Sparse Supervised Classification Methods Predict and Characterize Nanomaterial Exposures: Independent Markers of MWCNT Exposures

Details

• Personal Author:
  Bishop L ; Castranova, Vincent ; Erdely A ; Kodali VK ; Orandle MS ; Roberts, Jennifer R. ; Yanamala N ; Zeidler-Erdely PC
• Description:
  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 profiling studies to evaluate decisions related to dose/time-response relationships that could delineate experimental variables from exposure markers. [Description provided by NIOSH]
• Subjects:
  Occupational Health Pathology Safety Toxicology
• Keywords:
  Artificial Intelligence Author Keywords: Fibrosis Markers Comparative Toxicology Computational Toxicology Multi-walled Carbon Nanotubes MWCNT Nanomaterials Nanotoxicology Predictive Models Support Vector Machines
• ISSN:
  0192-6233
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 3 ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  HELD - Health Effects Laboratory Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  14-27
• Volume:
  46
• Issue:
  1
• NIOSHTIC Number:
  nn:20050489
• Citation:
  Toxicol Pathol 2018 Jan; 46(1):14-27
• Contact Point Address:
  Naveena Yanamala, NIOSH, Exposure Assessment Branch (MS-3030), 1095 Willowdale Road, Morgantown, WV 26505, USA
• Email:
  nyanamala@cdc.gov
• CAS Registry Number:
  Carbon nanotubes (CAS RN 308068-56-6)
• Federal Fiscal Year:
  2018
• NORA Priority Area:
  Manufacturing
• Peer Reviewed:
  True
• Source Full Name:
  Toxicologic Pathology
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:6858dd27552191de722f9ac398a8a1513ccd440de37f56226686fac5ee7da5211fa9cce7302349abd2ee9bad608df69eb1b0eee0bada022036f84aea0101d276
• Download URL:
  https://stacks.cdc.gov/view/cdc/210808/cdc_210808_DS1.pdf
• File Type:
  [PDF - 825.61 KB ]