Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews最新文献

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Mechanistic ecotoxicology and environmental toxicology. 机械生态毒理学和环境毒理学。
Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews Pub Date : 2018-01-01 Epub Date: 2018-09-10 DOI: 10.1080/10590501.2018.1492201
William H Tolleson
{"title":"Mechanistic ecotoxicology and environmental toxicology.","authors":"William H Tolleson","doi":"10.1080/10590501.2018.1492201","DOIUrl":"https://doi.org/10.1080/10590501.2018.1492201","url":null,"abstract":"Ecotoxicology is a multidisciplinary research area in which biologists, chemists, geologists, statisticians, and computer modelers study the toxic effects of environmental agents on biological populations, communities, and ecosystems. Environmental toxicology, a related field, investigates the effects of toxic agents on individual organisms, organs, tissues, cell types, organelles, and biochemical reactions. The Journal of Environmental Science and Health, Part C (JESH-C) aims to publish outstanding scientific review articles and original research reports presenting important and timely subjects in the fields of ecotoxicology and environmental toxicology. Articles providing novel and relevant mechanistic insights related to the toxicity of natural and manmade materials present in the environment are of special interest to JESH-C and its readers. Deeper mechanistic understandings of how toxic agents affect biological systems adversely may contribute to the development of better methods for control or remediation and improved biomarkers for exposure (Figure 1). In 2016, JESH-C published a review by Liyanage et al. describing the toxicology of freshwater cyanobacteria. The authors found an association between chronic kidney disease of unknown etiology in humans and the presence of harmful cyanobacteria in drinking water which, along with other types of data, utilized the detection of cyanotoxin biosynthesis genes as biomarkers for the presence of harmful algal species. Combinations of anthropogenic and non-anthropogenic processes influence the distribution, mobilization, chemical conversions, and deposition of toxic agents in the environment. These factors also influence the modes of exposure to hazardous agents that biological systems will experience and the magnitudes of those exposures. Mishra and Bharagava included perspectives of this type in their review of hexavalent chromium in the environment, along with mechanistic insights associated with the ecotoxic effects of chromium VI on microbes, plants, animals, and humans. Similarly, the ecotoxic effects of arsenic were presented in an article by Jha et al., together with a comparison of municipal water treatment methods used to prevent or minimize exposure to arsenic based on its chemical and physical properties.","PeriodicalId":51085,"journal":{"name":"Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews","volume":"36 3","pages":"164-166"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10590501.2018.1492201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36473417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A review on machine learning methods for in silico toxicity prediction. 硅毒性预测的机器学习方法综述。
Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews Pub Date : 2018-01-01 Epub Date: 2019-01-10 DOI: 10.1080/10590501.2018.1537118
Gabriel Idakwo, Joseph Luttrell, Minjun Chen, Huixiao Hong, Zhaoxian Zhou, Ping Gong, Chaoyang Zhang
{"title":"A review on machine learning methods for in silico toxicity prediction.","authors":"Gabriel Idakwo,&nbsp;Joseph Luttrell,&nbsp;Minjun Chen,&nbsp;Huixiao Hong,&nbsp;Zhaoxian Zhou,&nbsp;Ping Gong,&nbsp;Chaoyang Zhang","doi":"10.1080/10590501.2018.1537118","DOIUrl":"https://doi.org/10.1080/10590501.2018.1537118","url":null,"abstract":"<p><p>In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.</p>","PeriodicalId":51085,"journal":{"name":"Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews","volume":"36 4","pages":"169-191"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10590501.2018.1537118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36840770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 70
Machine learning models for predicting endocrine disruption potential of environmental chemicals. 预测环境化学物质内分泌干扰潜力的机器学习模型。
Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews Pub Date : 2018-01-01 Epub Date: 2019-01-10 DOI: 10.1080/10590501.2018.1537155
Marco Chierici, Marco Giulini, Nicole Bussola, Giuseppe Jurman, Cesare Furlanello
