Evidence map›Paper›PMID 31323852›Full record

ArticleInternational journal of environmental research and public health2019

Inhalation Exposure Analysis of Lung-Inhalable Particles in an Approximate Rat Central Airway.

Jingliang Dong, Jiawei Ma, Lin Tian, Kiao Inthavong, Jiyuan Tu

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Jingliang DongIndoor Environment Engineering Research Center of Fujian Province, College of Ecological Environment and Urban Construction, Fujian University of Technology, Fuzhou, Fujian 350118, China.
Jiawei MaSchool of Engineering, RMIT University, PO Box 71, Bundoora, VIC 3083, Australia.
Lin TianSchool of Engineering, RMIT University, PO Box 71, Bundoora, VIC 3083, Australia. lin.tian@rmit.edu.au.
Kiao InthavongSchool of Engineering, RMIT University, PO Box 71, Bundoora, VIC 3083, Australia.
Jiyuan TuSchool of Engineering, RMIT University, PO Box 71, Bundoora, VIC 3083, Australia. jiyuan.tu@rmit.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rats have been widely used as surrogates for evaluating the adverse health effects of inhaled airborne particulate matter. This paper presents a computational fluid and particle dynamics (CFPD) study of particle transport and deposition in an approximate rat central airway model. The geometric model was constructed based on magnetic resonance (MR) imaging data sourced from previous study. Lung-inhalable particles covering a diameter range from 20 nm to 1.0 µm were passively released into the trachea, and the Lagrangian particle tracking approach was used to predict individual particle trajectories. Overall, regional and local deposition patterns in the central airway were analyzed in detail. A preliminary interspecies data comparison was made between present rat models and previously published human data. Results showed deposition "hot spots" were mainly concentrated at airway bifurcation apexes, and a gravitational effect should also be considered for inertia particles when using a rat as a laboratory animal. While for humans, this may not happen as the standing posture is completely different. Lastly, the preliminary interspecies data comparison confirms the deposition similarity in terms of deposition enhancement factors, which is a weighted deposition concentration parameter. This interspecies comparison confirms feasibility of extrapolating surrogate rat deposition data to humans using existing data extrapolation approach, which mostly relies on bulk anatomical differences as dose adjustment factors.

Indexed as

AerosolsAnimalsHumansHydrodynamicsInhalation ExposureMaleModels, BiologicalParticle SizeParticulate MatterRatsRats, WistarRespiratory SystemAerosolsParticulate MatterCFPDdeposition enhancement factorextrapolationhumanlung-inhalable particlesrat central airway

Identifiers

PMID31323852
PMCPMC6678433

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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.