Effect-directed analysis of androgenic compounds from sewage sludges in China.

The safety of municipal sewage sludge has raised great concerns because of the accumulation of large-scale endocrine disrupting chemicals in the sludge during wastewater treatment. The presence of contaminants in sludge can cause secondary pollution owing to inappropriate disposal mechanisms, posing potential risks to the environment and human health. Effect-directed analysis (EDA), involving an androgen receptor (AR) reporter gene bioassay, fractionation, and suspect and nontarget chemical analysis, were applied to identify causal AR agonists in sludge; 20 of the 30 sludge extracts exhibited significant androgenic activity. Among these, the extracts from Yinchuan, Kunming, and Shijiazhuang, which held the most polluted AR agonistic activities were prepared for extensive EDA, with the dihydrotestosterone (DHT)-equivalency of 2.5 - 4.5 ng DHT/g of sludge. Seven androgens, namely boldione, androstenedione, testosterone, megestrol, progesterone, and testosterone isocaproate, were identified in these strongest sludges together, along with testosterone cypionate, first reported in sludge media. These identified androgens together accounted for 55 %, 87 %, and 52 % of the effects on the sludge from Yinchuan, Shijiazhuang, and Kunming, respectively. This study elucidates the causative androgenic compounds in sewage sludge and provides a valuable reference for monitoring and managing androgens in wastewater treatment.

Impacts of biochar aging on its interactions with As(III) and the combined cytotoxicity.

Despite the extensive use of biochar (BC) in soil and aqueous media for pollutant immobilization, the environmental behaviors and health risks of aged BC with multiple pollutants, especially with metal ions possessing various valence states, remain unexplored. Here, we prepared fresh banana peel BC (BP-BC) and aged BP-BCs by acidification (ABP-BC) and oxidation (OBP-BC). ABP-BC was then chosen to explore its environmental behaviors (i.e., adsorption, desorption, and arsenic valence transfer) towards As(III)-Cu(II) and the combined cytotoxicity of BCs with As(III)-Cu(II) was investigated in Human Gastric epithelium cells (GES-1). Our results demonstrate that the aging process notably alters the physicochemical properties of BP-BC, including surface morphology, elemental composition, and surface functional groups, which are key factors affecting the long-term environmental behaviors of BC with As(III)/Cu(II). Specifically, the aging process significantly enhanced the adsorption of As(III) on BC but reduced the adsorption of Cu(II). Although the oxidation of As(III) to As(V) did not change much, the aging process improved the stability of ABP-BC-metal ion complexes, alleviating the release of As(III) in acidic solution. Consequently, the combined cytotoxicity induced by ABP-BC-As(III)-Cu(II) was reduced compared to BP-BC-As(III)-Cu(II). The study highlights the critical roles of the aging process in regulating the As(III) adsorption/desorption dynamics on BCs and their combined cytotoxicity in the presence of multiple metal ions.

Multitask Deep Learning Enabling a Synergy for Cadmium and Methane Mitigation with Biochar Amendments in Paddy Soils.

Biochar has demonstrated significant promise in addressing heavy metal contamination and methane (CH4) emissions in paddy soils; however, achieving a synergy between these two goals is challenging due to various variables, including the characteristics of biochar and soil properties that influence biochar's performance. Here, we successfully developed an interpretable multitask deep learning (MTDL) model by employing a tensor tracking paradigm to facilitate parameter sharing between two separate data sets, enabling a synergy between Cd and CH4 mitigation with biochar amendments. The characteristics of biochar contribute similar weightings of 67.9% and 62.5% to Cd and CH4 mitigation, respectively, but their relative importance in determining biochar's performance varies significantly. Notably, this MTDL model excels in custom-tailoring biochar to synergistically mitigate Cd and CH4 in paddy soils across a wide geographic range, surpassing traditional machine learning models. Our findings deepen our understanding of the interactive effects of Cd and CH4 mitigation with biochar amendments in paddy soils, and they also potentially extend the application of artificial intelligence in sustainable environmental remediation, especially when dealing with multiple objectives.

Polystyrene nanoplastic exposure induces excessive mitophagy by activating AMPK/ULK1 pathway in differentiated SH-SY5Y cells and dopaminergic neurons in vivo.

