Chemometric modeling to predict air half-life of persistent organic pollutants (POPs)

ChemFREE: a one-stop comprehensive platform for ecological and environmental risk evaluation of chemicals in one health world

Addressing health and safety crises stemming from various environmental and ecological issues is a core focus of One Health (OH), which aims to balance and optimize the health of humans, animals, and the environment. While many chemicals contribute significantly to our quality of life when properly used, others pose environmental and ecological health risks. Recently, assessing the ecological and environmental risks associated with chemicals has gained increasing significance in the OH world. In silico models may address time-consuming and costly challenges, and fill gaps in situations where no experimental data is available. However, despite their significant contributions, these assessment models are not web-integrated, leading to user inconvenience. In this study, we developed a one-stop comprehensive web platform for freely evaluating the eco-environmental risk of chemicals, named ChemFREE (Chemical Formula Risk Evaluation of Eco-environment, available in http://chemfree.agroda.cn/chemfree/). Inputting SMILES string of chemicals, users will obtain the assessment outputs of ecological and environmental risk, etc. A performance evaluation of 2935 external chemicals revealed that most classification models achieved an accuracy rate above 0.816. Additionally, the $Q_{F1}^2$ metric for regression models ranges from 0.618 to 0.898. Therefore, it will facilitate the eco-environmental risk evaluation of chemicals in the OH world.

Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants

Persistent organic pollutants (POPs) are ubiquitous and bioaccumulative, posing potential and long-term threats to human health and the ecological environment. Quantitative structure-activity relationship (QSAR) studies play a guiding role in analyzing the toxicity and environmental fate of different organic pollutants. In the current work, five molecular descriptors are utilized to construct QSAR models for predicting the mean and maximum air half-lives of POPs, including specifically the energy of the highest occupied molecular orbital (HOMO_Energy_DMol3), a component of the dipole moment along the z-axis (Dipole_Z), fragment contribution to SAscore (SAscore_Fragments), subgraph counts (SC_3_P), and structural information content (SIC). The QSAR models were achieved through the application of three machine learning methods: partial least squares (PLS), multiple linear regression (MLR), and genetic function approximation (GFA). The determination coefficients (R2) and relative errors (RE) for the mean air half-life of each model are 0.916 and 3.489% (PLS), 0.939 and 5.048% (MLR), 0.938 and 5.131% (GFA), respectively. Similarly, the determination coefficients (R2) and RE for the maximum air half-life of each model are 0.915 and 5.629% (PLS), 0.940 and 10.090% (MLR), 0.939 and 11.172% (GFA), respectively. Furthermore, the mechanisms that elucidate the significant factors impacting the air half-lives of POPs have been explored. The three regression models show good predictive and extrapolation abilities for POPs within the application domain.

Paradox of 'ideal correlations': improved model for air half-life of persistent organic pollutants

The persistence of organic pollutants is an important environmental property due to the extended possibility to have an impact of corresponding substances. In many cases, the experimental values of the thousands of contaminants are missing. The object of the study is novel computational modelling for air pollutions. Quantitative structure-property relationship (QSPR) for air half-life has been built using the Monte Carlo method with applying the index of ideality of correlation (IIC). The basis of the predictive model of air half-life is the representation of the molecular structure by simplifying molecular input-line entry system (SMILES) and numerical data on the above endpoint (expressed by hours) converted to a decimal logarithm. The statistical quality of the model has been checked up with different validation metrics and is quite good. Paradoxically, the improvement of the statistical quality via the IIC for the validation set is done in detriment to the training set. The new model has performed better than those obtained previously on the same set of compounds, for the prediction of new compounds in the validation set. Some semi-quantitative indicators for the mechanistic interpretation of the model are suggested.

