A Meta‐Analysis of Carbon Nanotube Pulmonary Toxicity Studies—How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes
Identifieur interne : 003155 ( Istex/Corpus ); précédent : 003154; suivant : 003156A Meta‐Analysis of Carbon Nanotube Pulmonary Toxicity Studies—How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes
Auteurs : Jeremy M. Gernand ; Elizabeth A. CasmanSource :
- Risk Analysis [ 0272-4332 ] ; 2014-03.
Abstract
This article presents a regression‐tree‐based meta‐analysis of rodent pulmonary toxicity studies of uncoated, nonfunctionalized carbon nanotube (CNT) exposure. The resulting analysis provides quantitative estimates of the contribution of CNT attributes (impurities, physical dimensions, and aggregation) to pulmonary toxicity indicators in bronchoalveolar lavage fluid: neutrophil and macrophage count, and lactate dehydrogenase and total protein concentrations. The method employs classification and regression tree (CART) models, techniques that are relatively insensitive to data defects that impair other types of regression analysis: high dimensionality, nonlinearity, correlated variables, and significant quantities of missing values. Three types of analysis are presented: the RT, the random forest (RF), and a random‐forest‐based dose‐response model. The RT shows the best single model supported by all the data and typically contains a small number of variables. The RF shows how much variance reduction is associated with every variable in the data set. The dose‐response model is used to isolate the effects of CNT attributes from the CNT dose, showing the shift in the dose‐response caused by the attribute across the measured range of CNT doses. It was found that the CNT attributes that contribute the most to pulmonary toxicity were metallic impurities (cobalt significantly increased observed toxicity, while other impurities had mixed effects), CNT length (negatively correlated with most toxicity indicators), CNT diameter (significantly positively associated with toxicity), and aggregate size (negatively correlated with cell damage indicators and positively correlated with immune response indicators). Increasing CNT N2‐BET‐specific surface area decreased toxicity indicators.
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DOI: 10.1111/risa.12109
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tel: 412‐268‐3756; casman@andrew.cmu.edu.</affiliation></author><idno type="istex">CFB9ADF5408CEC25EC46A94D8D48AF3A7CEFBC25</idno><idno type="ark">ark:/67375/WNG-M9F1205S-K</idno><idno type="DOI">10.1111/risa.12109</idno><idno type="unit">RISA12109</idno><idno type="toTypesetVersion">file:RISA.RISA12109.pdf</idno></analytic><monogr><title level="j" type="main">Risk Analysis</title><title level="j" type="alt">RISK ANALYSIS</title><idno type="pISSN">0272-4332</idno><idno type="eISSN">1539-6924</idno><idno type="book-DOI">10.1111/(ISSN)1539-6924</idno><idno type="book-part-DOI">10.1111/risa.2014.34.issue-3</idno><idno type="product">RISA</idno><imprint><biblScope unit="vol">34</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="583">583</biblScope><biblScope unit="page" to="597">597</biblScope><biblScope unit="page-count">15</biblScope><date type="published" when="2014-03"></date></imprint></monogr></biblStruct></sourceDesc></fileDesc><profileDesc><abstract style="main"><p>This article presents a regression‐tree‐based meta‐analysis of rodent pulmonary toxicity studies of uncoated, nonfunctionalized carbon nanotube (CNT) exposure. The resulting analysis provides quantitative estimates of the contribution of CNT attributes (impurities, physical dimensions, and aggregation) to pulmonary toxicity indicators in bronchoalveolar lavage fluid: neutrophil and macrophage count, and lactate dehydrogenase and total protein concentrations. The method employs classification and regression tree (CART) models, techniques that are relatively insensitive to data defects that impair other types of regression analysis: high dimensionality, nonlinearity, correlated variables, and significant quantities of missing values. Three types of analysis are presented: the RT, the random forest (RF), and a random‐forest‐based dose‐response model. The RT shows the best single model supported by all the data and typically contains a small number of variables. The RF shows how much variance reduction is associated with every variable in the data set. The dose‐response model is used to isolate the effects of CNT attributes from the CNT dose, showing the shift in the dose‐response caused by the attribute across the measured range of CNT doses. It was found that the CNT attributes that contribute the most to pulmonary toxicity were metallic impurities (cobalt significantly increased observed toxicity, while other impurities had mixed effects), CNT length (negatively correlated with most toxicity indicators), CNT diameter (significantly positively associated with toxicity), and aggregate size (negatively correlated with cell damage indicators and positively correlated with immune response indicators). 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The resulting analysis provides quantitative estimates of the contribution of CNT attributes (impurities, physical dimensions, and aggregation) to pulmonary toxicity indicators in bronchoalveolar lavage fluid: neutrophil and macrophage count, and lactate dehydrogenase and total protein concentrations. The method employs classification and regression tree (CART) models, techniques that are relatively insensitive to data defects that impair other types of regression analysis: high dimensionality, nonlinearity, correlated variables, and significant quantities of missing values. Three types of analysis are presented: the RT, the random forest (RF), and a random‐forest‐based dose‐response model. The RT shows the best single model supported by all the data and typically contains a small number of variables. The RF shows how much variance reduction is associated with every variable in the data set. The dose‐response model is used to isolate the effects of CNT attributes from the CNT dose, showing the shift in the dose‐response caused by the attribute across the measured range of CNT doses. It was found that the CNT attributes that contribute the most to pulmonary toxicity were metallic impurities (cobalt significantly increased observed toxicity, while other impurities had mixed effects), CNT length (negatively correlated with most toxicity indicators), CNT diameter (significantly positively associated with toxicity), and aggregate size (negatively correlated with cell damage indicators and positively correlated with immune response indicators). Increasing CNT N<sub>2</sub>‐BET‐specific surface area decreased toxicity indicators.