BACKGROUND: Microplastics and nanoplastics (MNPs) are emerging environmental contaminants detected in human samples, and have raised concerns regarding their potential risks to human health, particularly neurotoxicity. This study aimed to investigate the deleterious effects of polystyrene nanoplastics (PS-NPs, 50 nm) and understand their mechanisms in inducing Parkinson's disease (PD)-like neurodegeneration, along with exploring preventive strategies. METHODS: Following exposure to PS-NPs (0.5-500 µg/mL), we assessed cytotoxicity, mitochondrial integrity, ATP levels, and mitochondrial respiration in dopaminergic-differentiated SH-SY5Y cells. Molecular docking and dynamic simulations explored PS-NPs' interactions with mitochondrial complexes. We further probed mitophagy's pivotal role in PS-NP-induced mitochondrial damage and examined melatonin's ameliorative potential in vitro. We validated melatonin's intervention (intraperitoneal, 10 mg/kg/d) in C57BL/6 J mice exposed to 250 mg/kg/d of PS-NPs for 28 days. RESULTS: In our in vitro experiments, we observed PS-NP accumulation in cells, including mitochondria, leading to cell toxicity and reduced viability. Notably, antioxidant treatment failed to fully rescue viability, suggesting reactive oxygen species (ROS)-independent cytotoxicity. PS-NPs caused significant mitochondrial damage, characterized by altered morphology, reduced mitochondrial membrane potential, and decreased ATP production. Subsequent investigations pointed to PS-NP-induced disruption of mitochondrial respiration, potentially through interference with complex I (CI), a concept supported by molecular docking studies highlighting the influence of PS-NPs on CI. Rescue experiments using an AMPK pathway inhibitor (compound C) and an autophagy inhibitor (3-methyladenine) revealed that excessive mitophagy was induced through AMPK/ULK1 pathway activation, worsening mitochondrial damage and subsequent cell death in differentiated SH-SY5Y cells. Notably, we identified melatonin as a potential protective agent, capable of alleviating PS-NP-induced mitochondrial dysfunction. Lastly, our in vivo experiments demonstrated that melatonin could mitigate dopaminergic neuron loss and motor impairments by restoring mitophagy regulation in mice. CONCLUSIONS: Our study demonstrated that PS-NPs disrupt mitochondrial function by affecting CI, leading to excessive mitophagy through the AMPK/ULK1 pathway, causing dopaminergic neuron death. Melatonin can counteract PS-NP-induced mitochondrial dysfunction and motor impairments by regulating mitochondrial autophagy. These findings offer novel insights into the MNP-induced PD-like neurodegenerative mechanisms, and highlight melatonin's protective potential in mitigating the MNP's environmental risk.

Neurotoxicity of the Cu(OH)2 Nanopesticide through Perturbing Multiple Neurotransmitter Pathways in Developing Zebrafish.

The copper hydroxide [Cu(OH)2] nanopesticide is an emerging agricultural chemical that can negatively impact aquatic organisms. This study evaluated the behavioral changes of zebrafish larvae exposed to the Cu(OH)2 nanopesticide and assessed its potential to induce neurotoxicity. Metabolomic and transcriptomic profiling was also conducted to uncover the molecular mechanisms related to potential neurotoxicity. The Cu(OH)2 nanopesticide at 100 µg/L induced zebrafish hypoactivity, dark avoidance, and response to the light stimulus, suggestive of neurotoxic effects. Altered neurotransmitter-related pathways (serotoninergic, dopaminergic, glutamatergic, GABAergic) and reduction of serotonin (5-HT), dopamine (DA), glutamate (GLU), γ-aminobutyric acid (GABA), and several of their precursors and metabolites were noted following metabolomic and transcriptomic analyses. Differentially expressed genes (DEGs) were associated with the synthesis, transport, receptor binding, and metabolism of 5-HT, DA, GLU, and GABA. Transcripts (or protein levels) related to neurotransmitter receptors for 5-HT, DA, GLU, and GABA and enzymes for the synthesis of GLU and GABA were downregulated. Effects on both the glutamatergic and GABAergic pathways in zebrafish were specific to the nanopesticide and differed from those in fish exposed to copper ions. Taken together, the Cu(OH)2 nanopesticide induced developmental neurotoxicity in zebrafish by inhibiting several neurotransmitter-related pathways. This study presented a model for Cu(OH)2 nanopesticide-induced neurotoxicity in developing zebrafish that can inform ecological risk assessments.

Surface functional groups and biofilm formation on microplastics: Environmental implications.

