Dataset Information

Analysis of model PM_2.5-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling.

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 (R² > 0.65 for leave-one-out validations) of the resulted consensus models indicated that this approach could be a universal tool to predict and analyze the potential toxicity of model PM2.5, ultimately understanding and evaluating the ambient PM2.5-induced toxicity.

SUBMITTER: Liu G

PROVIDER: S-EPMC7018436 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of model PM<sub>2.5</sub>-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling.

Liu Guohong G Yan Xiliang X Sedykh Alexander A Pan Xiujiao X Zhao Xiaoli X Yan Bing B Zhu Hao H

Ecotoxicology and environmental safety 20200120

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 ...[more]

PMID: 31972454

Similar Datasets

Project description:IntroductionDespite the London Underground (LU) handling on average 2.8 million passenger journeys per day, the characteristics and potential health effects of the elevated concentrations of metal-rich PM2.5 found in this subway system are not well understood.MethodsSpatial monitoring campaigns were carried out to characterise the health-relevant chemical and physical properties of PM2.5 across the LU network, including diurnal and day-to-day variability and spatial distribution (above ground, depth below ground and subway line). Population-weighted station PM2.5 rankings were produced to understand the relative importance of concentrations at different stations and on different lines.ResultsThe PM2.5 mass in the LU (mean 88??g?m-3, median 28??g?m-3) was greater than at ambient background locations (mean 19??g?m-3, median 14??g?m-3) and roadside environments in central London (mean 22??g?m-3, median 14??g?m-3). Concentrations varied between lines and locations, with the deepest and shallowest submerged lines being the District (median 4??g?m-3) and Victoria (median 361??g?m-3 but up to 885??g?m-3). Broadly in agreement with other subway systems around the world, sampled LU PM2.5 comprised 47% iron oxide, 7% elemental carbon, 11% organic carbon, and 14% metallic and mineral oxides. Although a relationship between line depth and air quality inside the tube trains was evident, there were clear influences relating to the distance from cleaner outside air and the exchange with cabin air when the doors open. The passenger population-weighted exposure analysis demonstrated a method to identify stations that should be prioritised for remediation to improve air quality.ConclusionPM2.5 concentrations in the LU are many times higher than in other London transport Environments. Failure to include this environment in epidemiological studies of the relationship between PM2.5 and health in London is therefore likely to lead to a large exposure misclassification error. Given the significant contribution of underground PM2.5 to daily exposure, and the differences in composition compared to urban PM2.5, there is a clear need for well-designed studies to better understand the health effects of underground exposure.

Dataset Information

Analysis of model PM_2.5-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling.

Publications

Analysis of model PM<sub>2.5</sub>-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Analysis of model PM2.5-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling.

Publications

Analysis of model PM<sub>2.5</sub>-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Analysis of model PM_2.5-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling.