods, the 2D-SG-2nd-df-PARAFAC provided the components without peak shifts and better fitting for Cu2+-DOM complexation model, demonstrating it to be a more reliable technique compared to only traditional PARAFAC for DOM characterization and quantifying metal-DOM in wastewater.

[Application of automatic injection device based on automatic hemostasis in injection of radiopharmaceutical bolus injection].

In clinical practice, radiopharmaceutical dynamic imaging technology requires the bolus injection method to complete injection. Due to the failure rate and radiation damage of manual injection, even experienced technicians still bear a lot of psychological burden. This study combined the advantages and disadvantages of various manual injection modes to develop the radiopharmaceutical bolus injector, and explored the application of automatic injection in the field of bolus injection from four aspects: radiation protection, occlusion response, sterility of injection process and effect of bolus injection. Compared with the current mainstream manual injection method, the bolus manufactured by the radiopharmaceutical bolus injector based on the automatic hemostasis method had a narrower full width at half maximum and better repeatability. At the same time, radiopharmaceutical bolus injector had reduced the radiation dose of the technician's palm by 98.8%, and ensured more efficient vein occlusion recognition performance and sterility of the entire injection process. The radiopharmaceutical bolus injector based on automatic hemostasis has application potential in improving the effect and repeatability of bolus injection.

Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms.

The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.

Integrating ultra-high-performance liquid chromatography tandem mass spectrometry and imaged capillary isoelectric focusing for in-depth characterization of complex fusion proteins.

RATIONALE: Fc-fusion proteins represent a successful class of biopharmaceutical products, which combine the tailored pharmacological properties of biological ligands with the multiple functions of the fragment crystallizable domain of immunoglobulins. There is great diversity in terms of possible biological ligands creating highly diverse structures, therefore the analytical characterization of fusion proteins is far more complex than that of monoclonal antibodies and requires the use and development of additional product-specific methods over conventional generic/platform methods. METHODS: Employing etanercept analogues as studied fusion proteins, the Orbitrap mass analyzer with ultra-high performance liquid chromatography (UHPLC-MS) and imaged capillary isoelectric focusing (icIEF) were utilized for the in-depth fusion protein characterization. RESULTS: The amino acid sequence coverage, peptide mapping, and post-translational modifications of etanercept analogues were analyzed by UHPLC-MS. The post-translational modification results were complemented by imaged capillary isoelectric focusing to produce quality research on etanercept analogues. CONCLUSIONS: The developed workflow integrating UHPLC-MS and icIEF provided an innovative strategy for characterizing complex fusion proteins in the process of quality control and manufacturing.

Nano-TiO2 modifies heavy metal bioaccumulation in Daphnia magna: A model study.

Due to special properties, nano-TiO2 will interact with heavy metals and other pollutants in water, thus affecting the environmental behavior and ecotoxicity of these pollutants. However, the exact manner in which nano-TiO2 affects the bioaccumulation mechanisms of heavy metals is still unclear now. In the present study, quantitative structure bioaccumulation relationship (QSBAR) models were established to explore the relationships between physicochemical parameters of heavy metals and their accumulation in Daphnia magna in the absence and presence of nano-TiO2 at low metal exposure concentrations. The results showed that different physicochemical parameters affected the bioaccumulation of metals in Daphnia magna. The metal accumulation could be described by means of a Comprehensive Parameter composed of seven parameters, i.e., atomic number (AN), relative atomic weight (AW), atomic radius (AR), atomic ionization potential (AN/ΔIP), covalent index (X2r), second ionization energy (I2) and electrochemical potential (E0), in the absence of nano-TiO2, whereas the metal accumulation increased with the increase in Van Der Waals radius (rw) of metals in the presence of nano-TiO2. It was demonstrated that the bioaccumulation mechanism of the metals to Daphnia magna changed in the presence of nano-TiO2. Moreover, the bioaccumulation of more than 85% of the metals increased in the presence of nano-TiO2, but it increased differently for different metals. The present study provides an alternative approach to understand the mechanism of heavy metal bioaccumulation at low metal exposure concentrations and the effect of nano-TiO2 on metal bioaccumulation.

Water Quality Criteria and Ecological Risk Assessment of Typical Transition Metals in South Asia.

Transition metal pollution in rivers in South Asia is more serious than in other regions because of the lack of adequate freshwater management measures. Water quality criteria (WQC) for South Asia is urgently needed to protect regional aquatic environments because of the occurrence of transboundary rivers. The present study established non-parametric kernel density estimation species sensitivity distribution (NPKDE-SSD) models and then derived the acceptable hazardous concentration for protection of 95% of all aquatic species (HC5) and WQC of six typical transition metals in South Asia. The results showed that the order of acute and chronic WQC was Mn > Fe > Cd > Zn > Cu > Hg and Cu > Fe > Cd, respectively. A risk assessment of these metals in the Indus River, the Ganges River, the Brahmaputra River, the Meghna River, and the Bagmati River was also carried out. Based on the results, these major rivers in South Asia were highly polluted with transition metals, with significant ecological risks for a large number of aquatic species. This study can contribute to a better understanding of ecological risks in South Asia and provide a scientific basis for the updating of water quality standards and the increase in overall water quality.

Production of Reactive Oxygen Species via Nanobubble Water Improves Radish Seed Water Absorption and the Expression of Aquaporin Genes.

Nanobubbles (NBs) stimulate seed germination; however, the mechanism of the promotion effect of NBs remains unclear. The impact of NBs on seed water absorption was investigated; we subsequently studied the genes associated with the response of radish seeds to NB water and used RNA sequencing to generate their expression profiles, especially those of aquaporin genes. NB water significantly promoted germination. The times at which 50% of the germinating seeds achieved germination (T50) for the submerged radish seeds in NB and control water were 11.6 and 17.4 h, respectively. NB water-germinated radish seeds showed a water uptake rate coefficient that was 15% higher than that of those germinated in control water. Through GO enrichment and cluster analyses, it was evident that NB water significantly increased the level of expression of the genes associated with the following activities: oxidoreductase, peroxidase, and antioxidant. Our results demonstrated that NB water increases the water uptake rate of radish seeds via two mechanisms. The NB water-produced exogenous hydroxyl radical (•OH) increases the seed coat's water permeability and enhances cell wall loosening, and NB water increases the aquaporin gene expression level of radish seeds.

Enhanced Removal of Free Radicals by Aqueous Hydrogen Nanobubbles and Their Role in Oxidative Stress.

Elevated levels of reactive oxygen radicals caused by environmental stress are the key triggers of inflammation, aging, and disease; thus, it is critical to develop novel reactive oxygen radical scavenging methods with high efficiency and low toxicity. As a result of their selective reactive oxygen radical removal, hydrogen molecules are strong candidates, but their application is limited by the small hydrogen supply and short duration of action. In this study, we for the first time combined nanobubble (NB) technology and hydrogen water to remove reactive oxygen species (ROS) using copper ions as a representative environmental pollutant and Tetrahymena thermophila as a model organism. Hydrogen NBs displayed a remarkable capability of removing H2O2 and O2•- at molar ratios of 8:1 and 240:1, respectively, which were unable to be removed by dissolved hydrogen molecules only. During the oxidative defense phase, hydrogen NB water either directly removed ROS or increased the activity and relative expression of glutathione peroxidase (GSH-Px). During the oxidative inhibition phase, hydrogen NB water exerted antioxidant effects mainly by increasing the activities of superoxide dismutase and GSH-Px as well as the expression of the corresponding genes. Our results provide an important theoretical support for the wide application of hydrogen NBs in empowering the antioxidant defense system.

Effects of water quality and bacterial community composition on dissolved organic matter structure in Daihai lake and the mechanisms.

The migration of organic matter in salinized lakes was critical in maintaining ecological balance and material circulation process of inland shallow lakes. To clarify the ecological and microbial mechanism of material transport and transformation, the microbial community structure and the characteristics of dissolved organic matter (DOM) in the sediment of Daihai Lake, a typical saline lake at the Yellow River Basin, were explored with three-dimensional excitation and emission matrix fluorescence (3DEEM), parallel factor analysis (PARAFAC) and 16 S rRNA techniques. The correlation between environmental factors, DOM composition and the bacterial community structure were also studied for identifying the key factors of community formation. DOM in the lake demonstrated both terrigenous and endogenous characteristics. Protein-like materials accounted for 74% of the total fluorescence intensity in the sediment, where 1127 species, 671 genera, 468 families, 157 classes, 317 orders, 59 phyla of microorganisms were detected. Among the top 10 abundant taxa of each level, Firmicutes, Actinobacterota, Acidimicrobiia and Alphaproteobacteria had the greatest influence on the composition and structure of DOM (|R| > 0.7, p < 0.01). Microbial metabolism was a key process of transforming sediment organic matter from terrestrial humic-like to protein-like matter, accounting for 81% of total fluorescence signal in saline lake samples, while salinity, temperature, dissolved oxygen and electrical conductivity also had significant impacts during the process (|R|>0.7, p < 0.05). The research provides fundamental data and enlightenment for the improvement of the saline inland lake environment.