Microplastics (MPs) contamination is becoming a significant environmental issue, as the widespread omnipresence of MPs can cause many adverse consequences for both ecological systems and humans. Contrary to what is commonly thought, the toxicity-inducing MPs are not the original pristine plastics; rather, they are completely transformed through various surface functional groups and aggressive biofilm formation on MPs via aging or weathering processes. Therefore, understanding the impacts of MPs' surface functional groups and biofilm formation on biogeochemical processes, such as environmental fate, transport, and toxicity, is crucial. In this review, we present a comprehensive summary of the distinctive impact that surface functional groups and biofilm formation of MPs have on their significant biogeochemical behavior in various environmental media, as well as their toxicity and biological effects. We place emphasis on the role of surface functional groups and biofilm formation as a means of influencing the biogeochemical processes of MPs. This includes their effects on pollutant fate and element cycling, which in turn impacts the aggregation, transport, and toxicity of MPs. Ultimately, future research studies and tactics are needed to improve our understanding of the biogeochemical processes that are influenced by the surface functional groups and biofilm formation of MPs.

4-Methylbenzylidene camphor triggers estrogenic effects via the brain-liver-gonad axis in zebrafish larvae.

4-Methylbenzylidene camphor (4-MBC), an emerging contaminant, is a widely-used ultraviolet (UV) filter incorporated into cosmetics because it protects the skin from UV rays and counters photo-oxidation. Despite the well-established estrogenic activity of 4-MBC, the link between this activity and its effects on neurobehavior and the liver remains unknown. Thus, we exposed zebrafish larvae to environmentally relevant concentrations of 4-MBC with 1.39, 4.17, 12.5 and 15.4 µg/mL from 3 to 5 days postfertilization. We found that 4-MBC produced an estrogenic effect by intensifying fluorescence in the transgenic zebrafish, which was counteracted by co-exposure with estrogen receptor antagonist. 4-MBC-upregulated estrogen receptor alpha (erα) mRNA, and an interaction between 4-MBC and ERα suggested ERα's involvement in the 4-MBC-induced estrogenic activity. RNA sequencing unearthed 4-MBC-triggered responses in estrogen stimulus and lipid metabolism. Additionally, 4-MBC-induced hypoactivity and behavioral phenotypes were dependent on the estrogen receptor (ER) pathway. This may have been associated with the disruption of acetylcholinesterase and acetylcholine activities. As a result, 4-MBC increased vitellogenin expression and caused lipid accumulation in the liver of zebrafish larvae. Collectively, this is the first study to report 4-MBC-caused estrogenic effects through the brain-liver-gonad axis. It provides novel insight into how 4-MBC perturbs the brain and liver development.

Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds.

Machine learning has made significant progress in assessing the risk associated with hazardous chemicals. However, most models were constructed by randomly selecting one algorithm and one toxicity endpoint towards single species, which may cause biased regulation of chemicals. In the present study, we implemented comprehensive prediction models involving multiple advanced machine learning and end-to-end deep learning to assess the aquatic toxicity of chemicals. The generated optimal models accurately unravel the quantitative structure-toxicity relationships, with the correlation coefficients of all training sets from 0.59 to 0.81 and of the test sets from 0.56 to 0.83. For each chemical, its ecological risk was determined from the toxicity information towards multiple species. The results also revealed the toxicity mechanism of chemicals was species sensitivity, and the high-level organisms were faced with more serious side effects from hazardous substances. The proposed approach was finally applied to screen over 16,000 compounds and identify high-risk chemicals. We believe that the current approach can provide a useful tool for predicting the toxicity of diverse organic chemicals and help regulatory authorities make more reasonable decisions.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation.

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Computer-Aided Discovery of Potent Broad-Spectrum Vaccine Adjuvants.

Adjuvants stimulate the immune system to vigorously respond to a vaccine. While current adjuvants such as aluminum salts and oil-in-water emulsions have been used for decades, they do not generate broad and long-lasting responses in many vaccines. Consequently, more potent adjuvants are needed. Here, using computer-aided molecule design and machine learning, we discovered 2 new, broad-spectrum adjuvants that can boost vaccine responses. Our library containing 46 toll-like receptor (TLR)-targeting agonist ligands were assembled on Au nanoparticles. Comprehensive in vitro, ex vivo and in vivo studies showed both leads promoted dendritic cell activation via multiple TLRs and enhanced antigen presentation to T cells. When used together with tumor-specific antigens to immunize mice against B16-OVA melanoma and 4T1-PD1 breast cancer, both adjuvants unleashed strong immune responses that suppressed tumor growth and lung metastases. Our results show computer-aided design and screening can rapidly uncover potent adjuvants for tackling waning immunity in current vaccines.