Graph Attention Network Model with Defined Applicability Domains for Screening PBT Chemicals

In silico models for screening environmentally persistent, bio-accumulative, and toxic (PBT) substances are necessary for sound management of chemicals. Due to the complex structure-activity landscapes (SALs) on the PBT attributes, previous models for screening PBT chemicals lack either applicability domain (AD) characterizations or interpretability, restricting their applications. Herein, graph attention networks (GATs), a novel neural network architecture, were introduced to construct models for screening PBT chemicals. Results show that the GAT model not only outperformed those in previous studies but also exhibited interpretability since it optimizes attention weight parameters (P_AW) that indicate contributions of each atom to the PBT attributes. An AD characterization termed AD_FP-AC, which considers both molecular fingerprint (FP) similarities and compounds at activity cliffs (ACs) of SALs, was proposed to describe the ADs, which further assured the performance of the GAT model. Eight previously unidentified classes of compounds were identified as PBT chemicals from the Inventory of Existing Chemical Substances in China. The GAT model together with the AD_FP-AC characterization may serve as efficient tools for screening PBT chemicals, and the modeling methodology can be applied to other physicochemical, environmental, behavioral, and toxicological parameters of chemicals that are necessary for their risk assessment and management.

Silica nanoparticles induce pyroptosis and cardiac hypertrophy via ROS/NLRP3/Caspase-1 pathway

Growing literatures suggest that silica nanoparticles (SiNPs) exposure is correlated with adverse cardiovascular effects. Cardiac hypertrophy is one of the most common risk factors for heart failure. However, whether SiNPs involved in cardiac hypertrophy and the underlying mechanisms was remained unexploited. Our study aimed to investigate the molecular mechanisms of SiNPs on pyroptosis and cardiac hypertrophy. The in vivo results found that SiNPs induced ultrastructural change and histopathological damage, accompanied by oxidative damage occurred and increased levels of inflammatory factors (IL-18 and IL-1β) in heart tissue. In addition, SiNPs could upregulate the expressions of cardiac hypertrophy-related special marker including ANP, BNP, β-MHC, it also elevated the pyroptosis-related protein, such as NLRP3, Cleaved-Caspase-1, GSDMD, IL-18 and Cleaved-IL-1β in vivo. For in vitro study, SiNPs increased the intracellular ROS generation and activated the NLRP3/Caspase-1/GSDMD signaling pathway in cardiomyocytes. Whereas, the NADPH oxidase (NOX) inhibitor VAS2870 had effectively inhibited the ROS level and suppressed the expression of NLRP3, ASC, Pro-Caspase-1, Cleaved-Caspase-1, N-GSDMD, IL-18, Cleaved-IL-1β, ANP, BNP and β-MHC. Moreover, transfected with si-NLRP3 or adopted with Caspase-1 inhibitor VX-765 in cardiomyocytes showed an inhibitory effect on SiNPs-induced pyroptosis and cardiac hypertrophy. In summary, our results demonstrated that SiNPs could trigger pyroptosis and cardiac hypertrophy via ROS/NLRP3/Caspase-1 signaling pathway.

Metal-Organic Frameworks-Based Sensors for Food Safety

Food contains a variety of poisonous and harmful substances that have an impact on human health. Therefore, food safety is a worldwide public concern. Food detection approaches must ensure the safety of food at every step of the food supply chain by monitoring and evaluating all hazards from every single step of food production. Therefore, early detection and determination of trace-level contaminants in food are one of the most crucial measures for ensuring food safety and safeguarding consumers’ health. In recent years, various methods have been introduced for food safety analysis, including classical methods and biomolecules-based sensing methods. However, most of these methods are laboratory-dependent, time-consuming, costly, and require well-trained technicians. To overcome such problems, developing rapid, simple, accurate, low-cost, and portable food sensing techniques is essential. Metal-organic frameworks (MOFs), a type of porous materials that present high porosity, abundant functional groups, and tunable physical and chemical properties, demonstrates promise in large-number applications. In this regard, MOF-based sensing techniques provide a novel approach in rapid and efficient sensing of pathogenic bacteria, heavy metals, food illegal additives, toxins, persistent organic pollutants (POPs), veterinary drugs, and pesticide residues. This review focused on the rapid screening of MOF-based sensors for food safety analysis. Challenges and future perspectives of MOF-based sensors were discussed. MOF-based sensing techniques would be useful tools for food safety evaluation owing to their portability, affordability, reliability, sensibility, and stability. The present review focused on research published up to 7 years ago. We believe that this work will help readers understand the effects of food hazard exposure, the effects on humans, and the use of MOFs in the detection and sensing of food hazards.