Effect of organic matter on the trophic transfer of silver nanoparticles in an aquatic food chain.

The behavior and toxicity of nanoparticles could be affected significantly by the ubiquitous natural organic matter (NOM) in aquatic environments. However, the influence of NOM on nanoparticles along the food chain remains largely unknown. This study constructed bacteria Escherichia coli (E. coli) - protozoa Tetrahymena thermophila (T. thermophila) to evaluate the influence of NOM on the bioaccumulation, trophic transfer and toxicity of silver nanoparticles (Ag NPs). Results demonstrated that NOM could reduce the toxicity of Ag NPs to E. coli and T. thermophila by different influence mechanisms (e.g., reduce Ag NPs accumulation or complex with dissolved silver ion (Ag+)) which related to the type of NOM and organisms. Moreover, Ag NPs can be transferred and biomagnified to T. thermophila via trophic transfer. Three typical NOM could significantly increase the trophic transfer factors of Ag NPs ranging from 1.16 to 2.49, which may be ascribed to NOM reducing the capacity for T. thermophila to excrete total silver (Ag) as NOM could significantly change the form of Ag. These findings provide a novel insight into the impact of NOM on the ecological risk posed by Ag NPs through the food chain and emphasize the need to understand further the interactions between nanoparticles and NOM in various ecosystems.

Potential application of Au core labeling for tracking Ag nanoparticles in the aquatic and biological system.

The entering of silver nanoparticles (Ag NPs) in natural environments constantly increases due to their widespread production and application. While the environmental behavior, impacts, and fate of Ag NPs were critically assessed, the main challenge represents continuous tracking and quantification of Ag NPs in environmental and biological matrices. A group of labeled Ag NPs with gold cores (Au@Ag NPs) was developed for distinguishing between pristine Ag NPs and their other forms, and we comprehensively compared their physicochemical properties, environmental behavior, and biological effects with unlabeled Ag NPs. The electron transfer process from the Au core to the Ag shell gradually decreased with the increase of Ag shell thickness, then the inhibition of Ag+ release induced by the Au core was gradually alleviated, but the generation of superoxide radicals was intensified sharply. Then, the effect of the Au core on the dissolution capacity and free radicals' generation significantly altered the biological toxicity of Ag NPs, and the influence degree was related to the test organism's species. Nevertheless, the Au core retained the surface properties of Ag NPs, leading to the uptake of Au@Ag NPs, entirely consistent with the behavior of unlabeled Ag NPs. These findings confirmed that Au core labeling provides new opportunities for tracking Ag NPs in environmental and biological systems, and the exposure conditions and test organisms should be carefully assessed before employing the Au core labeling technology.

Global Exposure to Per- and Polyfluoroalkyl Substances and Associated Burden of Low Birthweight.

Low birthweight (LBW) is a worldwide public health concern, while the global burden of LBW attributable to endocrine-disrupting chemicals, such as per- and polyfluoroalkyl substances (PFAS), has not yet been evaluated. Here, we established a large dataset for the biomonitoring of seven representative congeners of PFAS by examining data from 2325 publications. Global exposure to perfluorooctanesulfonic acid (PFOS) was the highest, followed by perfluorohexanesulfonic acid (PFHxS) and perfluorooctanoic acid (PFOA). Spatiotemporal exposure to PFAS varied considerably, with daily intake estimated in the range of 0.01-1.7 ng/kg/day. Moreover, decreasing trends in PFOS, PFHxS, and PFOA exposure were noted in most regions of the world over the past two decades, but such trends were not observed for other PFAS with long carbon chains, especially in East Asia. Furthermore, we estimated that human exposure to PFOA contributed to approximately 461,635 (95% confidence interval: 57,418 to 854,645) cases per year of LBW during the past two decades, predominantly from Asian regions. Although our estimation may be constrained by uncertainties from the dose-response curve and data availability, this study has unveiled that PFAS might be a contributor to global LBW prevalence during 2000-2019, supporting continuous actions to mitigate PFAS contamination.