Chemical speciation determines combined cytotoxicity: Examples of biochar and arsenic/chromium.

As both electron donors and acceptors, biochars (BCs) may interact with multivalent metal ions in the environment, causing changes in ionic valence states and resulting in unknown combined toxicity. Therefore, we systematically investigated the interaction between BCs and Cr (Cr(III) & Cr(VI)) or As (As(III) & As(V)) and their combined cytotoxicity in human colorectal mucosal (FHC) cells. Our results suggest that the redox-induced valence state change is a critical factor in the combined cytotoxicity of BCs with Cr/As. Specifically, when Cr(VI) was adsorbed on BCs, 86.4 % of Cr(VI) was reduced to Cr(III). In contrast, As(III) was partially oxidized to As(V) with a ratio of 37.2 %, thus reaching a reaction equilibrium. Meanwhile, only As(V) was released in the cell, which could cause more As(III) to be oxidized. As both Cr(III) and As(V) are less toxic than their corresponding counterparts Cr(VI) and As(III), different redox interactions between BCs and Cr/As and release profiles between BCs and Cr/As together lead to reduced combined cytotoxicity of BP-BC-Cr(VI) and BP-BC-As(III). It suggests that the valence state changes of metal ions due to redox effects is one of the parameters to be focused on when studying the combined toxicity of complexes of BCs with different heavy metal ions.

Unraveling the joint toxicity of transition-metal dichalcogenides and per- and polyfluoroalkyl substances in aqueous mediums by experimentation, machine learning and molecular dynamics.

The environmental fate of transition-metal dichalcogenides (TMDCs) may be further complicated by interacting with existing pollutants, especially per- and polyfluoroalkyl substances (PFAS). However, due to their sheer volume, it is impossible to explore all possible interactions by simply utilizing experimental methods. Herein, we used two model TMDC nanosheets, molybdenum disulfide (MoS2) and tungsten disulfide (WS2), and seven PFAS to explore their interactions and subsequent impacts on model cell lines and zebrafish. Utilizing experimental methods and machine learning approaches, we showed that TMDCs-PFAS interactions can pose unique challenges due to their interaction-specific toxicity niches towards cell lines. Further in vivo experiments, together with molecular dynamics simulation, suggested that TMDCs-PFAS interactions in aqueous environments significantly increased their bioaccumulation in zebrafish towards different target organs, mostly due to the differences in loading PFAS. Such enhanced bioaccumulation increased the oxidative stress in zebrafish liver and intestine, as demonstrated by the increased reactive oxygen species (ROS) level and other enzyme activities, which eventually led to obvious histopathological alterations in the liver and intestine. Our study highlights the importance of exploring interactions between emerging and existing contaminants with state-of-art techniques in aqueous environments and its significance in safeguarding aquatic environment health.

Threats to Terrestrial Plants from Emerging Nanoplastics.

Nanoplastics are ubiquitous in ecosystems and impact planetary health. However, our current understanding on the impacts of nanoplastics upon terrestrial plants is fragmented. The lack of systematic approaches to evaluating these impacts limits our ability to generalize from existing studies and perpetuates regulatory barriers. Here, we undertook a meta-analysis to quantify the overall strength of nanoplastic impacts upon terrestrial plants and developed a machine learning approach to predict adverse impacts and identify contributing features. We show that adverse impacts are primarily associated with toxicity metrics, followed by plant species, nanoplastic mass concentration and size, and exposure time and medium. These results highlight that the threats of nanoplastics depend on a diversity of reactions across molecular to ecosystem scales. These reactions are rooted in both the spatial and functional complexities of nanoplastics and, as such, are specific to both the plastic characteristics and environmental conditions. These findings demonstrate the utility of interrogating the diversity of toxicity data in the literature to update both risk assessments and evidence-based policy actions.

Linking electron ionization mass spectra of organic chemicals to toxicity endpoints through machine learning and experimentation.