{"title":"Machine learning models for predicting endocrine disruption potential of environmental chemicals.","authors":"Marco Chierici,&nbsp;Marco Giulini,&nbsp;Nicole Bussola,&nbsp;Giuseppe Jurman,&nbsp;Cesare Furlanello","doi":"10.1080/10590501.2018.1537155","DOIUrl":"https://doi.org/10.1080/10590501.2018.1537155","url":null,"abstract":"<p><p>We introduce here ML4Tox, a framework offering Deep Learning and Support Vector Machine models to predict agonist, antagonist, and binding activities of chemical compounds, in this case for the estrogen receptor ligand-binding domain. The ML4Tox models have been developed with a 10 × 5-fold cross-validation schema on the training portion of the CERAPP ToxCast dataset, formed by 1677 chemicals, each described by 777 molecular features. On the CERAPP \"All Literature\" evaluation set (agonist: 6319 compounds; antagonist 6539; binding 7283), ML4Tox significantly improved sensitivity over published results on all three tasks, with agonist: 0.78 vs 0.56; antagonist: 0.69 vs 0.11; binding: 0.66 vs 0.26.</p>","PeriodicalId":51085,"journal":{"name":"Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews","volume":"36 4","pages":"237-251"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10590501.2018.1537155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36840349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Computational prediction models for assessing endocrine disrupting potential of chemicals. 评估化学物质内分泌干扰潜能的计算预测模型。
Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews Pub Date : 2018-01-01 Epub Date: 2019-01-11 DOI: 10.1080/10590501.2018.1537132
Sugunadevi Sakkiah, Wenjing Guo, Bohu Pan, Rebecca Kusko, Weida Tong, Huixiao Hong
{"title":"Computational prediction models for assessing endocrine disrupting potential of chemicals.","authors":"Sugunadevi Sakkiah,&nbsp;Wenjing Guo,&nbsp;Bohu Pan,&nbsp;Rebecca Kusko,&nbsp;Weida Tong,&nbsp;Huixiao Hong","doi":"10.1080/10590501.2018.1537132","DOIUrl":"https://doi.org/10.1080/10590501.2018.1537132","url":null,"abstract":"<p><p>Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.</p>","PeriodicalId":51085,"journal":{"name":"Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews","volume":"36 4","pages":"192-218"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10590501.2018.1537132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36844966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Deep learning for predicting toxicity of chemicals: a mini review. 预测化学物质毒性的深度学习:一个小回顾。
Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews Pub Date : 2018-01-01 Epub Date: 2019-03-01 DOI: 10.1080/10590501.2018.1537563
Weihao Tang, Jingwen Chen, Zhongyu Wang, Hongbin Xie, Huixiao Hong
{"title":"Deep learning for predicting toxicity of chemicals: a mini review.","authors":"Weihao Tang,&nbsp;Jingwen Chen,&nbsp;Zhongyu Wang,&nbsp;Hongbin Xie,&nbsp;Huixiao Hong","doi":"10.1080/10590501.2018.1537563","DOIUrl":"https://doi.org/10.1080/10590501.2018.1537563","url":null,"abstract":"<p><p>Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a \"big data\" era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure-activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.</p>","PeriodicalId":51085,"journal":{"name":"Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews","volume":"36 4","pages":"252-271"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10590501.2018.1537563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37013309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 49
Realizing the promise of computational prediction in toxicology applications. 实现计算预测在毒理学应用中的前景。
{"title":"Realizing the promise of computational prediction in toxicology applications.","authors":"","doi":"10.1080/10590501.2018.1537560","DOIUrl":"https://doi.org/10.1080/10590501.2018.1537560","url":null,"abstract":"Toxicant screening is only as efficient and effective as the underlying methods. Unfortunately, chemical toxicity screening is predominated by slow, laborious, and costly methods some of which raise ethical concerns. Certain methods involve animal testing, and others involve tedious in vitro work. Accurate toxicity prediction is needed to enable regulatory decision making and for accelerating the drug development process. Current standard wet laboratory methods cannot keep pace with the increasingly varied panoply of potential toxicants that both human beings and fellow wildlife are bathed in. Other fields have benefitted from faster compute times as well as algorithmic advances in artificial intelligence. The increased computational power, improvement in