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With the promotion of carbon neutrality, it is also important to synchronously promote the assessment and sustainable management of chemicals so as to protect public health. Humans and animals are possibly exposed to endocrine disruptors that have inhibitory effects on thyroid stimulating hormone receptor (TSHR). As such, it is important to identify chemicals that inhibit TSHR and to develop models to predict their inhibitory activity. In this study, 5952 compounds derived from a cyclic adenosine monophosphate (cAMP) analysis, a key signaling pathway in thyrocytes, were used to establish a binary classification model comparing methods that included random forest (RF), extreme gradient boosting (XGB), and logistic regression (LR). The prediction model based on RF showed the highest identification accuracy for revealing chemicals that may inhibit TSHR. For the RF model, recall was calculated at 0.89, balance accuracy was 0.85, and its receiver operating characteristic (ROC) curve-area under (AUC) was 0.92, indicating that the model had very high predictive capacity. The lowest CDocker energy (CE) and CDocker interaction energy (CIE) for chemicals and TSHR were determined and were subsequently introduced into the predictive model as descriptors. A regression model, extreme gradient boosting-Regression (XGBR), was successfully established yielding an R 2  = 0.65 to predict inhibitory activity for active compounds. Parameters that included dissociation characteristics, molecular structure, and binding energy were all key factors in the predictive model. We demonstrate that QSAR models are useful approaches, not only for identifying chemicals that inhibit TSHR, but for predicting inhibitory activity of active compounds., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Exposure of cells to xenobiotic human-made products can lead to genotoxicity and cause DNA damage. It is an urgent need to quickly identify the chemicals that cause DNA damage, and their toxicity should be predicted. In this study, recursive partitioning (RP), binary logistic regression, and one machine learning approach, namely, random forest (RF) classifier, were used to predict the active and inactive compounds of a total 5036 data based on the assay conducted by a β-lactamase reporter gene under control of the p53 response element (p53RE) from Tox21 library. Results show that the binary logistic regression model with a threshold of 0.5 has a high accuracy rate (83%) to distinguish active and inactive compounds. The RF classifier method has satisfactory results, with an accuracy rate (84.38%) approximately higher than that of binary logistic regression. The models established can identify compounds that induce DNA damage and activate p53, and provide a scientific basis for the risk assessment of organic chemicals in the environment., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Mitochondria are significant targets in cells for many environmental chemicals. Mitochondrial damage and dysfunction can lead to apoptosis and death of fish. The objectives of this study were to compare the modes of action (MOAs) between fish, cell and mitochondrial toxicity. To achieve the goal, toxicity correlation, excess toxicity and quantitative structure-activity relationship (QSAR) were investigated between these three toxicity endpoints for a wide range of compounds. Results showed that fish toxicity is well correlated to cytotoxicity, but overall fish toxicity is relatively greater than the cytotoxicity. On the other hand, fish or cell toxicity is poorly related to mitochondrial toxicity, suggesting some compounds share same toxic mechanism but some not. The excess toxicity calculated from toxicity ratio (TR) shows that specifically-acting compounds in cytotoxicity, such as insecticides, fungicides, herbicides, dyes and medications used to treat cancer, depression, heart failure and blood pressure, are active compounds in mitochondrial toxicity. However, the less inert compounds identified in fish and cell toxicity exhibit greatly mitochondrial toxicity. QSAR models reveal that fish or cell toxicity is closely related to the chemical hydrophobicity, ionization, energy of lowest unoccupied molecular orbital, hydrogen bonding potential and stability. These descriptors reflect chemical bio-uptake, reactivity and interaction with target receptors. On the other hand, binomial model reveals that mitochondrial toxicity is closely related to the chemical hydrophobicity and polarizability/dipolarity, indicating bio-uptake and Van der Waals interaction play key roles in mitochondrial toxicity. Theoretical equations have been used to explain the toxicity correlation, excess toxicity and QSAR for fish, cell and mitochondrial toxicity. Above results suggest that cytotoxicity can serve as a surrogate for fish toxicity and be used in the safety evaluation of organic pollutants in aqueous environment, but not mitochondrial toxicity, although some compounds share same modes of action between fish or cell toxicity and mitochondrial toxicity., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Microplastics (MPs), a growing class of emerging pollutants in the environment, have attracted widespread attention due to their adsorption properties. Recent research on MPs has mainly concentrated on seawater, and little work has been conducted on freshwater. Investigating and predicting the adsorption behavior of organic pollutants by MPs are necessary in freshwater. In this study, the adsorption behavior of 13 organic chemicals by polyethylene (PE) and chlorinated polyethylene (CPE) MPs was determined under freshwater conditions. Results shows the majority of the organic chemicals exhibit no distinctive differences in their adsorption on two MPs. However, the adsorption of polycyclic aromatic hydrocarbons and chlorobenzene on CPE is obviously stronger than that on PE, and the result is a counter for two pesticides. Quantitative structure activity relationship (QSAR) analysis was performed for the prediction of adsorption capacity. A QSAR model with acceptable performance (R 2  = 0.8586) was built to predict the adsorptive affinity (expressed as logK d ) of organic compounds on the PE MPs via multivariable linear regression (MLR) on forty-nine determined and collected data. The octanol/water partition coefficient (logK ow ) and excess molar refractive index (E) play dominant roles in the model. A QSAR model with satisfactory performance (R 2  = 0.9302) was also established for logK d values from CPE MPs in freshwater by using 13 adsorption data determined. The logK ow and most negative charge on Cl atom (Q - max,cl ) play decisive roles in the adsorption. The findings can provide a scientific basis for the risk assessment of waters contaminated by MPs and organic pollutants., (Copyright © 2021 Elsevier Inc. All rights reserved.)