Quantitative structure-activity relationship (QSAR) modeling has been widely used to predict the potential harm of chemicals, in which the prediction heavily relies on the accurate annotation of chemical structures. However, it is difficult to determine the accurate structure of an unknown compound in many cases, such as in complex water environments. Here, we solved the above problem by linking electron ionization mass spectra (EI-MS) of organic chemicals to toxicity endpoints through various machine learning methods. The proposed method was verified by predicting 50% growth inhibition of Tetrahymena pyriformis (T. pyriformis) and liver toxicity. The optimal model performance obtained an R2 > 0.7 or balanced accuracy > 0.72 for both the training set and test set. External experimentation further verified the application potential of our proposed method in the toxicity prediction of unknown chemicals. Feature importance analysis allowed us to identify critical spectral features that were responsible for chemical-induced toxicity. Our approach has the potential for toxicity prediction in such fields that it is difficult to determine accurate chemical structures.

Developmental toxicity of fenbuconazole in zebrafish: Effects on mitochondrial respiration and locomotor behavior.

Triazole fungicides are used to control the disease of cereal crops but may also cause adverse effects on non-target organisms. There is a lack of toxicity data for some triazoles such as fenbuconazole in aquatic organisms. This research was conducted to evaluate the toxicity of fenbuconazole at environmentally relevant concentrations with attention on the mitochondria, antioxidant system, and locomotor activity in zebrafish. Zebrafish were exposed to one concentration of 5, 50, 200 or 500 ng/L fenbuconazole for 96 h. There was no effect on survival nor percentage of fish hatched, but exposure to 200 and 500 ng/L fenbuconazole resulted in malformation and hypoactivity in zebrafish. Oxygen consumption rates (OCR) of embryos were measured to determine if the fungicide impaired mitochondrial respiration. Exposure to 500 ng/L fenbuconazole reduced basal OCR and oligomycin-induced ATP linked respiration in exposed fish. Fenbuconazole reduced mitochondrial membrane potential and reduced the activities of mitochondrial Complex II and III. Transcript levels of both sdhc and cyc1, each related to Complex II and III, were also altered in expression by fenbuconazole exposure, consistent with mitochondrial dysfunction in embryos. Fenbuconazole activated the antioxidant system, based upon both transcriptional and enzymatic data in zebrafish. Consistent with mitochondrial impairment, molecular docking confirmed a strong binding capacity of the fungicide at the Qi site of Complex III, revealing this complex is susceptible to fenbuconazole. This study reveals potential toxicity pathways related to fenbuconazole exposure in aquatic organisms; such data can improve risk assessments for triazole fungicides.

Biosafety-inspired structural optimization of triazolium ionic liquids based on structure-toxicity relationships.

Ionic liquids (ILs), owing to their low vapor pressure and excellent solvating ability, are being increasingly applied in various industries to replace highly toxic organic solvents. They mainly pollute aquatic environment and soils, directly endangering eco-environment and human health. Therefore, it is critical to understand and optimize structural motifs of ILs with reduced toxicity. Considering human oral exposure is the major route, our investigations employed a human cell panel (modeling oral exposures) including human stomach (GES-1), intestinal (FHC), liver (HepG2) and kidney (HEK293) cells using a series of experimental and computational approaches to explore the cytotoxicity and molecular mechanism of ILs. We discovered that the cytotoxicity of triazolium and imidazolium ILs was human cell line-dependent with cytotoxicity in an order of FHC > GES-1 > HepG2 > HEK293. For this reason, a toxicity assay using a single cell line was highly inappropriate. Compared to anions (Br-, OTs-, OTMBS-) we tested, the cation of ILs played a major role in causing cytotoxicity. Ionic liquids with cations having longer hydrophobic sidechains (IL09 vs. IL01) readily insert into cell membranes with enhanced membrane and lipidomic perturbations, induce cytotoxicity by triggering cell cycle arrest and apoptosis. Reducing sidechain length and incorporating three nitrogen atoms (triazolium) instead of two (imidazolium) in the cation core alleviated cytotoxicity by reducing cell membrane perturbations and cell function interference. These findings provide important guiding principles for the design of the next-generation of "green" and safe ILs.

Predicting cytotoxicity of binary pollutants towards a human cell panel in environmental water by experimentation and deep learning methods.