computational methods, and increasing availability of databases have empowered a new age of toxicology prediction. Many computational predictive tools recognize the potential toxicants far faster and for less cost than an in vitro or in vivo assay possibly can, while still providing mechanistic insights. In this issue of JESH-C, we published two reviews on the newest advanced algorithms for toxicity prediction. Tang et al. focused on deep learning and detailed how the advent of this novel computational method combined with recent massive datasets enables increasingly accurate prediction. The authors reviewed big data sources relevant to the reader looking to feed a toxicology-centered deep learning algorithm and outlined the use of neural networks as a tool to construct quantitative structure–activity relationship (QSAR) models. Building on this, Idakwo et al. zoomed in on machine learning applications for the toxicity prediction field. Data cleaning is absolutely critical in any computational prediction method, and the authors provided a very helpful overview on this topic. Concentrating on a specific toxicological aspect, Sakkiah et al. detailed the utility of computational methods for predicting endocrine disrupting chemicals. Here instead of predicting general toxicity, the focus was on predicting chemicals which could bind to the estrogen receptor, the androgen receptor, alpha-phetoprotein, or other specific endocrine targets. The models reviewed in this paper could likely be applied to other toxicology prediction cases where a short list of targets of concern can readily be generated. As mentioned by Tang et al., Idakwo et al., and Sakkiah et al., current computational methods show great promise but are faced by a number of challenges. In this issue, Li et al. presented a novel computational toolkit called Target-specific Toxicity Knowledgebase (TsTKb) to address shortcomings of previous works. They curated various molecular descriptors from more than 100,000 chemicals across datasets and conducted molecular modeling to determine protein–ligand interactions. Building on such a rich compendium of datasets, they outperformed traditional QSAR modeling. Similarly, Chierici et al. presented ML4","PeriodicalId":51085,"journal":{"name":"Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews","volume":"36 4","pages":"167-168"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10590501.2018.1537560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37232099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Target-specific toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology. 目标特异性毒性知识库(TsTKb):一个新的工具箱,在计算机预测毒理学。
Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews Pub Date : 2018-01-01 Epub Date: 2018-11-14 DOI: 10.1080/10590501.2018.1537148
Yan Li, Gabriel Idakwo, Sundar Thangapandian, Minjun Chen, Huixiao Hong, Chaoyang Zhang, Ping Gong
{"title":"Target-specific toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology.","authors":"Yan Li,&nbsp;Gabriel Idakwo,&nbsp;Sundar Thangapandian,&nbsp;Minjun Chen,&nbsp;Huixiao Hong,&nbsp;Chaoyang Zhang,&nbsp;Ping Gong","doi":"10.1080/10590501.2018.1537148","DOIUrl":"https://doi.org/10.1080/10590501.2018.1537148","url":null,"abstract":"<p><p>As the number of man-made chemicals increases at an unprecedented pace, efforts of quickly screening and accurately evaluating their potential adverse biological effects have been hampered by prohibitively high costs of in vivo/vitro toxicity testing. While it is unrealistic and unnecessary to test every uncharacterized chemical, it remains a major challenge to develop alternative in silico tools with high reliability and precision in toxicity prediction. To address this urgent need, we have developed a novel mode-of-action-guided, molecular modeling-based, and machine learning-enabled modeling approach for in silico chemical toxicity prediction. Here we introduce the core element of this approach, Target-specific Toxicity Knowledgebase (TsTKb), which consists of two main components: Chemical Mode of Action (ChemMoA) database and a suite of prediction model libraries.</p>","PeriodicalId":51085,"journal":{"name":"Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews","volume":"36 4","pages":"219-236"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10590501.2018.1537148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36663876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Organism-derived phthalate derivatives as bioactive natural products. 生物衍生的邻苯二甲酸酯衍生物作为生物活性天然产物。
Huawei Zhang, Yi Hua, Jianwei Chen, Xiuting Li, Xuelian Bai, Hong Wang
