Sparse Supervised Classification Methods Predict and Characterize Nanomaterial Exposures: Independent Markers of MWCNT Exposures. (January 2018)
- Record Type:
- Journal Article
- Title:
- Sparse Supervised Classification Methods Predict and Characterize Nanomaterial Exposures: Independent Markers of MWCNT Exposures. (January 2018)
- Main Title:
- Sparse Supervised Classification Methods Predict and Characterize Nanomaterial Exposures: Independent Markers of MWCNT Exposures
- Authors:
- Yanamala, Naveena
Orandle, Marlene S.
Kodali, Vamsi K.
Bishop, Lindsey
Zeidler-Erdely, Patti C.
Roberts, Jenny R.
Castranova, Vincent
Erdely, Aaron - Other Names:
- Hubbs Ann guest-editor.
Monteiro-Riviere Nancy guest-editor.
Orandle Marlene guest-editor. - Abstract:
- Recent experimental evidence indicates significant pulmonary toxicity of multiwalled carbon nanotubes (MWCNTs), such as inflammation, interstitial fibrosis, granuloma formation, and carcinogenicity. Although numerous studies explored the adverse potential of various CNTs, their comparability is often limited. This is due to differences in administered dose, physicochemical characteristics, exposure methods, and end points monitored. Here, we addressed the problem through sparse classification method, a supervised machine learning approach that can reduce the noise contained in redundant variables for discriminating among MWCNT-exposed and MWCNT-unexposed groups. A panel of proteins measured from bronchoalveolar lavage fluid (BAL) samples was used to predict exposure to various MWCNT and determine markers that are attributable to MWCNT exposure and toxicity in mice. Using sparse support vector machine–based classification technique, we identified a small subset of proteins clearly distinguishing each exposure. Macrophage-derived chemokine (MDC/CCL22), in particular, was associated with various MWCNT exposures and was independent of exposure method employed, that is, oropharyngeal aspiration versus inhalation exposure. Sustained expression of some of the selected protein markers identified also suggests their potential role in MWCNT-induced toxicity and proposes hypotheses for future mechanistic studies. Such approaches can be used more broadly for nanomaterial risk profilingRecent experimental evidence indicates significant pulmonary toxicity of multiwalled carbon nanotubes (MWCNTs), such as inflammation, interstitial fibrosis, granuloma formation, and carcinogenicity. Although numerous studies explored the adverse potential of various CNTs, their comparability is often limited. This is due to differences in administered dose, physicochemical characteristics, exposure methods, and end points monitored. Here, we addressed the problem through sparse classification method, a supervised machine learning approach that can reduce the noise contained in redundant variables for discriminating among MWCNT-exposed and MWCNT-unexposed groups. A panel of proteins measured from bronchoalveolar lavage fluid (BAL) samples was used to predict exposure to various MWCNT and determine markers that are attributable to MWCNT exposure and toxicity in mice. Using sparse support vector machine–based classification technique, we identified a small subset of proteins clearly distinguishing each exposure. Macrophage-derived chemokine (MDC/CCL22), in particular, was associated with various MWCNT exposures and was independent of exposure method employed, that is, oropharyngeal aspiration versus inhalation exposure. Sustained expression of some of the selected protein markers identified also suggests their potential role in MWCNT-induced toxicity and proposes hypotheses for future mechanistic studies. Such approaches can be used more broadly for nanomaterial risk profiling studies to evaluate decisions related to dose/time–response relationships that could delineate experimental variables from exposure markers. … (more)
- Is Part Of:
- Toxicologic pathology. Volume 46:Number 1(2018)
- Journal:
- Toxicologic pathology
- Issue:
- Volume 46:Number 1(2018)
- Issue Display:
- Volume 46, Issue 1 (2018)
- Year:
- 2018
- Volume:
- 46
- Issue:
- 1
- Issue Sort Value:
- 2018-0046-0001-0000
- Page Start:
- 14
- Page End:
- 27
- Publication Date:
- 2018-01
- Subjects:
- fibrosis markers -- computational toxicology -- nanotoxicology -- predictive models -- support vector machines
Pathology -- Periodicals
Toxicology -- Periodicals
Pathology
Toxicology
615.9 - Journal URLs:
- http://tpx.sagepub.com/ ↗
http://online.sagepub.com/ ↗ - DOI:
- 10.1177/0192623317730575 ↗
- Languages:
- English
- ISSNs:
- 0192-6233
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8873.015000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 8659.xml