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A Meta‐Analysis of Carbon Nanotube Pulmonary Toxicity Studies—How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>A Meta‐Analysis of Carbon Nanotube Pulmonary Toxicity Studies</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>A Meta‐Analysis of Carbon Nanotube Pulmonary Toxicity Studies—How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes</title></titleInfo><name type="personal"><namePart type="given">Jeremy M.</namePart><namePart type="family">Gernand</namePart><affiliation>Engineering and Public Policy, Carnegie Mellon University, PA, Pittsburgh, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Elizabeth A.</namePart><namePart type="family">Casman</namePart><affiliation>Engineering and Public Policy, Carnegie Mellon University, PA, Pittsburgh, USA</affiliation><affiliation>E-mail: casman@andrew.cmu.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-6N5SZHKN-D">article</genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><dateIssued encoding="w3cdtf">2014-03</dateIssued><dateCreated encoding="w3cdtf">2013-08-14</dateCreated><copyrightDate encoding="w3cdtf">2014</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract>This article presents a regression‐tree‐based meta‐analysis of rodent pulmonary toxicity studies of uncoated, nonfunctionalized carbon nanotube (CNT) exposure. The resulting analysis provides quantitative estimates of the contribution of CNT attributes (impurities, physical dimensions, and aggregation) to pulmonary toxicity indicators in bronchoalveolar lavage fluid: neutrophil and macrophage count, and lactate dehydrogenase and total protein concentrations. The method employs classification and regression tree (CART) models, techniques that are relatively insensitive to data defects that impair other types of regression analysis: high dimensionality, nonlinearity, correlated variables, and significant quantities of missing values. Three types of analysis are presented: the RT, the random forest (RF), and a random‐forest‐based dose‐response model. The RT shows the best single model supported by all the data and typically contains a small number of variables. The RF shows how much variance reduction is associated with every variable in the data set. The dose‐response model is used to isolate the effects of CNT attributes from the CNT dose, showing the shift in the dose‐response caused by the attribute across the measured range of CNT doses. It was found that the CNT attributes that contribute the most to pulmonary toxicity were metallic impurities (cobalt significantly increased observed toxicity, while other impurities had mixed effects), CNT length (negatively correlated with most toxicity indicators), CNT diameter (significantly positively associated with toxicity), and aggregate size (negatively correlated with cell damage indicators and positively correlated with immune response indicators). Increasing CNT N2‐BET‐specific surface area decreased toxicity indicators.</abstract><note type="funding">National Science Foundation (NSF)</note><note type="funding">Environmental Protection Agency (EPA) - No. EF‐0830093; </note><note type="funding">Center for the Environmental Implications of NanoTechnology (CEINT)</note><note type="funding">Carnegie Institute of Technology (CIT) Dean's Fellowship</note><note type="funding">Prem Narain Srivastava Legacy Fellowship</note><note type="funding">Neil and Jo Bushnell Fellowship</note><note type="funding">Steinbrenner Institute for Environmental Education and Research (SEER)</note><subject><genre>keywords</genre><topic>Carbon nanotubes</topic><topic>inhalation</topic><topic>meta‐analysis</topic><topic>regression tree</topic><topic>toxicity</topic></subject><relatedItem type="host"><titleInfo><title>Risk Analysis</title></titleInfo><titleInfo type="abbreviated"><title>Risk Analysis</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><subject><genre>article-category</genre><topic>Original Research Article</topic></subject><identifier type="ISSN">0272-4332</identifier><identifier type="eISSN">1539-6924</identifier><identifier type="DOI">10.1111/(ISSN)1539-6924</identifier><identifier type="PublisherID">RISA</identifier><part><date>2014</date><detail type="volume"><caption>vol.</caption><number>34</number></detail><detail type="issue"><caption>no.</caption><number>3</number></detail><extent unit="pages"><start>583</start><end>597</end><total>15</total></extent></part></relatedItem><identifier type="istex">CFB9ADF5408CEC25EC46A94D8D48AF3A7CEFBC25</identifier><identifier type="ark">ark:/67375/WNG-M9F1205S-K</identifier><identifier type="DOI">10.1111/risa.12109</identifier><identifier type="ArticleID">RISA12109</identifier><accessCondition type="use and reproduction" contentType="copyright">© 2014 Society for Risk Analysis© 2013 Society for Risk Analysis</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-L0C46X92-X">wiley</recordContentSource></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/document/CFB9ADF5408CEC25EC46A94D8D48AF3A7CEFBC25/metadata/json</uri></json:item></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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