Evidence map›Paper›PMID 25646681›Full record

ArticleACS nano2015

Use of a pro-fibrogenic mechanism-based predictive toxicological approach for tiered testing and decision analysis of carbonaceous nanomaterials.

Xiang Wang, Matthew C Duch, Nikhita Mansukhani, Zhaoxia Ji, Yu-Pei Liao, Meiying Wang, Haiyuan Zhang, Bingbing Sun, Chong Hyun Chang, Ruibin Li and 5 more

Abstract read
In one paragraph

Article in ACS nano, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
11.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

50 citing papers in PubMed, 117 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. CeONano today · 2022
    Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 2 institutions in 1 country.

Xiang Wang
Matthew C Duch§Departments of Materials Science and Engineering, Chemistry, and Medicine, Northwestern University, Evanston, Illinois 60208, United States.
Nikhita Mansukhani§Departments of Materials Science and Engineering, Chemistry, and Medicine, Northwestern University, Evanston, Illinois 60208, United States.
Zhaoxia Ji
Yu-Pei Liao
Meiying Wang
Haiyuan Zhang
Bingbing Sun
Chong Hyun Chang
Ruibin Li
Sijie Lin
Huan Meng
Tian Xia
Mark C Hersam§Departments of Materials Science and Engineering, Chemistry, and Medicine, Northwestern University, Evanston, Illinois 60208, United States.
André E Nel
University of California, Los Angeles · USNorthwestern University · US

Funding

Scientific Core: Acquisition, Synthesis, and Characterization of NanomaterialsU19ES019528 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI NEL, ANDRE ELIAS · 2010 to 2014
$5.5M
Carbon Nanotube Structure-Activity Relationships for Predictive ToxicologyR01ES022698 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HERSAM, MARK C., NEL, ANDRE ELIAS · 2014 to 2018
$2.1M
NIEHS NIH HHS R01 ES022698NIEHS NIH HHS R01ES022698NIEHS NIH HHS U19 ES019528NIEHS NIH HHS U19ES019528
6 · The paper itself

Abstract

Engineered carbonaceous nanomaterials (ECNs), including single-wall carbon nanotubes (SWCNTs), multiwall carbon nanotubes (MWCNTs), graphene, and graphene oxide (GO), are potentially hazardous to the lung. With incremental experience in the use of predictive toxicological approaches, seeking to relate ECN physicochemical properties to adverse outcome pathways (AOPs), it is logical to explore the existence of a common AOP that allows comparative analysis of broad ECN categories. We established an ECN library comprising three different types of SWCNTs, graphene, and graphene oxide (two sizes) for comparative analysis according to a cell-based AOP that also plays a role in the pathogenesis of pulmonary fibrosis. SWCNTs synthesized by Hipco, arc discharge and Co-Mo catalyst (CoMoCAT) methods were obtained in their as-prepared (AP) state, following which they were further purified (PD) or coated with Pluronic F108 (PF108) or bovine serum albumin (BSA) to improve dispersal and colloidal stability. GO was prepared as two sizes, GO-small (S) and GO-large (L), while the graphene samples were coated with BSA and PF108 to enable dispersion in aqueous solution. In vitro screening showed that AP- and PD-SWCNTs, irrespective of the method of synthesis, as well as graphene (BSA) and GO (S and L) could trigger interleukin-1β (IL-1β) and transforming growth factor-β1 (TGF-β1) production in myeloid (THP-1) and epithelial (BEAS-2B) cell lines, respectively. Oropharyngeal aspiration in mice confirmed that AP-Hipco tubes, graphene (BSA-dispersed), GO-S and GO-L could induce IL-1β and TGF-β1 production in the lung in parallel with lung fibrosis. Notably, GO-L was the most pro-fibrogenic material based on rapid kinetics of pulmonary injury. In contrast, PF108-dispersed SWCNTs and -graphene failed to exert fibrogenic effects. Collectively, these data indicate that the dispersal state and surface reactivity of ECNs play key roles in triggering a pro-fibrogenic AOP, which could prove helpful for hazard ranking and a proposed tiered testing approach for large ECN categories.

Indexed as

NanostructuresAnimalsCarbonCell LineEngineeringGraphiteHumansInterleukin-1betaLysosomesMaleMiceOxidesPulmonary FibrosisToxicity TestsTransforming Growth Factor beta1CarbonGraphiteInterleukin-1betaOxidesTransforming Growth Factor beta1engineered carbonaceous nanomaterialsgraphenegraphene oxideNLRP3 inflammasome lung fibrosisSWCNT

Identifiers

PMID25646681
PMCPMC4539018
OpenAlexW1969397027

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.