To estimate spatial distribution, source analysis and uncertainty of heavy metals (Pb, Cd, Cr, Hg, As, Cu, Zn, and Ni) based on geographic information system (GIS), positive matrix factorization model (PMF) and bootstrap (BS) using 382 soil samples collected from cultivated soils in Lanzhou. The mean contents of Cd, Hg, Cu, Zn and Ni were high as 1.7,1.7, 2.1, 1.5 and 1.3 times local background values, mean contents of Pb, Cr and As were lower than local background values. However, the mean contents of eight heavy metals were lower environmental quality risk control standard for soil contamination of agricultural soil. Proportions of four sources were identified: Cr was predominantly contributed by natural sources (29.14%), Cu, Zn and Ni was primarily from industrial sources (25.26%), Hg and As were mainly of agricultural sources (27.49%), Pb and Cd mainly came from traffic source and smelting-related activities (18.09%). Uncertainties analysis contained three aspects: bootstrap runs, factor contributions in the PMF solution, and coefficient of variation (CV) values. By combining the four pollution source factors with bootstrap runs, the accuracy of the four pollution source factors were reliable based on PMF model. The median values in the BS runs was considered the most true factor contribution, and the 5th-95th quartile interval represents the variability of each factor, Factor 4 (traffic source) R 2 was 0.70 and lower variability. The highest CV value usually means a significantly deviation degree. In this study, the CV values of Cr in Factor 1, Cu, Zn, and Ni in Factor 2, Hg, and As in Factor 3, Pb, and Cd in Factor 4 were lower, indicates a lower deviation degree. and with the lowest content among heavy metals usually was also with the greatest uncertainties. In this study improves understanding of the reduction of heavy metal pollution in cultivated soil, and also serves as reference for pollution source apportionment in other regions., (Copyright © 2021. Published by Elsevier Inc.)

Cellular exposure to xenobiotic human-made products will lead to oxidative stress that gives rise to DNA damage, as well as chemical or mechanical damage. Distinguishing the chemicals that will induce oxidative stress and predicting their toxicity is necessary. In the present study, 4270 compounds in the ARE-bla assay were investigated to predict active and inactive compounds by using simple algorithms, namely, recursive partitioning (RP) and binomial logistic regression, and to develop the quantitative structure-activity relationship (QSAR) models of chemicals that activate the ARE pathway to induce oxidative stress and exert toxic effects on cells. A decision tree based on scaffold-based fragments obtained through RP analysis showed the best identification accuracy. However, the overall identification accuracy of this model for active compounds was unsatisfactory due to limited fragments. Furthermore, a binomial logistic regression model was developed from 638 active compounds and 3632 inactive chemicals. The model with a cutoff of 0.15 could predict chemicals that were active or inactive with the prediction accuracy of 69.1%. Its area under the receiver operating characteristic (ROC) curve metric (AUROC) was 0.762, which indicated the acceptable predictive ability of this model. The parameters nBM (number of multiple bonds) and H% (percentage of H atom) played dominant roles in the prediction of the activity (inactive or active) of chemicals. A global QSAR model was developed to predict the toxicity of active chemicals. However, the model displayed an unsatisfactory result with R 2  = 0.316 and R 2 ext  = 0.090. Active chemicals were then classified on the basis of structure. A total of 79 compounds with carbon chains could be predicted with acceptable performance by using a QSAR model with six descriptors (R 2  = 0.722, R 2 ext  = 0.798, Q 2 Loo  = 0.654, Q 2 Boot  = 0.755, Q 2 ext  = 0.721). The simple models established here contribute to efforts on identification compounds inducing oxidative stress and provide the scientific basis for risk assessment to organisms in the environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The ozonation-based advanced oxidation process is a promising treatment technology for wastewater with micropollutants. The second-order reaction rate constant (k O3 ) of ozone (O 3 ) with organic compounds is an important index for estimating removal efficiency of organic pollutants in engineered treatment; however, the experimental k O3 values are currently only available for hundreds of chemicals. In this study, two quantitative-structure activity relationship (QSAR) models were developed to predict k O3 of various organic chemicals with multiple linear regression (MLR) and support vector machine (SVM) methods. The built QSAR models cover a large dataset (136 chemicals) and more structurally diverse chemicals as compared to the existing models. The MLR model possesses satisfactory goodness-of-fit (R 2 tr  = 0.734), robustness (Q 2 LOO  = 0.700, Q 2 BOOT  = 0.772) and predictive ability (R 2 ext  = 0.797, Q 2 ext  = 0.794), and the SVM model also has good fitness (R 2 tr  = 0.862) and predictability (R 2 ext  = 0.782, Q 2 ext  = 0.775). The applicability domain of the models has been extended and includes chemicals (especially some emerging pollutants) that are rarely covered in many previous models. The underlying molecular structural factors influencing ozonation are revealed. The energy of the highest occupied molecular orbital (E HOMO ) and the phenol/enol/carboxyl OH group (O-057) are the two most important molecular structural factors governing the reactivity of organic compounds with ozone. The developed models can serve as a prescreening tool for the removal prediction of organic pollutants by ozone., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The acute toxicity of organic pollutants to fish, Daphnia magna, Tetrahymena pyriformis, and Vibrio fischeri was investigated. The results indicated that the Toxicity Ratio (TR) threshold of log TR =1, which has been based on the distribution of toxicity data to fish, can also be used to discriminate reactive or specifically acting compounds from baseline narcotics for Daphnia magna and Vibrio fischeri. A log TR=0.84 is proposed for Tetrahymena pyriformis following investigation of the relationships between the species sensitivity and the absolute averaged residuals (AAR) between the predicted baseline toxicity and the experimental toxicity. Less inert compounds exhibit relatively higher toxicity to the lower species (Tetrahymena pyriformis and Vibrio fischeri) than the higher species (fish and Daphnia magna). A greater number of less inert compounds with log TR greater than the thresholds was observed for Tetrahymena pyriformis and Vibrio fischeri. This may be attributed to the hydrophilic compounds which may pass more easily through cell membranes than the skin or exoskeleton of organisms and have higher bioconcentration factors in the lower species, leading to higher toxicity. Most of classes of chemical associated with excess toxicity to one species also exhibited excess toxicity to other species, however, a few classes with excess toxicity to one species exhibiting narcotic toxicity to other species and thus may have different MOAs between species. Some ionizable compounds have log TR much lower than one because of the over-estimated log K OW . The factors that influence the toxicity ratio calculated from baseline level are discussed in this paper., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Autosomal dominant polycystic kidney disease (ADPKD) is a common monogenetic disease characterized by the progressive development of renal cysts with further need for effective therapy. Here our aim was to investigate the effect of Ganoderma triterpenes (GT) on the development of kidney cysts. Importantly, GT attenuated cyst development in two mouse models of ADPKD with phenotypes of severe cystic kidney disease. Assays for tubulogenesis showed that GT promoted epithelial tubule formation in MDCK cells, suggesting a possible effect on epithelial cell differentiation. The role of GT in regulating key signaling pathways involved in the pathogenesis of PKD was further investigated by immune blotting. This showed that GT specifically downregulated the activation of the Ras/MAPK signaling pathway both in vitro and in vivo without detectable effect on the mTOR pathway. This mechanism may be involved in GT downregulating intracellular cAMP levels. Screening of 15 monomers purified from GT for their effects on cyst development indicated that CBLZ-7 (ethyl ganoderate C2) had a potent inhibitory effect on cyst development in vitro. Additionally, like GT, CBLZ-7 was able to downregulate forskolin-induced activation of the Ras/MAPK pathway. Thus, GT and its purified monomer CBLZ-7 may be potential therapeutic regents for treating ADPKD., (Copyright © 2017 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)