Predicting anionic surfactant toxicity to Daphnia magna in aquatic environment: a green approach for evaluation of EC50 values

The median effective concentration (EC₅₀) is the concentration of a substance expected to produce a specific effect in 50% of the populations with a certain density under defined conditions. This parameter is expressed as an acute toxicity and is obtained via chemical toxicity testing. But, the laboratory work is time-consuming, expensive, and not eco-friendly. Therefore, to predict EC₅₀ for new anionic surfactants, a quantitative structure-activity relationship (QSAR) tool was studied for modeling the EC₅₀ of anionic surfactants on Daphnia magna based on the molecular descriptors. The best model (R² = 0.901 and F = 118.077, p<0.01) included 3 variables of the number of carbons, hydrogens, and the octanol-water partition coefficient logarithm. The main contribution to the toxicity was the octanol-water partition coefficient logarithm descriptor that had a negative effect on the toxicity of surfactants. The QSAR approach exhibited good results in predicting anionic surfactants EC₅₀, which allows the building of a simple, valid, and interpretable model that can be utilized as potential tools for rapidly predicting the lnEC50 of new or untested anionic surfactants to Daphnia magna.

A critical review on the formation, fate and degradation of the persistent organic pollutant hexachlorocyclohexane in water systems and waste streams

The environmental impacts of persistent organic pollutants (POPs) is an increasingly prominent topic in the scientific community. POPs are stable chemicals that are accumulated in living beings and can act as endocrine disruptors or carcinogens on prolonged exposure. Although efforts have been taken to minimize or ban the use of certain POPs, their use is still widespread due to their importance in several industries. As a result, it is imperative that POPs in the ecosystem are degraded efficiently and safely in order to avoid long-lasting environmental damage. This review focuses on the degradation techniques of hexachlorocyclohexane (HCH), a pollutant that has strong adverse effects on a variety of organisms. Different technologies such as adsorption, bioremediation and advanced oxidation process have been critically analyzed in this study. All 3 techniques have exhibited near complete removal of HCH under ideal conditions, and the median removal efficiency values for adsorption, bioremediation and advanced oxidation process were found to be 80%, 93% and 82% respectively. However, it must be noted that there is no ideal HCH removal technique and the selection of removal method depends on several factors. Furthermore, the fates of HCH in the environment and challenges faced by HCH degradation have also been explained in this study. The future scope for research in this field has also received attention.

Metabolomic characteristics of hepatotoxicity in rats induced by silica nanoparticles

Silica nanoparticles (SiNPs) have become one of the most widely studied nanoparticles in nanotechnology for environmental health and safety. Although many studies have devoted to evaluating the hepatotoxicity of SiNPs, it is currently impossible to predict the extent of liver lipid metabolism disorder by identifying changes in metabolites. In the present study, 40 male Sprague-Dawley (SD) rats were randomly divided into control group and 3 groups with different doses (1.8 mg/kg body weight (bw), 5.4 mg/kg bw, 16.2 mg/kg bw), receiving intratracheal instillation of SiNPs. Liver tissue was taken for lipid level analysis, and serum was used for blood biochemical analysis. Then, the metabolites changes of liver tissue in rats were systematically analyzed using ¹H nuclear magnetic resonance (¹H NMR) techniques in combination with multivariate statistical analysis. SiNPs induced serum alanine aminotransferase (ALT), aspartate aminotransferase (AST) and triglyceride (TG) elevation in treated groups; TG and low-density lipoprotein cholesterol (LDL-C) were significantly higher in SiNPs-treated groups of high-dose, however high-density lipoprotein cholesterol (HDL-C) showed a declining trend in liver tissue. The orthogonal partial least squares discriminant analysis (OPLS-DA) scores plots revealed different metabolic profiles between control and high-dose group (Q² =0.495, R²Y=0.802, p = 0.037), and a total of 11 differential metabolites. Pathway analysis indicated that SiNPs treatment mainly affected 10 metabolic pathways including purine metabolism, glucose-alanine cycle and metabolism of various amino acids such as glutamate, cysteine and aspartate (impact value>0.1, false discovery rate (FDR)< 0.05). The result indicated that exposure to SiNPs caused liver lipid metabolism disorder in rats, the biochemical criterions related to lipid metabolism changed significantly. The obviously changed metabolomics in SiNPs-treated rats mostly occurred in amino acids, organic acids and nucleosides.