{"title":"Organism-derived phthalate derivatives as bioactive natural products.","authors":"Huawei Zhang,&nbsp;Yi Hua,&nbsp;Jianwei Chen,&nbsp;Xiuting Li,&nbsp;Xuelian Bai,&nbsp;Hong Wang","doi":"10.1080/10590501.2018.1490512","DOIUrl":"https://doi.org/10.1080/10590501.2018.1490512","url":null,"abstract":"<p><p>Phthalates are widely used in polymer materials as a plasticizer. These compounds possess potent toxic variations depending on their chemical structures. However, a growing body of evidence indicates that phthalate compounds are undoubtedly discovered in secondary metabolites of organisms, including plants, animals and microorganisms. This review firstly summarizes biological sources of various phthalates and their bioactivities reported during the past few decades as well as their environmental toxicities and public health risks. It suggests that these organisms are one of important sources of natural phthalates with diverse profiles of bioactivity and toxicity.</p>","PeriodicalId":51085,"journal":{"name":"Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews","volume":"36 3","pages":"125-144"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10590501.2018.1490512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36690506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 23
Application of molecular imaging technology in neurotoxicology research. 分子成像技术在神经毒理学研究中的应用。
Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews Pub Date : 2018-01-01 Epub Date: 2018-09-10 DOI: 10.1080/10590501.2018.1492200
Xuan Zhang, Qi Yin, Marc Berridge, Che Wang
{"title":"Application of molecular imaging technology in neurotoxicology research.","authors":"Xuan Zhang,&nbsp;Qi Yin,&nbsp;Marc Berridge,&nbsp;Che Wang","doi":"10.1080/10590501.2018.1492200","DOIUrl":"https://doi.org/10.1080/10590501.2018.1492200","url":null,"abstract":"<p><p>Molecular imaging has been widely applied in preclinical research. Among these new molecular imaging modalities, microPET imaging can be utilized as a very powerful tool that can obtain the measurements of multiple biological processes in various organs repeatedly in a same subject. This review discusses how this new approach provides noninvasive biomarker for neurotoxicology research and summarizes microPET findings with multiple radiotracers on the variety of neurotoxicity induced by toxic agents in both the rodent and the nonhuman primate brain.</p>","PeriodicalId":51085,"journal":{"name":"Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews","volume":"36 3","pages":"113-124"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10590501.2018.1492200","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36476886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Boron inhibits aluminum-induced toxicity to citrus by stimulating antioxidant enzyme activity. 硼通过刺激抗氧化酶活性抑制铝对柑橘的毒性。
Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews Pub Date : 2018-01-01 Epub Date: 2018-09-10 DOI: 10.1080/10590501.2018.1490513
Lei Yan, Muhammad Riaz, Xiuwen Wu, Chenqing Du, Yalin Liu, Bo Lv, Cuncang Jiang
{"title":"Boron inhibits aluminum-induced toxicity to citrus by stimulating antioxidant enzyme activity.","authors":"Lei Yan,&nbsp;Muhammad Riaz,&nbsp;Xiuwen Wu,&nbsp;Chenqing Du,&nbsp;Yalin Liu,&nbsp;Bo Lv,&nbsp;Cuncang Jiang","doi":"10.1080/10590501.2018.1490513","DOIUrl":"https://doi.org/10.1080/10590501.2018.1490513","url":null,"abstract":"<p><p>Aluminum (Al) toxicity is a major factor limiting plant productivity. The objective of the present study was to develop the mechanisms of boron (B) alleviating aluminum toxicity in citrus. The results showed that aluminum toxicity severely hampered root elongation. Interestingly, under aluminum exposure, boron supply improved superoxide dismutase activity while reducing peroxidase, catalase and polyphenol oxidase activities. Likewise, the contents of H<sub>2</sub>O<sub>2</sub>, lipid peroxidation, protein and proline in roots were markedly decreased by boron application under aluminum exposure. Our results demonstrated that boron could alleviate aluminum toxicity by regulating antioxidant enzyme activities in the roots.</p>","PeriodicalId":51085,"journal":{"name":"Journal of Environmental Science and Health Part C-Environmental Carcinogenesis & Ecotoxicology Reviews","volume":"36 3","pages":"145-163"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10590501.2018.1490513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36472968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 18
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