Evidence map›Paper›PMID 31972454›Full record

ArticleEcotoxicology and environmental safety2020

Analysis of model PM

Guohong Liu, Xiliang Yan, Alexander Sedykh, Xiujiao Pan, Xiaoli Zhao, Bing Yan, Hao Zhu

Open access · greenAbstract read
In one paragraph

Article in Ecotoxicology and environmental safety, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

8 citing papers in PubMed, 30 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. International journal of environmental research and public health · 2022
    Article
  7. Article
  8. Article
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

7 authors at 3 institutions in 3 countries.

Guohong LiuSchool of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China.
Xiliang YanSchool of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China; The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA.
Alexander SedykhThe Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA; Sciome, Research Triangle Park, NC, 27709, USA.
Xiujiao PanSchool of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China.
Xiaoli ZhaoDepartment of Physiological Science, Eastern Virginia Medical School, Norfolk, VA, 23507, USA.
Bing YanKey Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Institute of Environmental Research at Greater Bay, Guangzhou University, Guangzhou, 510006, China; School of Environmental Science and Engineering, Shandong University, Jinan, 250100, China. Electronic address: drbingyan@yahoo.com.
Hao ZhuThe Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA; Department of Chemistry, Rutgers University, Camden, NJ, 08102, USA. Electronic address: hao.zhu99@rutgers.edu.
Rutgers, The State University of New Jersey · USShandong University · CNEastern Virginia Medical School · US

Funding

Translational Research Support CoreP30ES005022 · NIEHS · UNIV OF MED/DENT NJ-R W JOHNSON MED SCH · PI ROBERT John LAUMBACH · 1988 to 2026
$47.4M
Predictive Computational Acute Toxicity Modeling by Profiling Chemicals With PublR15ES023148 · NIEHS · RUTGERS THE STATE UNIV OF NJ CAMDEN · PI ZHU, HAO · 2013 to 2018
$941k
NIEHS NIH HHS P30 ES005022NIEHS NIH HHS R15 ES023148
6 · The paper itself

Abstract

Health risks induced by PM2.5 have become one of the major concerns among living populations, especially in regions facing serious pollution such as China and India. Furthermore, the composition of PM2.5 is complex and it also varies with time and locations. To facilitate our understanding of PM2.5-induced toxicity, a predictive modeling framework was developed in the present study. The core of this study was 1) to construct a virtual carbon nanoparticle library based on the experimental data to simulate the PM2.5 structures; 2) to quantify the nanoparticle structures by novel nanodescriptors; and 3) to perform computational modeling for critical toxicity endpoints. The virtual carbon nanoparticle library was developed to represent the nanostructures of 20 carbon nanoparticles, which were synthesized to simulate PM2.5 structures and tested for potential health risks. Based on the calculated nanodescriptors from virtual carbon nanoparticles, quantitative nanostructure-activity relationship (QNAR) models were developed to predict cytotoxicity and four different inflammatory responses induced by model PM2.5. The high predictability (R

Indexed as

Models, MolecularAir PollutantsCarbonComputer SimulationEnvironmental MonitoringHumansInflammationNanoparticlesParticulate MatterQuantitative Structure-Activity RelationshipAir PollutantsCarbonParticulate MatterMachine learningNanodescriptorsPM2.5-induced toxicityVirtual carbon nanoparticle library

Identifiers

PMID31972454
PMCPMC7018436
OpenAlexW3002284633

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.