Biological assays are useful in water quality evaluation by providing the overall toxicity of chemical mixtures in environmental waters. However, it is impossible to elucidate the source of toxicity and some lethal combination of pollutants simply using biological assays. As facile and cost-effective methods, computation model-based toxicity assessments are complementary technologies. Herein, we predicted the human health risk of binary pollutant mixtures (i.e., binary combinations of As(III), Cd(II), Cr(VI), Pb(II) and F(I)) in water using in vitro biological assays and deep learning methods. By employing a human cell panel containing human stomach, colon, liver, and kidney cell lines, we assessed the human health risk mimicking cellular responses after oral exposures of environmental water containing pollutants. Based on the experimental cytotoxicity data in pure water, multi-task deep learning was applied to predict cellular response of binary pollutant mixtures in environmental water. Using additive descriptors and single pollutant toxicity data in pure water, the established deep learning model could predict the toxicity of most binary mixtures in environmental water, with coefficient of determination (R2) > 0.65 and root mean squared error (RMSE) < 0.22. Further combining the experimental data on synergistic and antagonistic effects of pollutant mixtures, deep learning helped improve the predictive ability of the model (R2 > 0.74 and RMSE <0.17). Moreover, predictive models allowed us identify a number of toxicity source-related physiochemical properties. This study illustrates the combination of experimental findings and deep learning methods in the water quality evaluation.

Emerging impacts of ionic liquids on eco-environmental safety and human health.

Owing to their unique physicochemical properties, ionic liquids (ILs) have been rapidly applied in diverse areas, such as organic synthesis, electrochemistry, analytical chemistry, functional materials, pharmaceutics, and biomedicine. The increase in the production and application of ILs has resulted in their release into aquatic and terrestrial environments. Because of their low vapor pressure, ILs cause very little pollution in the atmosphere compared to organic solvents. However, ILs are highly persistent in aquatic and terrestrial environments due to their stability, and therefore, potentially threaten the safety of eco-environments and human health. Specifically, the environmental translocation and retention of ILs, or their accumulation in organisms, are all related to their physiochemical properties, such as hydrophobicity. Based on results of ecotoxicity, cytotoxicity, and toxicity in mammalian models, the mechanisms involved in IL-induced toxicity include damage of cell membranes and induction of oxidative stress. Recently, artificial intelligence and machine learning techniques have been used in mining and modeling toxicity data to make meaningful predictions. Major future challenges are also discussed. This review will accelerate our understanding of the safety issues of ILs and serve as a guideline for the design of the next generation of ILs.

Comprehensive Interrogation on Acetylcholinesterase Inhibition by Ionic Liquids Using Machine Learning and Molecular Modeling.

Quantitative structure-activity relationship (QSAR) modeling can be used to predict the toxicity of ionic liquids (ILs), but most QSAR models have been constructed by arbitrarily selecting one machine learning method and ignored the overall interactions between ILs and biological systems, such as proteins. In order to obtain more reliable and interpretable QSAR models and reveal the related molecular mechanism, we performed a systematic analysis of acetylcholinesterase (AChE) inhibition by 153 ILs using machine learning and molecular modeling. Our results showed that more reliable and stable QSAR models (R2 > 0.85 for both cross-validation and external validation) were obtained by combining the results from multiple machine learning approaches. In addition, molecular docking results revealed that the cations and organic anions of ILs bound to specific amino acid residues of AChE through noncovalent interactions such as π interactions and hydrogen bonds. The calculation results of binding free energy showed that an electrostatic interaction (ΔEele < -285 kJ/mol) was the main driving force for the binding of ILs to AChE. The overall findings from this investigation demonstrate that a systematic approach is much more convincing. Future research in this direction will help design the next generation of biosafe ILs.

Cytotoxicity Induction by the Oxidative Reactivity of Nanoparticles Revealed by a Combinatorial GNP Library with Diverse Redox Properties.

It is crucial to establish relationship between nanoparticle structures (or properties) and nanotoxicity. Previous investigations have shown that a nanoparticle's size, shape, surface and core materials all impact its toxicity. However, the relationship between the redox property of nanoparticles and their toxicity has not been established when all other nanoparticle properties are identical. Here, by synthesizing an 80-membered combinatorial gold nanoparticle (GNP) library with diverse redox properties, we systematically explored this causal relationship. The compelling results revealed that the oxidative reactivity of GNPs, rather than their other physicochemical properties, directly caused cytotoxicity via induction of cellular oxidative stress. Our results show that the redox diversity of nanoparticles is regulated by GNPs modified with redox reactive ligands.