Aquaporin-3 (AQP3), a transporter of water, glycerol and H 2 O 2 , is expressed in basolateral membranes of principal cells in kidney collecting duct. Here, we report that AQP3 deletion in mice affects renal function and modulates renal injury. We found collecting duct hyperplasia and cell swelling in kidneys of adult AQP3 null mice. After mild renal ischemia-reperfusion (IR), AQP3 null mice had significantly greater blood urea nitrogen (57mg/dl) and creatinine (136μM) than wild-type mice (35mg/dl and 48μM, respectively), and showed renal morphological changes, including tubular dilatation, erythrocyte diapedesis and collecting duct incompletion. MPO, MDA and SOD following IR in AQP3 null mice were significantly different from that in wild-type mice (1.7U/g vs 0.8U/g, 3.9μM/g vs 2.4μM/g, 6.4U/mg vs 11U/mg, respectively). Following IR, AQP3 deletion inhibited activation of mitogen-activated protein kinase (MAPK) signaling and produced an increase in the ratios of Bax/Bcl-2, cleaved caspase-3/caspase-3 and p-p53/p53. Studies in transfected MDCK cells showed that AQP3 expression attenuated reduced cell viability following hypoxia-reoxygenation, with reduced apoptosis and increased MAPK signaling. Our results support a novel role for AQP3 in modulating renal injury and suggest the mechanisms involved in protection against hypoxic injury., (Copyright © 2017. Published by Elsevier B.V.)

Prediction of the biodegradability of organic pollutants is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. In this paper, linear and nonlinear relationships between biological oxygen demand (BOD) and molecular descriptors/fragments have been investigated for 1130 organic chemicals. Significant relationships have been observed between the simple molecular descriptors and %BOD for some homologous compounds, but not for the whole set of compounds. Electronic parameters, such as E(HOMO) and E(LUMO), are the dominant factors affecting the biodegradability for some homologous chemicals. However, other descriptors, such as molecular weight, acid dissociation constant and polarity still have a significant impact on the biodegradation. The best global model for %BOD prediction is that developed from a chain-based fragmentation scheme. At the same time, the theoretical relationship between %BOD and molecular descriptors/fragments has been investigated, based on a first-order kinetic process. The %BOD is nonlinearly, rather than linearly, related to the descriptors. The coefficients of determination can be significantly improved by using nonlinear models for the homologous compounds and the whole data set. After analysing 1130 ready and not ready biodegradable compounds using 23 simple descriptors and various fragmentation schemes, it was revealed that biodegradation could be well predicted from a chain-based fragmentation scheme, a decision tree and a %BOD model. The models were capable of separating NRB and RB with an overall accuracy of 87.2%, 83.0% and 82.5%, respectively. The best classification model developed was a chain-based model but it used 155 fragments. The simplest model was a decision tree which only used 10 structural fragments. The effect of structures on the biodegradation has been analysed and the biodegradation pathway and mechanisms have been discussed based on activating and inactivating fragments., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The individual toxicities of Cu and 11 nitroaromatic compounds to Photobacterium phosphoreum were determined. The toxicity was expressed as the concentrations causing a 50% inhibition of bioluminescence after 15 min exposure (IC(50)). To evaluate the joint effect between the metal ion and the 11 nitroaromatic compounds, the joint toxicity of Cu and 11 nitroaromatic compounds were measured at different Cu concentrations (0.2IC(50), 0.5IC(50) and 0.8IC(50)), respectively. The result shows that the binary joint effect between Cu and nitroaromatic compounds is mainly simple addition at the low Cu concentration (0.2IC(50)). However, an antagonism effect, 55% and 64%, was observed between Cu and 11 nitroaromatic compounds for Cu at medium and high concentrations (0.5IC(50) and 0.8IC(50)). Quantitative structure-activity relationship (QSAR) analysis was performed to study the joint toxicity for the 11 nitroaromatic compounds. The result shows that the toxicity of nitroaromatic compounds is related to descriptors of Connolly solvent-excluded volume (CSEV) and dipolarity/polarizability (S) at low Cu concentration. On the other hand, the toxicity is related to Connolly accessible area (CAA) at medium and high Cu concentrations. The result indicates that different QSAR models on complex mixtures need to be developed to assess the ecological risk in real environments. Using single toxic data to evaluate the toxic effect of mixtures may result in wrong conclusions., (Copyright © 2012 Elsevier B.V. All rights reserved.)